Risk Assessment, Modeling and Decision Support: Strategic Decisions

Risk, Governance and Society Volume 14 Editors Jeryl Mumpower, Texas A&M University, College Station, USA Ortwin Renn, ...

Author: Ann Bostrom | Steven P. French | Sara J. Gottlieb | Editors

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Risk, Governance and Society Volume 14 Editors Jeryl Mumpower, Texas A&M University, College Station, USA Ortwin Renn, Center for Technology Assessment in Baden-Württemberg, Germany

Ann Bostrom · Steven French · Sara Gottlieb Editors

Risk Assessment, Modeling and Decision Support Strategic Directions

123

Ann Bostrom Associate Dean of Research & Associate Professor Daniel J. Evans School of Public Affairs University of Washington 327 Parrington Hall Box 353055 Seattle, Washington 98195-3055 USA [email protected]

ISBN 978-3-540-71157-5

Professor Steven French Sara Gottlieb Georgia Institute of Technology Center for Geographic Information Systems College of Architecture Atlanta, GA 30332 USA [email protected] [email protected]

e-ISBN 978-3-540-71158-2

DOI 10.1007/978-3-540-71158-2 Library of Congress Control Number: 2007936685 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig Cover design: WMX Design GmbH, Heidelberg Printed on acid-free paper 987654321 springer.com

Contents

1 Introduction ............................................................................................ 1 1.1 Roadmap to the Book ....................................................................... 2 2 A Brief History of Seismic Risk Assessment ........................................ 5 2.1 Introduction ...................................................................................... 5 2.2 Terminology ..................................................................................... 6 2.3 Overview of Seismic Risk Assessment ............................................ 9 2.4 A Brief Chronology of Selected Events in Seismic Risk Assessment ........................................................................................... 11 2.5 How We Got Here.......................................................................... 11 2.6 Vulnerability................................................................................... 30 2.7 Special Vulnerability Relations...................................................... 40 2.8 Asset Identification and Attributes................................................. 44 2.9 Risk Analysis Methods................................................................... 47 2.10 Assessment ................................................................................... 50 2.11 Current Status ............................................................................... 53 2.12 Concluding Remarks .................................................................... 55 2.13 Acknowledgments ........................................................................ 56 References ............................................................................................ 57 3 Perspectives on the History of Seismic Risk Assessment .................. 83 Introduction .......................................................................................... 83 3.1 Lessons Leaned from Current Practice and Future Needs in Regional Loss Estimation..................................................................... 84 3.2 The Dawn of Earthquake Investigations and Cross-Continent Interactions ........................................................................................... 89 3.3 Social Science Perspectives on Seismic Risk Assessment............. 96 3.4 Seismic Risk Assessment: History and Strategic Directions.......... 97 References ............................................................................................ 99

VI

Contents

4 Strategic Directions in Seismic Modeling: HAZUS® Development and Current Applications for Catastrophe Planning......................... 101 4.1 Introduction .................................................................................. 101 4.2 HAZUS®MH Earthquake Model Overview ................................... 102 4.3 Use of HAZUS®MH to Support Catastrophe Planning .................. 104 4.4 Use of HAZUS®MH to Link Risk Assessment, Mitigation and Disaster Response............................................................................... 110 4.5 Utilization of a Web-Based Data Management Portal System..... 113 4.6 Conclusions .................................................................................. 115 References .......................................................................................... 115 5 Perspectives on Development and Current Applications for Catastrophe Planning............................................................................ 117 Introduction ........................................................................................ 117 5.1 Recommended Improvements for HAZUS®MH ............................ 118 5.2 User Dynamics and HAZUS® Development................................ 123 5.3 Perspectives from a HAZUS® User.............................................. 127 5.4 Strategic Directions for HAZUS® and Current Applications for Catastrophe Planning.......................................................................... 129 References .......................................................................................... 132 6 Loss Estimation Models and Metrics................................................ 135 6.1 Introduction .................................................................................. 135 6.2 Loss Dimensions .......................................................................... 136 6.3 Components of the HAZUS® Loss Estimation Methodology ...... 139 6.4 Current Limitations ...................................................................... 143 6.5 Loss Calibration Studies............................................................... 145 6.6 General Guidelines on the Use of Loss Estimates........................ 150 6.7 Research Topics ........................................................................... 153 References .......................................................................................... 154 Bibliography of Additional HAZUS® Studies.................................... 156 7 Perspectives on Loss Estimation Models and Metrics..................... 171 Introduction ........................................................................................ 171 7.1 Model Validation and Priorities in Loss Metrics ......................... 172 7.2 Additional Considerations in Loss Estimation ............................. 175 7.3 Social and Economic Considerations in Loss Estimation Modeling ............................................................................................ 179 7.4 Strategic Directions for Loss Estimation Models and Metrics..... 181 References .......................................................................................... 183

Contents

VII

8 Seismic Risk Mitigation Decisions Under Uncertainty ................... 185 8.1 Introduction .................................................................................. 185 8.2 Seismic Risk Analysis for Nuclear Power Plants......................... 186 8.3 Nuclear Power Plant Seismic Design ........................................... 187 8.4 Recommendations for Seismic Risk Mitigation Decisions .......... 193 8.5 Importance of Uncertainties ......................................................... 195 8.6 Summary ...................................................................................... 197 References .......................................................................................... 197 9 Perspectives on Seismic Risk Mitigation Decisions Under Uncertainty............................................................................................. 199 Introduction ........................................................................................ 199 9.1 Perspectives from a Risk Assessment Practitioner....................... 200 9.2 Seismic Risk Assessment and Mitigation: Current Issues............ 208 9.3 On the Importance of Network Considerations in Lifeline Risk Assessment ......................................................................................... 214 9.4 Strategic Directions for Seismic Risk Mitigation Decisions Under Uncertainty ......................................................................................... 222 References .......................................................................................... 224 10 Modeling Seismic Mitigation Strategies ......................................... 227 10.1 Introduction ................................................................................ 228 10.2 Structure of Catastrophe Models ................................................ 229 10.3 A Comparison of Models ........................................................... 230 10.4 The Exceedance Probability Curve ............................................ 233 10.5 Choice of Seismic Mitigation Strategies .................................... 235 10.6 Cost-Benefit Analysis................................................................. 237 10.7 A Mitigation Example Using an Insurance-Based Model.......... 238 10.8 Consideration of Decision Processes.......................................... 242 10.9 Improving the Modeling of Seismic Mitigation Strategies ........ 243 References .......................................................................................... 244 11 Perspectives on Modeling Seismic Mitigation Strategies .............. 247 Introduction ........................................................................................ 247 11.1 Catastrophe Modeling Paradigm Shift ....................................... 248 11.2 A Structural Engineering Perspective on Modeling Seismic Mitigation Strategies .......................................................................... 252 11.3 The Role of Risk Modeling in Mitigation .................................. 254 11.4 Adopting Mitigation ................................................................... 255 11.5 Strategic Directions for Modeling Seismic Mitigation Strategies ............................................................................................ 256 References .......................................................................................... 259

VIII

Contents

12 Visualizing Uncertainty in Natural Hazards.................................. 261 12.1 Introduction ................................................................................ 261 12.2 From Concepts to Representations............................................. 263 12.3 Uncertainty Visualization........................................................... 269 12.4 Task-Oriented Visual Mappings................................................. 282 12.5 Hazards Visualization................................................................. 285 12.6 Challenges .................................................................................. 290 12.7 Acknowledgments ...................................................................... 291 References .......................................................................................... 291 13 Perspectives on Visualizing Uncertainty in Natural Hazards ...... 295 Introduction ........................................................................................ 295 13.1 Preferred Data Visualization Techniques May Not Lead to Comprehension and Use of Hazard Information................................ 296 13.2 Putting Seismic Risk and Uncertainty on the Map..................... 306 13.3 Keep Representations Simple for Effective Communication..... 311 13.4 Strategic Directions for Visualizing Uncertainty in Natural Hazards............................................................................................... 313 References .......................................................................................... 314 14 Conclusion ......................................................................................... 319 Index ....................................................................................................... 323 List of Contributing Authors................................................................ 329

List of Abbreviations

AAL ABAG ABV AEBM ALA ANSI ANSS ANSYS ASCE ASTM ATC AWSS BCA BI BOCA

BPT BSSA BSSC CBA CBC CBRM CDMG CEB CGS CM COSMOS CUBE

Average Annual Loss Association of Bay Area Governments Assembly Based Vulnerability Advanced Engineering Building Module American Lifelines Alliance American National Standards Institute Advanced National Seismic System A suite of software products for engineering simulation by ANSYS, Inc. American Society of Civil Engineers American Society for Testing and Materials Applied Technology Council Auxiliary Water Supply System Benefit Cost Analysis Business Interruption Building Officials and Code Administrators International, Inc. One of three statutory members of the ICC. Producer of the National Building Code (NBC). Brownian Passage Time Bulletin of the Seismological Society of America Building Seismic Safety Council Cost-Benefit Analysis California Building Code. The 2001 CBC is based on the 1997 UBC model code. Consequence Based Risk Management California Division of Mines and Geology, recently renamed as CGS (q.v.) Comité Euro-Internationale du Béton California Geological Survey Catastrophe Modeling Consortium of Organizations for Strong Motion Observation Systems California Institute of Technology-USGS-Broadcast of Earthquakes

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CUSEC DEM DMA DOE DOI DPM DPRI EAL ECHO EERI ELF EOC EP EPEDAT EPRI ETABS EQE ERI FEM FEMA HMGP FRISK

GIS GPS GSHAP GSN HAZMAT HAZUS® HAZUS®97 HAZUS®99 HAZUS®99-SR1 HAZUS®99-SR2 HAZUS®MH HAZUS®MH-MR1 HAZUS®MH-MR2 HEC HSV

Central United States Earthquake Consortium digital elevation model Disaster Management Act of 2000 Department of Energy Department of Insurance (California, aka CDI) Damage Probability Matrices Disaster Prevention Research Institute (Kyoto University, Japan) Expected Annualized Losses Earth Change and Hazard Observatory Earthquake Engineering Research Institute Equivalent Lateral Force Emergency Operations Center Exceedance Probability Early Post-Earthquake Damage Assessment Tool Electric Power Research Institute Software for Integrated Analysis, Design and Drafting of Building Systems by Computers & Structures, Inc. (CSI) EQE International, Consulting Company Earthquake Research Institute, founded 1923 from IEIC Finite Element Method Federal Emergency Management Agency Hazard Mitigation Grant Program A Windows-Based Program for Seismic Hazard Evaluation, developed by Robin McGuire, Risk Engineering Inc., Boulder, Colorado Geographic Information Systems Global Positioning System Global Seismic Hazard Assessment Program Global Seismic Network Hazardous Materials Hazards U.S. Hazards U.S. 1997 Release Hazards U.S. 1999 Release Hazards U.S. 1999 Service Release 1.0 (2001) Hazards U.S. 1999 Service Release 2.0 (2002) Hazards U.S. Multi-Hazards Hazards U.S. Multi-Hazards Maintenance Release 1.0 (2005) Hazards U.S. Multi-Hazards Maintenance Release 2.0 (2006) Hydrologic Engineering Center (USACE) Hue, Saturation, Value

List of Abbreviations IAEE IBC ICBO

ICC

ICES

ICMA IDNDR IEIC InSAR IRIS ISC ISDR IT JMA LRFD LessLoss LFRS LIDAR LLNL LRFD LUPM M MAE Center MAEViz

MBT

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International Association of Earthquake Engineering International Building Code. Published as a model code by ICC since 2000. International Conference of Building Officials. One of three statutory members of the ICC. Producer of the UBC. International Code Council. Formed from three statutory members: BOCA, ICBO, and SBCCI. Producer of the IBC. Integrated Civil Engineering System, a visionary project at MIT in the 60s to develop an integrated system of software – included COGO, STRUDL, BRIDGE, LEASE, PROJECT, ROADS and TRANSET (only STRUDL is explained in this Glossary). Internal languages include ICETRAN and CDL. See "An Integrated Computer System for Engineering Problem Solving", D. Roos, Proc SJCC 27(2), AFIPS (Spring 1965). International City/County Management Association International Decade for Natural Disaster Reduction Imperial Earthquake Investigation Committee (Japan), founded 1892 following Nobi Earthquake Interferometric synthetic aperture radar Incorporated Research Institutions for Seismology International Seismological Centre International Strategy for Disaster Reduction Information technology Japan Meteorological Agency Load and Resistance Factor Design EU project for Risk Mitigation for Earthquakes and Landslides (http://www.lessloss.org) Lateral force resisting system Light detection and ranging Lawrence Livermore National Lab First-Generation Limit States Land Use Portfolio Modeler Moment magnitude Mid-America Earthquake Center A joint effort between the Mid-America Earthquake (MAE) Center and the National Center for Supercomputing Applications (NCSA) to develop the next generation of seismic risk assessment software using consequancebased risk management methods. Model Building Types

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MCEER MFL Mmax MMI MSK

MULTIFRAME Mw NASA NCEER NCSEA NCGIA NEES NEHRP NEIS NERIES NFIP NFPA NGDC NIBS NISEE NIST NOAA NOLA NORMA NPP NPV NRC NSF NSMP OES

Multidisciplinary Center for Earthquake Engineering Research (State University of Buffalo) Maximum Forseeable Loss Maximum magnitude Modified Mercalli Intensity Medvedev-Sponheuer-Karnik or MSK64 seismic intensity scale (evolved into the European Macroseismic Scale) Software for 2D and 3D modeling and simulation by Daystar Software, Inc. Moment Magnitude National Aeronautics and Space Administration National Center for Earthquake Engineering Research National Council of Structural Engineers Associations, of which SEAOC is one. National Center for Geographic Information and Analysis Network for Earthquake Engineering Simulation National Earthquake Hazard Reduction Program National Earthquake Information Service Network of Research Infrastructures for European Seismology National Flood Insurance Program National Fire Protection Association National Geophysical Data Center National Institute of Building Sciences National Information Service for Earthquake Engineering National Institute of Standards and Technology National Oceanographic and Atmospheric Administration New Orleans, Louisiana Nonprofit Risk Management Center Nuclear Power Plant Net Present Value National Research Council or Nuclear Regulatory Commission National Science Foundation National Strong-Motion Program Office of Emergency Services

List of Abbreviations OpenSEES

OpenSHA

PARMA PBEE PBO PDE PEER PERI PGA PGV PHIVOLCS P[LS] PMF PML PRA PREC8 PRIMA PSHA RANN RAMP RIMS RISA3D RSM Sa SAFERR SAP2000 SBCCI

SCDF SCEC SDC

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Free open-source software developed by the Pacific Earthquake Engineering Center (PEER) to simulate the performance of structural and geotechnical systems subjected to earthquakes. Open Seismic Hazard Analysis – an effort to develop object-oriented, web- & GUI-enabled, open-source, and freely available code for conducting Seismic Hazard Analyses (SHA) sponsored by the Southern California Earthquake Center and USGS http://www.opensha.org Public Agency Risk Management Association Performance-Based Earthquake Engineering Plate Boundary Observatory Preliminary Determination of Epicenters Pacific Earthquake Engineering Research Public Entity Risk Institute Peak ground acceleration Peak ground velocity Philippine Institute of Volcanology and Seismology Limit State Probability Probable Maximum Flood Probable Maximum Loss Probabistic Risk Analysis Prenormative Research in Support of Eurocode8 Public Risk Management Association Probabilistic Seismic Hazard Analysis Research Applied to National Needs, a program at NSF in the 1970s Regional Assessment of Mitigation Priorities Risk and Insurance Management Society 3D Structural Engineering Software by RISA Technologies Regional Spectral Model Spectral acceleration Safety Assessment for Earthquake Risk Reduection Integrated software for structural analysis and design by Computers & Structures, Inc. (CSI) Southern Building Code Congress International. One of three statutory members of the ICC. Producer of the Standard Building Code (SBC). Seismic Core Damage Frequency Southern California Earthquake Center Seismic Design Category

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SDDA SEAOC SFFD ShakeMap

SHA SIC SQUG

SRA SSA SSJ SSHAC STAAD

STRUDL ST-RISK TCLEE UBC UNAM UMB URM USACE USAR USCGS USGS USQUAKE Vis5D

WASH-1400 WWSSN ZEUS-NL

Seismic Design Decisions Analysis – a project at MIT 1973-78 (R.V. Whitman, PI) Structural Engineers Association of California San Francisco Fire Department A product of the U.S. Geological Survey Earthquake Hazards Program in conjunction with regional seismic network operators. ShakeMap sites provide near-realtime maps of ground motion and shaking intensity following significant earthquakes. seismic hazard analysis Standard Industrial Classification Seismic Qualifications Utility Group – an owners group that collects, evaluates, and facilitates the use of earthquake and testing experience data on behalf of its member companies. The group is organized under the auspices of the Electric Power Research Institute (EPRI). Seismic Risk Assessment Seismological Society of America Seismological Society of Japan Senior Seismic Hazard Analysis Committee Structural engineering software product for 3D model generation, analysis and multi-material design by Research Engineers International. STRUctured Design Language Software Tool for Site-specific Earthquake Risk Management by Risk Engineering, Inc. Technical Council on Lifeline Earthquake Engineering Uniform Building Code Universidad Nacional Autónoma de México Unreinforced Masonry Building Unreinforced Masonry U.S. Army Corps of Engineers Urban Search and Rescue U.S. Coast and Geodetic Survey U.S. Geological Survey Software for risk analysis, by EQECAT, Inc. A software system for interactive visualization of large 5-D gridded data sets such as those produced by numerical weather models Reactor Safety Study (1975) aka Rasmussen Report World Wide Standardized Seismographic Network Analysis and simulation platform of the MAE Center

1 Introduction

Ann Bostrom1, Steven P. French2 1,2

Georgia Institute of Technology, Atlanta, Georgia, USA

The first years of the 21st century brought horrific loss of life and property from earthquakes and tsunamis worldwide. Briefly, the world focused on international disaster prevention, response and recovery. Terrorism loomed large as well, after 9–11, leading to the creation of the Department of Homeland Security in the United States, and a plethora of related efforts globally. Many of these focus on the built environment. In the U.S. and elsewhere, large-scale infrastructure is stressed; roads, bridges, sewers, and dams built in the last century are deteriorating. Rising population is taxing existing infrastructure more and more as its reliability declines. As a society, we are developing dependencies on new kinds of infrastructure; these too are fragile and may age even less gracefully than sewers and roads. Our infrastructure – including human services, financial, and information – is both increasingly vulnerable and increasingly critical to society. Around the world, we are extending the built environment into increasingly fragile natural environments, raising the potential for catastrophe from natural disasters. Social, economic and environmental disparities are also growing between groups, both within the U.S. and between developed and developing countries, putting vulnerable groups even more at risk from extreme events. In the United States, recognition of these vulnerabilities has led to renewed efforts to develop risk and loss assessment tools, in particular HAZards US-Multi-Hazard (HAZUS®MH). HAZUS® is a federally funded GIS-based risk assessment tool for losses from winds, floods and earthquakes. Mandated for use by communities in the U.S. for loss assessment, HAZUS® is now being applied internationally as well, in response to the wide-felt need for improved information and decision support. This vol-

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Ann Bostrom, Steven P. French

ume addresses strategic directions for developing HAZUS® and related tools. While there is a significant body of research on related topics, there is no single source summarizing the state of seismic risk assessment modeling and decision support. This volume summarizes the current status of risk assessment, modeling and decision support, its history and current status for seismic risks, and future directions. The authors provide a positive vision of what the next generation HAZUS® could be (especially with regard to earthquakes) and how natural hazard modeling can be used to communicate risk and support individual and public decision making more effectively. They go beyond providing a guide for software development, however, and suggest the emergence of a new discipline from the confluence of ideas from modeling, engineering, sociology and psychology. The volume is the product of a workshop held in Boulder, Colorado in July 2006, enhanced by lively discussion at the workshop. This format allowed for a great deal of synthesis and cross-pollination between experts from a wide variety of disciplines all working in the areas of natural hazards and risk communication. The resulting synthesis defines ways to improve the applications and uses of natural hazards modeling. While the papers and discussion focus primarily on HAZUS® for earthquakes, the material is applicable to other extreme events, including floods and hurricanes.

1.1 Roadmap to the Book The collected papers in this volume integrate the results from current research efforts in earthquake engineering with research from the larger risk assessment community. The authors include researchers from the major U.S. earthquake centers and some from abroad as well as programmers and analysts from leading insurance and risk assessment firms. Four key components of risk assessment and decision support software are analyzed in detail in the papers: treatment of uncertainty, loss assessment, modeling of seismic mitigation strategies, and visualization of risk and uncertainty. Contributions come from senior experts in the natural hazards research community, as well as from senior scholars from other related fields. The volume lays out a road map for future developments in seismic risk modeling and decision support and helps earthquake engineering researchers position their work within the family of risk analysis tools and techniques. After this brief introductory chapter, the volume opens with a comprehensive historical account of the emergence of seismic risk modeling and

1 Introduction

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decision support, by Charles Scawthorn, followed by additional perspectives on that history from Dennis Mileti, Robert Whitman and Amr Elnashai in Chapter three. The fourth Chapter is authored by key developers of HAZUS®, Tom Durham, Pushpendra Johari, and Doug Bausch and describes the current status and directions for HAZUS®. The following chapter, with contributions from Rachel Davidson, Phil Schneider and Subramanyam Muthukumar touches on the role of HAZUS® in planning and directions for future development and uses of the software. Ron Eguchi and Hope Seligson, two veterans of loss estimation modeling, are the authors of Chapter six. This chapter focuses on earthquake loss estimation modeling, including the history of the development of these tools, summaries of several case studies that have been used to calibrate and validate specific models and general guidelines on the use of loss estimates. The subsequent commentaries by Stephanie Chang, Walter Peacock and Steve French in Chapter seven outline the limitations of loss estimation modeling due to lack of empirical data and complex linkages between physical and social damage, and emphasize the importance of setting appropriate priorities in loss determination. In Chapter eight, Robin McGuire proposes the use of Probabilistic Seismic Hazard Analysis for seismic risk mitigation decisions, and includes analysis of the effectiveness of this approach in the context of nuclear power plant design Mitchell Small, Bruce Ellingwood and Leonardo Dueñas-Osorio put McGuire’s analysis into context in Chapter nine, highlighting the decision context, particular analytical challenges, and the importance of continuing to press on system-level social and network analyses, and on analyzing and communicating risks and uncertainties for policy makers and general audiences. Patricia Grossi provides an overview and comparison of proprietary insurance-based models and the federal government’s model for assessing hazard risk exposures from an economic perspective in Chapter ten. Lively discussion of this paper in Chapter eleven by Laurie Johnson, Mary Beth Hueste, Robert Olshansky and Yang Zhang touches on the limitations of mitigation modeling including the inability of economic models to fully account for social losses and loss of life and how risk perception affects users’ willingness to adopt recommendations implied by the models. As many of the earlier chapters make clear, models, metrics, and mitigation strategies are of little use to society if a strong case is not made for the high level of risk in natural hazards, taking into account their high level of uncertainty. In Chapter twelve, Alex Pang provides a visually rich discussion of different ways to present complex, multi-dimensional data in ways that are sensitive to user needs. The commentaries in Chapter thirteen by

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Ann Bostrom, Steven P. French

three experts in the area of risk communication, Ellen Peters, Ann Bostrom and Susan Cutter, provide insight into the ways that the lay public perceives numeric, geographic and graphical information – a perspective that is essential to the acceptance of any decision support system. The volume concludes with a proposal for strategic development of seismic risk modeling and decisions support, in the context of multi-hazard software tools like HAZUS. Open source software, internet access for users, transparency regarding the sources and quality of data, and advanced data collection through sensors, linked to adaptive data analysis and visualization techniques are among the recommendations emerging from the volume. We hope this work will be useful not only to professionals and students in earthquake engineering, but also to readers from the larger natural hazards and risk assessment research communities. Those of you with a more general interest in science and technology policy and risk communication may find herein insights on the relationship between earthquake engineering analyses and support for earthquake policy and mitigation decisions. This volume would not have been possible without the support of the National Science Foundation (Award Number 9701785), the Mid-America Earthquake Center, Georgia Institute of Technology, the generous contributions of the contributing authors and other workshop participants, and Sara’s outstanding coordination and editing. Thank you.

2 A Brief History of Seismic Risk Assessment

Charles Scawthorn1 Kyoto University, Kyoto, Japan

2.1 Introduction Seismology and earthquake engineering have rich histories, perhaps due to the fact that earthquakes tend to affect almost everything around us – after all, one can take shelter from a storm, but not from an earthquake. Great minds – Aristotle, Plato, Da Vinci and Kant to name a few – have grappled with the problems of earthquakes. When issues of risk are added to those of earthquakes, the field and history are further enriched (think of adding Pascal, Fermat, Bernoulli, Keynes, etc.), and the challenge of writing a history increased commensurately. The task is daunting – Housner (1984) observed: “Earthquake engineering is a 20th Century development, so recent that it is yet premature to attempt to write its history … Although 1984 is too soon to write a definitive history, it is an appropriate time for an historical view of earthquake engineering development to see where we were, where we now are, and where we are going…”

This writer agreed entirely when he heard those words in 1984, but two more decades later the perspective is greatly improved, at least as regards seismic risk assessment. In fact, as we shall see, in many ways we were just on the verge of seismic risk analysis and assessment in 1984, whereas today we have very significant capabilities.

1

© C. Scawthorn, 2006.

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Charles Scawthorn

This paper addresses developments in seismology, engineering and related fields narrowly, and only to the extent that the development of seismic risk assessment was directly affected. We stop at seismic risk assessment, and do not discuss seismic risk management. We begin by defining selected terms, since some confusion still lingers in that regard, and provide a very brief overview of seismic risk assessment. We then get to the heart of the matter – the key historical events, developments and influences by which seismic risk assessment came into being. This discussion is broadly structured according to Hazard, Vulnerability, Assets, and Methods. Following is a summary assessment, selected references, tables and figures. An exhaustive bibliography is available from the author.

2.2 Terminology Seismic risk is the potential or probability of a loss due to the occurrence of an earthquake. The risk is the combination of three main elements – the earthquake hazard, the assets at risk (i.e., the value that is threatened by the earthquake), and the vulnerability of the assets to the effects of the earthquake. Figure 2.1 depicts this conceptually. While earthquake hazard has a relatively narrow scope of simply the physical effects (faulting, shaking, liquefaction, landsliding, tsunami etc.) of the earthquake event on the natural environment, whether defined deterministically or probabilistically, the elements of assets at risk and vulnerability need explanation. Assets at risk is used here in the broadest sense – it includes not only those physical items that have financial value, but also non-physical items that have financial value (e.g., business interruption, reputation) and very importantly, those items more intangible yet of great value, such as social cohesiveness and peace, public confidence, political union, education, mental health and so on. There are not a few instances of governments overthrown due to their inadequate response to a great earthquake (e.g., 1755 Lisbon, 1972 Managua). Vulnerability is the susceptibility of the assets to the hazard, so that vulnerability is defined here just as broadly as assets, although in most usage throughout this paper a somewhat narrower more technical meaning is employed.


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Fig. 2.1. Elements of Seismic Risk

The process of analyzing the risk involves a fourth crucial aspect – the mathematical and theoretical methods by which the three elements are combined to more or less rigorously and accurately estimate the risk. Seismic risk analysis is not an end in itself – rather, the risk must then be judged or assessed as to its acceptability, relative to social norms, and to other priorities. Seismic risk assessment is still not the end – rather, it is the foundation for seismic risk management, the appropriate and efficient allocation of resources for reducing the risk to acceptable levels. While we begin by defining some terms, and other terms where used, we assume a knowledgeable reader: x Earthquake: Tectonic movements of the earth’s crust resulting in propagating broad-banded vibratory motion (volcanic earthquakes and human-induced events such as blast are not considered here). x Assets: the humans, animals, property, business operations and other items of value whose reduction in value (loss) is a concern. In general, the economy and built, social and natural environments that may be affected by an earthquake. Similar terms include exposure, inventory, and portfolio. Note that assets include intangibles such as intellectual property, reputation, etc.

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Charles Scawthorn

x Damage: Death, collapse, physical injury or other degradation in condition of assets. x Seismic hazard: The phenomena and/or probability of an earthquakerelated agent of damage, such as fault rupture, vibratory ground motion (i.e., shaking), inundation (e.g., tsunami, seiche, dam failure), various kinds of permanent ground failure (e.g., liquefaction, landsliding), fire, or hazardous materials release. x Seismic Intensity: A metric of the effect, or the strength, of an earthquake hazard at a specific location, commonly measured qualitatively using scales such as MMI, MSK, and JMA, or quantitatively with measures such as Peak Ground Acceleration (PGA), response spectral acceleration, etc. x Loss: the decrease in asset value resulting from damage. Loss may be direct, or indirect (the terms ‘direct’ and ‘indirect’ loss are used variously – see Table 3 for an example, and concordance of usage). x Vulnerability generically refers to the probability of damage given the occurrence of a hazard. If damage is defined as the median damage, the relation with hazard is termed a vulnerability function or curve. In some cases damage and loss are combined, so that vulnerability can be the probability of loss given the hazard. Note also that in the social domain vulnerability has a somewhat different meaning, referring more to the lack of capacity of populations and social systems to cope with disasters. x Fragility: complementing vulnerability is the concept of fragility, which is the probability of being in a damage state (e.g., “1%” damage, or “light” damage) given the hazard. x Seismic risk: potential or probability of a loss due to the occurrence of earthquakes and associated hazards. x Risk analysis: Systematic use of information to identify sources and to estimate risk. Information can include historical data, theoretical analysis, informed opinions, etc. x Risk assessment: Overall process of risk analysis and risk evaluation. x Risk evaluation: Process of comparing the estimated risk against given risk criteria to determine the significance of the risk. Risk evaluation may be used to assist in the decision to accept or to mitigate a risk. x Risk management: the process and acts to reduce risk in an orderly, efficient and desirable manner. Appendix 3 provides an example of some of these terms, and other terms, and also shows that there is still some confusion in terminology in the indirect loss arena.


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2.3 Overview of Seismic Risk Assessment As shown in Fig. 2.1, seismic risk analysis is the combination of three main factors – hazard, vulnerability, and asset value. Seismic hazard is the result of a process combining the probabilistic occurrence of one or more earthquakes (the ‘source’), with the estimated effects of the earthquake at a site (‘attenuated’ to the site via the ‘path’), considering the specific conditions at the site (‘site-effects’). While the hazard is what ‘nature does’, assets are generally what humankind ‘puts in the way of nature’, although even the natural environment will be disturbed by an earthquake and may lose ‘value’ from a scenic or other perspective. Assets are people, buildings, contents, communications, organizational operations, etc. Any asset will have many relevant attributes, such as the age, health and income of a person, the location, age, size, materials of construction, lateral force resisting system and occupancy of a building, the location, size etc, nature and criticality of operations, and redundancy, of a financial data center, or water or other lifeline system. Vulnerability or fragility functions are developed for each asset based on the general or specific seismic resistive characteristics of the asset. In general, one or both of two methods are employed (see Fig. 2.2): 1. empirical, in which observations of the performance of similar assets are implicitly or explicitly assessed. Empirical sources can further be divided into three important sub-classes: a) Field survey data – that is the qualitative and statistical record of observed damage in actual earthquakes. b) Experimental and laboratory data, derived from tests of components and/or small scale models (although recently full scale model testing is becoming more feasible). c) Expert opinion-derived, which is the estimate by knowledgeable persons with first-hand experience observing earthquake performance of structures. The expert opinions may be derived and expressed by more or less rigorous methods. 2. analytical, in which the properties of the asset are analyzed using a theoretical model based on a mechanics of materials or other theoretical framework. Ideally, the two methods should agree, or be used in conjunction in a vulnerability function.

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Charles Scawthorn

Fig. 2.2. Vulnerability Development Methods

If empirically-derived data are employed to calibrate an analytically-based model, the result is termed a hybrid or correlative model. Relatively few hybrid models have been developed. Seismic risk assessment takes the results of a seismic risk analysis (SRA) and compares it against societal, organizational or personal norms.


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These norms vary with each situation – they may be legally mandated, or may be decided on the basis of more or less rational decision making criteria, such as benefit cost analysis.

2.4 A Brief Chronology of Selected Events in Seismic Risk Assessment Appendix 1 presents a chronology of selected events key to the development of seismic risk analysis. The column headings Hazard, Vulnerability, Assets and Risk are rubrics, intended generally to refer respectively to developments in the Earth Science, Built Environment and Socio-Economic spheres, and to those developments that contribute to integrating or analyzing those three spheres into an assessment of the likelihood of loss – that is, seismic risk. Many events cut across more than one of these spheres. An example is seismic intensity – quantification of damage can be a measure of shaking intensity, or a measure of structural integrity, depending on what one regards as the independent variable. That is, one measures the strength of shaking at various sites by observing damage to similar construction at those sites (intensity is the independent variable), or one takes the intensity of shaking as given (dependent variable), and judges the relative seismic resistiveness of structures (damageability is the independent variable) subjected to the same intensity. Historically, and confusingly, seismic intensity has been used both ways – isoseismals are mapped and earthquake magnitudes thereby estimated, and damage is estimated based on intensity (e.g., ATC-13 1985). There is a significant although not complete tautology in this usage – the tautology is diluted in that human reactions and natural phenomena (e.g., liquefaction) are also used in estimating intensity, but not typically vice-versa. In Appendix 1, developments with respect to intensity cut across Hazard and Vulnerability. By allocating the space in the table in this way, large empty swaths show lagging development.

2.5 How We Got Here In his keynote speech to the 1984 8th World Conference of Earthquake Engineering in San Francisco, George Housner also noted “… there are still many people that remember relevant information and would be severe critics of a history. To write an acceptable history, it is necessary to wait till most of the poorly known facts have disappeared from memory and litera-

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ture, then, with a coherent blend of fact and fiction, history can be written.” (Housner 1984). So, let us attempt a “coherent blend of fact and fiction”, knowing full well that “there are still many … critics of a history.” 2.5.1 Hazard and Some Related Matters – Early Days The history of seismology, and earthquake engineering, begins with Robert Mallet (1810–1881). His interest was two-fold – the pursuit of pure knowledge, but also the reduction of death and destruction – embodying the complementary natures of the scientist and the engineer. Space does not permit a full discussion of his accomplishments, but his Catalogue of global seismicity and 1857 map, Fig. 2.3 (Mallet and Mallet, 1858) foresaw truths painfully learned much later – plate tectonics, seismic zones, microzonation, seismic risk. He was followed by Rossi, Forel, Mercalli, who in the same spirit developed scales that serve to both measure the size of the natural event, and the effects of that event on humankind. Milne went to Japan, experienced an earthquake and never went back to geology. With Ewing and Gray he immediately developed excellent seismographs and the network infrastructure they demand, and founded the Seismological Society of Japan (his perspicacity can be judged by the fact that the Seismological Society of America (SSA) was not founded until 25 years later). Japan was fertile ground, not only in its seismicity but in the expansive energy of a people recently released from feudal bonds. Spurred by the Nobi earthquake of 1891, within a few years a large cadre of solid scientists and engineers (Omori, Suyehiro, Sano, and others) had built a solid understanding of earthquake mechanics and seismic design. (It should be noted that the Seismological Society of Japan was dissolved in 1892, the year after the Nobi earthquake, perhaps due to the establishment of the Imperial Earthquake Investigation Committee. SSJ was re-established in 1929). The 1906 San Francisco earthquake was not the seminal event it should have been. Much storied and gloried, it allowed Reid to recognize elastic rebound, but not much else of value – seismological, engineering or seismic risk – emerged. SSA was established, but it had been the fire, and seismic considerations did not make their way into San Francisco’s, or any US, building code. This is particularly ironic given the solid risk management that was emerging in the fire insurance field – since the civil war, Sanborn and others had been developing detailed risk data, and US cities, tired of their conflagrations, were spending big money to build special high pressure water systems. The 1908 Messina (Italy) earthquake, with 70,000 dead, was much more of a seminal event. The Italians appointed an


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excellent commission, who developed principles of seismic design still in use today (the Japanese did the same contemporaneously and independently). It was in Southern California where the next progress was made. In 1920 there was no seismological observatory in Southern California. Harry Wood was brought to the California Institute of Technology by Robert Millikan to fill that vacuum. Wood was a pragmatic fellow who was more interested in studying earthquakes in his backyard rather than from thousands of miles away, since the local events were potentially damaging. He and John Anderson immediately built a practical seismograph which began to generate good quality data which Charles Richter, when he arrived in 1925, began to analyze. In 1923 the Tokyo earthquake had motivated Japan to adopt a rational seismic design procedure in its building code, using the Equivalent Lateral Force (ELF) method developed by Sano early in the century (and independently, by the Italians). The 1925 Santa Barbara earthquake, combined with a seminal series of papers in 1923–1924 by Stanford professor Bailey Willis in the Bulletin of the Seismological Society of America, led to adoption in 1927 of a similar provision in the first Uniform Building Code. The Italians, Japanese and American engineers all agreed that an ELF of about 10%, adjusted for soils and transient stresses, should suffice, due in part to the successful performance of buildings in the 1923 Tokyo earthquake that had been designed in this manner by Tachu Naito. Independently, insurance companies had ‘taken a beating’ in the 1925 Santa Barbara earthquake, which caught the attention of a remarkable engineer and insurance executive named John Ripley Freeman. Space does not permit telling of the full story, but suffice it to say that Freeman (1930) looked into the situation and was shocked: The American structural engineer possesses no reliable accurate data about form, amplitude, or acceleration of the motion of the earth during a great earthquake to withstand which he must design a structure. Notwithstanding there are upward of fifty seismograph stations in the country and an indefinitely large number of seismologists, professional and amateur; their measurements of earthquake motion have been all outside of the areas so strongly shaken as to wreck buildings … Most of the prominent American textbooks on theory of structures say nothing about earthquakeresisting design.

Freeman decided the US needed a strong motion program, started with the local officials and went up the ladder to the Secretary of Commerce and even President Hoover. The National Bureau of Standards was put on the job, and designed and built a standard instrument for the US Coast and

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Geodetic Survey, who deployed it, all within a year or so and just in time to catch the strong motions from the 1933 Long Beach earthquake. Engineering seismology was born, a prerequisite for seismic design. The maximum PGA recorded in 1933 was 280 gals, several times greater than the recently promulgated ELF requirements in the UBC and Japanese codes. Inexplicably given this evidence, California passed the Riley Act, requiring buildings to be designed for a minimum lateral force (quite small, in practice). Also passed was the much more influential Field Act, which required all K-12 public schools in California to be designed under the supervision of the State Architect. Following 1933, the growth in strong motion observations (Fig. 2.4), was very slow, from 27 in 1933 to a total of about 1,000 in 1971, when the total was doubled by the 1971 San Fernando earthquake. Figure 2.5 shows the global growth since – today, there are an estimated 150,000 records worldwide (author’s estimate), which have shown that PGA of 0.7g is not unrealistic for sites close to a fault in a large earthquake. Figure 2.5 also shows the geographic distribution of those records – Japan’s strong motion programs today dwarf those of most of the rest of the world, and Japan has recorded about 60% of the global archive2. In 1935 Richter, who had been struggling with the growing body of data that the Wood-Anderson seismographs were generating, read a paper by Wadati which suggested that the maximum amplitude of a seismograph correlated with the size of an earthquake. This led to Richter defining earthquake magnitude as the maximum amplitude of the Wood-Anderson seismographs at a standard distance. The concept of magnitude, although simple and borrowed from astronomy, was very powerful, not only for communicating to the public but also as a simple measure that could be employed in statistical studies. By 1941 Gutenberg and Richter had accumulated enough data to sort out global seismicity and publish “Seismicity of the Earth”, on the basis of which they could state a power law for earthquake occurrence – the well-known Gutenberg-Richter magnitude frequency relations log N = a – b M, where N is the number of events greater than magnitude M3. They had also developed relations between magnitude, energy, acceleration and intensity, which allowed estimation of the strong motion given the magnitude and distance, and estimation of the in-

2 Actually, there is no single global archive, although COSMOS and NOAA’s National Geophysical Data Center are relatively large useful international archives. 3 Ishimoto-Iida (1939) had independently found a comparable relation, but it wasn’t widely known.


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tensity given the acceleration4. Wood and Neumann in 1931 had modified the Mercalli Scale for US construction, so that a correlation between intensity and damage was also now at hand.

Fig. 2.3. (This figure also appears in the color insert) (a) Mallet’s 1857 map; (b) Global Earthquakes 1900–1990 (Agnew 2002); (c) Global Seismic Hazard Analysis Project Map (GSHAP 1998)

4 They noted the possibility, but declined at that time, to define magnitude in terms of total event energy. That would wait for Kanamori (1978), and Hanks and Kanamori (1979), to define moment magnitude.

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Fig. 2.4. Cumulative number of strong motion records in the COSMOS database, from 27 in 1933 to 27,000 today. COSMOS is probably the most complete and representative database of strong motion records, although an estimated 150,000 strong motion records are estimated to have been recorded (author’s estimate). Also shown in the graph is the maximum peak ground acceleration (PGA) by decade – the more observations, the greater the maxima. The highest recorded PGA was 2,322 gals in the 1985 Nahanni (Canada) earthquake.

Fig. 2.5. Number of Strong Motion records by country (compilation by the author). Japan has almost 100,000 records, or about 60% of the world total.


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Therefore, by about 1940 seismology was able to instrumentally estimate the size of earthquake on a defined scale (magnitude), and estimate the probability of various size events in a future period (log N = a – b M, with a and b defined for different regions). There were about 100 strong motion recordings which had been used to develop estimates of acceleration, and intensity, as a function of magnitude and distance. And, intensity defined damage. Therefore, all the elements were at hand for estimating the probability of damage. However, perhaps due to World War II, no one seems to have made that leap. At about the same time, also at the California Institute of Technology, Biot and Housner developed the concept of response spectra, which was immediately recognized as of great value as it permitted consideration of multiple modes of vibration in earthquake response. 2.5.2 1950’s During most of the 40s, ‘seismologically speaking’ not much seems to have happened, undoubtedly due to World War II. Following the War, the Earthquake Engineering Research Institute (EERI) was founded in 1949 to promote research. Research was needed – in 1948 the US Coast and Geodetic Survey issued the first national seismic ‘probability’ map, although the map was not probabilistically based. Rather, it was a mapping of the maximum observed intensities, clustered into ‘zones’. The map had problems – Zone 3 was contiguous with Zone 1 in some places, Zone 2 with Zone 0 – but its main problem seems to have been resistance to designing for earthquakes. The map was revised the following year, so that Charleston, South Carolina moved from Zone 3 to Zone 2, and then the following year the map was withdrawn. As Beavers (2002) relates: “… the USCGS made the following official statement: ‘The Seismic Probability Map of the United States, SMC-76, issued by the US Coast and Geodetic Survey in 1951, has been withdrawn from circulation because it was found to be subject to misinterpretation and too general to satisfy the requirements of many users.”

And, quoting a 1996 study (Mittler, as quoted in Beavers 2002): “... the United States Geodetic Survey retracted a map in the 1950’s because business and scientific interests applied pressure on the grounds that the map (like all science) was subject to misinterpretation. There also appears to have been resistance by the public and other federal agencies to design buildings and facilities

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Charles Scawthorn to earthquake loads where earthquakes were not generally known to occur.”

Nevertheless, the map was adopted for the 1949 edition of the UBC, and served as the national seismic zonation map until 1970. In contrast, in Japan in 1951 Kawasumi put the elements of earthquake magnitude, frequency and strong motion attenuation together, to produce a probabilistic acceleration map of Japan, Fig. 2.6. In his paper, Kawasumi (1951) stated: “… and if we want to know, for example, expectancies of … total amount of damage in current price or number of casualties etc., they may be determined from this mean frequency, since these damages are respectively functions of the intensity of an earthquake. These expectancies themselves also serve as the practical indices of earthquake danger. It is also to be noted that we can also derive a physical index of earthquake danger.”

It is interesting to compare seismic hazard maps in use in the Japan and the U.S. in 1951, Fig. 2.6 and 2.7. The Japanese map is probabilistically based and offers substantial detail, clearly reflecting significant variation in seismic hazard. The US map, not probabilistically based, divides California, roughly the same size as Japan, into three zones, with Zone 3 covering more than half of the state. Housner in 1952 developed an approach for probabilistic seismic mapping similar to that of Kawasumi, in a report for the Office of Naval Research which however doesn’t seem to have been widely used or published. Liquefaction had been noted in many earthquakes (e.g., by Dutton in the 1886 Charleston event) but it was only in the 1950s that Mogami and Kubo (1953), noting the virtually total loss of strength of vibrated sands, coined the term liquefaction, and Housner (1958) wrote a beautiful analysis of the ‘mechanics of sand blows’. However, as Housner noted in 1997 in reference to his own paper “neither I nor anyone else…seemed to grasp the practical significance of the phenomenon …” When buildings overturned in the 1964 Niigata (Japan) earthquake, liquefaction was ‘discovered’ and became the subject of intensive and extended research.


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Fig. 2.6. Expected maximum acceleration in 200 years (reproduced from Kawasumi 1951)

Fig. 2.7. Seismic ‘probability’ map, 1949 Uniform Building Code (Roberts and Ulrich, 1951) – this map, which is not probabilistically based, was retracted by the US Coast and Geodetic Survey in 1952, but remained in the UBC until 1970.

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An outgrowth of the organization of EERI was the World Conference of Earthquake Engineering, held at Berkeley in 1956, on the 50th Anniversary of the 1906 San Francisco Earthquake. While the 1929 World Conference of Engineering in Tokyo had had the 1923 earthquake as one of its foci, the 1956 conference was the first conference focused solely on earthquakes. Forty papers were presented in 1956, the participants found the event very valuable, and it has led to a series of World Conferences of Earthquake Engineering, the 13th of which was held in Vancouver, B.C. in 2004. Figure 2.8 shows the growth in World Conference contributed papers, the fraction of such papers related to hazard or risk, and in (b) the number of peer-reviewed journal papers with titles relating to earthquake hazard or risk. Starting with work at Stanford and the California Institute of Technology in the 1920s and 30s, earthquake structural dynamics had developed in the US to the point where John Blume, Henry Degenkolb and other leading post-WW2 ‘second generation’ earthquake engineers wanted to introduce those concepts into the design practice. A group gathered in San Francisco and in 1952 produced “Lateral Forces of Earthquake and Wind” which by the end of the decade had developed into the SEAOC “Blue Book” (Recommended Lateral Force Requirements), which for the next 40 years was the earthquake section of the UBC, used by virtually most of the rest of the world (excepting Japan and the Soviet bloc). The Blue Book was a good code, influential and representing the best thinking of the time. Not in the Blue Book itself, but contained in its supporting materials was a statement that structures designed in accordance with the Blue Book should: x Resist minor earthquake shaking without damage x Resist moderate earthquake shaking without structural damage but possibly with some damage to nonstructural features x Resist major levels of earthquake shaking with both structural and nonstructural damage, but without endangerment of the lives of occupants This came to be known as the life-safety criterion and it became common knowledge among engineers, but not the public, that the intent of the code was to protect life safety, and that it did not prevent damage or protect investments or operations – that therefore the code was a minimum standard. The author should note at this point that the first time he read this statement, about 1973, he was immensely struck with its absurdity. The statement was one of the primary reasons his career shifted from structural design to risk analysis. The third bullet in the Blue Book philosophy provides for life-safety. However, it also provides for the situation that, if all buildings in a major city (like San Francisco) are designed per these provisions, then all buildings – the entire city – would sustain structural


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damage when subjected to major levels of earthquake shaking. What was the basis for that decision? How would an entire city cope with all buildings being damaged? Why design for that to happen? There were no satisfactory answers.

Fig. 2.8. (a) World Conferences, growth of papers; (b) Journals with paper titles containing the words ‘risk’ or ‘hazard’

And, other issues and questions were emerging – during the 50s and 60s society was being fundamentally transformed:

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x The digital computer came into existence (see Appendix 1 for some key dates), allowing increased analytic capability. x Like the digital computer, Operations Research was developed during World War II, and its statistical, probabilistic and structured approach was seeping into many areas of technology. x Another product of the war, atomic power, was being converted to ‘atoms for peace’. In the 50s only a few nuclear power plants were built, but construction accelerated quickly in the 60s, Fig. 2.9. x The 1927 Mississippi and other floods led to the Navigation Act of 1936, which introduced cost-benefit analysis, including the discounted cost of damage, into engineering and federal thinking (but in the flood arena, not seismic). There was a hiatus during the War, but by the 50s flood protection was a major effort, which had to be justified. To support this, the US Army Corps of Engineers formed the Hydrologic Engineering Center in 1963, to develop tools for estimating flood probability and avoided damage. x The fire insurance industry, which had led the way in accumulating detailed accurate exposure data (and loss statistics) since the Civil War, came to the conclusion that their loss statistics were probably adequate for future underwriting, and the legions of surveyors compiling Sanborn maps were no longer necessary. x Also in the insurance industry, Thomas F. Malone and Don Friedman brought the science of meteorology and risk analysis to Travellers Insurance, to model hurricane losses for the insurance industry. They came to Hartford from MIT to develop data and probabilistic estimates with enough rigor for the actuaries. x Ian McHarg taught ‘design with nature’ at the University of Pennsylvania. He and urban planners everywhere were struggling with how to rationally and efficiently process the many factors they recognized as important. Towards the end of the 60s, Geographic Information Systems emerged, with the development of Canadian GIS in 1967, and the founding of Environmental Systems Research Institute, Inc. (ESRI) in 1969. x Vance Packard wrote The Hidden Persuaders (1957) showing the manipulative tactics of Madison Avenue; Jane Jacobs in The Death and Life of Great American Cities (1961) critiqued isolated, unnatural urban spaces; Rachel Carson’s Silent Spring (1962) gave birth to the environmental movement; the outrageous demolition of New York’s Penn Station (1964) spawned the conservation movement; and Ralph Nader’s Unsafe at Any Speed (1965), started the consumer movement. These statements and the heartfelt public responses were indicative of an erosion of faith in U.S. institutions and their ‘trust me’ paternalism, and of


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an aversion to risks that unconsidered growth was creating. There arose a demand for demonstrable safety, which Carson, Nader and others showed Americans weren’t getting.

Fig. 2.9. U.S. Nuclear power growth and some related events

Technical demands – the risk of nuclear power, investment in offshore platforms – and a growing consciousness that explicit consideration of risk was both possible and desirable combined to set the stage for a new model. Steinbrugge (1968) spoke for the earthquake community in a landmark monograph Earthquake hazard (sic) in the San Francisco Bay Area; a continuing problem in public policy, which brought the discussion of earthquake risk into the public forum. The changing focus was signaled by the creation of the first center to provide research into the social science aspects of disasters, the Disaster Research Center at Ohio State University founded by Russell Dynes and Enrico L. Quarantelli in 1969.

2.5.3 1968 And then, in 1968, there appeared C. Allin Cornell’s paper Engineering Seismic Risk Analysis. In the field of seismic risk, no paper has had the impact that Cornell’s did. Perhaps Richter’s definition of magnitude had a

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similar impact on seismology in 1935. It was a breakthrough publication – everyone got it, the paradigm shift in earthquake engineering to a probabilistic way of thinking. Although it appeared to come out of the blue, it hadn’t. At UNAM, Luis Esteva and Emilio Rosenblueth (where Cornell had been a visiting professor) were working on similar lines, Kawasumi had produced national maps 17 years earlier, and Housner had been there, in a 1952 paper. In fact, Cornell in the first sentence of the paper cites Blume, 1965; Newmark, 1967; Blume, Newmark and Cornell 1961; Housner, 1952; Muto, Bailey and Mitchell, 1963; and Gzovsky, 1962. But the Cornell paper nailed it. The next sentence goes on to say, “The engineer professionally responsible for the aseismic design of a project must make a fundamental trade-off between costly higher resistances and higher risks of economic loss”. Simply put, the paper laid out a transparently clear, reproducible algorithm for integrating the probabilistic contributions of any number of faults (‘sources’), to determine the total ‘risk’ at a site – that is, the probability distribution of ground motion (an irony of the paper is that the risk in Cornell 1968 refers to hazard, as commonly used today). While the paper strives to provide a closed form solution, in general it couldn’t do that, and to this day virtually all seismic hazard estimates are developed via numerical integration using special purpose codes (discussed further below). Others soon followed – W.G. Milne and A.G. Davenport in 1969 used a similar methodology to develop a complete seismic hazard mapping of Canada, similar to Kawasumi’s mapping of Japan (Milne and Davenport submitted their paper seven months after Cornell, but before his appeared. This writer does not know if they were aware of Cornell’s work. However, while their paper is very good, it’s an application, and doesn’t read with the same clarity and impact as Cornell’s). Also in 1969, S.T. Algermissen, in the first sentence of his paper on page one of the 4th World Conference of Earthquake Engineering, observed “The zoning of the United States for seismic risk has not received the attention … that the subject has enjoyed in other parts of the world, notably in countries such as Japan, the U.S.S.R and New Zealand.” His paper went on to present a “seismic risk map” [Algermissen’s quotes] of the United States, Fig. 2.10a, which however he was careful to point out was not frequency (i.e., probabilistically) based and which was an interim map – more work was in the offing. Nevertheless, it was quickly adopted by the UBC, replacing the 1949 ‘withdrawn’ map. In 1976 a fully probabilistic map was released, Fig. 2.9b, using attenuation equations for hard rock developed by Per B. Schnabel. and H. Bolton Seed in 1973, modified for slower attenuation in the Eastern and Central US. The level of detail for California shown in the inset in Fig. 2.10b (not unlike the detail of Ka-


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wasumi’s 1951 map), when compared with the 1970 map, shows the analytical capability the Cornell method provided.

Fig. 2.10. (a) Algermissen (1969) map; (b) Algermissen (1976) map

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2.5.4 1970’s to Today From the early 70s to today, the seismic hazards field has generally been one of consolidation, with much effort on more and better data, and refinement of concepts and models. The basic elements for seismic hazard analysis are sources (derived from geological and seismological data), strong ground motion attenuation, site effects, and methods to analytically integrate the data. We summarize source, attenuation and analysis here, omitting site effects due to space. Source data availability has advanced enormously since Cornell’s paper. Plate tectonics emerged in the 60s and that framework has guided much source identification. Hanks and Kanamori (1979) reduced the plethora of magnitude scales that had developed following Richter in 1935, to the moment magnitude scale, Mw, that is increasingly the international standard. Global catalogs have been compiled from the various national catalogs, and much work has been done to remove duplicate entries and correct other errors. “The International Seismological Centre is a nongovernmental organization charged with the final collection, analysis and publication of standard earthquake information from all over the world. Earthquake readings are received from almost 3,000 seismograph stations representing every part of the globe. The Centre's main task is to redetermine earthquake locations making use of all available information, and to search for new earthquakes, previously unidentified by individual agencies.” (http://www.isc.ac.uk/). An issue for probabilistic hazard estimates like Milne and Davenport’s or Algermissen’s is how complete and homogeneous is an earthquake catalog? In 1972 Carl Stepp developed a useful test for catalog homogeneity, which has improved the quality of seismic hazard estimates. The problem is that a catalog may contain large events from long ago, but smaller events from that far back in history will likely be missing, due to lack of instrumentation, or simply lost records. For more recent periods, larger events will likely not yet have occurred. These gaps in the record result in underestimates of seismicity, relative to the long term record. To fill these gaps, many of the most famous names in seismology, such as Robert Mallet, Comte Montessus de Ballore and Charles Davison to name a few, have pored over historical documents to rediscover ancient earthquakes. One of the most diligent of modern investigators in this regard has been Prof. Nicholas Ambraseys of Imperial College (UK) who since the 60s has carefully researched the historical record, particularly in the Trans-Alpide belt (ie, from the Mediterranean through to the Himalayas). Careful examination of ancient documents can date an event and, by ascribing intensities to the descriptions, draw approximate isoseismals


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from which earthquake magnitude for the event can be back-calculated. The result of the effort of the many investigators doing this work is that the historical record of seismicity now dates back about 3,000 years, albeit very incompletely in the early period. To go back beyond the historical record, beginning in the 60s Wallace and co-workers began using paleoseismological techniques to extend earthquake catalogs back to prehistoric times. Examples of such techniques include dating of C14 deposits in ancient liquefaction sites, or in drowned forests, and dendrochronology (dating of tree rings). In 1984 these techniques were dramatically rewarded when evidence was found near Los Angeles by Kerry Sieh of repeated Ft. Tejon-like earthquakes going back a millennium. Another effort has been the patient mapping of earthquake faults by geologists over many decades. The result is that many regions now have detailed fault maps (http://earthquake.usgs.gov/regional/qfaults/ for the entire US; http://unit.aist.go.jp/actfault/english/products/index_e.html for parts of Japan). A large amount of effort has been spent on building strong motion networks – the first as discussed above was in Southern California in 1933, and the Japanese followed in the 1950s. Networks were established in the 1960s in Mexico, Europe, New Zealand, India, and in the 1970s in Turkey, Iran, the Philippines and Taiwan (where a very large amount of data was recorded in the 1999 ChiChi earthquake), and exist in many other countries. Figure 2.4 shows the growth in number of recordings, and Fig. 2.11 shows two of the larger strong motion networks in Japan, which, in sheer number of instruments, appear to dwarf networks anywhere else.

Fig. 2.11. (This figure also appears in the color insert) Two of the larger Japanese Strong Motion Networks. Reprinted with permission from Kashima (2000)

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With increasing numbers of strong motion recordings, better and better attenuation equations have emerged. A relation developed by Donovan (1973) was one of the first to use the data from the 1971 San Fernando earthquake, and was widely used until well into the 80s. Douglas cites 121 relations developed between 1969 and 2001 for PGA, and 76 for spectral ordinates – key workers in this field have been Esteva, Blume, Donovan, Ambraseys, Trifunac, McGuire, Idriss, Milne, Faccioli, Campbell, Joyner, Boore, Bolt, Abrahamson, Crouse, K. Sadigh, Annaka, Somerville, Youngs, Yamazaki, Bommer, and Bozorgnia, approximately ordered according to their earliest contribution. The great majority of strong motion attenuation relationships developed to date have an empirical basis. In the 80s and 90s a few attenuation relations were developed based on theoretical / analytical considerations (e.g., relations by David M. Boore, Otto W. Nuttli and Robin K. McGuire), and there is now an increasing trend toward analytical or “physics-based” models. In 1976 McGuire, a student of Cornell’s and then at the USGS, published open source software (McGuire, 1976, 1978) which greatly facilitated the numerical integration of the Cornell methodology. The freely available FRISK (Fault Risk) code was quickly used by many researchers and engineers around the world as the core of numerous computer codes, and greatly stimulated high quality seismic hazard analysis. To this day, pieces of the FRISK code are buried deep within many codes. The HAZUS® software (discussed in more detail below) is free GISbased software available from FEMA that permits the estimation of seismic intensities for scenario events anywhere in the US. The project to develop the current US National Seismic Hazard Maps developed a new generation of software5, and there is now a project led by Ned Field at USGS to develop an open source distributed hazard software code, OpenSHA6. Many advances in seismic hazard analysis arose from the needs of the nuclear and offshore industries for state-of-the-art analyses. With the ability to better quantify seismic hazard, the U.S. Nuclear Regulatory Commission (NRC) realized that there were many unknowns with the increasing number of nuclear power plants, particularly after WASH-1400 (discussed below), the 1975 Brown’s Ferry near-disaster and the 1979 Three Mile Island disaster. From 1981 to 1989, the NRC funded the Lawrence Livermore National Laboratory (LLNL) to develop a Probabilistic Seismic Hazard Analysis (PSHA) method for the eastern United States, which was paralleled by a utilities sponsored study. A key development 5 6

http://earthquake.usgs.gov/research/hazmaps/publications/docmaps.php http://www.opensha.org/


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was more rational and accurate treatment of uncertainties, beginning by distinguishing between epistemic and aleatoric uncertainties. Epistemic or ‘modeling’ uncertainties are based on a lack of scientific understanding that may be reduced in the future, and exemplified by different results from two models using the same basic data. Aleatory or “random” uncertainties are typified by the inherent variability in seismicity or other phenomena, and cannot be reduced for all practical purposes (National Academy Press 1997). The use of large logic trees simplified the task of treating uncertainties, such as whether a traditional Gutenberg-Richter or other magnitude-frequency relation (e.g., “characteristic earthquake”) was most appropriate. In a sense, Cornell’s (1968) paper marked the end of one chapter, and the beginning of the next. The chapter that ‘ended’ was that of seismic hazard, which in another sense had just begun. This may sound contradictory, but with Cornell’s paper the problem of determining the probability distribution of the hazard had been solved, particularly given the concurrent emergence during the 60s of the theory of plate tectonics. The earth sciences now had a complete framework, of sources (i.e., faults), ground motion and the theoretical framework to link them. The next several decades saw exciting work, as better ground motion models, magnitudefrequency relations, other work and data, data, data filled in the framework. The ground motion modeling was largely empirical, and it was only in the 90s that analytical strong ground motion modeling began to emerge. The US seismic hazard mapping program continued its development to where today it is by far the most advanced in the world. During the 90s, an international team (key members included D. Giardini, H. Gupta, K. Shedlock, G. Grunthal, M. Garcia, B. Iben Brahim, D. Slejko, C. Pannonian, R. Musson, S. Balassanian, V. Ulomov, M. G. Ashtiany, K. Atakan, I. Nyambok, P. Zhang, K. McCue, E. Engdahl, R. McGuire, and D. MayerRosa) developed a global seismic hazard map (GSHAP 1998), Figure 2.3. The effort not only produced the first consistent global estimate of seismic hazard, but had many derivative positive effects in promoting international cooperation and enhancing standards and capabilities. It is based on 150 years of hard work and advances in the seismological, geological and other sciences. And yet, Mallet’s map comes off rather well. The difference is we now understand, at least to a better extent, many of the things that Mallet could only see as a glimmer.

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2.6 Vulnerability The chapter that now began had to do with vulnerability. This is ironic, in that most people’s expectations, including the expectations of those in earthquake engineering, is that the engineers can analyze structures to an infinitesimal degree, whereas it’s the earth scientists that are always in the ‘discovery’ mode. And yet, in a very real sense and from the perspective of seismic risk analysis, we know more about the earth than about what humankind has built. A note on the rubric of vulnerability – first, it refers to both vulnerability functions (i.e., functions directly relating hazard to final loss or consequence, such as a curve of repair cost as a function of MMI for a selected structural type), and fragility functions (which provide a damage state conditioned on hazard, such as Heavy Damage as a function of PGA – the fragilities can then be related to consequences, such as business interruption). Second, we also include under the heading of Vulnerability advances contributing to vulnerability and fragility function development, particularly structural engineering analytical techniques, although we keep discussion of the contributing advances as brief as possible. Just as with ground motion and many other phenomena, there are two fundamental approaches to vulnerability – analytical, and/or empirical. Ideally, the two approaches are employed in a complementary manner to arrive at a hybrid model, but in seismic risk this has rarely been attempted. Regarding the analytical approach, consider what is involved if one is asked to estimate the seismic risk of a building: x In a few moments, for any site in the US, one can access the USGS website and obtain key points on the site’s hazard curve, in terms of PGA, spectral acceleration, etc. If the site is not in the US, there is still an extensive global literature of earthquake faults, as well as earthquake catalogs, which can be employed in FRISK or other codes to estimate the hazard. Or, admittedly with less confidence, one can access GSHAP and obtain an approximate estimate of the hazard. In either of the above cases, the hazard will need to be adjusted for site specific soil conditions but readily available geologic maps for most of the US and many other localities often will provide sufficient information for at least a preliminary adjustment. x However, regarding the building structure, if the engineer is lucky drawings will be available. Even with drawings, the engineer will still have to make a site visit to ascertain if the building today is the building the drawings show – every engineer has had the experience of finding seismic bracing cut for a new door. The drawings will provide limited


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information on the nominal material strengths but, for structural analysis material samples (particularly of the concrete) will typically be taken, as well as non-destructive confirmation of reinforcing bar location (similar investigations would be made for wood or other structural materials). x With the drawings, all that an engineer can do at first is identify whether a lateral force resisting system (LFRS) was incorporated by design. If there is no LFRS, then in a sense the job is easy – the building can probably be judged to be a high risk, and the engineer can proceed to design of strengthening (or other measures) to reduce the risk. However, if an LFRS is found to exist, by design or imputed, then the engineer has several choices – several increasingly complex ELF type analyses, or increasingly complex dynamic structural analysis. The decision as to what level of analysis to employ depends on the client’s need for accuracy and confidence, and the potential liability involved in rendering a professional opinion. However, any analysis involves a considerable level of effort. To begin with, there is the data acquisition and reduction effort – the masses of parts of the structure must be estimated, and the structural stiffnesses of each structural member must be determined. When this data is compiled, it must be built into a structural model, and that model analyzed. Until the 1960s a dynamic structural analysis was infeasible for ordinary practice. The advent of the Finite Element Method (FEM), which had its roots in the 1920s but didn’t develop into a usable tool until the 1960s, and the advent of a modicum of computing power, permitted the development of software that made linear dynamic structural analysis feasible, particularly with the invention of the Fast Fourier Transform. However, generally speaking, linear analysis is not an analysis for structural damage which by definition is an excursion of the structure into the nonlinear range. Nonlinear dynamic analysis, typically performed in the time domain, only became feasible in the late 80s, and is still today an advanced method infrequently employed. Today, commercial software such as ETABS, SAP2000, ANSYS, STRUDL, RISA3D, STAAD and MULTIFRAME permit linear and nonlinear analysis and are integrated packages with visualization, Figure 2.12, section properties, CAD and other features. There are a surprising number of free software packages available (see http://www.structuralengineering.fsnet.co.uk/free.htm for an extensive list). OpenSees (http://opensees.berkeley.edu/) is a free open-source software developed by the Pacific Earthquake Engineering Center (PEER) to simulate the performance of structural and geotechnical systems subjected to earthquakes. The Mid-America Earthquake Center (MAE) has developed ZEUS-NL a free

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3D static and dynamic analysis platform for earthquake engineering applications (http://mae.cee.uiuc.edu/software_and_tools/zeus_nl.html).

Fig. 2.12. Example of visualization of deformed structure (from ETABS, reprinted with permission from CSI, Inc.)

So, while seismic hazard is available literally at one’s finger tips today (at least for the U.S., via the USGS website), the determination of the potential damage given that hazard (i.e., the vulnerability) of a structure is still a time-consuming process, with substantial effort for data acquisition and preparation, and still significant effort for analysis, in which substantial approximations are more the norm than the exception. And, the above discussion has been for one defined building where the engineer has access to all the available information. If a vulnerability estimate of a portfolio (i.e., a collection of facilities, such as all the schools in a city), or of a region (i.e., just a larger portfolio, but involving all the buildings, plus perhaps some or all other infrastructure such as water, wastewater, power, transportation, telecom etc, collectively referred to as lifelines) is required, then the determination of vulnerability using traditional detailed structural engineering techniques is often impractical (for a portfolio), and simply not possible at the regional scale. Other means for development of vulnerability functions must be resorted to. Thus, there are two approaches for development of vulnerability functions – empirical, and analytical. We next briefly review (non-exhaustively) some key events in the development of each.


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2.6.1 Empirical Approaches Empirical development of vulnerability functions involves the collection of damage observations and data for variety of structures or other assets at risk, the organization before or after collection of the variety of assets into some schema, and the processing of the observations and data for each category within the schema to determine a relation for vulnerability as a function of a measure of hazard. As noted earlier, vulnerability is a general term – when used in the specific, measures of vulnerability are one of the three D’s (Deaths, Dollars or Downtime – that is, human injury, financial loss, or temporal disruption). The general term vulnerability also refers to fragility, meaning a relation for damage state (Light, Heavy, Collapse, etc) as a function of a measure of hazard. Measures of hazard range from qualitative intensity (MMI, JMA, etc.) to quantitative (PGA, Sa, Sv, etc.). The first empirically-based vulnerability functions were the intensity scales themselves, as developed by Rossi, Forel, Mercalli and others although as discussed above there is some circular reasoning in their usage. The investigations following the 1906 San Francisco earthquake and 1923 Tokyo earthquake included detailed examination of structures but this data does not appear to have been processed in any manner for development of a vulnerability function. Freeman (1932) presents some general estimates of damage based on a review of the 1906, 1923 and other events for several categories of buildings, Table 2.1. Chick in 1934 presented very similar ratios following the 1933 Long Beach Earthquake. Martel in 1936 investigated “if significant differences in damage [in an earthquake] resulted from differences in the building’s subtype, occupancy, or adjacency to other buildings” by looking at 1,261 unreinforced-masonry buildings (UMBs) in Long Beach, CA, and a number of woodframe residences in Compton, CA, shaken by the 1933 Long Beach earthquake. Following World War II, Japanese investigators and the US Army documented the 1948 Fukui (Japan) earthquake, but the data doesn’t seem to have been employed for any statistical purposes. The 1952 Kern County (California) earthquakes seem to have been the first US field investigation of earthquakes since the 1933 Long Beach earthquake, and involved a ‘new generation’ of investigators (e.g., Steinbrugge, Degenkolb, Moran), who did a good job of documenting the earthquake and its aftershocks. However, the 1964 Prince William Sound (Alaska) earthquake was extensively documented (US Coast and Geodetic Survey, 1966–1969. A number of engineers from California (Henry J. Degenkolb, Karl V. Stein-

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Table 2.1. Building Earthquake Loss Ratios (after Freeman 1932)

Class of construction 1. Steel-frame buildings on reinforced concrete mat foundation, having rigid cross-bracing, with strong gusset plates uniting columns to strong horizontal girders between windows, with curtain walls or reinforced concrete poured around the steel frame, and with ordinary interior finish. Not more than 100 feet tall. (Expected damage, chiefly cracked plaster.) 2. Tall steel-frame buildings with less-rigid crossbracing than Class 1, with ordinary brick curtain walls and rock-concrete floors and uncertain foundations. Not more than 100 feet tall 3. Tall reinforced concrete buildings without riveted or welded structural steel-frame and with ample strength at column connections and having ample horizontal cross-bracing by walls around windows, particularly in first story. Not over 100 feet tall. 4. Wood-frame dwellings, set on good foundation walls (not on posts or slender piers), not above 2 ½ stories high, excluding stucco exteriors. (Expected damage chiefly cracked plastering and chimneys.) If on tall posts or slender piers the loss ration will probably be 5 to 10 times as great 5. Factory buildings of good design having bearing walls of brick in cement mortar, or of reinforced concrete. Strong wood floors, with little expensive interior finish. No plastered walls or ceilings. Not more than 4 stories tall 6. Ordinary brick residence, mercantile and office buildings, of excellent design with brick bearing walls and wood floors. General average of unrated risks not exceeding 2 ½ stories 7. Same as Class 6, but for general average of unrated risks, not exceeding 4 stories 8. Brick veneered, wood-frame or concrete-frame residence, mercantile and office buildings, or stucco exterior on wood lath, or with hollow-tile partitions. 9. General average of commercial buildings with reinforced concrete frames and columns, (no steel frame) with curtain walls and partitions of hollow-tile, and large window openings in lower story 10. Buildings of doubtful quality of design and construction, uncertain wall ties, unanchored parapets, uncertain quality of mortar 11. Concrete-block and hollow-tile buildings

Expected Average Loss Ratio Per cent. of Sound Value

Soft Ground Factor Add nothing

Bed Rock or Stable Ground Factor Deduct nothing Perhaps Add?

3% “

“

“

“

2 to 4 times average loss Ratio.

¼ to ½ average loss Ratio.

5%

8%

3%

5%

6% 10% 25% 10% to 20% 20% to 40% 50%+

To be modified by structural conditions

To be modified by structural conditions

“

“

“

“

“

“

“

“

“

“


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brugge, among others) surveyed the damage to modern construction, and were shocked: “I know when I came from Alaska, I figured from now on we’re designing buildings as if the earthquake is going to happen in another five years, and we’re going to have to answer for all the mistakes. It sure stiffens up your back.” (Degenkolb, EERI, 1994)

The next year Karl V. Steinbrugge wrote Earthquake Hazards in the San Francisco Bay Area: A Continuing Problem in Public Policy (Steinbrugge, 1968). That publication was very influential – it caught the attention of State Senator Alfred E. Alquist and contributed to the establishment of the California Seismic Safety Commission, for example. However, from our perspective, it started a series of investigations into what a large earthquake might do in a US urban center. A small group including Karl Steinbrugge, Frank McClure, Henry Lagorio, Henry Degenkolb and others were motivated to develop loss estimates for U.S. urban areas (discussed below). In the first of numerous studies on earthquake damage and potential losses, they laid the basis for probabilistic earthquake hazard estimation and, particularly Frank McClure in Chapter III of that report, gathered dollar value loss, by class of construction, for 1,139 buildings that were reported as damaged by the M7.6 Kern County earthquake of July 21, 1952, and its aftershocks. The objective was to estimate the fraction of all structures, by class of construction and “amount of lateral bracing,” that were demolished, repaired, or undamaged as a result of the earthquake. A matrix relating 24 dwelling construction classes with the 12 Modified Mercalli intensities is used; each of the boxes in this 24 × 12 matrix contains a damage ratio (cost of repair as a percent of replacement cost) and a damage factor (the percentage of buildings of this class and located in that intensity zone that would actually experience the specified damage ratio). Seed investigated the July 29, 1967 Caracas Venezuela earthquake (M6.4), in which four 10- to 12-story buildings totally and one l2-story building partially collapsed (over 200 lives lost). His basic finding was that damage could be approximately correlated with spectral velocity normalized by the building’s lateral design coefficient. The 1971 San Fernando (California) earthquake then occurred and caused a significant amount of damage to low- and high-rise buildings, collapsing the Veterans and new Olive-View hospitals. McClure did a detailed study of 169 single-family dwellings in the epicentral region of the 1971 San Fernando earthquake (i.e., PGA 0.25g to 1.0g), almost all of which experienced damage in excess of $5,000 (1971$). Hafen analyzed the 1971 San Fernando earthquake for low and high-rise damage data, correlating it with Blume’s Engineering Intensity Scale. Rinehart estimated

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earthquake losses to single-family dwellings on a detailed empirical basis. Scholl examined the 1971 San Fernando earthquake and several underground nuclear explosions to correlate low-rise building damage with an envelope of spectral accelerations in the 0.05 to 0.2 second range (this being the range of natural periods of USA low-rise buildings). Whitman developed the Damage Probability Matrix (DPM) based on the 1971 San Fernando (and other) data. These correlate discrete damage states with MMI (which are discrete ground motion states). Part of the same Seismic Design Decision Analysis (SDDA) project later attempted correlations of the San Fernando damage experience with response spectral measures, finding spectral acceleration or velocity to be satisfactory, though no measure of the correlation is given. Algermissen employed MMI, 5 classes of buildings, oval isoseismals and judgmental intensity-loss relations to develop an estimation methodology for mid-rise buildings. This last study was probably most significant for its use of a detailed building inventory. The MIT Seismic Design Decision Analysis (SDDA) project deserves special mention, as it was an extensive integrated approach to the entire issue of seismic design. Led by R.V. Whitman, it produced at least 33 reports over five and a half years (Appendix 2). The program’s contributions went beyond the topic of this subsection, and included more sophisticated seismic studies, introduction of Damage Probability Matrices (DPM), seismic design cost studies, incorporation of incident losses (e.g., lives lost, business interruption), and the introduction of multi-attribute decision making. Many of the project’s studies continued the practice of using MMI as the intensity parameter (but not all – Wong used response spectra) while recognizing that more objective measures would be better. Overall, the project was a very significant step forward in many areas, including seismic risk analysis. In Japan, an extensive literature has been built up describing earthquake damage. Mochizuki surveyed existing structures in Tokyo and related these to damage ratios providing a good match with the 1923 Kanto earthquake. Shiga, for 1 to 5 story Japanese RC buildings, related damage in the 1968 Tokachi-oki earthquake to wall ratio and nominal shear stress in columns and walls, providing a probabilistic estimate for building damage. In 1985 the Applied Technology Council (ATC) published ATC-13, Earthquake Damage Evaluation Data for California, which has been a major influence ever since. The study used a modified Delphi process for expert opinion elicitation to poll 85 experts on their estimates of damage to 78 different classes of buildings and infrastructure. Building stock was categorized by material of construction (e.g., W = Wood) and lateral force resisting system (e.g., W1 = single family dwellings, S5 = low-rise light


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metal steel buildings). Derivatives of this categorization, not changed very much, have continued through two decades of work in the US, including HAZUS®, and are the model building types (MBT) likely to continue in use for much longer. The experts’ opinions were fitted to beta distributions and presented in the form of Damage Probability Matrices. The resulting ground motion-loss vulnerability functions were not explicitly derived from statistical data, but were still empirical in that they were based on the observations of the experts. Substantial guidance was given on compilation of building inventories and related matters. The ATC-13 report has stood the test of time very well. Key persons leading the effort included Chris Rojahn, Roland Sharpe, Anne Kiremidjian, Roger Scholl and Richard Nutt, and the Project Engineering Panel consisted of Milton A. Abel, J. Marx Ayres, John A. Blume, George E. Brogan, Robert Cassano, Ted M. Christensen, Henry J. Degenkolb, Homer H. Given, Henry J. Lagorio, Le Val Lund, Ferd F. Mautz and James L. Stratta. Following the 1994 Northridge earthquake, a project (ATC-38) gathered data on 530 buildings located within 300 meters of strong-motion recording sites that were strongly shaken by the earthquake, with the goal “to correlate the relationship between recorded ground shaking”. The resulting data did not achieve its purpose of developing new correlations, in part due to the relatively light damage of many of the buildings in the vicinity of the seismographs. One lesson that was learned is that the placement of seismographs needed to be reviewed, so as to place more in areas of anticipated higher damage. Schierle also examined woodframe dwelling losses of the 1994 Northridge earthquake, with the objective to create seismic vulnerability functions for six categories of dwelling, but with similar lack of dramatic new findings. The above discussion has focused on buildings, and lifelines will be discussed below. A third important class is equipment, since industrial and critical facilities are often more dependent on their equipment functionality than building functionality (short of collapse). A major effort to develop empirically-based equipment fragility functions was funded by the nuclear industry’s Seismic Qualifications Utility Group (SQUG) during the 80s and 90s. Yanev summarized the extensive database of the observed seismic performance of industrial equipment and nonstructural components that was developed under the auspices of the Electric Power Research Institute (EPRI). The focus of the database is on facilities related to electric power, including power plants, electrical-distribution substations, oil refineries, and natural-gas processing and pumping stations. There are also extensive entries related to the earthquake performance of water-treatment and pumping facilities, large commercial facilities, hospitals, and conven-

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tional buildings. By 1990, the database reflected equipment performance at more than 100 major facilities, many smaller facilities, and hundreds of buildings that experienced strong motion (typically peak ground acceleration of 0.15g or greater). Surveys at that time included experience in 42 events since the 1971 San Fernando Earthquake. 2.6.2 Analytical Approaches Until very recently, there have been fewer attempts at developing analytically-based vulnerability functions, for some of the reasons discussed above, and due to the recognition that empirically-based functions would more likely reflect actual built conditions and have more credibility. Friend and Blume (1975) summarized and compared three damage prediction techniques: the spectral matrix method (an analytical probabilistic method), the seismic element method (‘an eclectic method using a theoretical basis but modified to include observed behavior’) and the decision analysis method (empirical) finding advantages in each, depending on application. Czarnecki for the SDDA project at MIT, found that building component energy absorption over energy capacity roughly measured damage. Design level didn't seem to affect the damage level greatly. Kuribayashi in 1978 used an average seismic coefficient dividing elastic from elastoplastic behavior, together with a normal distribution, to provide a damage estimator, although not relating it to the structural system. Sakamoto in l974 used a trilinear force-displacement curve together with standard displacement response spectra to determine a maximum acceleration for wooden houses. Scawthorn et al. (1982) developed relatively simple but nonlinear dynamic models for low- and mid-rise buildings in Japan, and used empirical data from the 1978 Miyagiken-oki and other earthquakes to calibrate the models, marking one of the first attempts at a hybrid model. Kustu, Scholl and co-workers at J.A. Blume and Associates employed a similar technique but de-aggregated the building into its sub-components. Most recently, Porter has extended that approach and developed the Assembly Based Vulnerability (ABV) method, which is the first fully rational approach to vulnerability. It consists of a detailed nonlinear dynamic analysis of a building, the results of which are employed to determine damage states for the structural and all significant non-structural components (e.g., gypsum wall board, windows, suspended ceilings, mechanical and plumbing). Detailed cost data is then used to estimate all contributing repair costs for each component. While it can be applied to an individual building, it also can be used for classes or model building types, by defining


39

‘index buildings’ – in this mode it was used to examine the benefits and costs of retrofitting wood frame construction in California. The methodology is used by the California Earthquake Authority (i.e., the state insurance company). The HAZUS® software uses a semi-hybrid approach, in that it employs the Capacity Spectrum method developed by S. Freeman in 1998 to estimate the non-linear behavior of the structure, to determine a fragility curve for the structure. Many of the parameters for the structural capacity however can be traced back to ATC-13, which means the methodology has an empirical basis. The translation from fragility to cost is at the overall structural, not component, level, although the method could easily be extended to the component level. Lastly, for equipment, it should be mentioned that analytical techniques have also been developed, which are mostly applied in nuclear power and other applications where high reliability is required. Kennedy, Ravindra, Campbell, Short, Hardy, Reed and co-workers have led the field in developing these methods. 2.6.3 Lifelines The foregoing discussion has focused on building structures. Lifelines are much more varied than buildings, and space does not permit a review of all the work done in that regard – suffice it to say that lifelines emerged as a separate seismic field following the 1971 San Fernando earthquake, due in particular to efforts by Prof. C.M. Duke at UCLA, who was instrumental in founding ASCE’s Technical Council on Lifeline Earthquake Engineering (TCLEE). TCLEE has led the field in the US since that time. Key leaders of TCLEE have included Hall, Schiff, Lund, Eguchi, T.D. O’Rourke, M. O’Rourke, Taylor, Tang, Werner, Elliott, Cooper, Ballantyne, Shinozuka, Kiremidjian, and many others (see http://www.asce.org/community/ disasterreduction/tclee_home.cfm). Most non-US contributions in the lifelines area have come either from New Zealand and especially Japan, where Kubo, Toki, Kameda, Katayama, Hamada, Sato and Takada have been key leaders. A noteworthy venue for developments in this arena has been a series of U.S.–Japan meetings since about 1980 on the topic of lifelines seismic risk, led by the above-named Japanese and US researchers. Several studies regarding lifelines vulnerability functions are worth noting: x In the early 1990s FEMA authorized a major study of the seismic vulnerability of the US national infrastructure (FEMA, 1991). The scena-

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rio-based study by the Applied Technology Council (ATC) examined 489,892 km of highways, 144,785 highway bridges; 270,611 km of railroad right-of-way), 17,161 civil and general aviation airports, 2,177 ports, the national electric power grid (4,551 substations; 441,981 km of transmission lines), gas and liquid fuel transmission systems (77,109 km of crude oil pipelines; 85,461 km of refined oil pipelines; 67,898 km of natural gas pipelines.); 29,586 emergency broadcast facilities, 6,973 medical care centers, and 3,575 km of water aqueducts. Scenario earthquakes considered were Cape Ann M7, Charleston M7.5, New Madrid M7, and M8, Mountain Wasatch Front M7.5, Puget Sound M7.5, Southern California Fort Tejon M8, Northern California Hayward M7.5. The study estimated both direct damage and indirect economic effects, which were modeled using an economic Input-Output (I-O) model for the US. The results guided FEMA’s seismic lifeline program during the 1990s. x Lund and Schiff in 1991 developed a database for recording and compiling pipeline damage records. The database is composed of records, one record for each pipe failure. Each record consists of 51 data fields, indicating the associated earthquake, the pipeline owner, pipe break location, soil condition, details of construction and installation, and nature of the break. The database, which contains information about 862 pipe breaks in the 1989 Loma Prieta earthquake, is defined to facilitate appending pipe-break data from future earthquakes. Similar, larger, databases have been developed in Japan by Takada, Hamada and others. x An analytically based approach to the vulnerability of buried pipe (O’Rourke and Liu, 1999).

2.7 Special Vulnerability Relations The above discussion has focused on vulnerability of typical engineering topics – buildings and infrastructure. Because earthquakes affect virtually all aspects of society, a number of relatively specialized vulnerability relations have been developed, dealing with injuries, ground failure, Fire Following Earthquake, liquefaction and other aspects, for which we highlight some of the key developments.


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2.7.1 Injuries The estimation of human injuries is a key task for risk analysis. Steinbrugge had provided estimates of injuries in the USGS and NOAA studies in the 70s, but based only on a simple rule of thumb. Mitchell was one of the first to seriously consider this issue, based on Turkish earthquakes. The 1985 Mexico City earthquake had a particular impact on this topic, due to (a) the large number of high-rise building collapses, resulting in an extended period of search and rescue for the large numbers of trapped victims, with tragically little to show in the end, and (b) the large number of hospitals that had collapsed, doubly compounding the disaster medical response. The US and other countries began to examine issues of search and rescue, and there was a large amount of activity internationally having to do with search dogs and many kinds of technology (infrared, sound, heartbeat detectors, micro-cameras, etc.). FEMA sponsored work on heavy search and rescue (FEMA, 1988), and developed the USAR (Urban Search and Rescue) team concept in place today. Friedman in 1988 developed an estimate of Workers Compensation losses for a “Worst Case” event in Greater Los Angeles, for the insurance industry. Durkin and Thiel (1992) collected injury data and examined the damage to health care facilities. Jones, Noji, Krimgold, and Smith convened a key workshop in 1989 on the topic, which was followed a year later on the other coast by a second workshop – these two workshops were the state-of-the-art at the time, although the field was still developing. They were followed in 1992 by an international conference on the topic. The HAZUS® software contains an injury module. Most recently, Shoaf, Seligson and co-workers have been working on a complete injury model, including valuing the cost of injuries, and various investigators have been collecting injury-related data. 2.7.2 Liquefaction While a large effort has been spent on liquefaction since its ‘discovery’ in the 1964 Niigata (Japan) earthquake, almost all the effort has focused on either estimating the potential for liquefaction, or mitigating it, for specific conditions. Almost no work has been done on developing functions to estimate the distribution of damage to a class of buildings in a locality given the occurrence of liquefaction. In fact, one might say that most of the liquefaction related work has been at the “hazard” or “micro” scale. The first research this writer is aware of regarding estimation of damage to buildings given the occurrence of liquefaction was by this author (in 1981) who investigated the probability of damage to Japanese low-rise wood build-

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ings given the occurrence of significant liquefaction, finding that about 10% are destroyed and 25% damaged. 2.7.3 Fire Following Earthquake The problem of fire following earthquake is a potentially very serious earthquake problem in regions with large wood building inventories, as the 1906 San Francisco, 1923 Tokyo and 1995 Kobe earthquakes show. The insurance industry had long been concerned about this issue and the problem had been addressed in Japan, although only in piecemeal manner. The problem was unaddressed in the US until the early 80s, when a stochastic model of the Fire Following Earthquake process was developed by Scawthorn. Steinbrugge highly evaluated the model for insurance applications, and it was subsequently widely adopted by the insurance industry. Fire Following Earthquake modeling in Japan improved dramatically in the 80s, and recent researchers in Japan include Sekizawa, Murasaki and Tanaka, as well as on-going work by the Tokyo Fire Dept. and Japan’s National Fire Research Institute. New Zealand is also concerned about the problem, and investigators there include Cousins and the New Zealand Fire Service. 2.7.4 Hazardous Materials Environmental impacts resulting from release of hazardous materials (HAZMAT) in earthquakes have received relatively little attention. Preparedness and response to natural hazards have tended to focus on human casualties, structural damage, and property losses, and environmental impacts have only been reported when they have resulted in major incidents, such as the petroleum fires that followed the 1964 Niigata earthquake. This situation is changing, due to a number of factors, including better recording of HAZMAT incidents in natural hazards (e.g., 387 such incidents in the 1994 Northridge earthquake, not to mention the enormous environmental impacts of Hurricane Katrina in New Orleans), and more stringent standards for environmental liability. Reitherman was perhaps the first in the US to look at the problem of hazardous materials releases in earthquakes, in 1982, and he, Selvaduray and Perkins have developed databases and methods for the problem. The HAZUS® project reviewed the state-of-the-art as of 1992 and found three models as of that time. The model developed by Tierney et al. focused on the likelihood of gaseous releases, and its potential effect on surrounding populations. However, it was not found to be suitable for risk assessment


43

efforts by local jurisdiction personnel due to the level of detailed analysis required. The study by Ravindra (1992) was very similar to the Los Angeles County Fire Department methodology, and was really intended for seismic vulnerability analysis of individual facilities, requiring significant expert input. The HAZUS® model provides a listing and mapping of HAZMAT facilities vs. seismic intensity. More recently, Steinberg and Cruz have closely investigated the 1999 Marmara Earthquake (Turkey), where a number of hazmat releases occurred. 2.7.5 Socio-Economic Impacts and Business Interruption While estimates of casualties, cost of repair and other ‘direct damage’ measures come first to mind for most applications, the economic impact of an earthquake on a business, and the economic impact of a great earthquake on the overall regional economy, are important issues. Dacy and Kunreuther in 1969 and Mukerjee in 1971 were probably the first to examine the broader economic impacts of natural hazards, while Cochrane in1974 was the first to quantify the economic impacts of a large regional earthquake, in this case the ‘coming San Francisco earthquake’, for which he found about half the overall economic impact was ‘secondary’: “A reoccurrence of the 1906 San Francisco Earthquake in 1974 would likely cost the Bay area in excess of $13 billion, approximately one half of which would take the form of lost income due to a regional economic recession. The unemployment rolls would be swelled by as many as one quarter million. The probability of such large scale social disruption following an extreme geophysical event signals the need for a broadened perspective in planning for such potentialities, and the need to review the choice of adjustments to mitigate these effects. Concentration on mean annual damages or direct damage ensuing from disaster may not take into consideration the social benefits of measures pursued to affect damage reduction. The importance of these findings lies in the implications for public policy which may need some rethinking if the potential for large scale economic chaos is to be avoided.” (Cochrane, 1974)

The economic aspects received further attention in the 80s, as Rose and coworkers examined the economic costs of loss of infrastructure, and Ellison, Milliman and Roberts (1984) examined the regional economic effects of earthquakes and earthquake prediction. Another aspect of considering economic impacts was at the ‘micro-scale’, where FEMA funded development of methodologies for assessing the cost-benefit of seismic retrofit-

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ting. Beginning in the mid-90s, Rose, Chang and co-workers for the HAZUS® project developed a well-founded model of the regional economic impacts of a large earthquake, which employs a computable general equilibrium model designed to rebalance a region's inter-industry trade flows based on discrepancies between sector supplies and demands. Modeling of economic impacts, like the other aspects of modeling discussed here, must be based on good data. A number of investigators have collected data from various earthquakes, but two efforts were particularly important: x Tierney and Dahlhamer (1998) performed surveys of disaster-related business impacts of the 1994 Northridge Earthquake (as well as 1993 Midwest floods), focusing on eight aspects related to determining business interruption: business characteristics, nature of physical damage, lifeline service interruption, business closure, business relocation, insurance and disaster-assistance programs, disaster preparedness, and losses. These studies contributed to understanding the particular vulnerabilities in natural disasters of small and mid-sized businesses. x A study of the Northridge Earthquake by Eguchi and co-workers at EQE International, Inc., and the Governor’s Office of Emergency Services was perhaps the most-thorough effort to collect data and compile a database of the effects of an earthquake. The data included the seismological and geotechnical aspects of the earthquake; the charac-teristics of the building stock exposed to strong motion; building damage data including ATC-20 safety evaluations and repair-cost estimates; coroner data on earthquake-related fatalities; relocation and injury data from cities, the Red Cross, and the Salvation Army; and insurance losses reported by the California Department of Insurance.

2.8 Asset Identification and Attributes The third ingredient for a seismic risk analysis, beyond the hazard and a vulnerability function conditioned on the hazard, is the identification of and attributes of the assets at risk. As noted earlier, over 100 years ago, detailed fire insurance maps were available for all major urban areas of the US – an underwriter literally had at his fingertips up to date information on the size, occupancy, materials of construction and protective features (for fire) for every building in a city. That was lost in the mid 20th Century. Steinbrugge and succeeding investigators trying to develop accurate informative loss estimates from the 70s until very recently have struggled with trying to determine what is out there?, and what are its attributes


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relative to seismic vulnerability? (and, where is it?). For an individual structure, drawings will typically be available, but for a regional loss study, similar information on the ‘portfolio’ or ‘inventory’ is typically so widely dispersed as to be inaccessible. Tax assessor records have been a chimera – they are difficult to acquire, are different in every county, and in most counties don’t contain much reliable information on structural attributes. Selected counties’ assessor records (e.g., Los Angeles, San Francisco) have relatively useful amounts of structural information (although still with significant errors), but still require significant processing. Perhaps the only academic pursuing the problem of built inventory in the 70s and 80s was Barclay Jones, at Cornell University, who investigated relationships between population size and techniques for estimating size and characteristics (floor area and height) of building stocks in urban areas – case studies were cities in Colombia and Turkey as well as the boroughs of New York City. Jones et al. (1987) addressed losses from earthquakes, noting that estimating elements at risk had received less attention to date. A complete enumeration of the building stock of a moderate-sized metropolitan area in the United States, Wichita, Kansas, was compared to previous and less complete studies. The number and area of buildings disaggregated by use was given and replacement costs calculated. The spatial distribution of buildings by rings outward from the center was also determined. The techniques developed were shown to approximate existing building stock of a metropolitan area in greater detail than previous work, and regularities in the composition and distribution suggested that the techniques were generally applicable. In the early 80s, a study by Scawthorn and Gates (1983) of earthquake losses was one of the first to develop ‘proxy’ measures of many attributes, and in 1985 the ATC-13 study provided an excellent survey of practices to date, and guidance for filling in the gaps in data. The FEMA 224 study (FEMA, 1991) benefited from better information on infrastructure. Since that time, GIS has penetrated most municipalities, who are tending towards integrated databases of all facets of their operations. However, the detailed structural attributes desired by risk analysts is still lacking. The insurance industry, under the influence of the modeling companies, has improved its data enormously. Most recently, remote sensing and related techniques hold out some hope of providing automated data acquisition at the regional level. The one major advance has been the development of a national database of assets at risk, by the HAZUS® project. The specific inventory provided for the general building stock are (FEMA 2006):

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x Square footage by occupancy. Estimated floor area by specific occupancy (e.g., COM1). x Full Replacement Value by occupancy. Estimated replacement values by specific occupancy (e.g., RES1). x Building Count by occupancy. Estimated building count by specific occupancy (e.g., IND1). x General Occupancy Mapping. A general mapping of the inventory data from the specific occupancy to general building type (e.g., wood). x Demographics. Housing and population statistics for the study region. These data were compiled from various sources (2000 Census of Population and Housing, U.S. Census Bureau 2002: Summary Tape File 1B Extract; 2000 Census of Population and Housing, U.S. Census Bureau 2002: Summary Tape File 3; Dun & Bradstreet, Business Population Report aggregated by Standard Industrial Classification (SIC) and Census Block, 2002; Department of Energy, Housing Characteristics 1993 and related 1995 and 1997 data). This data is then mapped to Model Building Types using a pro ration scheme. While proxy-based and no substitute for detailed local information, the resulting inventory data appears reasonably correct in the mean, is consistent across a region, in fact nationally, and is freely and readily available on the HAZUS® CDs. The HAZUS® database is an enormous advance, at least for a first approximate analysis (useful for determining the overall magnitude and significant of the earthquake risk, and for planning more detailed studies), which is the primary purpose of HAZUS®. Outside the U.S., the issue of developing a portfolio generally still remains although in selected regions excellent databases have been developed. A surprising number of municipalities in developing economies tend to have relatively sophisticated GIS systems. Some regions (e.g., parts of Japan) tend to have better building data (relative to the U.S.). The problem can be tackled head-on – an impressive recent effort was Istanbul, where over one summer, school teachers (who were on summer vacation) were hired, trained and then surveyed every building in the City, to compile a database for earthquake loss estimation purposes (M. Erdik, personal communication). This data was all loaded into an existing high-quality municipal GIS system. A recent contribution to the problem of asset attributes has been the World Encyclopedia of Housing, developed by a global group lead by EERI members and sponsored by the IAEE. The WHE contains a wealth of information on the construction and seismic resistive aspects of housing and other building types around the world, including information on seismic strengthening of various building types specific to the region.


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2.9 Risk Analysis Methods The fourth aspect of a seismic risk analysis, beyond the hazard, vulnerability and asset attributes, is the mathematical and theoretical analysis methods by which the three elements are combined to more or less rigorously and accurately estimate the risk. Willis in 1923 compiled some loss ratios but Freeman (1932) was probably the first to systematically compile damage data and develop economic loss ratios for a wide variety of building types (reading the literature, it would appear that insurance underwriters had compiled and used loss ratios prior to 1932, but Freeman would have been very familiar with such work, and clearly felt the information he offered in his book was badly needed). The concept of Probable Maximum Loss (PML, also variously referred to as Maximum Probable Loss, and other variations) has long been used in the fire insurance business, probably since the 19th century. PML is actually one of three ‘levels’ for consideration while underwriting for fire: Probable Loss, PML, and Maximum Foreseeable Loss (MFL). As of 2006, the Insurance Services Office defines PML for fire as: “PML (Probable Maximum Loss) is an estimate of the largest loss that a building or a business in the building is likely to suffer – considering the existing mitigation features – because of a single fire. The PML is the maximum expected loss, expressed as a percentage of the building’s value, when critical protection systems are functioning as expected. The Maximum Foreseeable Loss (MFL) is an estimate of the largest fire loss likely to occur if a key loss-reduction system fails. The PML and MFL percentages depend on many factors, including: construction of building combustibility of contents susceptibility of contents (likelihood of damage from fire, smoke, and water) private and public fire protection For example, in a fire-resistive building with a fire-division wall between each occupant, chances are the entire building wouldn't burn if a fire broke out. But in a building without adequate protection features, a single occurrence might involve a significant loss, and the probability is high that both the building and its occupants would experience extensive damage in the same occurrence. And occupants such as flower and pet shops can experience severe

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Charles Scawthorn losses even in small, quickly extinguished fires”. (Insurance Services Office, 2006)

Guy Carpenter, the leading reinsurance broker offers: “MFL (Maximum Forseeable Loss) The anticipated maximum property fire loss that could result, given unusual or the worst circumstances with respect to the nonfunctioning of protective features (firewalls, sprinklers, a responsive fire department … as opposed to PML (Probable Maximum Loss), which would be a similar valuation but under the assumption that such protective features function normally.” (Strain 1997)

Other authorities defining PML can be cited – today, it is still a widely used term in the insurance and related industries, for fire. Regarding earthquake, on the other hand, Freeman (1932) did not use the term, and the concept of PML was long recognized as problematic. McGuiness (1969) observed: The term “PML” or “probable maximum loss” is one of the most widely used terms in property insurance underwriting. But it represents one of the least clear concepts in all insurance. This fact is reflected by the results of a four-year study that involved collecting the personal and company definitions of PML from over one hundred underwriters and underwriting executives. No two of their definitions fully agree.

However, despite problems for using the term in regard to earthquake, it was used by McClure in 1969, probably due to the influence of Steinbrugge, who worked in the insurance industry. In the aftermath of the San Fernando earthquake in 1971, concern about the exposure of the insurance industry to earthquakes greatly increased, leading the California Insurance Department to issue Rule 226, which requires all licensed insurers to report each year their insured exposures for earthquake shake damage on residential and commercial structures in California. To assist insurers in complying with Rule 226, the California Insurance Department developed a simple but useful method for insurers to estimate their probable maximum loss (PML). While it was recognized by the Department that definitions of PML varied widely within the industry, the methodology developed by the California Department of Insurance and its consultant K.V. Steinbrugge has been used by the State of California since about 1980 to monitor insurance industry exposure. In that methodology: x Building Class PML (i.e., for an individual building of a specific class, such as wood frame, see Tables 32.3 and 32.4) is defined as the expec-


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ted maximum percentage of monetary loss which will not be exceeded for nine out of ten buildings, where the building is located on firm alluvial ground, subjected only to the vibratory motion from the maximum probable earthquake (i.e., not astride a fault or in a resulting landslide). x Aggregate PML is the sum of all of the PML values in a PML zone, plus factored PML values for buildings located outside of the PML zone but still within the earthquake underwriting zone. A factored PML is a reduced PML value based on reduced intensity (i.e., damage) with increasing distance away from the causative fault. Using this methodology, insurance companies in California are required to report their aggregrate PML each year to the California Department of Insurance. This is of interest to the department, as it wishes to assure adequate company surplus to assure payment of claims in the event of a large earthquake. Almost from the same time, however, others have all strongly discouraged use of the term, with ASTM (1999) stating: “The long used notion of “probable maximum loss” (PML) has become, for many, a catch phrase to encapsulate all earthquake issues into a simple number that can be used to qualify or disqualify a potential commitment. Unfortunately, there has been no previous industry or professional consensus on what PML means or how it is computed … use of the term Probable Maximum Loss (PML) is not encouraged for future use”.

and have proposed alternative measures. In the 1970s the SDDA project, discussed above, was developing a fully probabilistic methodology for integrating vulnerability over hazard, accounting for uncertainty. This methodology, in varying forms, is the standard model used today, although all forms don’t necessarily derive directly from the SDDA project – that is, the methodology was developed concurrently by others. For example, Oliveira considered the seismic risk of a site and a metropolitan area and determined the final probability distribution of the maximum response of a single-degree-of-freedom (SDOF) system which, when related to damage through a damage ratio function (characterized by random variables yield and collapse) and integrated over the region gave the global loss.

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2.10 Assessment Having traced the history of seismic risk analysis, the next step would be to trace the corresponding development of assessment of the analytical results – that is, the norms and criteria by which the analytical results are assessed. However, space does not permit an adequate discussion of this aspect, so the following thoughts will simply be noted. First, seismic risk assessment would appear to be a natural development of a society less interested in risk-taking, and more interested in assuring safety. This would appear to be a development paralleled in many fields, such as consumer safety, environmentalism, etc, as briefly discussed above. Whether this is a long-term trend, or a temporary (but influential) movement à la the Progressive Era (ca. 1900–1917), remains to be seen. Second, generally speaking, there would not appear to be any explicit specific norms or standards for seismic risk assessment, in California or, in this writer’s experience, anywhere else, with the probable exception of California standards for public K-12 schools, and hospitals. A study of various laws and programs would provide inferences as to patterns of norms and standards for seismic safety. Examples of items for study would include: x Seismic provisions in the 1927 UBC and subsequent building codes (which were not mandated in the UBC until 1960, and not until later in other model codes). x 1933 Field and Riley Acts (school standards strict and effective; building standards lax and ineffective). x 1959 Blue Book (tolerates widespread structural damage). x 60s requirement for seismic safety elements in California. x 1972: Alquist Priolo Act, requiring Special Studies in fault zones. x 1986: SB 547 (section 8875 of the California Code, setting ‘soft’ standards for URM mitigation, but ultimately generally effective. x 1983: California Hospital Seismic Safety Act, SB 1953, and its varied path and effectiveness. x Caltrans bridge seismic retrofit program – started following 1971 San Fernando earthquake, but it took another two earthquakes to finally complete it (generally speaking). x Bay Bridge – the delays in retrofitting and replacing these spans deserve a careful study. x Record of the Insurance Industry in California (and elsewhere) – 1983 Coalinga earthquake and ‘concurrent causation’.


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– 1994 Northridge earthquake and the effective industry withdrawal from market due to the requirement for a mandatory offer of earthquake insurance. – The first attempt at a California state earthquake program under Commissioner Garamendi, and its failure. – The second and successful attempt under Commissioner Quackenbush, and the establishment of the California Earth-quake Authority.

Fig. 2.13. Vision 2000 Performance Levels (reprinted with permission from Structural Engineers Association of California 1995)

Perhaps the best attempt by the profession to codify an acceptable standard for seismic performance was the Vision 2000 report by the Structural Engineers Association of California (1995), in which several performance levels were defined, Fig. 2.13. The Performance-based earthquake engineering tools needed for implementation of Vision 2000 are however still emerging, and the real impact of Vision 2000 still remains to be seen. Third, there don’t appear to be any explicit frameworks for assessing seismic risk. Various decision-making frameworks are employed, including benefit cost analysis, life cycle cost analysis, internal rate of return, and least regret. These are all relatively fixed frameworks, and other paradigms are probably more appropriate. Fourth, seismic risk assessment cannot be seen in a vacuum – there are other developments which directly influence seismic risk assessment – to name a few: x NRC – the U.S. Nuclear Regulatory Commission has been deeply influential in the development of seismic risk methods and, by its example, in influencing the assessment of the results of those methods in non-nuclear arenas. The nuclear field is currently on the verge of an apparent renaissance – what effects might that have?

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x Basel II requires reliability standards for financial institutions, including for natural hazards. The impacts of those requirements or their example on other institutions has not been felt, but may occur in the future. x Sarbanes-Oxley Act of 2002 requires considerable transparency in corporate management. This transparency should extend to manage-ment of natural hazards, but doesn’t seem to have had an impact yet. Will it? x Deregulation – the deregulation of the telephone, airlines and electric power industries (with the catastrophic experience in California and with Enron) raises issues as to their robustness for large natural hazards. How should that be assessed? x Gramm-Leach-Bliley Financial Services Modernization Act of 1999 repealed Glass Steagall, so that banks, insurance companies and securities firms can cooperate and compete. Does this potential concentration of capital imply greater vulnerability? including to natural hazards? Might standards emerging from these new conditions offer insights for seismic risk assessment and decision-making? Similar questions arise when consumer safety, product liability and trans-portation safety norms and standards are considered. That such seemingly distant examples might have influences in seismic risk assessment is not implausible. Conversely, consider examples much closer to home: x In the earthquake strong motion field, there is a long history of institutional cooperation and archiving of data. COSMOS, NGDC, and the International Seismological Center are just three of the most prominent examples. Yet, complementing the hazard arena, there is no cooperation or archiving of vulnerability data. Why not? The root cause probably lies in an identified and vital earth sciences agency (the USGS) but comparable national engineering agency. Dishearteningly, in this regard, EERI recently had its reconnaissance funding (the Learning from Earthquakes program) drastically cut. x In the 1960s the National Flood Insurance Program emerged, as a model coordinated approach to flood mitigation. Communities are provided insurance, but levees and other flood protection measures are required. About the same time, the California Dept. of Insurance began monitoring insurance company exposure via its Probable Maximum Loss (PML) reporting requirements. Recently, the California Dept. of Insurance seems to have relaxed its vigilance. Yet, the Florida Hurricane Commission, as compared with California, strictly monitors its insurance companies, and requires full confidential disclosure of all


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insurance modeling software employed in the state. These disparities need examination. x Hurricane Katrina is an object lesson in what not to do. The community had been warned many times, with articles about the risk in scientific and popular magazines years and months prior to the disaster. What does this tell us about people’s perceptions and attitudes toward natural hazards? About the value of risk assessments? About how to plan for recovery and reconstruction, should San Francisco or Los Angeles be devastated by an earthquake?

2.11 Current Status As of this writing (June, 2006), the status of seismic risk assessment is that the US remains the center of innovation and almost the center of application, although that is rapidly changing. The global insurance industry is served basically by the three modeling firms, all of which are based in the US, with some significant contribution from London. These three firms however are fiercely competitive and closely guard their technology, so that while innovation occurs it is closely held and the merit is difficult to judge. This is partially compensated by rating agencies such as Moodys and Standard & Poor’s requiring confidential disclosure from the modeling firms, and the Florida Hurricane Commission’s detailed inquiries into the hurricane models. A major development in the US has been HAZUS®, which has funded an extensive collation of technology, so that methods for earthquake, hurricane and flood loss estimation are clearly laid out. However, while the software is distributed free of charge, the source code is closed, so that it is regarded as something of a ‘black box’, and has had limited acceptance. As a result, while to some extent serving risk-based mitigation, HAZUS® has also tended to stifle innovation, in that potential supporters of new risk-based software question why they should compete with ‘free’ software; while at the same time the inaccessibility of the source code precludes its free and open enhancement. This is a typical defect of any attempt to have an ‘authorized’ version (viz. parallels in IBM-Apple, and Windows-Linux). In contrast, probably the most successful series of risk models anywhere has been the flood frequency and loss estimation software developed by the US Army Corps of Engineers (USACE), at the Hydrologic Engineering Center (HEC). HEC-RAS and similar programs have succeeded by being free, with source code available to many users. Therefore, independent

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vetting has occurred. It helped that flood modeling was required by the NFIP, and that USACE had something of a monopoly on this, in the US. This situation is now changing however, with DHI’s Mike series of software making significant inroads in the US, based on more advanced dynamic flood modeling, versus the ‘static’ modeling in the HEC software. Outside of the US, some national authorities have embraced risk-based modeling and the UN’s ISDR, the World Bank and other institutions are strongly encouraging a risk-based approach to disaster risk management. An interesting observation is the place of Japan in this development. Arguably, Japan should be the leader in earthquake engineering worldwide, due to the size of its earthquake risk and its technological capabilities. From 1880 to 1930 it was the leader, which Freeman and Martel very clearly observed in the late 20s, leading Freeman to push for the strong motion program in California, and to invite Suyehiro to lecture at Berkeley, Stanford, California Institute of Technology and MIT. From that moment, the US surpassed Japan, developing the first strong motion program, the magnitude scale, the magnitude frequency relation, response spectra and other innovations. Admittedly, many of these innovations were developed independently in Japan about the same time, but their visibility and application were much lower. Following World War 2, Kawasumi published his probabilistic hazard map of Japan (1951), about two decades ahead of its time. But, while many innovations still continued in Japan, Kawasumi’s map seems to have been the end of probabilistic and risk thinking in Japan. When this writer was at Kyoto University in the late 70s, some work was being done in Japan on hazard analysis, but risk analysis was almost unknown. As the WASH-1400 report introduced probabilistic analysis to engineers in general, and Wiggins, Blume, Whitman and then Yanev and colleagues, introduced seismic risk management in the US in the 70s and 80s, this writer’s observations and recollections for the 70s to 90s is that the field was totally ignored in Japan. Seismic design was very advanced in Japan, but it was deterministic in nature, and systems or enterprise risk management approaches just did not exist, despite their burgeoning development in the US. It was only following the 1995 Kobe earthquake and the demonstrated seismic vulnerability of even ‘modern’ Japanese construction, that interest in risk management in Japan emerged. The first seismic retrofit of a high-rise building in Japan was designed by a US consulting firm, in 1997.


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2.12 Concluding Remarks The rational analysis and mitigation of risk due to natural and other hazards is founded on a large body of work developed over the last 150 years. If one were asked to list some contributions essential to the development of seismic risk assessment, the list might be something like: 1. Mallet – his investigations and founding of seismology in the UK about 1850. 2. Milne – his arrival in Japan in 1880 and development of seismology and training of seismologists in Japan, development of the first practical seismograph, and the founding of the Seismological Society of Japan. 3. Reid’s Elastic Rebound Theory (1910) and Wegener’s theory of continental drift (1913); however, Wegener’s ideas were rejected at the time, and not accepted until the 1960s with the theory of plate tectonics. 4. Freeman – in the few short years of about 1927 to 1932, his strong encouragement of earthquake engineering in the USA, role in founding the US strong motion program, and book laying out building damage experience and reduction of that experience to loss ratios – and Neumann and colleagues for translating Freeman’s ideas into actual deployed instruments in time for the 1933 Long Beach earthquake. 5. Caltech – does credit go to Millikan for gathering Wood, Richter, Martel, Gutenberg, Benioff, Housner and Hudson? or do they get the credit for development of the – Magnitude scale (Richter 1935) – magnitude-frequency relation, log N = a – b M (Gutenberg and Richter 1941) – response spectra (Biot 1940; Housner 1941). 6. Cornell’s 1968 BSSA paper on engineering seismic risk analysis. Items 1–6 are necessary and sufficient for estimation of seismic hazard. Freeman laid the basis for a rational approach to seismic risk assessment, including vulnerability functions. The proper development of vulnerability functions however still required: 7. The Finite Element Method (Argyris, Turner, Clough et al., 40s to 60s), and development of associated structural analysis software (e.g., Wilson, 60s to now).

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8. Karl Steinbrugge and the group around him, Algermissen, McClure, Lagorio and others, for focusing on the goal of assessing (and reducing) the risk (60s to 80s). 9. The SDDA project at MIT (1973–78, Whitman, Cornell, Vanmarcke, Veneziano et al.), for a consistent approach to the entire problem, and 10. ATC-13 (1985) for developing a consistent open set of vulnerability functions (Rojahn, Sharpe, Kiremidjian et al.). These and selected other developments are shown in Fig. 2.14, overlaid on a background of the global growth in natural hazards loss estimation. While much more remains to be done, recent developments in information technology permit leveraging of this body of knowledge in ways not previously possible. Key to enhanced seismic risk mitigation is dissemination of the capability to analyze risk, in an open and transparent manner, and better doctrine on using the results.

Fig. 2.14. (This figure also appears in the color insert) General developmental trend of seismic risk assessment 1950–2000, overlaid on natural hazard losses and also showing selected related trends (background: Munich Re)

2.13 Acknowledgments The support of the Mid-America Earthquake Center in inviting this paper is gratefully acknowledged. Too many persons to acknowledge here have educated me over the years on the various topics discussed above – while


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not named, all their efforts have been highly appreciated. The formal reviews of this paper by Profs. Robert Whitman (MIT), Amr Elnashai (U. Illinois) and Dennis Mileti (U. Colorado) were most helpful and much appreciated, as was their kindness. Dr. Keith Porter (Caltech) is thanked for his reading of an early draft and many helpful comments. I leaned heavily on several papers for historical information, especially Ben-Menaheim (1995), various writings of Steve Tobriner (U. California at Berkeley) and Bob Reitherman (CUREE Exec. Director), the Oral Histories published by EERI, and Lee’s two volume International Handbook of Earthquake & Engineering Seismology (esp. the Japan national report, App. 79_33).

References Gov. Wilson puts cost of earthquake in Los Angeles as high as $30 billion. (1994, 21 January 1994). Wall Street Journal, p 3 Agnew DC (2002) History of seismology. In: Lee HK, Kanamori H, Jennings PC, Kisslinger C (eds.), International handbook of earthquake and engineering seismology. Academic Press, New York, pp 3–12 Algermissen ST (1969) Seismic risk studies in the United States. In: Proceedings of the Fourth World Conference on Earthquake Engineering. Santiago de Chile, pp A1: 14–27 Algermissen ST, Hopper M, Campbell K, Rinehart WA, Perkins D, Steinbrugge KV, Lagorlo HJ, Moran DF, Cluff LS, Degenkolb HJ, Duke CM, Gates GO, Jacobson NN, Olson RA, Allan CR (1973) A study of earthquake losses in the Los Angeles, California area. The Federal Disaster Assistance Administration, Washington, DC Algermissen ST, Perkins DM (1976) A probabilistic estimate of maximum acceleration in rock in the contiguous United States (Open-File Report 76–416). USGS Algermissen ST, Riehart W, Dewey J, Steinbrugge KV, Lagorio HJ, Degenkolb HJ, L SC, McClure FE, Scott S, Gordon RF (1972) A study of earthquake losses in the San Francisco Bay area: Data and analysis. National Oceanic and Atmospheric Administration (NOAA), Washington, DC American Society of Civil Engineers (ASCE) (1952) Lateral forces of earthquake and wind Applied Technology Council (1978, amended 1982) Tentative provisions for the development of seismic regulations for buildings (ATC-3-06). Applied Technology Council, Washington, DC Applied Technology Council (1985). Earthquake damage evaluation data for California (ATC-13) Redwood City, CA ASTM (1999) ASTM E2026 Standard guide for the estimation of building damageability in earthquakes. ASTM International

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Eguchi RT, Goltz JD, Seligson HA, Flores PJ, Blais NC, Heaton TH, Bortugno E (1997) Real-time loss estimation as an emergency response decision support system: The early post-earthquake damage assessment tool (EPEDAT). Earthquake Spectra 13:815–832 Ellison R, Milliman J, Roberts R (1984) Measuring the regional economic effects of earthquakes and earthquake predictions. Journal of Regional Science 24:559–579 FEMA (1988) Earthquake Damaged Buildings: An Overview of Heavy Debris and Victim Extrication, prepared by C. Scawthorn for the Applied Technology Council under sub-contract to the Federal Emergency Management Agency (ATC 21-2; FEMA 158), Washington. FEMA (1991) Seismic vulnerability and impact of disruption of lifelines in the conterminous United States, prepared by C. Scawthorn and M. Khater for the Applied Technology Council (ATC-25). Federal Emergency Management Agency, Washington, DC FEMA (2006) Multi-hazard loss estimation methodology, earthquake model, HAZUS®MH MR2, technical manual, developed by Department of Homeland Security, Federal Emergency Management Agency, Mitigation Division. National Institute of Building Sciences, Washington, DC Freeman JR (1930) Engineering data needed on earthquake motion for use in the design of earthquake-resisting structures. Bulletin of the Seismological Society of America 20:67–87 Freeman JR (1932) Earthquake damage and earthquake insurance. McGraw-Hill, New York Friend I, Blume ME (1975) The demand for risky assets. American Economic Review 65:900-922 Gallagher RB (1956) Risk management: A new phase of cost control. Harvard Business Review GSHAP (1998) Global seismic hazard assessment program, closing report to the IDNDR/STC, 1992 – 1998 Gutenberg B, Richter CF (1941) Seismicity of the earth (Vol. 34). Geological Society of America, New York Gutenberg B, Richter CF (1942) Earthquake magnitude, intensity, energy, and acceleration. Bulletin of the Seismological Society of America 32:163 Gzovsky MV (1962) Tectonics and earthquake forecasting. Bulletin of the Seismological Society of America 52:485–505 Hanks TC, Kanamori H (1979) A moment magnitude scale. Journal of Geophysical Research 84:2348–2350 Hess HH (1962) History of ocean basins. Petrologic Studies: A 599–620 Housner G (1952) Spectrum intensities of strong motion earthquakes. In: Proceedings of the Proceedings of a Symposium on Earthquakes and Blast Effects on Structures. Los Angeles, pp 21–36 Housner GW (1941) Calculating the response of an oscillator to arbitrary ground motion. Bulletin of the Seismological Society of America 31:143-149 Housner GW (1952) Intensity of ground motion during strong earthquakes, second technical report under office of naval research contract N6ONR-244, Task

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Order 25, Project Designation NR - 081 – 095. Earthquake Research Laboratory, California Institute of Technology, Pasadena Housner GW (1958) The mechanism of sandblows. Bulletin of the Seismological Society of America 48:155–161 Housner GW (1984) An historical view of earthquake engineering. In: Proceedings of the 8th World Conference on Earthquake Engineering. San Francisco, pp 25–38 Insurance Services Office. (2006) Definition of probable maximum loss. from http://www.iso.com/products/2400/prod2453.html Isacks BL, Oliver J, Sykes LR (1968) Seismology and the new global tectonics. Journal of Geophysical Research 73:5855–5899 Ishimoto M, Iida K (1939). Observations of earthquakes registered with the microseismograph constructed recently. Bulletin of the Earthquake Research Institute of the University of Tokyo 17:443–478 Jacobs J (1961) The death and life of great American cities. Random House, New York Jones BG, Manson DM, Hotchkiss CM, Savonis MJ (1987) Determination of building stocks for urban vulnerability analysis and earthquake loss estimation. Cornell Institute for Social and Economic Research, Program in Urban and Regional Studies, Ithaca Jones NP, Noji EK, Krimgold F, Smith GS (eds) (1989). Proceedings of the international workshop on earthquake injury epidemiology for mitigation and response. Johns Hopkins University, Baltimore Kashima T (2000) Strong earthquake motion observation in Japan. Building Research Institute, Tsukuba, Japan Kawasumi H (1951) Measures of earthquake danger and expectancy of maximum intensity throughout Japan as inferred from the seismic activity in historical times. Bulletin of the Earthquake Research Institute 29:471–482 Knight FH (1921) Risk, uncertainty and profit. University of Chicago Press, Chicago Kornfield L, Rojahn C, Scawthorn C (2006) Estimated losses from scenario earthquakes affecting San Francisco: CAPSS – part 2. In: Proceedings of the 8th US National Conference on Earthquake Engineering. San Francisco Law DG (1997) Chapter 6.0 seismic risk models, part 4: Computer models of probable maximum loss. In: Brun SE, Etkin D, Law DG, Wallace L, White R (eds.), Coping with natural hazards in Canada: Scientific, government and insurance industry perspectives, a study written for the round table on environmental risk, natural hazards and the insurance industry. Environmental Adaptation Research Group, Environment Canada and Institute for Environmental Studies, University of Toronto, Toronto Le Pichon X (1968) Sea-floor spreading and continental drift. Journal of Geophysical Research 73:661–697 Le Pichon X, Heirtzler JR (1968). Magnetic anomalies in the indian ocean and sea-floor spreading continents. Journal of Geophysical Research 73:2101 Lee HK, Kanamori H, Jennings PC, Kisslinger C (eds) (2002). International handbook of earthquake and engineering seismology. Academic Press, New York


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Mallet R (1862) Neapolitan earthquake of 1857. The first principles of observational seismology. Chapman and Hall, London Mallet, R and Mallet, JW (1858). The Earthquake Catalogue of the British Association, with the Discussion, Curves, and Maps, Etc. (Including Seismographic Map of the World, Dated 1857), Taylor and Francis, London. Markowitz H (1952) Portfolio selection. The Journal of Finance 7:77–91 Martel RR (1936) A report on earthquake damage to Type III buildings (Special Publication No. 201). U.S. Department of Commerce Coast and Geodetic Survey, Washington, DC McGuinness JS (1969) Is “Probable Maximum Loss” (PML) a useful concept? In: Proceedings of the Proceedings of the Casualty Actuarial Society, pp. 31–39 McGuire RK (1974) Seismic structural response risk analysis incorporating peak response regressions on earthquake magnitude and distance (Pub. R74-51). MIT, Cambridge, MA McGuire RK (1976) Fortran computer program for seismic risk analysis (USGS Series Open-File Report Number 76-6) McGuire RK (1978) FRISK-computer program for seismic risk analysis using faults as earthquake sources, (U.S.G.S. Open-File Report, 78–1107) Milne J (1888) The movement produced in certain buildings by earthquakes. Transactions of the Seismological Society of Japan 12:67–75 Milne J (1898) Seismology. Kegan Paul, Trench, Trubner and Co, London Milne J, Burton WK (1892) The Great Japan Earthquake of 1891. Lane, Crawford & Co, Yokohama Milne WG, Davenport AG (1969) Distribution of earthquake risk in Canada. Bulletin of the Seismological Society of America 59:729 Mogami T, Kubo K (1953) The behavior of soil during vibration. In: Proceedings of the Third International Conference of Soil Mechanics and Foundation Engineering, pp 152–155 Morgan WJ (1968) Rises, trenches, great faults, and crustal blocks. Journal of Geophysical Research Morgenstern O, Von Neumann J (1953) Theory of games and economic behavior. J. Wiley and Sons, New York Mukerjee T (1971) Economic analysis of natural hazards: A preliminary study of adjustments to earthquakes and their costs, working paper 17. Natural Hazards Center, University of Colorado, Boulder Muto K, Bailey RW, Mitchell KJ (1963) Special requirements for the design of nuclear power stations to withstand earthquakes. Proceedings of Institutional Mechanical Engineering 117:155–203 Nader R. (1965) Unsafe at any speed. New York: Knightsbridge Publishing Co. National Academies Press (1999) The impacts of natural disasters: A framework for loss estimation. National Academies Press, Washington, DC Newmark NM (1967) Design criteria for nuclear reactors subjected to earthquake hazards. Urbana, IL Nigg JM, Eisner RK. (1997) Earthquake response: Intergovernmental structure and policy innovation (keynote paper). Paper presented at the 5th U.S.-Japan Workshop on Urban Earthquake Hazard Reduction, Pasadena

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O'Rourke, MJ and Liu X (1999) Response of Buried Pipelines Subject to Earthquake Effects, Monograph, Multidisciplinary Center for Earthquake Engineering Research, State University of New York, Buffalo Packard V (1957) The hidden persuaders. Penguin, London Petak WJ, Atkisson AA (1982) Natural hazard risk assessment and public policy. Springer-Verlag, New York Press NA (1997) Review of recommendations for probabilistic seismic hazard analysis: Guidance on uncertainty and use of experts. National Academy Press, Washington, DC Rasmussen NC (1975) Reactor safety study: An assessment of accident risks in us commercial nuclear power plants. National Technical Information Service Ravindra M (1992) Seismic assessment of chemical facilities under California risk management and prevention program. In: Proceedings of the Proceedings, International Conference on Hazard Identification and Risk Analysis, Human Factors and Human Reliability in Process Safety Reid HF (1910) Elastic rebound theory. Bulletin of the Department of Geological Sciences, University of California 6:92–120 Reitherman RK (1982) Earthquake-caused hazardous materials releases. In: Proceedings of the Hazardous Materials Spills Conference. Milwaukee, WI Richter CF (1935) An instrumental earthquake magnitude scale. Bulletin of the Seismological Society of America 25:1–32 Roberts EB, Ulrich FP (1951) Seismological activities of the U.S. Coast and geodetic survey in 1949. Bulletin of the Seismological Society of America 41:205–220 Scawthorn C, Gates WE (1983) Estimation of earthquake losses in Los Angeles: Damage scenarios under varying earthquake research effort. Dames & Moore, Sponsored by U.S. Geological Survey Contract No. 14-08-000119822, San Francisco Scawthorn C, Yamada Y, Iemura H (1982) A model for urban post-earthquake fire hazard. Disaster: The International Journal of Disaster Studies and Practice (London) 5:125–132 Schnabel P, Seed B (1973) Accelerations in rock for earthquakes in the western united states. Bulletin of the Seismological Society of America 63:501 Schneider PJ, Schauer BA (2006) HAZUS® – its development and its future. Natural Hazards Review Special Issue: Multihazards Loss Estimation and HAZUS® edited by C Scawthorn, PJ Schneider, and BA Schauer 7: 40–45 Scholl RE (1974) Statistical analysis of low-rise building damage caused by the San Fernando earthquake. Bulletin of the Seismological Society of America 64:1–23 Seismology Committee SEAoC (1959) Recommended lateral force requirements and commentary. Structural Engineers Association of California, Sacramento Spangle WE (1987) Pre-earthquake planning for post-earthquake rebuilding (pepper). Southern California Earthquake Preparedness Project, Los Angeles, California


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Steinbrugge KV (1968) Earthquake hazard in the San Francisco Bay area: A continuing problem in public policy. University of California Institute of Governmental Studies, Berkeley Steinbrugge KV (1982) Earthquakes, volcanoes, and tsunamis: An anatomy of hazards. Skandia America Group, New York Steinbrugge KV, Moran DF (1954). An engineering study of the southern California earthquake of July 21, 1952 and its aftershocks. Bulletin of the Seismological Society of America 44:201–462 Stepp JC (1972) Analysis of completeness of the earthquake sample in the Puget Sound area and its effect on statistical estimates of earthquake hazard. In: Proceedings of the First Microzonation Conference, Seattle, WA, pp 897–909 Strain RW (1997) Reinsurance. Strain Publishing, Inc., Athens, TX Structural Engineers Association of California (SEAOC) (1995). Performancebased seismic engineering of buildings. Vision 2000 report (OC #95-9) Sykes LR (1971) Aftershock zones of great earthquakes, seismicity gaps and prediction. Journal of Geophysical Research 76:8021–8041 Tierney KJ (1997) Impacts of recent disasters on businesses: The 1993 midwest floods and the 1994 Northridge earthquake. In: Jones BG (ed), Economic consequences of earthquakes: Preparing for the unexpected, NCEER SP-0001. Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY, pp 189–222 Tierney K, Dahlhamer, JM (1998) Business disruption, preparedness and recovery: Lessons from the Northridge earthquake. In: Proceedings of the NEHRP Conference and Workshop on Research on the Northridge, California Earthquake of January 17, 1994, pp IV-171 – IV-178 U.S. Census Bureau (2002) 2000 census of population and housing. Retrieved from http://www.census.gov/prod/cen2000/index.html. Wadati K (1933) On the travel time of earthquake waves. Geophysical Magazine 7:101–111 Wegener K (1913) Die seism registrierangen am samo-observatorium etc. Nachr Koniglichen Akad Wiss Gottingen, Math-Phys 3 Weigel RL (ed) (1966). Earthquake engineering. Prentice Hall, Englewood Cliffs, NJ Willis B (1923) Earthquake risk in California. Bulletin of the Seismological Society of America 13:89–99 Willis B (1923) A fault map of California. Bulletin of the Seismological Society of America 13:1–12 Wilson JT (1965) A new class of faults and their bearing on continental drift. Nature 207:343–347 Wood HO, Neumann F (1931). Modified Mercalli Intensity scale of 1931. Bulletin of the Seismological Society of America 21:277–283 Yanev PI (1975) Peace of mind in earthquake country. Chronicle Books, San Francisco

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Table 2.2. Chronology of Seismic Risk Assessment

Appendix 1. Chronology of Seismic Risk Assessment

Table 2.2. (continued)


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Appendix 2. Reports from MIT SDDA (Seismic Design Decision Analysis) Project

Ackroyd MA, Biggs JM (1974) The formulation and experimental verification of mathematical models for predicting dynamic response of multistory buildings.1 Cornell CA, Merz Cornell HA (1974) A seismic risk analysis of Boston. MIT-CE R74-2 1 Whitman RV (1973) Damage probability matrices for prototype buildings. MITCE R74-8 1 De Neufville R (1975) 1939- How do we evaluate and choose between alternative codes for design and performance? 1,2 Heger FJ, Luft RW (1977) Structural evaluation of existing buildings in Massachusetts for seismic resistance MIT-CE R77-44 1,2 Hein KH, Whitman RV (1976) Effects of earthquakes on system performance of water lifelines, MIT-CE R76-23 1,2 Isbell JE, Biggs JM (1974) Inelastic design of building frames to resist earthquakes 1,2 Krimgold F (1977) Seismic design decisions for the Commonwealth of Massachusetts state building code 1 Larrabee RD, Whitman RV (1976) Costs of reinforcing existing buildings and constructing new buildings to meet earthquake codes 1,2 Munroe T, Blair C (1975) Economic impact in seismic design decision analysis: a preliminary investigation 1,2 Panoussis G (1974) Seismic reliability of lifeline networks. SDDA Report No. 15. MIT-CE R74-58 1 Schumacker B, Whitman RV (1977) Models of threshold exceedance and loss computations of non-homogeneous spatially distributed facilities 1,2 Taleb-Agha G, Whitman, RV (1975) Seismic risk analysis of discrete systems 1,2 Taleb-Agha G (1975) Seismic risk analysis of lifeline networks 1,2 Taleb-Agha G (1974). Sensitivity analyses and graphical method for preliminary solutions 1 Tong, W-H (1975) Seismic risk analysis for two-sites case 1,2 Unemori AL, Whitman RV (1976) Nonlinear inelastic dynamic analysis with soilflexibility in rocking. SDDA Report No. 25. 1,2 Veneziano D (1975) Probabilistic and statistical models for seismic analysis 1,2

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M.I.T. Dept. of Civil Engineering, Cambridge, MA. Available from National Information Service, U.S. Dept. of Commerce, Springfield VA. 2


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Whitman RV, Protonotarios JN (1977) Inelastic response to site-modified ground motions 1,2 Whitman RV et al. (1973) Summary of methodology and pilot application. SDDA Report No. 9. 1 Whitman RV et al. (1974) Ground motion amplification studies, Bursa, Turkey 1 Whitman RV et al. (1974) Methodology and pilot application 1 Whitman RV et al. (1974) Seismic design decision analysis 1 Whitman RV, Aziz TS, Wong EH (1977) Preliminary correlations between earthquake damage and strong ground motion 1,2 Wong EH (1975) Correlations between earthquake damage and strong ground motion. MIT-CE R75-23 1,2 Yegian MK (1976) Risk analysis for earthquake-induced ground failure by liquefaction 1,2

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M.I.T. Dept. of Civil Engineering, Cambridge, MA. Available from National Information Service, U.S. Dept. of Commerce, Springfield VA. 2

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Table 2.3. Terminology and Concordance

Appendix 3. Terminology and Concordance



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3 Perspectives on the History of Seismic Risk Assessment

Responses to Scawthorn’s paper “A Brief History of Seismic Risk Assessment”

Introduction Each of the following authors brings his own distinguished viewpoint on the history and future directions for [seismic] risk assessment. Robert Whitman focuses on the lessons learned from regional seismic risk assessments, including the need for clearly defined objectives for successful outcomes; accounting for user needs in loss estimates; assessing uncertainty; including lifelines in analyses; and developing a meaningful inventory prior to analysis. Amr Elnashai extends the history of scientific inquiry into earthquakes back to the 1600’s and fills in some important events, while emphasizing how Europe, Japan, and the United States interacted in the development of seismic research and building codes. Finally, Dennis Mileti urges practitioners to take sociological perspectives into account, as losses that extend beyond traditionally assessed risk are harder to quantify, but no less important. Scawthorn’s history makes an important contribution to our understanding of the field of risk assessment. Further consideration of the historical context highlights the importance of considering how loss estimation tools developed for applications in the past need to be better customized to appeal to current audiences. Although they are costly, there is still a need for more on-the-ground sensors, using multiple technologies to provide better base data for predictions about earthquakes. Lessons learned in the development of seismic risk assessment are broadly applicable to the assessment of risk due to other natural hazards. Indeed, the primary tool used by the U.S. disaster management agencies,

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HAZUS®, brings together models for earthquakes, wind and floods under one umbrella to help communities estimate potential losses from all of these hazards. In all areas of risk analysis, broadening analyses will likely produce more useful outcomes.

3.1 Lessons Leaned from Current Practice and Future Needs in Regional Loss Estimation Robert V. Whitman Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

3.1.1 Impacts of Risk Analyses In Section 2.5, Scawthorn lists a number of regional seismic risk assessments, and in almost every case summarizes the impact of the assessment. With just a few exceptions, the impact is “unknown,” “as yet unknown,” or is given in vague, general terms. This summary is in line with that extracted from the user needs questionnaire and workshop conducted during the study for the 1989 “Estimating Losses …” NRC report. In that study, the users questioned were unable to cite specific benefits from the regional loss estimates that had been conducted, but “the most important use of these studies … was … in educating elected officials and the public about the seriousness of seismic threat and the need to take action.” Of the studies cited by Scawthorn, there were specific and tangible impacts only from those with more limited and specific objectives; e.g.: Worker’s Compensation benefits, and Fire Following Earthquake (San Francisco and Los Angeles). These observations emphasize the importance of having clear objectives in advance of a study, arrived at in co-operation with the intended user. The ability to operate in this fashion is one reason for the successful cooperation between insurance companies and private-sector firms performing risk analyses for them. The recent Seattle Fault scenario study should be added to the list of important regional loss estimates (EERI 2005).


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3.1.2 User Needs The aforementioned 1989 NRC study included circulating questionnaires and holding a workshop in an effort to understand what potential users most wanted from a regional loss estimate. The users in this case were primarily officials and workers from smaller cities and, to a lesser degree, from regional and state governments. The following recommendations were gleaned from the results: x Involve the users in the entire loss estimate study process. x Clearly indicate the level of potential error as well as confidence in the loss estimates. x There should be an inventory base that can be disaggregated to the smallest political and geographic unit, and that is in a form that can be used and maintained by local governments. x Loss estimate studies should contain a scenario earthquake that is relatively probable and yet large enough to cause serious loss. x To increase the likelihood of access and use, reports should be as nontechnical as possible. More emphasis should be placed on the implications of the findings for seismic policy and planning adoption. In the 1989 report, credibility and clarity to relatively lay audiences jump out. The Seattle Fault study meets these tests very well. There was also a large effort to ascertain user needs in connection with the development of HAZUS®. A sizable committee, reflecting the same type of users identified for the 1989 NRC study, met several times and developed a long “wish list.” The list covered types of facilities for which losses were desired (e.g. hospitals, fire stations, electric systems, etc.,) and types of losses to be evaluated (e.g. dollar losses, casualties, volume of debris, etc.) This list paralleled that from the 1989 NRC study (with one major exception mentioned in the next section). In particular, this group of potential users strongly wished to have the inventory, and losses associated with that inventory, de-aggregated to the smallest community size possible, which led to the use of a census tract as the basic geographic unit in HAZUS®. These potential users, like those contacted in the 1989 NRC study, strongly desired information concerning the post-earthquake serviceability of utility and transportation lifelines. The HAZUS® leaders and contractors sought to satisfy as many of these user needs as seemed possible at the time, also keeping in mind the needs of the real client – FEMA.

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3.1.3 Accuracy and Uncertainty Unlike the potential users in the 1989 NRC study, in my memory the group of potential users associated with the initial phases of the HAZUS® effort were not enthusiastic about being given a realistic range of estimated losses – especially when faced with estimates of how large these ranges might be. They felt such indications of large uncertainty would make it difficult to sell elected officials and the public on the need for actions to reduce losses. This is in stark contrast with the needs of the insurance industry for realistic assessment of uncertainties. Hence, it is not surprising that the greatest impetus for evaluating uncertainty has come from insurers. Hopefully, public users of loss estimates are today better prepared to face the reality of large uncertainties. Achieving realistic evaluation of uncertainties – especially as regard to vulnerability – does appear as an important outstanding challenge. Certainly every opportunity to compare predicted and actual losses – i.e. every significant earthquake – should be pursued to the fullest extent possible – and in detail and not just in aggregated losses. But acquiring empirical information in this way will be a slow process. As described by Scawthorn, multiple theoretical analyses – varying assumed configurations and properties – offer the possibility of better estimates for uncertainties of various classes of buildings and bridges. However, since every analysis must start from assumptions about the proper model for a structure, there will always remain doubts about the size of the model error. 3.1.4 Lifelines Disruptions resulting from serious damage to utility and transportation lifelines may have a greater impact upon a community as a whole than major damage to residential and commercial buildings. This should be of special concern to municipal officials and their planners. Outside help will assist with the casualties and displacements resulting from building damage, but the public will hold local officials responsible for slowness in restoring vital lifeline systems to a functioning state. Despite this need on the part of their potential users, the HAZUS® team concluded that it was only, at that time, feasible to assess damage to components of lifelines (e.g. bridges) – at best a very rough indicator of the operational capability of partly-damaged lifeline systems. Since then, quite a number of software packages for analyzing lifeline systems have been developed. They are complex, in part because of the special problems presented by spatially dispersed and redundant networks. These software


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packages obviously can be of great value to those responsible for the planning and functioning of lifeline systems. Moreover, there potentially are important interactions between utility lifelines and other aspects of disaster mitigation – fire-fighting being the most obvious example. At first sight, it might seem desirable to incorporate one or more such software packages into HAZUS®, so as to permit a more complete analysis of the economic and disruptive effects of an earthquake upon a community or region. Indeed, the HAZUS® team originally hoped that would be possible as part of a “Level 3” analysis. Clearly, the “closed source” policy for HAZUS® inhibits such linkages. With privately-owned utility systems, at least, there may be other obstacles as well. It is pertinent to note that those preparing the Seattle Fault scenario – while emphasizing potential problems with lifeline systems – apparently chose to rely upon engineering experience and judgment rather than running any formal analysis of the systems. Just how to most effectively integrate the analysis of lifelines into a comprehensive risk analysis is certainly is an important question. There are aspects of risk where building damage and lifeline performance overlap; for example, business interruption and fire-related loss. Such overlaps argue for formal linking of building damage and lifeline performance within a single analysis. On the other hand, one should question whether the supposed increase in accuracy is worth the additional complication. It may well be that informal linking of separate analyses in a scenario is usually a better alternative. 3.1.5 Inventory As noted by Scawthorn, developing a meaningful inventory – especially one that includes credible information concerning the seismic resistance of structures and facilities – is an important but difficult challenge. The more detail that is desired in the estimated losses, the more important the inventory becomes. Thus, uncertainty in estimated fatalities (which depend strongly upon the inventory of the most collapse-susceptible structures) is generally greater than uncertainty in gross damage cost (which can be estimated by averaging losses over many, many buildings). Credible estimates for the functionality of fire-fighting systems are dependent upon knowledge of the seismic resistance of fire stations and upon the functionality of water supply systems. The particular soil profile at the location of an essential facility can have an important influence upon the expected performance of that facility. Reliance solely upon default inventories, or any inventories lacking good information about seismic resistance, thus

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greatly limits the reliability of detailed loss estimates. The experiencebased judgment of local engineers – even without detailed studies and tabulations – can go a long way in improving upon default inventories – and hence improve estimates for the number of severely damaged structures. However, when a particular structure or facility is very important in a loss estimate, there can be no substitute for detailed knowledge concerning the structural system and the way in which it was designed and built. This will be true when estimated losses can be dominated by those occurring in a single (or a very few) large structures, or when a large facility (dam, LNG plant, etc.) might cause losses so large as to dominate those caused by damage in the balance of the community or region. Such situations should be treated separately from broad loss estimates that inevitably depend upon averaging uncertain losses over many, many entries in an inventory. 3.1.6 Immediate Post-Earthquake Response One offered role for a loss estimate is to provide guidance for responders immediately following an earthquake. Being able to obtain a ShakeMap (USGS) within minutes following an earthquake makes this a potential possibility. Obviously, how meaningful a role there is depends upon a detailed and up-to-date inventory – especially for critical structures (e.g. fire stations and hospitals) and for especially damageable buildings and facilities. 3.1.7 Risk Assessment Scawthorn mentioned the potential role for standards and norms for risk assessment. I particularly feel the need for a multi-attribute measure of the loss and disruption that might result from an earthquake – incorporating measures of direct damage loss, broader impacts on the economy, suffering and disruption. Here clearly is a challenge requiring close cooperation between the engineering and social science disciplines. Of course, standards imply controls over the software used for analyses. The desire to be able to compare loss estimates from various different communities was what drove FEMA to maintain control of the source code for HAZUS®. Here is a suggestion for another entry in the bibliography. Within the past several years, a group in Canada responsible for the safety of hydroelectric facilities has issued a comprehensive and excellent guideline for


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risk assessment (Hartford and Baecher, 2004). Earthquakes are only one of the risks considered. 3.1.8 Concluding Remarks I agree that risk assessment, and in particular risk analysis, has certainly come a long way during recent decades. There also certainly are opportunities for further advances in accomplishing more comprehensive and credible risk analyses. At the same time, perhaps the greatest challenge will be in learning how to effectively use risk assessment to achieve a safer society. It will be important to document applications of risk assessment tools for purposes of earthquake hazard mitigation – what was done and what results were achieved. Stimulating officials and planners to provide such documentation – which can serve as invaluable guides for others planning risk evaluations, as well as suggesting new developments aimed at improving loss estimates – will be a vital task.

3.2 The Dawn of Earthquake Investigations and CrossContinent Interactions Amr S. Elnashai University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

Congratulations to Scawthorn are in order, for the excellent information that he has provided and for setting a very ambitious goal that was largely achieved. He could have only reviewed Seismic Loss Assessment, meaning studies that have attempted to estimate the impact of an earthquake on a system or a region, or he could have limited the geographical or temporal coverage of his review. Instead, he attempted to cover the subject from Antiquity to modern day projects, some of which are indeed still underway. He has challenged himself and readers by challenging the statement made by George Housner, who is quoted as saying in 1984 that writing a history of earthquake engineering is premature. The author even delved into other related developments, such as digital computers and finite element methods. This enjoyable chapter reflects the wide and international experience of Scawthorn. Notwithstanding the ‘lack of comprehensiveness’ disclaimer in

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the paper, the contents cover almost every aspect of not only earthquake loss assessment but also its components of hazard and fragility, alongside decision-making and underlying technology and tools. As a consequence, there are large gaps both in time and space, in the chapter. I will attempt below to fill in some important events, and to highlight other gaps that I am not able to fill and leave for others. The four issues I will cover are: (1) the dawn of systematic and scientific research in earthquake causes and effects, (2) interactions between Europe, Japan and the USA in the early days of earthquake research, (3) seismic design code development, and (4) contributions from outside Europe, Japan and the USA, alongside present day projects in Europe. 3.2.1 Landmark Developments in the 17th and 18th Centuries I am sure that George Housner has a good explanation for his statement in 1984 that earthquake engineering is a 20th century subject. As far as my limited investigations indicate, it started three centuries earlier. The origins of modern earthquake engineering may be traced to four geographical nuclei of development, California, Japan, Italy and the British Isles (Housner, 1984, 1989). The earliest formal study was by Robert Hooke (1635–1703) who gave a series of lectures at the Royal Society during 1667 and 1668, which appeared as a book in 1705. These were entitled ‘Lectures and Discourses in Earthquakes and Sub-terranean Eruptions’. This was followed by the work of Thomas Young (1773–1829), who collated a bibliography on earthquake-related publications in Europe, published in 1807. Independently, Robert Mallet (1810–1881) was busy thinking of earthquake processes and the development of measuring devices. In 1847, he presented his paper entitled ‘On the Dynamics of Earthquakes’ at the Irish Academy, which was published subsequently. The paper included a blueprint for a seismograph that may have inspired later developments in Italy, California and Japan. Mallet also invented the term seismology, by joining the Greek words VHLVPRV (earthquake) with ORJLD(suffix meaning talking technically or explaining). However, the real trigger was provided by events elsewhere. In 1880, a small magnitude earthquake hit Yokohama (Kanai, 1989), causing minor damage to houses, but firing the enthusiasm of three English engineers who were visiting Japan. These were John Milne (1850–1913), James Alfred Ewing (1855–1912) and Thomas Gray (1850–1908). The former played a crucial role in the development of the subject and published a landmark book in England (Milne, 1898). There is some conflict in the literature about its formation, but it is likely that the Seismological Society of Japan was founded, at least in part,


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by John Milne and his colleagues, in collaboration with Fusakichi Omori (1868–unknown) and Seikei Sekiya (1855–1896). The latter was the world’s first professor of seismology at the Imperial College of Tokyo and is probably the first to observe the effect of topography on earthquake ground motion (Sekiya and Omori, 1891). Certainly, Milne had not left Japan, as some Japanese references state, by the time of the Great Gifu earthquake of 1891 (magnitude 8.0, 7273 deaths, 17,175 injured). John Milne’s A3-size book on observations from the earthquake is a prime example of his pioneering work and insight. Photographic evidence of high quality was given in the book of the collapse of early steel truss bridges supported on masonry piers, as shown in Fig. 3.1 alongside many other photographs of industrial and residential buildings. The report is comprehensive in its treatment of the event, and would still hold today as a model for investigative, as opposed to observational, earthquake reconnaissance reporting.

Fig. 3.1. Nagara Gawa Bridge damaged in the Gifu earthquake of 1891 (Milne and Burton 1892)

The Japanese nucleus continued to grow further with the formation of the Imperial Earthquake Investigation Committee in 1892, to investigate means of earthquake prediction and to minimize their impact. The Committee was replaced by the Earthquake Research Institute at the abovementioned Imperial College of Tokyo (where, in fact, John Milne was the first professor of Geology who studied earthquakes) in the wake of the Great Kanto earthquake of 1923, which killed 100,000 people. The first di-

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rector of the Institute was Dr. Kyoji Suyehiro, credited with the establishment of the sub-discipline and the term ‘Jishin Kogaku’ or Engineering Seismology (Suyehiro 1932). His lectures at California Institute of Technology (amongst whose academic staff were von Karman and Richter), University of California, Stanford and MIT in 1931 and 1932 stressed the need to obtain recordings of earthquakes, to establish their characteristics, hitherto completely unknown. Indeed, a paper in the proceedings of the American Society of Civil Engineering written by Professor C. Derleth, in the aftermath of the 1906 San Francisco earthquake stated that ‘An attempt to calculate earthquake stresses is futile. Such calculations could lead to no practical conclusions of value’! The cross-over from British to Italian earthquake engineering came in 1855 when Luigi Palmieri (1867–1896) built a seismograph similar in design to that published by Robert Mallet in 1848. Italian giant strides drew further vigor when the Messina earthquake of 1908 (Figure 3.2) caused about 83,000 deaths, thus exceeding the Great All Saints Day earthquake of 1755 which devastated Lisbon, killing 60,000. An Italian government committee of 13 practitioners and engineers wrote a report giving a procedure, for the first time, of applying earthquake forces on structures as a percentage of their weight. It seems that this approach was the work of Professor Modesto Panetti of the Politecnic of Turin who recommended that the first floor earthquake force be 1/12 of the weight above, changing to 1/8 of the weight above for the second and third floors. The method, an equivalent static approach still used to this day in design codes, was explained and articulated by Professor Arturo Danusso of the Politecnic of Milan in his paper ‘Statics of Anti-Seismic Construction’. The method was thereafter widely used in Japan and the USA for earthquake design (Blume 1958).


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Fig. 3.2. Damage to the Palazzo Navigazione Beneraio Italiano, Messina earthquake, 1908

3.2.2 Seismic Design Code Development Although it was the Italians who developed the equivalent static procedure, the first formal code for seismic design was due to the Japanese Building Ordinance, after the 1923 Great Kanto earthquake. The regulations stipulated that buildings should be designed to resist a force equivalent to 10% of their weight applied horizontally. In the US, seismic design became mandatory only after the 1933 Long Beach earthquake, as mentioned in Scawthorn’s paper. A seismic design coefficient of 8% of the weight of the structure was recommended, regardless of earthquake or building characteristics. This event also yielded the world’s first set of strong-motion records, from the newly installed network, at Long Beach, Los Angeles and Vernon (Heck and Neuman 1933). The record at Long Beach showed a peak acceleration of 0.23g, nearly 6 times (in response terms) the recommended design coefficient. Since few buildings collapsed, first evidence of ductile response and the benefits of energy absorption were obtained. Engineering seismology and earthquake engineering, now distinct but interrelated fields, have never looked back. The proliferation of, and indeed the contradictions between, seismic design codes in force around the world is best demonstrated by examining the world list published every four years

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by the International Association of Earthquake Engineering (IAEE). The leading codes that have been widely used and tested are the Uniform Building Code (UBC, mainly developed in California but used on many international projects), the Japanese Building Law (sometimes inspired or augmented by the Architectural Institute of Japan documents) and the New Zealand seismic design code (known to include advanced concepts of ductile seismic response). In recent years, Eurocode8, the pan-European design code of earthquake resistance, has emerged as a force to be reckoned with, and has now been adopted by not only all EU member states, but also several countries in their vicinity. For example, Slovenia, not a member of the EU, has adopted all Eurocodes, whilst the seismic design code of Egypt is a simplified and regionalized version of Eurocode8. The New Zealand seismic design code remains a powerful document that is in many ways distinct from and more advanced than others. In addition to seismic design codes, which are legally binding documents, there are a number of guidance notes that are either used in their own right (e.g. Federal Emergency Management Agency – FEMA – guidelines for assessing and strengthening existing structures) or as source documents for code developments (e.g. Comite Euro-internationale de Beton – CEB – model code 1990, and National Earthquake Hazard Reduction Program – NEHRP – guidelines for seismic design of structures). 3.2.3 Contributions from Other Countries and Recent Developments As discussed above, the main nuclei of earthquake investigations were Italy and Britain in Europe, California in the USA, and Japan. However, in the 20th century, significant contributions were made by other countries, some of which qualify as landmark contributions. For example, the concept of Capacity Design is well and truly a New Zealand contribution. Contrary to popular belief, it was not proposed by Thomas Paulay, but by a practitioner, the name of whom escapes me. It was, however, Thomas Paulay who quantified it especially for reinforced concrete, and used it extensively in his teaching, research and contributions to the practice in New Zealand. Major contributions came from India and Turkey, both of which hosted the World Conference on Earthquake Engineering (1977 and 1980, respectively); Jai Krishna and Refaat Yarar were founding members of the International Association of Earthquake Engineering. I hasten to add that I have not researched this section of my review, but relied on my memory of some notable contributions.


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With regard to the current situation, the author has again challenged established wisdom not to chronicle recent events; he has indeed chronicled current, not just recent, ones. I would like to at least point out that LessLoss is not undertaking earthquake loss assessment as we understand it, but is rather a loose alliance of a very large number of institutions (about 42) that cover almost all aspects of earthquake loss assessment, but without integrating the tools and products. Moreover, LessLoss is the successor of a number of important, and somewhat more focused, European collaborative projects, namely PREC8 (coordinator, Artur Pinto), ICONS (coordinator Gian Calvi) and SAFERR (coordinator Amr Elnashai), in addition to several other projects that were funded earlier in geotechnical earthquake engineering and seismology. Finally, alongside the existing and excellent software packages mentioned in Scawthorn’s paper, there are others that have made giant strides and deserve to be mentioned there. Examples are OpenSHA (from SCEC), MAEviz (from the Mid-America Earthquake Center) and software by ImageCat, RMS and others. 3.2.4 Closure As mentioned in the opening of this section, George Housner often said that it is too early to write an historical review of the development of our interesting and inter-disciplinary topic. This is indeed true, but without contributions along the way, the prospects of writing such a review diminish with time. The endeavor is certainly premature. Notwithstanding, Scawthorn’s paper is such a contribution, and a significant one too. It reminds us of the names of Robert Mallet and John Milne, alongside the early Japanese pioneers Sekiya, Suyehiro and Omori, who featured very strongly in earthquake investigations in the late 1800s early 1900s. The Italian engineers Palmieri, Panetti and Danusso also made essential contributions at a critical time. For reasons that still prevail today, once an idea is implanted in the USA, it takes root so vigorously that its flourishing is attributed solely to the fertility of the medium, not the potency of the cutting. This is not necessarily disputable, but due recognition of the originators would perhaps encourage further cross-fertilization. It is in this spirit that I have added more information and specific details on non-USA earthquake research contributions. Since earthquakes know neither national nor techno-disciplinary boundaries, seismic risk mitigation efforts should also abandon these boundaries and deploy all means and efforts to protect communities threatened by earthquake risk.

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3.3 Social Science Perspectives on Seismic Risk Assessment Dennis S. Mileti University of Colorado, Boulder, Colorado, USA

3.3.1 Definition of Seismic Risk Assessment Careful attention should be paid to the definition of seismic risk assessment and/or analysis. It can be cast as a combination of earthquake hazard and assets at risk, but it means so much more to so many others. For example, to many it also includes losses that extend beyond direct losses to include second and third level impacts, societal consequences in their varied forms, and so on (what the author has called “loss studies”). The most inclusive of all views for this paper would include all such items for the earthquake hazard and, thereby, enable the author to catalogue the contributions of all those who have worked in this very diverse field. 3.3.2 Basis for Seismic Risk Assessment Seismic risk assessment is a mixed bag. For example, it can be thought of as map based and linked to shaking intensity, the interplay between shaking intensity and the constructed environment via fragility curves, as a picture of a broken building element that constitutes a lesson learned (which provides risk information regarding like building in other seismic areas) after a post-event field investigation, and so on. This rich variety in the basis of what constitutes a seismic risk and loss assessment might be more interesting to flaunt than keeping to the more straight and narrow definition of the field. 3.3.3 Guideposts The history of anything in general, and the field of seismic risk assessment in particular, are punctuated with significant events that shape and direct the field. These events for seismic risk assessment could be more richly identified to provide the reader with guideposts to the area’s history. One


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way to capture the impact of these guideposts would be, perhaps, to use them as sub-section titles. Although many of these “guidepost events” are now included, others are not. What I mean by guideposts are significant events or documents that impacted the field. Some examples include the risk and losses scenarios written by Karl Steinbrugge, the 1964 Great Alaska Earthquake, Ted Algermissen’s national risk maps, the 1971 San Fernando Earthquake and the reports which followed from it, the California State Geologist’s shaking intensity maps published in the early 1980s, and many others. 3.3.4 Techniques and Applications Intertwine The history and development of seismic risk assessment is intertwined with the history of the application of seismic risk and loss assessment information. The current document might consider clarifying and documenting this relationship. For example, the California Seismic Safety Commission recently published a 100 year timeline of seismic safety legislation in the state that documents over 200 seismic safety laws in the state. Although the adoption of almost every one of these laws is linked to an actual earthquake, many – but certainly not all – are rooted in advances and/or work in risk and loss assessment. 3.3.5 Societal Losses and Vulnerability It is difficult in the year 2006 to accept the absence in the previous chapter of dramatic advances in the social sciences in the sub-fields of vulnerability and loss assessment. Some of these include, for example, those performed by the social sciences component of the MAE Center, and the Social Vulnerability GIS based Index developed by Susan Cutter and her colleagues at the University of Southern California (Cutter’s recent work with the Social Vulnerability Index may be analogous to the emergence of the first shaking intensity map for the nation).

3.4 Seismic Risk Assessment: History and Strategic Directions No forward-looking treatment of a field would be complete without an historical perspective on which to ground it. The previous chapter attempts to present this as comprehensively as possible for the broad field of seismic

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risk assessment. Charles Scawthorn outlined a well-annotated timeline for the major discoveries and developments from many sub-disciplines, including seismology, civil engineering and economics. These were drawn on the backdrop of major seismic events around the world, which spurred societal interest in assessing and mitigating the risk of catastrophic losses due to earthquakes. It is important to view the development of loss estimation technologies through the perspective of the institutions in which they have developed and to consider the appropriateness of these tools to current audiences. What has been the audience for and what has funded loss estimation methodologies developed so far? The drivers/clients for the technology in the past have been the insurance industry, nuclear power, and the federal government. Current audiences for the products of these technologies are less understood and are probably much more diverse than originally intended or designed for. In some cases, the technologies have exceeded the needs of the audiences, and there may be technologies looking for uses. Improving sensor technologies and applying them to broader seismic sensing networks is an essential part of loss estimation. A denser network of earthquake sensors is needed around the world, but this is currently limited by the cost of the sensors. The development of lower cost sensors should be a priority. The demand for sensors is higher where earthquakes are more frequent, more severe, and the potential for financial losses is greater (i.e., Japan). The use of multiple technologies (small sensors, teleseismics, remote sensing, etc.) will all help to develop better ground motion maps for making predictions and estimations of loss. As the field of seismic risk assessment moves forward, its practitioners must stay aware of the history of its major developments, and the environment in which these developments occurred. The evolution of technology and its applications are intertwined, as pointed out previously, and this history has important implications in terms of problem framing and potentially neglected analyses. The well-documented events and outcomes of historical and recent earthquakes provide fodder for improvements to analysis techniques and technology to provide better risk assessments. Finally, as one would expect, many major developments in the history of seismic risk assessment were motivated by earthquakes with major loss of life and high economic consequences, but forward-looking improvements in assessment methods and technologies need not await the next disaster. A theme that was repeated in the reviews and discussion is the importance of considering the audiences for which previous assessments were developed and broadening the utility of the outputs or at least being aware of interests that may be neglected by existing methods. In the future, risk assessment should be based on clearly-defined objectives that include fac-


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tors not historically accounted for (such as disruptions in lifelines and social consequences of disasters) and should produce output that is communicable to an audience that at least includes decision-makers with relatively little technical background. The rational analysis and mitigation of risk due to natural and other hazards has a solid and long-standing inter-disciplinary history that has resulted in significant improvements in risk assessment and mitigation through building design and code regulations in Japan, Western Europe, and the United States. The continued progression of positive impacts on mitigating losses will depend on expanding the factors included in analysis, audiences targeted for outputs, and pre- and post-disaster data collected to improve the models.

References Applied Technology Council (1978, amended 1982) Tentative provisions for the development of seismic regulations for buildings (ATC-3-06). Applied Technology Council, Washington, DC Blume JA (1958) Structural dynamics in earthquake resistant design Coburn A, Spence R (1992) Earthquake protection. John Wiley & Sons, West Sussex, England EERI (2005) Scenario for a magnitude 6.7 earthquake on the Seattle fault. Earthquake Engineering Research Institute, Oakland CA Elnashai AS (2002) A very brief history of earthquake engineering with emphasis on developments in and from the British Isles. International Journal of Chaos Solitons and Fractals 13:967–972 Hartford DND, Baecher GB (2004) Risk and safety in dam safety. Thomas Telford, London Heck NH, Neuman F (1933) Destructive earthquake motion measured for the first time. Engineering News Record 110:804 Housner GW (1984) An historical view of earthquake engineering. In: Proceedings of the 8th World Conference on Earthquake Engineering. San Francisco, pp 25–38 Housner GW (1989) Coping with natural disasters: The international decade for natural disaster reduction, the second Mallet-Milne lecture. Society of Earthquake and Civil Engineering Dynamics, UK Kanai K (1983) Engineering seismology. University of Tokyo Press, Tokyo, Japan Milne J (1898) Seismology. Kegan Paul, Trench, Trubner and Co, London Milne JB, Burton WK (1892) The great Japan earthquake of 1891, 1st edn. Lane, Crawford & Co, Yokohama National Research Council (1989) Estimating losses from future earthquakes, panel on earthquake loss estimation methodology. National Academy Press, Washington DC

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Sekiya S, Omori F (1891) Comparison of earthquake measurements made in a pit and on ground surface. Journal of College of Science, Imperial University of Japan IV:249 Suyehiro K (1932) Engineering seismology. Proceedings ASCE 58

4 Strategic Directions in Seismic Modeling: HAZUS® Development and Current Applications for Catastrophe Planning

Thomas S. Durham1 with Pushpendra Johari2 and Douglas Bausch3 1

PBS&J, Inc., Chantilly, VA, 20151 PBS&J, Inc., Atlanta, GA 30328 3 FEMA Region VII Denver, CO 80225 2

4.1 Introduction In the early 1990s, the Federal Emergency Management Agency (FEMA) and the National Institute of Building Sciences (NIBS) embarked on an initiative to develop a consistent, standardized approach to estimating losses from earthquakes. In 1997, FEMA released the first HAZUS® (Hazards U.S.) model for earthquakes to “provide state, local, and regional officials with the tools necessary to plan and stimulate the efforts to reduce risk from earthquakes and to prepare for emergency response and recovery from an earthquake” (FEMA and NIBS 2003). Today, HAZUS®MH (multihazards) has evolved into a powerful, GISbased risk assessment tool that estimates potential damage and losses from earthquakes, wind and floods. The latest version – MR2 – was released in June 2006 and incorporates important new enhancements into all three models. As HAZUS®MH continues to mature, new uses are being identified, particularly for risk assessment and mitigation (FEMA 2006). The emergence of catastrophic planning as a national priority under the Department of Homeland Security and FEMA presents a unique opportunity to “harness” the capabilities of HAZUS®MH as a regional loss estimation tool. This paper presents an approach to fully integrating the HAZUS®MH Earthquake Model into FEMA’s catastrophic planning initia-

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tive, with specific recommendations on model enhancements that will strengthen this national planning effort. The paper offers a new vision of catastrophic planning that integrates risk assessment and risk reduction with response and recovery planning. The rationale is that preparedness to respond to a major or catastrophic incident is in large part a function of the resiliency1 and capacity of our nation’s emergency management system – including essential facilities and lifelines (transportation and utilities) – to support response and recovery missions. Simply put, if emergency services, medical facilities and lifelines are not available or have limited capacity to support response, then our nation’s operational capability is undermined. As a regional loss estimation tool, HAZUS®MH can both quantify losses from hazard events, and quantify losses avoided through mitigation, and in the process show the role of mitigation in catastrophic planning. The paper is organized into five sections: 1) HAZUS®MH Earthquake Model Overview; 2) Use of HAZUS®MH to Support Catastrophic Planning; 3) Use of HAZUS®MH to Link Risk Assessment, Mitigation and Disaster Response; 4) Utilization of a Web-based Data Management Portal System; and 5) Conclusions.

4.2 HAZUS®MH Earthquake Model Overview HAZUS®MH is a regional loss estimation tool that enables the user to perform a wide range of analyses, from simplified estimates that require minimal input to refined calculations of building damage and loss of functionality. As reflected in Fig. 4.1, the HAZUS®MH Earthquake Model utilizes a modular approach to estimating hazard, building and lifelines damage and losses, induced physical damage, direct and indirect economic losses, and social losses. Because data in the earthquake model is aggregated at the census tract level in all 50 states, loss estimates can be generated at virtually any scale. One of the strengths of the methodology is the ability to generate and display GIS-based maps at multiple scales, a feature that is very useful in catastrophic planning. The model incorporates a comprehensive, national inventory of building stock, essential facilities, lifelines and hazard data 1 The need for a regional disaster resilience strategy is the focus of a report that was released in June, 2006 by The Infrastructure Security Partnership (TISP). Regional Disaster Resilience: A Guide for Developing an Action Plan, provides stakeholders with a comprehensive set of guidelines for improving regional preparedness from natural, technological and man-made hazards.

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that can be enhanced by the user, as discussed in a subsequent section of this paper.

Fig. 4.1. HAZUS®MH Earthquake Loss Estimation Methodology

One of the main components of the HAZUS®MH earthquake methodology is the estimates of the probability of various states of structural and nonstructural damage to buildings. Damage state probabilities are used by other components of the methodology to estimate various types of building loss, social and economic losses. Typically, buildings are grouped by occupancy and model building type and evaluated on a census tract basis. Buildings are grouped into 33 specific occupancies and 36 model building types (and for various combinations of seismic design level and performance) with users having the flexibility to add more model building types. To summarize, the HAZUS®MH Earthquake Model incorporates a flexible, standardized, modular loss estimation methodology that can be used in multiple ways to assess damage and loss from earthquakes. In April, 2006 the Earthquake Model was successfully used to estimate and display earthquake damage and loss to a 19 county region in the San Francisco Bay Area from a repeat of the 1906 earthquake. The following section highlights the findings of a study on the current state of practice in catastrophic planning in the U.S., and presents a strategy for using HAZUS®MH to enhance planning and preparedness for catastrophic earthquakes.

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4.3 Use of HAZUS®MH to Support Catastrophe Planning

4.3.1 Catastrophe Planning: A National Priority In the aftermath of Hurricane Katrina, the Bush administration elevated catastrophic planning2 as a national priority. In a speech in New Orleans following Katrina, President Bush directed the Secretary of the Department of Homeland Security (DHS) to undertake a review of emergency and catastrophic plans in the 50 states and 75 urban areas in the United States. The Nationwide Plan Review: Phase 2 Report, released on June 16, 2006 identifies multiple “areas of relative weakness”, measured by the level of action taken by the 50 states and 75 urban areas to develop and sustain preparedness capabilities for catastrophic incidents. Fundamentally, the report concludes that catastrophic planning as practiced in the nation is flawed. As stated in the report: “Current catastrophic planning is unsystematic and not linked within a national planning system. This is incompatible with 21st century homeland security challenges, and reflects a systematic problem: outmoded planning processes, products, and tools are primary contributors to the inadequacy of catastrophic planning” (U.S. Department of Homeland Security 2006, page viii). 4.3.2 Use of HAZUS®MH to Address Gaps in Catastrophic Planning The Nationwide Plan Review: Phase 2 Report (U.S. Department of Homeland Security 2006) identifies several key gaps in the catastrophic planning process, several of which can be addressed in part through the use of HAZUS®MH. Examples of specific weaknesses are highlighted below, with observations on how HAZUS®MH analysis can be used to address these weaknesses. x Few Emergency Operations Plans (EOPs) address the consequences of catastrophic disasters (p. 14). HAZUS®MH outputs (see Fig. 4.2) are uniquely adapted to functional planning under the “Emergency Support 2 Catastrophic disasters are defined as “any natural or manmade incident, including terrorism, that results in extraordinary levels of mass casualties, damage, or disruption severely affecting the population, infrastructure, environment, economy, national morale, and/or government functions” (U.S. Department of Homeland Security 2006).


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Function” (ESF) system in the National Response Plan (NRP). Several states, notably California, have made significant progress in using HAZUS®MH analysis to “scale mission requirements” for earthquake response. The catastrophic planning initiative provides an opportunity to standardize HAZUS®MH outputs for ESF planning under the NRP. x Assumptions in Basic Plans do not adequately address catastrophic incidents (p. 63). In large part, assumptions flow from an understanding of the consequences of hazards on buildings, lifelines and the population. One of the unique features of the HAZUS®MH Earthquake Model is the ability to demonstrate – both qualitatively and quantitatively – the consequences of major or catastrophic earthquakes. x Shelter needs and shelter capacity for catastrophic disasters need to be identified and quantified, and statewide plans developed (p. 26). The shelter module is among the most reliable in HAZUS®MH (in model validation studies, actual shelter requirements match up well with estimated requirements). To support catastrophic planning, the Earthquake Model can be used to estimate the number and distribution of displaced households and shelter requirements, and also characterize the population at risk. x The plans do not compare projected, quantified needs and listed resources that are needed for an effective emergency response (p.18). The ability to estimate damage and loss of functionality with HAZUS®MH enables users to identify and quantify resource require-ments that are needed for scenario events. 4.3.3 Integrating HAZUS®MH into the National Incident Management System (NIMS) Preparations for and response to a catastrophic disaster are guided by National Incident Management System, issued by the Department of Homeland Security on March 1, 2004 to provide a comprehensive and consistent national approach to all-hazard incident management at all jurisdictional levels and across functional disciplines. Key to using HAZUS®MH to support catastrophic planning is the ability to fully integrate the HAZUS® technology and HAZUS®MH team into the Planning Section under the NIMS. A key feature of NIMS is the Incident Command System (ICS), which comprises five functional areas: Command, Operations, Planning, Logistics, and Finance and Administration (Fig. 4.2). The ICS organizational structure is modular, extending to incorporate all elements necessary for the type, size, scope, and complexity of a given incident. The HAZUS®MH

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team should become an integral part of the Planning Section under the Incident Command System, and provide analysis (pre- and post-disaster) on potential consequences of scenario earthquakes on the building stock, essential facilities, lifelines, and population.

Incident Command System Command

Public Information

Operations

Planning

Safety Officer

Logistics

Finance & Admin.

HAZUS-MH

Fig. 4.2. Use of HAZUS®MH in the Incident Command System

The systematic application of HAZUS®MH for catastrophic planning should be guided by two basic tenets: 1. Priorities for the application of HAZUS®MH need to be established by the user of the analysis (e.g., Command staff and Planning Section), with full understanding of the limitations and uncertainties of the model outputs. Experience in the use of HAZUS®MH for disaster operations continues to reinforce the importance of a user driven approach, including training of decision-makers in the potential uses of HAZUS®MH analysis to support decisions. 2. Technical oversight in the use of HAZUS®MH is critical to the effective use of the model for impact assessment and decision-support. In using HAZUS®MH for a major, damaging earthquake, it is important that technical oversight be provided, preferably by a team HAZUS®MH specialists with domain knowledge of the earthquake hazard, the loss estimation methodology, and model outputs.


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4.3.4 Planning for a Catastrophic Earthquake in the New Madrid Seismic Zone In 2006, FEMA announced the administration’s intentions to implement a 5-year catastrophic planning initiative – funded at approximately $20 million – to better prepare the nation for the consequences of catastrophic disasters. The first scenario to be addressed is a catastrophic earthquake in the New Madrid Seismic Zone (NMSZ). The NMSZ catastrophic planning initiative can provide an important national platform to demonstrate the capabilities of HAZUS®MH. Figure 4.3 highlights key HAZUS®MH outputs that can be used – for pre-disaster planning and post-disaster assessment and response – in a national catastrophic planning initiative.

Fig. 4.3. (This figure also appears in the color insert) Potential Applications of HAZUS®MH to Support Catastrophic Planning in the New Madrid Seismic Zone

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Potential Applications of HAZUS®MH to Support Catastrophic Planning in NMSZ: Rapid Needs Assessment & Response: x Area of impact from ground shaking and liquefaction. x Population at risk, including estimates of casualties, displaced households, and short-term shelter requirements. x Damage and loss of functionality to essential facilities (fire, police, hospitals, schools, emergency operations centers) x Damage and loss of functionality to transportation system, including bridges x Estimates of economic losses x Potential search and rescue missions Recovery Planning: x Estimates of debris (by category) x Loss Ratios x Estimates of residential damage x Estimates of commercial damage x Estimates of business interruption In the context of a New Madrid scenario, there are several important applications of HAZUS®MH to support catastrophic planning. Some of these applications require enhancements to the Earthquake Model. Two potential uses with high potential value to the NMSZ planning effort are highlighted below. 1. Use of HAZUS®MH to identify and quantify the population in the study region with disabilities, defined by the Americans with Disabilities Act (ADA) as a broad range of people, including individuals who use wheelchairs or have other mobility disabilities; individuals who are blind or have low vision; individuals who are deaf, hard of hearing, or deaf-blind; as well as individuals with cognitive disabilities, psychiatric conditions, serious respiratory conditions, or other physical or mental impairments that substantially limit life activities. According to the 2000 Census, nearly 29% of American families include at least one person with a disability. As individuals with disabilities continue to live more independently and are more integrated into society, substantial improvement is necessary to integrate people with disabilities in emergency planning and readiness (U.S. Department of Homeland Security 2006). Acknowledging that special needs populations have significantly


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greater difficulties in coping with the consequences of disasters, the ability to incorporate this subset of the population into HAZUS®MH inventory and analysis would be an important contribution to emergency management planning. 2. Use of HAZUS®MH to estimate damage and loss of functionality to essential facilities. From a federal and state perspective, an “essential element of information” following a major disaster is the availability and functionality of hospitals and emergency services facilities and systems. A fundamental question becomes – when is it necessary for the federal government or state government to perform basic health and public safety services for an extended period of time because local government has become the victim and there is no capacity to perform essential services? Planning for a catastrophic New Madrid earthquake provides an opportunity to “push the envelope” in estimating the availability of essential facilities and lifelines – before and after a scenario event – and integrating this analysis into NIMS and the National Response Plan. This initiative should consider the following: 1) incorporation of HAZUS®MH analysis into training with Planning Section and Command staff under the NIMS, with emphasis given to treatment of uncertainties; 2) creation of standardized HAZUS®MH Earthquake Model analysis templates which can be used in training and disaster operations; 3) use of grid cells3 (e.g., census blocks) in aggregating data to improve resolution and increase accuracy of analysis; and 4) expansion of essential facility attribute data that is found in the HAZUS®MH inventory (e.g., bed capacity for hospitals), to include information for each essential facility that can be used by emergency managers in characterizing these facilities. Attribute data to be considered: updated contact information, specialized services or capabilities offered by the facility, and specialized resources that can be accessed. There is one major advantage to using HAZUS®MH to support analysis of damage and loss of functionality to essential facilities (and other analyses) – the model incorporates a national, uniform, standardized inventory of essential facilities, which is critical for multi-state analyses. The use of a uniform approach to assessing damage states of essential facilities promotes consistency in the use of the Earthquake Model from one state or Federal Region to another.

3 Wyoming and South Carolina are among the states that have used a grid cell (2 kilometer x 2 kilometer) approach to aggregating data – as opposed to aggregating by census tract – which improves resolution of analysis, addresses the “large census tract issue” (in large census tracts, analysis can appear distorted).

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To summarize, HAZUS®MH is a unique, nationally applicable loss estimation tool that can make a significant contribution to catastrophic planning and disaster response. As a regional loss estimation model, the strength and relative accuracy of HAZUS®MH matches well with analysis that is most needed immediately after an earthquake, including: area of impact; exposure and damage to essential facilities; quantification of population exposed to the earthquake hazard; estimates of shelter requirements; and identification and mapping of areas with moderate to high probability of search and rescue mission requirements (due to high concentrations of unreinforced masonry structures and other vulnerable buildings).

4.4 Use of HAZUS®MH to Link Risk Assessment, Mitigation and Disaster Response FEMA’s catastrophic planning initiative presents an opportunity to reexamine our nation’s approach to planning for and responding to hazard events (natural, technological or human caused) that generate extraordinary casualties, damages and economic disruption. In the final analysis, the ability of our society to respond to and recover from these events – including a catastrophic earthquake – is in large part a function of the disaster resilience of our population and the built environment (buildings, systems, infrastructure) that supports our daily routines at home and the workplace. Investments in stronger buildings, better prepared populations, and disaster resilient lifelines will reduce the burden on government at all levels to respond to major disasters. In essence, mitigation should become an integral component of the catastrophic planning equation. In this context, HAZUS®MH can become an important tool in assessing damage and losses avoided through the adoption of specific mitigation measures for buildings and lifelines, as follows: x Physical Damage - Buildings - Contents - Infrastructure - Landscaping - Site contamination x Loss of Function / Economic Impacts - Loss of business income - Loss of rental income - Lost wages


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Shelter costs Disruption time for residents - Loss of public services - Economic impacts associated with loss of transportation and utility functions x Casualties - Deaths - Injuries - Illnesses x Emergency Management Costs - Emergency Operations Center costs - Evacuation / Rescue costs - Debris removal and cleanup costs - Temporary measures costs -

-

An initiative to use HAZUS®MH to systematically integrate mitigation into the catastrophic planning process should include the following elements: 1. Formation of a multidisciplinary advisory team with expertise in the HAZUS®MH Earthquake Model (including the Advanced Earthquake Building Module), seismic/structural engineering, buildingspecific damage and loss functions, and functional planning under the National Incident Management System. The objective is to provide a forum for HAZUS®MH specialists with domain knowledge of the Earthquake Model and its uncertainties, and key users of the analysis for catastrophic planning for earthquakes. This initiative should be coordinated with: 1) The Infrastructure Security Partnership (TISP) and the Task Force on Regional Disaster Resilience; and 2) the Multihazard Mitigation Council (MMC).4 2. Selection of three pilot sites – California, the Central U.S, and Utah’s Wasach Front – to evaluate and document the use of the HAZUS®MH Earthquake Model to support catastrophic earthquake planning, and specifically the value of investing in vulnerability reduction measures to increase response and recovery capacities.

4 The MMC, a council of the National Institute of Building Sciences (NIBS), completed a landmark study, Natural Hazard Mitigation Saves: An Independent Study to Assess the Future Savings from Mitigation, which used HAZUS®MH to estimate direct property damage from earthquakes and hurricanes, and used these estimates to determine future losses avoided through mitigation.

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3. Collaboration with the FEMA catastrophic planning team to identify “most critical” of essential facilities relative to capacity to respond to a major earthquake. The ability of state and local government to effectively respond to a catastrophic disaster will be a function in large part of the availability of hospitals, public health departments, emergency response agencies, shelters and other facilities in the “emergency management system.” The objective of this task is to systematically identify and prioritize facilities and systems that – by virtue of their role and function in urban or regional response to a catastrophic earthquake – are most critical. Collection of this information, which can be incorporated into the HAZUS®MH inventory, should involve federal, state and local emergency managers. 4. Add a display feature to the existing library of capacity curves and fragility curves for the Earthquake Model to enable planners, engineers and others to assess potential losses from earthquakes based on the properties of a specific building type. The HAZUS®MH Hurricane Model, for example, enables the user to view building damage functions for all model building types. The model displays graphs of the probabilities of four different damage states for buildings, contents, or combined ratios for each wind building type as a function of peak gust wind. A similar display feature for the library of building loss curves for the Earthquake Model would be an excellent reference for HAZUS®MH users and emergency managers. A user-friendly collection of building loss curves would provide decision-makers with a readily available reference in identifying most vulnerable structures at specified levels of ground shaking, and to use this information in prioritization of mitigation measures. A library of building loss curves also needs to address the vulnerability of buildings and lifelines to ground deformation. This aspect of the HAZUS®MH Earthquake Model needs to be updated in light of the extensive engineering work completed at the Nation’s earthquake engineering centers and under National Science Foundation programs. In addition, the body on knowledge associated with vulnerability of the built environment to ground deformation in a catastrophic earthquake is relatively small compared to the body of research of the interaction of ground motions and the built environment. The level of research that assesses ground deformation influences is out of balance with the large contribution to losses, functionality, and utility and transportation impacts as a result of ground deformation in scenario earthquakes.


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5. Identify and prioritize mitigation measures to enhance the functionality of essential facilities and in the process increase the capacity of federal, state and local governments to respond to major or catastrophic earthquakes. Ultimately, the goal of this initiative is to provide federal, state and local emergency managers with: 1) HAZUS®MH inventory of “most critical” of essential facilities that are instrumental to urban and/or regional response to a major earthquake or other catastrophic disaster; 2) HAZUS®MH analyses that quantifies losses avoided as a result of the implementation of mitigation measures for high value essential facilities; and 3) analysis of enhancements to local and state capacity to respond as a result of the implementation of mitigation measures. The analysis should follow the functional approach (Emergency Support Functions or ESFs) to planning that is incorporated into the National Response Plan, including: x x x x x x

Transportation (ESF #1) Communications (ESF #2) Public Works and Engineering (ESF #3) Firefighting (ESF #4) Mass Care, Housing and Human Services (ESF #6) Public Health and Medical Services(ESF #8)

4.5 Utilization of a Web-Based Data Management Portal System Although HAZUS®MH was not originally developed as an emergency response tool, important developments have occurred since the 1994 Northridge earthquake to integrate the HAZUS®MH methodology with real-time information technologies to provide decision support following a earthquake (Eisner 2000). The California Office of Emergency Services has developed an interface between HAZUS®MH and ShakeMap, a web-based application that provides shaking intensity and ground motion in near realtime. This interface provides the California OES with a rapid, accurate and useful set of data for emergency response. In the Central and Eastern U.S., where soil and other conditions contribute to a seismic hazard that extends over a much broader region, a different approach to data management and the use of HAZUS®MH is being contemplated. A proposed HAZUS®MH Web-based Data Management Portal System is guided by the following objectives:

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1. To identify, prioritize and incorporate new datasets for User Defined Facilities that will provide a significantly more robust HAZUS®MH inventory. South Carolina, for example, has identified 14 additional categories of facilities and demographic data that the Emergency Management Division intends to add to the existing statewide HAZUS®MH inventory. These datasets will significantly improve risk assessment and planning in high risk coastal areas of the state. 2. To develop a data management system for HAZUS®MH that enables the user to place default and Level 2 inventory data in existing webbased portals for designated users. This will facilitate access to the data from multiple HAZUS®MH users (including state and local governments), and enable users to update the inventory data on a routine basis. 3. To refine the HAZUS®MH data management system to enable users to download and distribute pre-built study regions to federal, state and local governments to facilitate and expedite post-earthquake analysis of damages and losses. This capability is critical to the use of HAZUS®MH for earthquake impact assessment and response planning in the Central and Eastern U.S. Options include the development of a library of “HAZUS®MH runs” for a range of scenarios that can be accessed by FEMA and impacted states following an actual earthquake, and used to estimate casualties, damages and losses. 4. To prepare and test standard operating procedures for uploading, downloading, and managing HAZUS®MH datasets and analyses for disaster operations. To incorporate the HAZUS®MH Web-based Data Management Portal System into the Planning Section under the Incident Command System, and to utilize this capability during disaster exercises. The objective is to ultimately institutionalize the use of HAZUS®MH for risk assessment and emergency management planning. To train HAZUS®MH users to use this system to quickly assess damages and losses from earthquakes, floods and hurricanes. When fully implemented, the proposed HAZUS®MH data management system will provide planners and decision makers with a unique capability for risk assessment and response planning. In the process, the system will address some of the key issues identified in the Nationwide Plan Review (U.S. Department of Homeland Security 2006).


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4.6 Conclusions HAZUS®MH has two core features that make the model totally unique: 1) the national inventory of building stock, lifelines, essential facilities and population that enable the user to characterize the exposure of our 50 states to earthquakes, hurricanes and flood hazards; and 2) the capability to quantify damage and losses over a multi-state area. There is an unprecedented opportunity to apply the HAZUS®MH Earthquake Model and the enhancements that are outlined in this paper to support the catastrophic planning initiative. In assessing and prioritizing the potential uses of HAZUS®MH, a useful starting point is the findings of the Nationwide Plan Review, an excellent baseline study that identifies significant problems, issues and deficiencies in catastrophic planning in the 50 states and 75 urban areas. Finally, the Mitigation Directorate – in coordination with the National Institute of Building Sciences – can play an important role in supporting the FEMA’s catastrophic planning initiative through the use of HAZUS®MH. An understanding of the performance of essential facilities and lifelines in earthquakes, and the role of mitigation in reducing building damage and loss of functionality, can become an important dimension in the catastrophic planning program. The Mitigation Directorate can take the lead in demonstrating the return on investment in mitigation, expressed as the increase in federal, state, and local government capacity to respond and recover from major or catastrophic disasters. HAZUS®MH can become an important tool in this effort.

References Applied Technology Council (2003) Guidelines for using strong-motion data and shakemaps in post-earthquake response (ATC-54). Applied Technology Council, Redwood City, California Earthquake Engineering Research Institute (2006) Managing risk in earthquake country. Publication of the 100th anniversary earthquake conference - 1906 San Francisco earthquake. Earthquake Engineering Research Institute, San Francisco Federal Emergency Management Agency (FEMA), National Institute of Building Sciences (NIBS) (2003) HAZUS®MH MR1 Technical Manual. Federal Emergency Management Agency, Washington, DC Goltz J, Eisner R (2003) Real-time emergency management decision support: The California Integrated Seismic Network (CISN). In: Proceedings of the Disaster Resistant California National Institute of Building Sciences (2003) A guide to using HAZUS® for mitigation. NIBS, Washington, DC

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National Institute of Building Sciences (2005) Natural hazard mitigation saves: An independent study to assess the future savings from mitigation activities. Multihazard Mitigation Council, Washington, DC The Infrastructure Security Partnership (TISP) (2006) Regional disaster resilience: A guide for developing an action plan, Reston, VA U.S. Department of Homeland Security (2006) Nationwide plan review: Phase 2 report. FEMA, Washington, DC U.S. Department of Justice (2004) An ADA guide for local governments: Making community emergency preparedness and response programs accessible to people with disabilities. U.S. Department of Justice, Washington, DC

5 Perspectives on Development and Current Applications for Catastrophe Planning

Responses to Durham’s paper “Strategic Directions in Seismic Modeling”

Introduction The commentaries in this chapter represent three important perspectives: academic research in earthquake engineering; the federal agency that sponsors and oversees HAZUS®; and expert users. Rachel Davidson fills in some gaps in the background and suggests directions for the development of HAZUS®, including improving its mitigation planning capability and structural inventory data, as well as providing for web-based user overrides based on post-catastrophe observations. Phil Schneider presents several examples of user-driven developments in HAZUS® and the role of users in pushing forward improvements and enhancements that are currently in the works, by members of the self-established user/developer consortia. Finally, Subrahmanyam Muthukumar, an expert HAZUS® user, confirms Schneider’s proposition that user input based on experience with applications of the software provide significant pull for its development. Muthukumar suggests several improvements to HAZUS®, including more dynamic inventory data, better cost-benefit analysis capability, and lifeline/network models integrated into the overall system. In considering these perspectives it helps to keep in mind the purposes for which HAZUS® was developed and the institutional context in which it functions. Future development of HAZUS® and its potentially expanded role in catastrophe planning should proceed with input by the earthquake community.

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5.1 Recommended Improvements for HAZUS®MH Rachel Davidson Cornell University, Ithaca, New York, USA

5.1.1 Introduction The chapter by Durham, Johari, and Bausch discusses an “approach to fully integrating the HAZUS®MH Earthquake Model into FEMA’s catastrophic planning initiative, with specific recommendations on model enhancements that will strengthen this national planning effort.” In this section, I first comment on the text of the paper directly, then discuss some possible directions for the development of HAZUS®MH, building on ideas from the paper. 5.1.2 Comments on the Paper The ideas presented in the chapter by Durham et al. are useful guides for taking full advantage of HAZUS®MH. In such discussions, it is helpful to clarify definitions of key concepts (e.g., mitigation, resilience) and use them consistently. For example, one might define four main phases of the disaster cycle: (1) mitigation includes efforts to reduce losses long before an event, (2) preparedness includes efforts shortly before an event to reduce losses and prepare for the post-event period, (3) response includes efforts in the immediate aftermath of an event, and (4) recovery refers to post-disaster efforts in the longer-term. Pinpointing more specifically which aspect of the disaster cycle – mitigation, preparedness, response, and recovery – each proposed initiative would address can help tighten and strengthen the arguments. Another approach is to consider resilience as a central term, as is becoming more common. For example, using the Multidisciplinary Center for Earthquake Engineering Research (MCEER) definition of resilience as a function of robustness, rapidity, resourcefulness, redundancy (Bruneau et al. 2003), one could identify which aspect of resilience an effort aims to address. Is its aim to help reduce the immediate loss (improve robustness) or reduce the time to recover from them (improve rapidity)? Linking specific types of HAZUS®MH output to specific users and

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specific uses more directly and comprehensively could similarly clarify the arguments. It is not always clear which features of HAZUS®MH described in the chapter are already functional and which are proposed enhancements. For example, in the NMSZ initiative related to essential facilities and in the initiative to integrate mitigation into the catastrophic planning process, it is not clear which of the items listed are available and which are proposed improvements. The paper covers many topics in a short space, but could be improved with further development of some of ideas. For example, in explaining how mitigation can be integrated into planning in Section III, the authors suggest adding a feature to HAZUS®MH that would allow the user to display the library of capacity and fragility curves. While this would likely be a useful capability, it is but one piece of what would be required to enable HAZUS®MH to be used more effectively for mitigation planning. Perhaps more challenging issues include the current inability of HAZUS®MH to map physical mitigation projects (e.g., adding shear walls to a structure) to changes in the model parameters, to provide information about costs of various mitigation alternatives, or to acknowledge the many competing objectives and constraints that a user may have. In a few places, “unique features” or “advantages” of HAZUS®MH are mentioned. It is never really clear, however, to what alternative HAZUS®MH is being compared. Some of the most important distinguishing features of HAZUS®MH are assumed, such as that it is multihazard, comprehensive in the losses it estimates, in the public domain, and supported by FEMA. In the discussion of essential facilities in the NMSZ initiative, the need to emphasize the treatment of uncertainties and the idea of aggregating data by grid cells instead of census area units are both potentially important, but are only mentioned in passing. In the same section, the need to improve treatment of disabled populations is discussed, but other disproportionately vulnerable populations (e.g., the poor and children) are not. It might help to include a little more background on some topics in the paper, such as, NIMS, the National Response Plan (DHS 2004a), and the New Madrid Seismic Zone initiative. Background on how HAZUS®MH has been used successfully and why some organizations have chosen not to use it, would help motivate proposals for the future. The HAZUS® Annual Progress and Utilization Report for Fiscal Year 2004 offers some information in that regard, although it does not provide much insight into observed impediments to HAZUS® adoption (FEMA 2004).

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5.1.3 Directions for Development and Challenges Associated with Each

Identifying directions for development

Before discussing possible directions for future development of HAZUS®MH, it is worth commenting on the process used to identify and prioritize those directions. Directions for future development should emerge from the intersection of unmet user needs and technical feasibility. One could systematically consider specific users and the specific decisions they make (e.g., a local government planning identifying shelters for use after a catastrophic earthquake) and identify ways in which their ability to make those decisions could be improved. As before, decisions might relate to any of the phases of the disaster cycle (e.g., mitigation, response), the different dimensions of resilience (e.g., improving robustness or rapidity), and multiple hazards. In addition to improvements in the capabilities of the software itself, directions for development might relate to improvements in the way HAZUS®MH is supported (training, distribution) or used in the catastrophic planning process. Future surveys of HAZUS®MH users and potential users, like that in the HAZUS® Annual Progress and Utilization Report (FEMA 2004), could be used to better understand the users’ perspective on HAZUS®MH and the obstacles to its being used to its full potential. From the technical feasibility point-of-view, the limitations of HAZUS®MH can be identified (e.g., data requirements) and developments can be planned to help overcome them. In the following sections, I present a few potential directions, from the technical feasibility point-of-view and based on my assumptions about users’ needs. No attempt is made to prioritize these suggestions or assess the difficulty of implementing them. They are intended as fodder for discussion. Improve ability of HAZUS®MH to support mitigation planning

The capabilities of HAZUS®MH could be extended to support mitigation decision-making more directly. Mitigation is fundamental to reducing risk. In most cases, mitigation is more cost-effective than waiting until after an earthquake and paying for reconstruction. And deaths and injuries, psychological trauma, and other types of impact can only be lessened by preventing the damage from occurring in the first place. As the previous chapter explains, HAZUS®MH has the potential to help link risk assessment, mitigation, and disaster response. It was not originally intended to support mitigation decision-making, however, so currently HAZUS®MH can only be


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used with a trial-and-error approach to evaluate mitigation alternatives (FEMA 2002). Losses can be estimated with and without implementation of a particular mitigation alternative to evaluate the alternative’s effectiveness. The relative costs of the alternatives, the budget and other constraints, and the specific objectives are not incorporated, and only a small set of pre-defined mitigation alternatives can be considered. It is difficult to directly simulate the implementation of specific mitigation efforts, such as base isolating a steel frame structure or bracing the cripple walls of a wood frame house, in the model. Dodo et al. (2005) summarize different efforts to support mitigation decision-making with engineering models, and with Dodo et al. (2007) and Xu et al. (2007), introduces an optimization-based approach to allocating regional earthquake mitigation resources that builds on the current capabilities of HAZUS®MH. Improve structural inventory data

The structural inventory data used in HAZUS®MH could be improved, in its representation of both the current built environment and the built environment as it will exist at some future time at which the next earthquake occurs. The output of any HAZUS®MH analysis is only as good as the data on which it is based, and at least some portions of that data are known to include substantial errors. The use of remote sensing technology in disaster management has been advancing rapidly in the last several years; one potential use is developing more accurate building inventory data (e.g., Sarabandi et al. 2005). The use of remote sensing for improving inventory data could be explored. Further, the number, locations, value, and vulnerability of structures in a region change over time, sometimes rapidly, due to population and economic growth, urbanization, building code changes, changing construction practices, structural aging, and other factors. All of those changes can affect the losses that will result if the structures are subjected to an earthquake. If mitigation and response plans are developed using a HAZUS®MH analysis based on the current building inventory, the inventory, and therefore the losses, may be very different by the time the next earthquake occurs, say 50 years in the future. Failure to incorporate the dynamics of the built environment may result in loss estimation models that misestimate losses, thereby compromising any risk management plans that are based on those estimates. Focusing on regional hurricane risk, Jain et al. (2005), Davidson and Rivera (2003) and Jain and Davidson (2007a, 2007b) offer one approach to incorporate the dynamics of the built environment that could be integrated with HAZUS®MH. Alternatively, several scenarios of inventory change (e.g., rapid development vs. a static inventory) could be

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included to provide output that reflects the range of possible losses when changes in the built environment are considered. Develop web-based data management system for HAZUS®MH

As presented in the Durham et al. paper, a web-based data management system for HAZUS®MH could be developed to facilitate sharing of data and analysis results among many participants in all phases of the disaster planning cycle. This could be an excellent way to minimize duplication of data collection effort and improve communication among the participants in the catastrophic planning process. Beyond the technical challenges involved in developing such as system, effort would be required, for example, to develop a protocol to determine who would be allowed to modify the data and to ensure that it would be available immediately after an earthquake to support emergency response activities when electric power service may be interrupted. In using such a web-based system to support real-time catastrophe response (or even in HAZUS®MH as it currently is), one could also consider including a feature that would allow the user to begin with the HAZUS®MH-generated damage and loss estimates, then gradually override those estimates with observations of actual damage from the field as they become available. Updates could continue as subsequent repairs are made, thus capturing the changes in information about the situation as well as changes in the actual situation throughout the post-earthquake period. The HAZUS®MH environment would be used to record damage and loss in addition to estimating it before and immediately after an event. Some type of Bayesian updating features might be possible as well to update the initial HAZUS®MH damage estimates everywhere based on limited initial observations. That type of capability would be more difficult to implement and may not be as useful as the simple ability to override estimates with actual observations of damage and loss. 5.1.4 Present Uncertainties Explicitly The uncertainties in HAZUS®MH loss estimates could be quantified and presented to the user. Because of randomness in earthquake and damage occurrence, and modeling and data uncertainty, there is necessarily a great deal of uncertainty surrounding any loss estimate. While most people probably recognize that to some extent, greater effort could be made to clarify the magnitude and character of the uncertainty to HAZUS®MH users so that it would be more difficult to ignore it when using the results of


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HAZUS®MH analyses in supporting decisions. Such an effort would admittedly be difficult to undertake as it is not straightforward to determine how much uncertainty there is in a given output or to determine the best way to present it in a way that is helpful for decision support.

5.2 User Dynamics and HAZUS® Development Philip J. Schneider National Institute of Building Sciences, Washington, DC, USA

5.2.1 Introduction This review of the paper, HAZUS® Development and Current Directions for Catastrophic Planning, addresses the relationship of user dynamics to HAZUS® development. The HAZUS® program is now 14 years old. HAZUS®MH MR2, the seventh version of the Earthquake Model, was released in June 2006 by the Federal Emergency Management Agency (FEMA). HAZUS® development, which is managed for FEMA by the National Institute of Building Sciences (NIBS), has involved expert consultants overseen by an Earthquake Committee consisting of nationally recognized experts. Robert Whitman, Professor Emeritus at MIT, was its first Chairman, and is now the Chairman Emeritus. William Holmes, Rutherford and Chekene, is the current chair. PBS&J serves as the principal consultant for the Earthquake Model. 5.2.2 User-Driven Development Within HAZUS® Users have played a central role in HAZUS® development. When HAZUS® development began, a 30-member project oversight committee of stakeholders was convened to define HAZUS® functionality. After the software was developed, two pilot tests of HAZUS® usability were conducted – the first in Portland, Oregon, in 1995 and the second in Boston, Massachusetts, in 1996. Pilot testing also was conducted at a 1996 training workshop for the emergency managers from the 50 states. Since HAZUS® was first released in 1997, technical support and training work-

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shops have been ongoing and twenty-one HAZUS® consortia (http://www.HAZUS.org) have formed. Each furnishes an ongoing stream of suggested user improvements for HAZUS® with the most useful comments coming from users involved in HAZUS® applications, especially during the past three years. 5.2.3 Studies for FEMA Examples of these HAZUS® applications include FEMA’s 2001 national annualized loss study (FEMA 2001; Fig. 5.1), which is now being updated using the latest version of HAZUS®. HAZUS® also was used in the NIBS Multihazard Mitigation Council independent assessment of future savings from mitigation activities (Multihazard Mitigation Council 2006), which is notable for determining the metric that every $1 spent in mitigation represents $4 in future savings. HAZUS® was used to estimate losses following earthquakes in Seattle (Nisqually, 2001) and San Simeon (2003).

Fig. 5.1. (This figure also appears in the color insert) State Level Losses map from the annualized loss report (FEMA 2001) showing the relative level of risk across the country based on a standard form of hazard analysis and inventory.


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5.2.4 State Studies A multistate HAZUS® study was conducted in 2005 with the Central U.S. Earthquake Consortium (CUSEC), FEMA, and USGS for catastrophic planning purposes. This study involved simulating the 1811–1812 earthquakes in the New Madrid earthquake fault region (Fig. 5.2). HAZUS® also is being used by many states to develop state mitigation plans as part of the pursuit for funding under the Disaster Management Act of 2000.

Fig. 5.2. (This figure also appears in the color insert) Estimated total county building loss (CUSEC 2005) from an earthquake scenario in the New Madrid Region (reprinted with permission from CUSEC)

5.2.5 Regional Studies A significant study of earthquake risks and mitigation in the tri-state New York City metropolitan area was conducted by Princeton (Tantala et al. 2005). A similar study for the San Francisco Bay area was conducted in 2003 (Fig. 5.3).

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Fig. 5.3. Output from the San Francisco Bay earthquake risk and mitigation study meant to raise awareness

5.2.6 New Directions for Development from HAZUS® Applications Lessons learned from conducting the above studies have stimulated improvements in HAZUS® since the multihazard version of HAZUS®, HAZUS®MH, was released in February 2004. The annualized loss studies revealed that the HAZUS® valuation figures needed to be revised on a regional basis, which was accomplished in HAZUS®MH MR2. The rapid loss assessments in Washington and California demonstrated the need to run HAZUS® faster so that an analysis can be completed in a reasonable amount of time for response purposes. As a result, the Earthquake Model now has considerably improved speed. The USGS Shake Maps used in these events can now be ported into HAZUS® with the geodatabase format provided by ArcGIS. The tri-state New York study provided the impetus to customize building types within HAZUS® and to facilitate bringing in new building types, such as those found in New York City, San Francisco, and other large metropolitan areas. Further work in New York since the tri-state study has spurred improvement in the Building Inventory Tool (BIT). A data man-


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agement system has been initiated to improve adding Level 2 user-supplied data in HAZUS®. Grid analysis is an older HAZUS® improvement that resulted from state studies in FEMA Regions VIII and X. For western states with large census tracts, grid analysis more accurately locates ground shaking and losses by assigning values to cells instead of spreading them evenly over a single large census tract. 5.2.7 Conclusion – Push and Pull HAZUS® Development and Current Directions for Catastrophic Planning demonstrates the need for new HAZUS® technology for catastrophic planning – for example, implementing HAZUS® on the internet. The need for a catastrophic planning application and related HAZUS® development to support it represents the push into this new area. HAZUS® applications, once conducted, create the pull by their users for new development in the HAZUS® model that began with the original push by the earlier user groups. A dynamic between the push created by the need for applications and the pull that comes from users in applications continues to create new directions for HAZUS®. FEMA supports changes that are based on user demand.

5.3 Perspectives from a HAZUS® User Subrahmanyam Muthukumar Georgia Institute of Technology, Atlanta, Georgia, USA

5.3.1 Current Catastrophe Planning As an experienced HAZUS® user, some of my questions are detail oriented and assume a certain familiarity with HAZUS®. At Georgia Tech, we’ve done some projects looking at emergency response and incident management application software in place at the local and state level. It would be helpful to have an Application Programming Interface (API) where you could send HAZUS® products directly into the incident management ap-

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plication software to facilitate the distribution of those products to operations and logistics. When we worked in the hurricane section at FEMA in 2004 I made maps and printed labels, used HAZUS® outputs, and transferred them physically – copying and pasting into another folder. Logistics or operations would take it from there and print or email the resulting products. That takes time and is inefficient. Right now we have an extensive default inventory and demographic data based on the 2000 census, but we can’t wait every 10 years for the numbers to change. Mechanisms should be put in place within HAZUS® to allow for projections of demographic data, leading to changes in the buildings and materials in the inventory. 5.3.2 Risk Assessment, Mitigation and Disaster Response – Suggestions for HAZUS® Development A recurring theme in Durham et al.’s chapter is Essential Facilities. Essential Facilities change according to the requirements of the local population, so they are fairly dynamic. There are several comprehensive (and incredibly expensive) databases available that are frequently updated, on a monthly or quarterly basis, and contain information on hospital beds, clinics, location of doctors, etc. One such database is available for $180,000 per year. So such data are available, but how could the federal government obtain these data and make them part of the default inventory? HAZUS® does quantify losses well, but currently does not have the capacity to incorporate costs of mitigation measures, or to perform fullfledged benefit-costs analyses for various mitigation scenarios. How could these capacities be incorporated into HAZUS®? Analyzing the sensitivity of base scenarios to different building inventory characteristics currently takes a long time. One potential solution is to use existing research from insurance companies and come up with a set of parameters for creating fragility curves on the fly. Fragility curve sensitivity analyses would come from changing the inventory scenario; the cost of that change scenario would be incorporated into the cost analysis. Last is the glaring need to incorporate the interdependencies of transportation and lifeline systems into HAZUS®. At present the Transportation and Lifeline Systems are standalone systems. Leonardo Dueñas-Osorio (e.g., Dueñas Osorio 2004; 2006) has done some excellent work in looking at the interdependencies of these systems. Say the electrical system fails, then a pumping station doesn’t work and therefore you don’t get water.


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This all comes together in an earthquake scenario. This is a good opportunity to incorporate outside research into HAZUS®. There is no transportation network capability in HAZUS®, though it exists in ArcGIS. If HAZUS® is sitting on top of ArcGIS as its platform, then there is strong potential to use it, especially for response. If your study region is seven states, of course you can’t have street level for all seven states, but you can incorporate transportation information at the scale of arterials or highways. If you’re looking at a study region of a particular county then you can include street-level detail and use that in a transportation network model. This would be useful for emergency response, to magnify the accessibility of various locations within a study area, and therefore the accessibility of various populations or subgroups of populations to hospitals, fire stations, emergency facilities, during the response phase. The network model would be useful to quantify direct and indirect business interruption losses and long-term losses.

5.4 Strategic Directions for HAZUS® and Current Applications for Catastrophe Planning The open-source, U.S. government-sponsored hazard modeling software, HAZUS® (Hazards U.S.) is the de facto tool for local, state, and federal agencies for catastrophe planning. In the 14 years since its initial release, HAZUS® has grown largely through the input and custom development of its users. In the previous chapter, Tom Durham, along with colleagues who have been closely involved with the development and application of this important tool, provided an overview of the framework and methodologies in HAZUS®, as well as its organizational role in the Incident Command System. Durham also presented examples of potential applications of HAZUS® to support catastrophic planning and several suggestions for improving the software through collaborative, user-driven development. The perspectives in this chapter highlight some of the highest priorities for HAZUS® development. The HAZUS® program is rapidly evolving in response to the needs of and input from its diverse users. In addition to the evolution of the software and technical models, the role of HAZUS® in catastrophe planning has expanded and changed in the years since its initial release. There may be some value in reconsidering the appropriate organizational placement of HAZUS® to more fully utilize its capabilities, but awareness of its original intent and inherent limitations should be taken into account. Nevertheless, there is significant potential for improving

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HAZUS® by developing the data inventory in HAZUS®, better integrating the core program with modules to address concerns such as network disruptions and the needs of special populations, and developing outputs customized for specific uses and users with varying levels of familiarity with this type of information. The users of HAZUS® provide strong promotion and support for development of the software. An eye toward user needs in catastrophe planning should continue to guide its direction into the future. Although originally designed as a scenario development tool to estimate potential losses from natural hazards, recently HAZUS® has been used for mitigation. HAZUS® could play an important role in mitigating losses by providing cost-benefit analyses for various mitigation alternatives. However, the models in HAZUS® aren’t necessarily in the right form for looking at specific mitigation measures to realize cost savings in a straightforward manner. HAZUS® will have to evolve further in order to be more useable as a mitigation tool. Additionally, HAZUS® may be more appropriate as a mitigation tool at the state level, rather than at the federal level. The most appropriate placement for HAZUS® within organizations – either under Planning in the Incident Command System (ICS) or higher up, directly connected to Command – is an open question. HAZUS® is important and useful for situational awareness in ICS. It is unclear whether the currently limited use of HAZUS® is due to technical or institutional limitations. Those making policy decisions need analytic tools. Probabilistic hazard analysis is not currently being used. The outputs from probabilistic analyses may not be useful to (understandable by) those users who need it for planning. There is an institutional disconnect between mitigation and response in FEMA/DHS; these problems are not primarily technical, but institutional. FEMA has been reduced staff-wise and is a much smaller organization than it was ten years ago. Additionally, HAZUS® hasn’t been marketed well within FEMA itself. There is an effort underway to “spread the word” within FEMA about the types of analyses available through use of the software. Disaster planning focuses more now on response to floods and hurricanes rather than on mitigation, with little focus on earthquake planning. This is due in part to current events (major hurricanes in 2005 and very few large earthquakes in the US in the past decade), but is also institutional. The use of HAZUS® has also expanded into the area of disaster response; key to succeeding with this is increasing the speed at which scenarios can be run. Further, there is a widely recognized need for a greatly improved structural inventory database in HAZUS® to support more accurate analyses. Publicly available sources of data about the built environment are incomplete or infrequently updated, and private sources are expensive. Web-based data management and output delivery could provide a


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boon to users in terms of improved communication and flexibility to use on-the-ground observations in place of program defaults. Daytime and nighttime population distribution information is available from Oak Ridge National Lab (Landscan – http://www.ornl.gov/ sci/landscan/) but is not currently in HAZUS®. In order to do pre-event planning, this type of information is needed for accurate analyses that account for time of day. This may be an example of another institutional disconnect, since the information is available, but not integrated into HAZUS®, though there are proposals to do so. Use of improved and more realistic data sets makes the output more useful, believable, and accepted. Some consultants have had experience connecting better developed data sets to HAZUS® (as opposed to the default data sets) with success in generating better outputs. It may not be appropriate to mesh other software with HAZUS®, nor to use it for recovery. HAZUS® was developed for and is best used in planning for mitigation, not for human services recovery. It would take a major investment, including major advances in inventory data, in order to make HAZUS® appropriate for those purposes and fully functional for response-focused users. Presenting responders with HAZUS® in the midst of disaster recovery is potentially problematic. Additionally, users who are not familiar with HAZUS® may not trust it (Tom Durham has had this experience with Red Cross, for example) and if not, will not use the outputs. Expanding the role of HAZUS® in catastrophe planning, mitigation, and response must proceed with caution as increasingly sophisticated analyses may not necessarily serve the needs of those decision-makers who are ultimately responsible for using HAZUS® outputs to make choices that affect the lives and assets of many people. In order for HAZUS® to be used effectively for mitigation and response, software development and user training should go hand-in-hand. The appropriate organizational placement of HAZUS® is an important consideration in its effectiveness as well – its current placement within FEMA/DHS may not serve users well. Finally, improving the completeness and accuracy of the structural inventory and population data, implementing mechanisms for future updates, and integrating modules for network capability to better model disaster impacts on transportation and lifeline systems would greatly improve HAZUS® outputs.

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References Bruneau M, Chang S, Eguchi R, Lee G, O’Rourke T, Reinhorn A, Shinozuka M, Tierney K, Wallace A, von Winterfeldt D (2003) A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra 19:733–752 Davidson R, Rivera M (2003) Projecting changes in the Carolina building inventory and their effect on hurricane risk. Journal of Urban Planning and Development 129:211–230 Dodo A, Davidson R, Xu N, Nozick L (2007) Application of regional earthquake mitigation optimization. Computers and Operations Research Dodo A, Xu N, Davidson R, Nozick L (2005) Optimizing regional earthquake mitigation investment strategies. Earthquake Spectra 21:305–327 Dueñas-Osorio L, Craig JI, Goodno BJ (2004) Probabilistic response of interdependent infrastructure networks In: Proceedings of the 2nd annual meeting of the Asian-Pacific Network of Centers for Earthquake Engineering Research (ANCER) Dueñas-Osorio L, Leelardcharoen K, Craig JI, Goodno BJ (2006) Failure of interdependent networks. In: Proceedings of the 8th National Conference in Earthquake Engineering (8NCEE) Federal Emergency Management Agency (FEMA) (2001) HAZUS®99 estimated annualized earthquake loss for the United States (FEMA 366). Federal Emergency Management Agency (FEMA), Jessup, MD Federal Emergency Management Agency (FEMA) (2002) Guide to using HAZUS®for mitigation. FEMA, Washington, DC Federal Emergency Management Agency (FEMA) (2004) HAZUS annual progress and utilization report for fiscal year 2004. FEMA, Washington, DC Jain V, Davidson R (2007a) Application of a regional hurricane wind risk forecasting model for wood-frame houses. Risk Analysis 27(1):45–58 Jain V, Davidson R (2007b) Forecasting changes in the hurricane wind vulnerability of a building inventory. Journal of Infrastructure Systems 13(1):1–12 Jain V, Davidson R, Rosowsky D (2005) Modeling changes in hurricane risk over time. Natural Hazards Review 6:88–96 Multihazard Mitigation Council (2006) Hazard mitigation saves: An independent study to assess the future savings from mitigation activities. National Institute of Building Sciences (NIBS), Washington, DC Sarabandi P, Adams B, Kiremidjian A, Eguchi R (2005) Infrastructure inventory compilation using single high resolution satellite images. In: Proceedings of the 3rd International Workshop on Remote Sensing for Post-Disaster Response Tantala M, Nordenson G, Deodatis G, Jacob K, Swiren B, Augustyniak M, Dargush A, Marrocolo M, O’Brien D (2005) Earthquake risks and mitigation in the New York, New Jersey, Connecticut region 1999–2003 (MCEER-03SP02). The New York City Area Consortium for Earthquake Loss Mitigation, NYC


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U.S. Department of Homeland Security (DHS) (2004) National response plan. DHS, Washington, DC U.S. Department of Homeland Security (DHS) (2004) Nationwide incident management system. DHS, Washington, DC U.S. Department of Homeland Security (DHS) (2006) Nationwide plan review: Phase 2 report. DHS, Washington, DC Xu N, Davidson R, Nozick L, Dodo A (2007) The risk-return tradeoff in optimizing regional earthquake mitigation investment. Structure and Infrastructure Engineering

6 Loss Estimation Models and Metrics

Ronald T. Eguchi1, Hope A. Seligson2 1 2

ImageCat Inc., Long Beach, CA, USA ABS Consulting Inc., Irvine, CA, USA

6.1 Introduction Earthquake loss estimation – although around since the early 1970’s – has not been considered a decision-making tool until recently. Because early models were based on expert opinion with limited hand calculations, they lacked the ability to perform sensitivity studies (which could provide important insight into drivers of loss potential) or the ability to produce results in a timely fashion. The results of these early studies were generally displayed on maps and/or in the form of tables that listed potential losses to a limited number of exposure categories, e.g., residential, commercial or industrial. Today, the power of loss estimation is significant with both commercial and publicly-available models for earthquake, in addition to models for other hazards, e.g., flooding, hurricane wind and terrorist threats. Two major developments are responsible for this progress. In the early 1990’s, the area of geographic information systems (GIS) and associated relational database management systems (RDMS) made a major thrust into the disaster management area and because of this, user-friendly software systems (especially those that allowed the visualization of geospatial information) became widely available. The second development was FEMA’s major push behind the HAZUS® (Hazards U.S.) standardized loss estimation model. Beginning in 1995, FEMA launched a major development effort that has resulted in comprehensive loss estimation models for earthquake, riverine and coastal flooding, and hurricane wind. These models have been applied in a number of recent earthquakes including the 2000 Napa, 2001 Nis-

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qually, and 2003 San Simeon earthquakes, as well as several recent hurricane events. Because of these two major milestones (GIS/RDMS and HAZUS®), most emergency managers and many re/insurance companies currently use loss estimation methods in their analysis of natural hazard and manmade threats. As a result of the major development thrust by FEMA, HAZUS® has become the de facto standard for regional loss estimation for emergency planning, mitigation and response; therefore, any discussion of regional loss estimation for these uses by necessity focuses on HAZUS®. HAZUS®’ predecessors, e.g., EPEDAT (Early Post-Earthquake Damage Assessment Tool) informed HAZUS®’ development but were short-lived (Eguchi et al. 1997; Eguchi and Seligson 1994; Seligson et al. 1994). This paper will lay the foundation for a discussion of loss estimation models focusing on the dimensions of loss (social, economic), key loss estimation modules or elements, perceived and actual limitations of existing methodologies, and issues related to data development, usage and uncertainties. The discussion will continue by focusing on model verification and/or validation. Because of uncertainties associated with regional ground motion mapping, building/lifeline inventories and/or damage or fragility modeling, model results can be off by a factor of 2 or 3, when compared to actual tabulated losses from real events. This paper will provide several case studies on model verification/validation, with the intent on guiding the reader in how best to use the results of loss estimation studies for earthquake risk mitigation, earthquake scenario development (mainly for preparedness studies), and post-earthquake response and recovery. We consider this discussion an examination of metrics or ways in which to calibrate loss assessments. Finally, the authors will provide a list of studies/investigations that they believe will lead to the largest improvements in loss estimation methodologies, including database enhancements, expansion of loss categories, and damage or fragility model research.

6.2 Loss Dimensions The dimensions of earthquake loss estimation have evolved considerably in the last ten years. Early efforts focused mainly on estimating the cost to repair or replace damaged buildings (for example, see NOAA 1972, 1973; Steinbrugge 1982; ATC 1985). In some cases, building contents and nonstructural systems were also addressed. Estimating these costs required the use of damage functions that related damage ratios (repair cost divided by replacement cost) to ground motion indices (e.g., Modified Mercalli Inten-


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sity or peak ground acceleration) for a specific class of construction. The primary focus of HAZUS® was on the estimation of economic losses from damage to the regional building inventory, referred to in HAZUS® as the “general building stock”; in fact, the building damage methodology development represented a significant advance in the state-of-the-art, replacing Modified Mercalli Intensity (MMI) damage probability matrices with acceleration-based push-over curves and fragility functions. An area that remains controversial is the development of these damage functions, which inevitably change after each new major earthquake in order to better match the observations and data from that event. Another dimension that had early roots and has evolved considerably in the last decade is casualty estimation. Current loss estimation approaches tie the estimation of casualties primarily to the structural performance of buildings (e.g., HAZUS®). In comprehensive studies, other factors are also considered, e.g., non-structural elements, fire following earthquake, etc. The estimation of casualties has until recently been crude with wide range of uncertainties. A significant reason for this has been the lack of detailed information or data on deaths and especially injuries in past events, and the non-standardized manner in which available data were collected and presented. In response to the need for a standardized data collection system to improve casualty modeling, a multi-disciplinary team of researchers studying casualties in the 1994 Northridge earthquake assembled a standardized classification scheme intended to be useful to the many disciplines studying earthquake-related casualties (see Shoaf et al. 2002; Seligson and Shoaf 2003). The scheme (available on-line at: http://www.cphd.ucla.edu– /pdfs-/scheme.pdf) attempts to define a common language that can be used to study events across time and geography to improve our ability to estimate casualties in earthquakes. Furthermore, it provides a mechanism for understanding the risk factors associated with injuries in order to reduce those losses. Casualty data collected using the standardized definitions includes data from the 1994 Northridge (see Mahue-Giangreco et al. 2001; Peek-Asa et al. 2000, 2003; Shoaf, et al. 1998), 1999 Turkey, 1999 Taiwan, and the 2001 Nisqually earthquakes. The 1999 Turkey earthquake casualty data has subsequently been used to develop detailed casualty models specifically for non-ductile concrete frame buildings (Shoaf et al. 2005; Seligson et al. 2006). One of the reasons that credible models are so essential is that many cost-benefit studies rely on the estimation of casualty risk reductions to establish the cost-effectiveness of structural and non-structural mitigation measures.

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Finally, an area that has received considerable attention in the last decade is the estimation of direct and indirect economic losses. These losses are normally associated with impacts resulting from the damage to or disruption of physical facilities or systems (lifelines). For purposes of distinguishing direct and indirect economic losses from structural repair costs, the following definitions are used here: x Direct losses are interpreted as those associated with gross regional product losses that consider no “bottleneck” effect; in other words, each economic sector is assumed to be impacted by only a disruption of service and not additionally by supply input bottlenecks from other sectors, and x Indirect losses are calculated by subtracting these “non-bottleneck” losses from those gross regional product losses that do account for bottleneck effects. These indirect losses model the so-called “multiplier” or “ripple” effects. The above discussion on indirect economic losses is especially relevant when the performance of lifeline systems is considered. For example, MCEER (Shinozuka et al. 1998) has shown that with a disruption of electric power service after a major earthquake, direct and indirect economic losses combined can be as high as seven times larger than expected repair costs. This ratio applies to losses caused by the failure and disruption of electric power service in a large New Madrid Earthquake (M7.5). In addition to the loss categories defined above, a more comprehensive assessment of earthquake impacts may consider other losses, e.g., environmental, ecological, etc. A more comprehensive list is provided below (Porter 2005): x x x x x x x x x x x x x

Direct property damage (buildings, contents, lifelines) Indirect property damage (e.g., fires) Direct business interruption loss (e.g., factory shutdown) Indirect business interruption loss (ordinary economic ripple effects) Non-market environmental damage (e.g., wetlands, parks) Other non-market damage (historic sites) Societal losses (casualties, homelessness) Emergency response (ambulance service, fire protection) Governmental administrative cost (e.g., disaster field office) Public/private investment displacement Market spillovers (changes in property values and business climate) Long-term effects (permanent loss of market share) Household impacts (disruption to daily lives)


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6.3 Components of the HAZUS® Loss Estimation Methodology Figure 6.1 shows the key modules in a loss estimation methodology. This framework comes from the HAZUS®MH methodology, FEMA’s standardized methodology for loss estimation. Currently, HAZUS®MH analyzes earthquake, flood (coastal and riverine) and hurricane wind losses. The discussions below briefly describe the purpose and scope of each module, from an earthquake hazard perspective. For a complete description of the methodology and the various modules, the reader is referred to the following references: NIBS, FEMA (2003a, 2003b, 2003c). 1. POTENTIAL HAZARDS

2. INVENTORY DATA

3. DIRECT DAMAGE

4. INDUCED DAMAGE

5. SOCIAL LOSSES 6. ECONOMIC LOSSES

7. INDIRECT LOSSES

Fig. 6.1. Key Loss Estimation Modules in HAZUS®MH

Potential Hazards (1): The potential hazard module estimates the expected intensities or hazard severities for earthquake. In general, this would entail the estimation of ground motions and ground failure potential from landslides, liquefaction, and surface fault rupture. For a probabilistic analysis, the added element of frequency or probability of occurrence would be included. Inventory Data (2): A national-level exposure database of the built environment provided with HAZUS®MH allows the user to run a preliminary analysis without collecting additional local information or data. The default database includes information on the general building stock, essential

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facilities, transportation systems, and utilities, although the data for some transportation and utility lifelines may be limited. The general building stock data are classified by occupancy (residential, commercial, industrial, etc.) and by model building type (structural system, material of construction, and height). The default mapping schemes (identifying the distribution of structure types expected for each occupancy category) are statespecific for single-family dwellings and region-specific for all other occupancy types. In all cases, they are age and building-height specific. Direct Damage (3): This module estimates property damage for each of the four inventory groups (general building stock, essential facilities, transportation, and utilities), based on the level of exposure and the vulnerability of structures at different hazard intensity levels. Induced Damage (4): Induced damage is defined as the secondary consequence of a disaster event on property. Fire following an earthquake is an example of this type of damage. Social Losses (5): Societal losses are estimated in terms of casualties, displaced households, and short-term shelter needs. The casualty model provides estimates for four injury severity levels (minor injuries to deaths), for three times of day (2:00 a.m., 2:00 p.m., and 5:00 p.m.), and for seven population groups (commuting, hotel, educational, commercial, industrial, single family residential, and other residential). The number of displaced households is estimated based on the number of residential structures that are uninhabitable, which is in turn estimated by combining damage to the residential building stock with utility service outage relationships. Economic Losses (6): Direct economic losses are estimated in terms of structural and nonstructural damage, contents damage, losses to business inventory (collectively referred to within HAZUS® as “Capital Stock Losses”), relocation, capital-related income, wage and rental income losses (time-dependent losses collectively referred to as “Income Losses” within HAZUS®). Indirect Economic Losses (7): This module evaluates region-wide (“ripple”) and longer-term effects on the regional economy from earthquake, flood, and wind losses. Estimates provided include changes in income and employment by sector (i.e., commercial, industrial, retail). The output generated by HAZUS® is summarized in Table 6.1 for general building stock, essential facilities (including hospitals, police and fire stations, emergency operations centers, and schools).


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Table 6.1 HAZUS®MH Loss Estimation Results Exposure Type

General building stock

Default Data Provided Yes

Damage State Probabilities S / NS, BC/SF by DS6 S

Function- Economic Associated Social Losses ality2 Impacts NA Direct and Casualties, Indirect8 Shelter, Debris10 A NASB None

Essential Yes Facilities3 Military No S A NASB None Installations User-Defined No S A NASB None Structures User defined No S / NS7 NA Direct9 Casualties Structures Using AEBM4 Transportation Yes1 A A ARC None 5 Systems Utility Systems/ Yes1 A A ARC Number of Potable Water households Systems– Pipelines, without water Facilities Utility Systems/ Yes1 A A ARC None Wastewater Systems– Pipelines, Facilities Utility Systems/ Oil Yes1 None A A ARC & Natural Gas Systems– Pipelines, Facilities Utility Systems/ Yes1 A A ARC Number of Electric Power households Facilities without power Utility Systems/ Yes1 A A ARC None Communications Facilities Damage State Probabilities, S = Structural only, S/NS = Both Structural and NonStructural, A = Available at the facility level. Functionality, A = Available, NA = Not Available. Economic Losses, ARC = Available, Repair Cost Only, NASB = Not Available on a Site-Specific Basis. 1 Default data may be limited. 2 Functionality estimated at Day 1, 3, 7, 14, 30 and 90. 3 Essential Facilities include hospitals, police & fire stations, EOCs and schools.

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Table 6.1 (cont.) 4

Analyzed using the Advanced Engineering Building Module (AEBM) Transportation systems include the following subcategories: Highway Facilities – Bridges, Segments, and Tunnels; Railway & Light Rail Facilities – Bridges, Segments, Tunnels, and Facilities; Bus, Port & Ferry Facilities; Airports – Runways and Facilities. 6 Structural (S), Nonstructural (NS) acceleration-sensitive and NS drift-sensitive, damage states probabilities provided by structure type. Building counts (BC) and square footage (SF) in each damage state (DS) are also provided. 7 Structural (S), Nonstructural (NS) acceleration-sensitive and NS drift-sensitive, damage states probabilities provided. 8 Direct economic losses available by Structure Type and by occupancy (Structural, nonstructural, contents, and inventory damage, and relocation, rental income, business income, and wage losses.) Indirect economic income and employment losses by sector, with and without outside aid. 9 Direct economic losses available (Structural, nonstructural, contents, and inventory damage, and relocation, rental income, business income, and wage losses). 10 Casualties estimated at 3 times of day (2 am, 2 pm, 5 pm), by occupancy or structure type, Shelter (displaced households, short-term shelter needs), and Debris (thousands of tons of light & heavy debris). 5

Additional measures not produced by HAZUS® include: lifeline disruption costs (network disruption models and their economic and societal impacts); a rigorous estimation of losses resulting from fire following earthquake; environmental damage; building damage in terms of safety evaluation tagging (e.g., red, yellow, and green); damage and loss to historic structures; casualty estimates using medically meaningful injury classes (e.g., immediate deaths, die in hospital, admitted to hospital, treated and released, etc.); governmental response administrative costs; investment displacement; changes in property values; long-term effects (permanent loss of market share or business failures); community recovery; household impacts (i.e., disruption to daily lives and long-term housing recovery); and evacuation demand (primarily for hurricane and floods but also applicable to small heavily damaged areas in earthquake). Finally, an area which is not addressed by the current HAZUS® methodology is the quantification of uncertainty. Addressing the magnitude of uncertainty associated with loss output is an important issue in assessing the reliability of results.


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6.4 Current Limitations Current loss estimation methodologies have several limitations, owing mostly to data limitations. Although current methodologies have become more powerful, they continue to under-predict losses for large events and over-predict losses for small events. There are many possible reasons why these estimates are off. Some are related to poor databases (especially in the area of inventory quantification); other reasons relate to inaccurate or inappropriate loss models. Also, inaccuracies may occur because of the manner in which impacted regions are defined. For example, limiting loss estimates to areas experiencing Modified Mercalli Intensities of VIII or greater may limit the extent of losses estimated, particularly in very large earthquakes. One possible reason for overestimating earthquake losses in small events is that earthquake damage functions may over-predict the amount or level of damage experienced for areas experiencing low levels of ground shaking, e.g., Modified Mercalli Intensities less than VI. Damage functions developed in the last two decades are often based on building damage information collected from California earthquakes. In general, there is much interest in collecting damage data in events that cause significant overall loss. In California, there are two significant events in this regard: the 1971 San Fernando earthquake, and the 1994 Northridge earthquake. Both of these events were considered moderate in size (M6.4 and M6.7, respectively). Damage data collected from these two events generally focused on areas of strong ground shaking, e.g., areas of MMI greater than or equal to MMI VIII, essentially ignoring any loss potential in areas experiencing MMI VI or VII. A substantial amount of damage information, therefore, exists only for construction in areas of MMI VIII and above. When damage models are developed, in many cases, an empirical approach is used. Regression analyses are employed on damage information with the end result being a linear or non-linear function relating percent damage (in terms of replacement cost) to earthquake intensity. Since the process used in creating these models relies on interpolating or extrapolating limited datasets using predefined shapes or functions, it is possible that errors or inaccuracies could occur at the extreme ends of these damage functions (i.e., at very low intensities or very high intensities). So, one possible reason for overestimating earthquake losses for small events is that the extrapolation of the damage function generally developed from data in areas of MMI VIII and above to lower intensities produces erroneously large losses, likely because the damage function does not decrease in a linear fashion at these low levels of ground shaking.

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A possible reason for underestimation for large events is that the damage functions do not adequately represent losses at the higher intensity levels or that the total damage area is not considered in the analysis, e.g., because a large event will affect a very large area, the analysis may be limited to only those areas experiencing MMI’s VIII or greater, again ignoring any losses in areas of VI or VII. Another source of inaccuracy is in the quantification of the built environment (e.g., buildings, lifelines). These inaccuracies can occur because the correct number of buildings is not established, or the value of these buildings is not properly estimated, or the distribution of buildings by structural type is not effectively accounted for. There have been many different approaches proposed over the years to develop detailed databases of building inventories. Some of these are based on using tax assessor rolls as a source for building counts, building square footage, number of stories, building age, and rough assessment of structural type. While this approach may provide the most accurate compilation of building information, it is also the most time-consuming and costly approach for creating this data. One study conducted after the Northridge earthquake estimated the cost of constructing a detailed building inventory database for Los Angeles, Orange and Ventura counties at $50,000 per county. The best estimate of building inventory is generally built from tax assessor information on individual buildings aggregated to some geographic unit (e.g., census tract). In lieu of using tax assessor information, other approaches have used inference methods to create building inventories based on readily available information, e.g., HAZUS® uses census data (using housing unit counts and approximate square footage per unit to estimate total square footage) and Dun and Bradstreet information (estimating commercial and industrial square footage from employment data) to create its default building inventory. While these methods may be reasonable for many areas where the type of development or construction does not vary much (e.g., residential), they generally are less accurate in downtown areas, or areas of mixed development types (e.g., residential/commercial). A third area where loss estimates are highly uncertain is casualty estimation. As described above, this area has seen significant recent research; with a general consensus on what constitutes an earthquake-related death or injury, with more medically appropriate definitions of injuries, and with consistent data collection across events, we should see better or more applicable models in the future. Early estimates of casualty (see Seligson et al. 2002) were often crude, with the lower and upper bound estimates varying by an order of magnitude. While this output accurately reflected the degree of certainty that was present at the time these models were being


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applied, the wide range of uncertainty essentially made these model predictions inadequate for planning or emergency response purposes. A fourth area where loss estimation is limited is in determining the system impacts from lifeline failures. Lifelines, in this case, would include water supply, wastewater, electric power, communication, oil supply, natural gas, transportation and port and harbor operations. The limitation arises when the impacts of lifeline disruption are addressed. Usually, analysts have no problem in estimating which facilities will be damaged, or how the system will respond to individual or multiple failures. The difficulty comes when estimating how long it will take to repair damaged components, or in determining the extended impacts from lifeline disruption. In general, to estimate the “indirect” impacts of lifeline disruption requires a reasonably complete topological model of the system, estimates of the restoration times associated with particular types of facilities, resiliency models that relate the ability of businesses to continue operations without lifeline service (models that are often a function of time), and the level of dependency that each economic sectors has on other sectors. While the research community has made major strides in this area, there is much work needed to “operationalize” these methodologies.

6.5 Loss Calibration Studies In this section, we discuss several different studies where loss estimation methods were assessed and/or calibrated against actual earthquake data. In all three cases studies below, FEMA’s HAZUS® tool is used. The first, “the Boston HAZUS® pilot study” was an early earthquake validation study performed by EQE International (now ABSG Consulting Inc.) for the National Institute of Building Sciences (NIBS) and the Federal Emergency Management Agency (FEMA) using Boston as the pilot city. The second validation study was performed by Comartin-Ries – again for NIBS - using data from five California earthquakes. The final effort was a joint activity between ImageCat, Inc. and ABSG Consulting Inc. for the California Governor’s Office of Emergency Services (OES) where the study purpose was to develop guidelines for improving HAZUS® loss estimates by updating building and lifeline inventories. The details of each study are discussed below. However, before we introduce the results of each study, we present a brief summary of the HAZUS® methodology (some of which was discussed above). HAZUS® (HAZards U.S.) is a standardized, nationally applicable earthquake loss estimation methodology and public domain software package

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developed for the Federal Emergency Management Agency (FEMA) by the National Institute of Building Sciences (NIBS). First released as HAZUS®97, significant updates followed, resulting in the more widely used version known as HAZUS®99, as well as two enhanced service releases; HAZUS®99 Service Release 1.0 (2001) and HAZUS®99 Service Release 2.0 (2002). The latest version of HAZUS®, HAZUS®MH, provides updated inventory data and new flood and hurricane modeling capability. HAZUS®MH V1.0 was released in January of 2004, followed by the release of HAZUS®MH MR-1 (Maintenance Release 1) in January, 2005, and MR2 in June of 20061. The evolution of the HAZUS® model – the current “gold” standard for regional earthquake loss estimation – provides insight into the importance of verification, validation, and data enhancement. Default data built into HAZUS®MH allows a user to run a simplified or “Level 1” analysis, without collecting additional data (i.e. “straight out-of-the-box”). However, in many cases, the quality of default national data delivered with the software is less than optimal; it may originate from agencies other than FEMA, having been collected for applications other than earthquake loss estimation. Accordingly, the accuracy of HAZUS®MH results can be greatly improved with the input of various “user-supplied” data. An enhanced analysis is usually referred to as a “Level 2” analysis.

6.5.1 Boston Pilot Study The importance of data enhancement for the HAZUS® earthquake loss estimation model was demonstrated in the Pilot Test of the HAZUS®97 software conducted in 1996 for the City of Boston, Massachusetts (EQE International, 1996). While this was the second HAZUS® pilot test, it was the first real test of a fully-functioning model. The purpose of the study was to test the methodology, evaluate the results produced by the software, and to recommend improvements. (Many of the suggested improvements were made; HAZUS®99 reflected many differences from the original HAZUS®97 model. Accordingly, utilization of the 1996 study’s results should be done with caution, as the software has changed significantly over time). A substantial part of the project entailed collecting seismic hazard data and developing the required ground failure susceptibility maps. The development of general building and lifeline inventories was also necessary 1

In addition, the release of MR-3 is expected in Summer of 2007.


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in order to test the methodology’s capability to produce better results when the software program’s default data were substituted with better quality data. General building stock database enhancements included development of square footage exposure data from Assessor’s data, as well as custom mapping schemes (distributions of construction types for each occupancy category) by neighborhood. The study included sensitivity studies that compared estimates of loss and damage across the levels of analysis available within the software and methodology; Level 1 (all default data), Level “1E” (primarily default data, enhanced by incorporating a soils map, and a simple “age-height” distribution for building inventories), and Level 2/3 (analyses with a significant amount of user-input data including custom building and lifeline inventories); and by varying key data and/or parameters. Some of the lessons learned from the sensitivity studies are still relevant to the current version of the software: x One of the biggest contributors to variation in losses is the selection of attenuation relationship. The Pilot test compared losses for a M 7.0 scenario generated using the USGS Project ’97 attenuation relationship with that of Boore, Atkinson (1995); losses resulting from the USGS97 run were almost 250% higher than that using Boore, Atkinson.2 x While the ideal representation of the building inventory might appear to be mapping schemes (identifying the distribution of construction types for a given occupancy) for each census tract or for smaller sections of the study area, the development of multiple mapping schemes can take a prohibitive amount of time, and may not be worth the effort. In the Boston HAZUS®97 pilot test, Level 2/3 building inventory files were developed for the City of Boston, which has a total of 460 million square feet of exposure on just over 100,000 parcels. In a test of one “city-wide” mapping scheme versus 22 mapping schemes for sub-regions of the City, only a 5% change in building-related losses was found. x The study also demonstrated the importance of incorporating local inventory data. The “best estimate” inventory (i.e., utilizing the most detailed, local data available) derived from the Assessor’s data, including custom mapping schemes, was compared to two runs: run “A” utilizing default inventory data and mapping schemes, and run “B” using custom square footage data with default mapping schemes. The results indicated that run “A” produced building damage losses 41% higher than the 2 The wide range of losses resulting from various attenuation relationships was demonstrated again more recently for a Puente Hills earthquake in Los Angeles using HAZUS®MH (see Field et al. 2005).

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best estimate, while run “B” was actually within 1% of the best estimate building loss. This indicated that users with limited budget should consider concentrating their efforts on updating the square footage inventory data first.

6.5.2 California Validation Study The HAZUS® model was further refined following a “Validation” Study of the HAZUS®99 (SR-1) software conducted in 2001 (Comartin-Reis 2001). The study assembled a significant amount of earthquake impact data for comparison to losses estimated using HAZUS® for five California earthquakes; the 1983 Coalinga, 1987 Whittier, 1989 Loma Prieta, 1994 Northridge, and 2000 Napa/Yountville earthquakes. In assembling the data from various sources (including reconnaissance reports, government statistics and academic research), the study’s authors noted a wide range of completeness and accuracy of the available data, making some of the validation comparisons impossible or unreliable. Sixteen HAZUS® runs were completed, including one default run for each earthquake, and additional runs modifying various ground motion, soil or inventory parameters. The study’s observations included the following (Comartin-Reis 2001): Overall estimates of direct economic losses to buildings were typically within 50% of the documented reports. x “HAZUS® typically predicted more casualties than were reported, but generally the over predictions were less than three times the documented number. Considering deaths only, HAZUS® generally predicted less than twice the documented number. x For the more damaging earthquakes considered, Loma Prieta and Northridge, HAZUS® typically predicted the number of displaced households and people seeking shelter to be 50% to 200% of the documented numbers. The predictions were less accurate in the smaller events. x The use of site specific ground motion and soils information and of refined inventory data, generally improved loss predictions, as would be expected. However, the quality of the ground motion information can have a large impact on the results. For earthquakes in which the source information is contained in its database, HAZUS® does a better job of predicting ground motion than for runs using arbitrary source information only.”


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Suggested improvements included improved procedures for replacing default building inventory data, and inclusion of better default hazard data (seismic sources, soil and liquefaction data). 6.5.3 OES Validation Study More recently, the California Governor’s Office of Emergency Services (OES) has been sponsoring guidelines development research aimed at facilitating a comprehensive and standardized approach to loss estimation using HAZUS® for the State of California, which is also applicable throughout the U.S. The guidelines, Phase One “Data Standardization Guidelines for Loss Estimation – Populating Inventory Databases for HAZUS®99” (2004) and Phase Two “Data Standardization Guidelines for Loss Estimation – Populating Inventory Databases for HAZUS®MH MR-1” (2006), provide HAZUS® users with data update prioritization ratings and procedures for enhancement of default building stock and lifeline inventory data. The Phase Two effort also included a pilot test of the Guidelines, development of software tools to facilitate data updates in the HAZUS®MH framework, and calibration of the recommended updates using the 2003 San Simeon earthquake. The San Simeon validation study confirmed some of the earlier lessons learned in the implementation of HAZUS® at the default level, and for enhanced data sets. Losses generated using default HAZUS® runs (using the final USGS ShakeMap to represent ground motions) were compared to statistics on actual losses, as well as losses estimated from study regions where Assessor’s data was used to update building inventories and mapping schemes, as outlined in the Guidelines document. x The “best estimate” HAZUS® analysis (using updated inventory data and mapping schemes as described in the Guidelines) estimated economic losses associated with building damage within 20% of observed losses for San Luis Obispo County; total economic loss estimates were within 3% of observed. This can be compared to the default analysis, which overestimated building losses by about 73%, and total economic losses by 50%. x The San Simeon earthquake demonstrated the significant risk posed by URM structures; the collapse of one unreinforced masonry (URM) building during moderate levels of shaking in the City of Paso Robles resulted in two deaths. In California, local jurisdictions generally maintain inventories of URM structures. This data can be analyzed using HAZUS®’ Advanced Engineering Building Module (AEBM) with a

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moderate level of effort. For Paso Robles, AEBM results were significantly different from the HAZUS®MH estimates derived from default data. Therefore, for communities with an existing inventory of URM buildings in a region exposed to earthquake risk, implementing the AEBM was highly recommended. x Because default data on utility lifelines is often missing from HAZUS®, reliable estimates of utility performance after a disaster requires user input data. Data at the local level may be available from lifeline agencies.

6.6 General Guidelines on the Use of Loss Estimates Loss estimation methods are finding roles in many areas of earthquake planning. While early efforts concentrated mostly on planning scenarios for emergency response, newer activities have included near real-time loss estimation for emergency response (i.e., determining level of resources after a major event, and where to deploy them), loss estimation after a major earthquake (this currently applies mostly to insurance estimates of loss after a significant event), calibration of model improvements (e.g., quantification of loss implications associated with seismic hazard updates or refinements), and quantification of mitigation effectiveness (many recent studies have used loss estimation methodologies to develop benefit-cost estimates of specific categories of mitigation, where benefits are defined as potential reduction in losses after application of mitigation measures). In addition, the more conventional uses of loss estimation also apply, e.g., planning scenarios. The following discussion provides guidelines on how loss estimates can be used for different earthquake planning tasks. Earthquake Planning: Loss estimates can be used to quantify potential risks from future events. This information is generally useful in developing land use policies, information that will support the development and implementation of local building codes, and preparing maps for the public that allows a high-level assessment of risk. The following points should be considered in using loss estimations for the purposes above: x Loss estimates must consider the effects of local conditions to be useful. This would include local soil conditions (especially if soft soils are present), topographic effects that might suggest potential landslide problems, the presence of nearby dams or reservoirs that can inundate the area if failure occurs, high groundwater tables that might suggest a


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possible liquefaction problem, and if located on the coast, any conditions that might suggest a possible tsunami hazard. x Information must be presented at a level that a local homeowner can understand. Ultimately, this information must be conveyed to the public since it affects design costs, land use policies, and potential insurance premiums. Because of this, special attention must be paid to concepts such as annualized risks, maximum probable scenarios, etc., in order to not unnecessarily complicate the issue of earthquake risk for the homeowner. Earthquake Risk Mitigation: Loss estimates are an effective means of quantifying the efficacy of mitigation measures and/or strategies. In many earthquake mitigation studies (especially those that involve the disbursement of FEMA hazard mitigation funds), it is necessary to simulate what the future potential benefits are with prescribed mitigation measures. In general, the application of benefit-cost analyses requires that the anticipated or estimated future benefits associated with the implementation of a particular mitigation measure outweigh the initial cost of the mitigation (i.e., because greater than one). In order to estimate these potential benefits or savings (in this case), it is necessary that loss estimation methods be applied. Some of the points that should be considered in this application include: x A key decision for the analyst will be the selection of the proper discount rate to discount future benefits for inflation. This rate has varied from between 3% and 7% in recent years and does have an important effect on the amount of dollars saved over the lifetime of the project (Multihazards Mitigation Council 2005). In general, benefits (i.e., future savings) at a discount rate of about 3% begin to markedly drop off after 15 to 20 years. One possible strategy to address this issue is to perform the benefit-cost analysis at several different levels of discount rate. x Recent cost-benefit studies have shown that assessing the regional benefits of specific mitigation strategies (e.g., retrofitting suspended ceilings) offers another perspective on cost-effectiveness. Current approaches for estimating mitigation cost-effectiveness often focus on the efficacy of single projects. Whereas, this approach may be appro-priate for unique or non-standard mitigation activities, for mitigation activities that represent a broad application of simple, standard measures, a regional approach may be more appropriate. Adopting such an approach allows a simpler and more efficient procedure for actually assessing the cost-effectiveness and implementation of a broad-scale program, i.e.,

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ensuring that the overall benefits of the program are cost-effective (EQE International 1997; Seligson et al. 1998). Emergency Response and Recovery: Loss estimation methods play a vital role in post-earthquake response and recovery activities. Since the introduction of real-time earthquake magnitude and location systems (such as the CUBE system – Caltech-USGS-Broadcast of Earthquakes in Southern California), many earthquake analysts have been using loss estimation methods as a means of rapidly quantifying the impacts of large and moderate earthquakes (see Eguchi et al. 1997). Newer systems – like the ShakeMap system operated by the U.S. Geological Survey (see http://earthquake.usgs.gov/eqcenter/shakemap/) – offer near real-time maps of earthquake ground motions after most moderate-sized events. When combined with efficient loss estimation models, it is possible to produce estimates of economic loss within a matter of minutes. These loss estimates can be used to determine the level of government assistance required (manpower and financial resources), where emergency relief sites (temporary housing, government assistance) need to be set up and how many, and what the possible damage state is of complicated lifeline systems (e.g., water supply, transportation). A recent ATC study (ATC 2006) examined how real-time earthquake monitoring systems can be used in emergency response; some of the applications identified above are discussed. Some of the points that should be considered in this application are: x The output from a loss estimation analysis immediately after a major earthquake is a “static” view of the magnitude of the disaster. It does not account for any special situations that may have resulted after the event (e.g., dam breakage causing unforeseen damage or loss), or deviations from standard model predictions, e.g., earthquake source effects that may increase intensities beyond those predicted by standard ground motion attenuation functions (such as those observed after the 1994 Northridge earthquake). Therefore, there needs to be simple validation methods or tools that allow an analyst to upgrade or calibrate loss estimates with actual data collected after an event. Furthermore, independent assessment of damage or loss, such as those afforded by the application of remote sensing technologies, should be encouraged in order to maximize the reliability of overall loss estimates. x In small earthquakes, earthquake losses have been typically overestimated; in large events, losses have been generally underestimated. For many of the reasons stated above, loss estimates have not accurately matched actual loss totals in real events. Because of this, the analyst


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may wish to seek further validation of model predictions (such as those mentioned above) or err on the high side for large events, or cautiously use loss estimates in small events. In the future, as more sensors are placed within seismically-active regions, and as other technologies become tools in the loss estimator’s tool chest, the discrepancy between simulated loss results and actual measurements will narrow. x Loss estimation models can be useful tools for simulating a hypothetical event to assess alternative recovery strategies. Planning for recovery before a disaster strikes was the focus of early work by Spangle and Associates (1987); more recently, Miles and Chang (2006) have been developed community recovery models that are complementary to loss estimation models. Together, loss estimation and recovery models can assist decision-making for enhanced community resilience.

6.7 Research Topics The following topics offer suggestions for research that would significantly improve loss estimation capabilities for earthquake (presented in no order of priority): x Improving building inventory development methods by using remote sensing technologies. Various types of sensors are available that can help create more accurate and more updatable databases on exposed buildings. These sensors include LIDAR (light detection and ranging), SAR (synthetic aperture radar) and high-resolution optical imagery. x More rigorous collection of building and lifeline damage data in areas experiencing lower ground motion intensities (e.g., less than MMI VIII) to improve damage and fragility models. Data collection efforts should distinguish between structural and non-structural damage and loss. x Better integration of loss estimation models with near real-time ground motion monitoring systems. Seismologists must work closely with earthquake loss estimation specialists in order to ensure that the linkages between loss estimation models and earthquake monitoring systems are robust and can be implemented in all size events. x Better integration of cutting-edge loss modeling techniques (e.g., recovery modeling, fire-following modeling, and lifeline disruption modeling ) into the standard HAZUS® loss estimation framework, as well as additional research to development quantification methods for other measures including environmental damage, response costs and business failure.

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x Displaying the results of loss estimation studies on the internet or through web-based portals (e.g., the integration of ShakeMap data and HAZUS® loss outputs into Google Earth) will allow a larger population to view the impacts of the earthquake, and hopefully lead to better and more informed decisions regarding response and recovery. x More attention needs to be focused on developing standard data collection protocols for damage data from all types of earthquakes, i.e., large and small. These data should include the typical data types (e.g., building damage information, information about the structure, ground motion data, etc.) but also include information on actual repair costs, repair times (especially important for lifeline systems), and costs or losses associated with indirect impacts (such as business interruption). Collection of societal impact data, such as casualties and shelter utilization, should also be collected systematically using standardized protocols. x Explanation of the results of loss estimation studies and how these results can be used in the different phases of earthquake management is greatly needed. Especially important is explaining the value of these methods as well as limitations associated with their implementation. A standard manual or guide would be ideal.

References ATC (1985) Earthquake damage evaluation data for California (Report ATC-13). Applied Technology Council Report, Redwood City, California ATC (2006) Guidelines for using strong-motion data and ShakeMaps in postearthquake response (Report ATC-54). Applied Technology Council, Redwood City, California Comartin-Reis (2001) HAZUS®99 SR-1 validation study. National Institute of Building Sciences/Federal Emergency Management Agency, Washington, DC Eguchi RT, Goltz JD, Seligson HA, Flores PJ, Blais NC, Heaton TH, Bortugno E (1997) Real-time loss estimation as an emergency response decision support system: The Early Post-Earthquake Damage Assessment Tool (EPEDAT). Earthquake Spectra 13:815–833 Eguchi RT, Seligson HA (1994) Development of an early post-earthquake damage assessment tool for southern California, final technical report. (Technical Report prepared for United States Geological Survey under USGS Award Number 1434-93-G-2306) EQE International (1996) Second pilot test study of the Standardized Nationally Applicable Loss Estimation Methodology, Task 4.2.3. (Final report, technical report prepared for the National Institute of Building Sciences, Washington, DC)


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EQE International (1997) Ramp methodology and user’s guide. (Report prepared for the California Governor’s Office of Emergency Services, Hazard Mitigation Grant Program) Field EH, Seligson HA, Gupta N, Gupta V, Jordan TH, Campbell KW (2005). Loss estimates for a Puente Hills blind-thrust earthquake in Los Angeles, California. Earthquake Spectra 21: 329–338 Mahue-Giangreco M, Mack W, Seligson HA, Bourque LB (2001) Risk factors associated with moderate and serious injuries attributable to the 1994 Northridge earthquake, Los Angeles, California. Annals of Epidemiology 11:347– 357 Miles SB, Chang SE (2006) Modeling community recovery from earthquakes. Earthquake Spectra 22:439–458 Multihazard Mitigation Council (2005) Natural hazard mitigation saves: An independent study to assess the future savings from mitigation activities, vols 1 and 2. Federal Emergency Management Agency, Washington, DC NIBS, FEMA (2003a) Multi-Hazard Loss Estimation Methodology, Earthquake Model, HAZUS®MH Technical Manual, National Institute of Building Sciences and Federal Emergency Management Agency, Washington, DC, pp 690 NIBS, FEMA (2003b) Multi-Hazard Loss Estimation Methodology, Hurricane Model, HAZUS®MH Technical Manual, National Institute of Building Sciences and Federal Emergency Management Agency, Washington, DC, pp 557 NIBS, FEMA (2003c) Multi-Hazard Loss Estimation Methodology, Flood Model, HAZUS®MH Technical Manual, National Institute of Building Sciences and Federal Emergency Management Agency, Washington, DC NOAA (1972) A study of earthquake losses in the San Francisco Bay area; data and analysis. Office of Emergency Preparedness, Washington, DC NOAA (1973) A study of earthquake losses in the Los Angeles, California area. Federal Disaster Assistance Administration, Department of Housing and Urban Development, Washington. DC OES (2004) Data standardization guidelines for loss estimation – populating inventory databases for HAZUS®99 (Report prepared by ABS Consulting Inc. and Imagecat, Inc. for the California Governor’s Office of Emergency Services) OES (2006). Data standardization guidelines for loss estimation – populating inventory databases for HAZUS®MH MR-1 (Report prepared by ABSG Consulting Inc. and Imagecat, Inc. for the California Governor’s Office of Emergency Services) Peek-Asa C, Ramirez MR, Seligson HA, Shoaf KI (2003) Seismic, structural, and individual factors associated with earthquake-related injury. Journal of Injury Prevention 9: 62–66 Peek-Asa C, Ramirez MR, Shoaf KI, Seligson HA, Kraus JF (2000) GIS mapping of earthquake-related deaths and hospital admissions from the 1994 Northridge, California, earthquake. Annals of Epidemiology 10: 5–13 Porter KA (2005) Personal Communication to RT Eguchi Seligson HA, Blais NC, Eguchi RT, Flores PJ, Bortugno E (1998) Regional benefit-cost analysis for earthquake hazard mitigation: Application to the North-

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ridge earthquake. In: Proceedings of the Sixth U.S. National Conference on Earthquake Engineering. Seattle, Washington Seligson HA, Eguchi RT, Goltz JD (1994) Seismic vulnerability assessment for southern California. In: Proceedings of the Fifth U.S. National Conference on Earthquake Engineering. Chicago, Illinois Seligson HA, Shoaf KI (2003) Human impacts of earthquakes. In: Scawthorn C, Chen WF (eds) Earthquake engineering handbook. CRC Press, Boca Raton, Florida, pp 28–1 – 28–25 Seligson HA, Shoaf KI, Kano M (2006) Development of casualty models for nonductile concrete frame structures for use in PEER’s performance-based earthquake engineering framework. In: Proceedings of the 8th National Conference on Earthquake Engineering. San Francisco, California Seligson HA, Shoaf KI, Peek-Asa C, Mahue-Giangreco M (2002) Engineeringbased earthquake casualty modeling: Past, present and future. In: Proceedings of the Seventh U.S. National Conference on Earthquake Engineering. Boston, Massachusetts Shinozuka M, Rose A, Eguchi RT (eds) (1998) Engineering and socioeconomic impacts of earthquakes - an analysis of electricity lifeline disruptions in the New Madrid area. Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY Shoaf KI, Sareen HR, Nguyen LH, Bourque LB (1998) Injuries as a result of California earthquakes in the past decade. Disasters 22:218–235 Shoaf KI, Seligson HA, Peek-Asa C, Mahue-Giangreco M (2002) Advancing the science of casualty estimation: A standardized classification scheme. In: Proceedings of the Seventh U.S. National Conference on Earthquake Engineering. Boston, Massachusetts Shoaf KI, Seligson HA, Ramirez M, Kano M (2005) Appendix D - Fatality model for non-ductile concrete frame structures developed from Golcuk population survey data, in Van Nuys hotel building testbed report: Exercising seismic performance assessment (Pacific Earthquake Engineering Research Center Report 205/11). University of California, Berkeley Spangle WE (ed) (1987) Pre-earthquake planning for post-earthquake rebuilding. (Southern California Earthquake Preparedness Project of the Governor’s Office of Emergency Services in cooperation with the Federal Emergency Management Agency) Steinbrugge KV (1982) Earthquakes, volcanoes, and tsunamis: An anatomy of hazards. Skandia America Group, New York, NY

Bibliography of Additional HAZUS® Studies ATC (final publication pending) San Francisco’s earthquake risk: Report on potential earthquake impacts in San Francisco: Phase II, Task 2. (Impact Assessment Report prepared by the applied technology council (ATC), Redwood City, California, for the San Francisco Department of Building Inspection,


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Community Action Plan for Seismic Safety (CAPSS). City and County of San Francisco) CUSEC (2003) Comparison study of the 1985 CUSEC six studies study using HAZUS®. (Electronic report Prepared by the Central United States Earthquake Consortium http://www.cusec.org/Hazus/six_cities.htm) EERI (ed) (1997) Earthquake Spectra: Theme issue of earthquake loss estimation 13: 565–855 EQE International (2000) Assessment of the benefits of implementing the new seismic design criteria and inspection procedures. (Report prepared for the SAC Steel project) FEMA (2000) HAZUS®99 estimated annualized earthquake losses for the United States (Publication 366). Federal Emergency Management Agency (FEMA) Washington, D.C. King & Pierce Counties (2001) Port-to-port transportation corridor earthquake vulnerability (Conducted as part of a project impact partnership between King & Pierce Counties, “Creating Disaster Resistant Communities”) Rowshandel B, Reichle M, Wills C, Cao T, Petersen M, Branum D, Davis J (electronic version, accessed 26 January, 2007) Estimation of future earthquake losses in California (Report available from http://www.consrv.ca.gov/CGS/ rghm/loss/) Tantala MW, Nordenson GJP, Deodatis G (2001) Earthquake loss estimation study for the New York City area (Second year technical report prepared by Princeton University, Department of Civil and Environmental Engineering for MCEER and funded by FEMA) URS Corporation, Durham Technologies, Inc. II, Pacific Engineering & Analysis, S&ME, Inc. (2002) A comprehensive seismic vulnerability and loss evaluation of the state of South Carolina using HAZUS®. (Report prepared for the South Carolina Emergency Preparedness Division)

Fig. 2.3. (a) Mallet’s 1857 map; (b) Global Earthquakes 1900–1990 (Agnew 2002); (c) Global Seismic Hazard Analysis Project Map (GSHAP 1998)

Fig. 2.11. Two of the larger Japanese Strong Motion Networks. Reprinted with permission from Kashima (2000)

Fig. 2.14. General developmental trend of seismic risk assessment 1950–2000, overlaid on natural hazard losses and also showing selected related trends (background: Munich Re)

Fig. 4.3. Potential Applications of HAZUS®MH to Support Catastrophic Planning in the New Madrid Seismic Zone

Fig. 5.1. State Level Losses map from the annualized loss report (FEMA 2001) showing the relative level of risk across the country based on a standard form of hazard analysis and inventory.

Fig. 5.2. Estimated total county building loss (CUSEC 2005) from an earthquake scenario in the New Madrid Region (reprinted with permission from CUSEC)

Fig. 12.4. Uncertainty mapped to transparency. Reprinted with permission from Strothotte (1999)

Fig. 12.5. (a) Positional uncertainty is mapped to fuzzy surface. Reprinted with permission from Lee and Varshney 2002. (b) Positional uncertainty is shown using point clouds. Reprinted with permission from Grigoryan and Rheingans 2004

Fig. 12.6. Probability that a magnitude 5.0 or greater earthquake will hit the Boulder area in the next 10 years is mapped to a discrete color scale

Fig. 12.7. Left images show the 95% confidence interval where darker regions have higher values. Users can then progressively adjust the focus to only show those regions. Reprinted with permission from MacEachren et al (1993)

Fig. 12.8. (a) A contour is overlain on top of the data map. The contours show uncertainty and grow thicker as the uncertainty rises. Reprinted with permission from MacEachren et al. (1993). (b) A similar idea is used where the reliability of watershed boundaries is inversely mapped to the width of the boundary lines. Reprinted with permission from Miller and Morrice (1996)

Fig. 12.9. Yet another variation on how uncertainty can be depicted on contour lines. Reprinted with permission from Djurcilov and Pang (2000)

Fig. 12.10. A grid is superimposed over the 2D domain. The data values are mapped to color, while the grid is modified according to uncertainty values. On the left, it is mapped to amplitude, while on the right, it is mapped to noise. Reprinted with permission from Cedilnik and Rheingans (2000)

Fig. 12.11. Angular uncertainty represented as angular spread is mapped to the width of the arrow head. Reprinted with permission from Wittenbrink et al. (1995)

Fig. 12.12. Minimum and maximum pairs rendered as bars below and above the wireframe surface respectively. Reprinted with permission from Cliburn (2002)

Fig. 12.13. HSV color mapping of three variables. Reprinted with permission from Luo et al. (2003)

Fig. 12.14. An example of displaing the multi-value properties of the multi-value on a separate location or geometry. Reprinted with permission from Kao et al. (2001)

Fig. 12.16. The bottom plane is the mean field colored from non-forest (cyan) to forest (red). The upper plane is generated from three fields: the bumps on the surface is from the standard deviation field and colored by the interquartile range; the heights of the vertical bars are from the absolute value of the difference between the mean and median fields colored according to the mean field on the lower plane. Only difference values exceeding 3 are displayed as bars to reduce clutter. The data is the same as in Fig. 12.13. This approach uses parametric statistics and assumes the multi-values can be adequately described by these statistics. However, for ill-behaved distributions, or higher dimensional data sets, this thematic layered approach is inadequate (reprinted with permission from Kao et al. 2001)

Fig. 12.17. Contour lines showing where the multi-values in the 2D field are similar to the distribution shown on the right. Reprinted with permission from Kao et al. (2005)

Fig. 12.18. An isosurface using a reference temperature multi-value is shown on the right. The surface represents regions in the data where the multi-values are very similar to the reference multi-value. Color of the surface shows the standard deviation of the multi-value at each location. The data set is from an ocean circulation model of the Middle Atlantic Bight shelfbreak which is about 100 km wide and extends from Cape Hatteras to Canada. Both measurement data and ocean dynamics were combined to produce a 4D field that contains a time evolution of a 3D volume including variables such as temperature, salinity, and sound speed (reprinted with permission from Luo, Kao, and Pang 2003). An ensemble of 600 Monte-Carlo forecasts of each field was produced

Fig. 12.19. Streamline visualization of ensemble weather forecasts. Reprinted with permission from Luo et al. (2003)

Fig. 12.20. On the left, fault block diagram showing 9 of the 11 major faults in the Santa Clara Valley. Reprinted with permission from Jachens et al. (2001). On the right, the physical structure of a North Sea oil field derived from 3D seismic data Reprinted with permission from the Earthquake Engineering Research Institute (2003)

Fig. 12.21. Four direct volume rendered frames from an earthquake-induced ground motion simulation. Reprinted with permission from Ma (2003)

Fig. 12.22. Hazard map showing regions with the same 10% probability of exceedance in 50 years, but with different degrees of ground motion (Bostrom and Anselin 2005)

Fig. 12.23. Projected path of Hurricane Katrina

7 Perspectives on Loss Estimation Models and Metrics

Responses to Eguchi and Seligson’s paper “Loss Estimation Models and Metrics”

Introduction Several broad themes emerge in this chapter to point the way for strategic directions in the development of HAZUS® and other tools for estimating losses from disasters. These include expanding and enhancing data types, data sources and data collection schedules. New empirical data collection efforts – including network, engineering and building data, and measures of social and economic changes – have the potential to strengthen loss estimation models and metrics. Network data, data on non-economic losses, and additional input from sociologists and social models are seen as essential elements, currently missing from existing methodology. New data sources include ground-based sensor systems, satellite and LIDAR data, and automated systems to collect and manage data both before and after disasters. Improvements in the accuracy and credibility of loss estimates should result from the integration of loss assessment with data collection and analyses, and use of loss assessment models across a broader variety of potential disaster scenarios. Across all of these themes, proprietary software is widely seen as inhibiting development. Open-source software has the advantage of lending flexibility and modularity to models. As this chapter highlights, the validation and comparability of HAZUS® with proprietary insurance loss models remains a subject of open inquiry.

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7.1 Model Validation and Priorities in Loss Metrics Stephanie Chang University of British Columbia, Vancouver, British Columbia

The emphasis on model validation is an important aspect of Eguchi and Seligson’s paper. There have been a commendable series of efforts to establish the credibility of HAZUS®, largely by testing HAZUS® results against actual events. Nonetheless, the search for validation remains elusive. x Need for empirical data – A major effort is needed to systematically gather data on losses in disasters and institutionalize a process for gathering such data. Model validation – and the credibility of the entire loss estimation enterprise – requires systematic, reliable information on actual disaster losses. Data are needed to calibrate and update individual elements of loss estimation models (e.g., damage functions), as well as to verify aggregate losses. A national database has been called for by experts in the field for many years (e.g., Mileti 1999; National Research Council 2001; National Research Council 2006). Such a database is needed more than ever. x Pitfalls of calibrating to available data – It is important to be aware of the limitations of available data, even as efforts are being made to validate models against them. In an earlier article, Eguchi et al. (1998) conducted a thorough examination of loss data for the Northridge earthquake, probably the most thoroughly documented earthquake in U.S. history. They concluded that in contrast to the commonlyreferenced loss figure of $24 billion, total direct losses could be as high as $40 billion. The discrepancy owed largely to various forms of undocumented losses such as uninsured losses. Against which figure should loss estimation models be validated? If “loss estimates have not accurately matched actual loss totals in real events,” the question remains – in view of the limitations of “actual” loss data, should they? x Acknowledging uncertainty – The issue remains of how to acknowledge uncertainty in loss estimation results. Even while there may be insufficient empirical basis for quantifying uncertainty in a model, it is important to provide the user with a sense of the magnitude of the uncertainty in order to assess the reliability of the results.


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x Difficulties at the extremes – The paper makes the intriguing statement that current methodologies “continue to under-predict losses for large events and over-predict losses for small events.” This observation is important, and it would have been very useful to know on what basis it is made. Is this speculative, anecdotal, or solidly based on systematic study? Moreover, is the observation limited to human and property losses, or losses more broadly defined? I suspect that the insight is sound, and especially applicable in the case of broader regional impacts. In addition to the reasons identified by Eguchi and Seligson, I believe the underestimation of losses in extreme events also arises because loss estimation models typically neglect such non-linearities as the potential for long-term or permanent (structural change) impacts (see Chang (2000) for an instance of permanent loss of business at the Port of Kobe) and systems interactions such as critical infrastructure interdependencies. 7.1.2 Need to Rethink What Losses Matter Loss estimation modeling has focused on estimating property losses and human casualties, with minor attempts at a slightly broader scope that includes, for example, infrastructure outages and regional economic disruption. A number of important gaps in scope and emphasis remain. x Infrastructure impacts – Empirical studies have found that a sizable portion of social and economic disruption in disasters can be attributed to loss of transportation, electric power, water, telecommunications, and other infrastructure services. Despite major advances in modeling lifeline impacts, as Eguchi and Seligson point out, work remains to “operationalize” these models for loss estimation. In my view, there is a critical need for “reduced form” lifeline system models – i.e., simplified or statistical models that can capture the essential outcomes of complex systems models. x Economic disruption losses – Similarly, regional economic disruption has been shown to be sizable in disasters, and substantial progress has been made in modeling this issue (see, e.g., Okuyama and Chang 2004). But work remains in “operationalization.” The Indirect Economic Loss module may be the least used element of HAZUS®. Regrettably, even terminological confusion remains. For example, Eguchi and Seligson use the terms “direct” and “indirect” loss inconsistently within the paper, and reference “gross output” losses, rather than the more appropriate value-added or gross regional product losses. There is also a need for “reduced form”

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regional economic models, as well as for white papers that communicate the definition, importance, interpretation, and use of economic loss estimates. x Dynamics – There is a great need for recovery models (see Miles and Chang (2006) for a current attempt in this direction), but current loss estimation models do not serve this purpose because they are fundamentally static in concept. A truly dynamic model would need to (1) capture post-disaster decisions, interactions, and changes over time, and (2) contextualize the disaster within the underlying land-use, economic, and social changes of the study region. x Differential impacts – Much social science literature on disasters has emphasized issues of differential vulnerability and impacts between socio-economic groups, including groups defined by neighborhood, income, gender, race/ethnicity, language, age, disability, etc. These differential impacts are also of central interest in disaster preparedness, mitigation, and assistance policy. Because of the great detail entailed in loss estimation models such as HAZUS®, there is potential to use such models to better understand and forecast the differential effects of disasters and disaster reduction policies; for example, to identify neighborhoods that may need special assistance on the basis of not only damage but socio-economic vulnerability of the resident population. x Policy variables – There is a great need to better represent risk reduction and policy options within loss estimation models. Now that a community of practice (indeed, an entire industry) seems to have sprung up around HAZUS®, there is both greater need and opportunity for identifying and incorporating policy variables (e.g., mitigation options) that are relevant to different types of users and uses.


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7.2 Additional Considerations in Loss Estimation Walter Gillis Peacock Texas A&M University, College Station, TX USA

7.2.1 Overview Chapter six aims to lay a foundation for discussion of: x “loss estimation models in general, focusing on the dimensions of loss (social, economic)” x “key loss estimation modules or elements,” x “perceived and actual limitation of existing methodologies” x “and, issues related to data development, usage and uncertainties” Eguchi and Seligson offer case studies on model verification/validation, which they intend to guide use of loss estimation study results for risk mitigation, earthquake scenario development (mainly preparedness studies), and post earthquake response and recovery. They also provide a list of studies/investigation that they believe will lead to the largest improvements in loss estimation. On the whole chapter six focuses on HAZUS®, its loss estimations components and limitations, suggestions for its use, and future research that might improve HAZUS®. In the following is a brief discussion of each of the six sections in chapter six, highlighting their major strengths and weaknesses. 7.2.2 Review by Section The Loss Dimensions section offers a brief discussion of essentially three dimensions of loss – cost of repair/replacement, casualties, and economic losses mentioning both direct and indirect losses – along with a listing of potential losses from work by Porter (2005) that might be considered for a more comprehensive approach to loss estimation. Of the dimensions discussed, the discussion on casualties is the most thoughtful, although all are brief. Discussion as to why some dimensions of loss are perhaps more important than others and why estimation approaches have focused on some dimensions and not others would greatly enhance this paper.

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The Components of Loss Estimation section lists the 7 principle modules in HAZUS®MH’s methodology which include – Potential hazards, inventory data, direct damage, induced damage, social losses, economic losses, and indirect losses. More detail on these modules would be helpful. For example, if the interest is in capturing a broader theoretical discussion of the various components of loss estimation, it might be illustrative to discuss more fully how best to derive loss estimates given inventory data that might vary from individual structures to higher levels of aggregation such as census blocks, block-groups or tracts and what might be the consequences in terms of error? The Current Limitations section focuses on problems in loss estimation with particular attention to building losses, casualties, and lifelines. One of the most detailed discussions concerns the general problem of underpredicting losses for large events and over-predicting losses for smaller events. In general, the authors describe issues such as data collection activities focusing on higher earthquake intensities, hence generating error at lower intensity estimation analyses to inadequate modeling resulting in underestimating for higher intensity events. Problems in the “quantification of the built environment” are also addressed. The authors briefly discuss several “quantification” approaches and allude to potential errors generated in areas with mixed development particularly when inferential approaches are used. It is interesting to speculate whether the relative error given the heterogeneity of structures in different areas (tracts, blocks, etc.) might be estimated and taken into account when losses are aggregated (i.e., to what degree can the error arising from composition of building features in aggregated units such as census tracts be estimated and accounted for in loss estimation processes)? The Loss Calibration Studies section offers the most complete discussion in the paper. It provides the reader with insights into some of the potential issues and errors regarding loss estimation using HAZUS® in various settings and given various ‘levels’ of analysis. In general the discussion seems to suggest that while user provided data regarding building inventories improve estimation over default datasets, however more highly refined and detailed data may not necessarily provide major enhancements. Clarification of the potential payoffs of investment in dataset refinement will be important in the future. Furthermore, if the focus is not on overall estimates for an entire county or municipal area, but rather on sub-areas to promote planning and mitigation efforts, one might anticipate that these recommendations could change. Guidelines on the Use of Loss Estimates section examines various potential applications for loss estimation results. It does a good job of raising


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important issues related to utilizing loss estimation methodologies for earthquake planning, risk mitigation, and response and recovery. The authors for example note that “information must be presented at the level that a local homeowner can understand.” In the final analysis this is indeed where the rubber often hits the road and as a result if loss estimation models do not alter household behavior, as well as the behavior of developers, contractors, home builders, then their impacts on future behavior will be diminished. However the question of how models like HAZUS® can best be developed and utilized to alter household/individual behavior is an open question. A rather large literature addresses many issues related to how, for example, warning information should be structured to affect individual and household behavior exists (e.g., Lindell and Perry 1992; 2000; 2004; Tierney, Lindell and Perry 2001; National Research Council 2006). Perhaps findings of this literature should also inform the development of HAZUS® and other types of models to better insure their efficacy. In a similar vein, an argument could also be made that information must be relevant to local planners, emergency managers and other potential stakeholders such as community based organizations that often develop and shape local mitigation policies. In this context meaningful units of analysis might not be entire metropolitan areas or counties, but rather smaller units such as neighborhoods and smaller incorporated and unincorporated areas. Can loss estimation models generate information at a more refined scale to make it possible for this wide range of possible users to affect change? And, to what extent are these models being used by local planners and emergency managers? The authors suggest that “… most emergency managers and many re/insurance companies use loss estimation methods in their analysis of natural hazards and manmade threats.” While it is undoubtedly true that many insurance and reinsurance companies make use of proprietary methodologies, it is probably overly optimistic to suggest such widespread usage among planners and emergency managers in general. Although my assessment is certainly contingent on the meaning of the concept “use” and perhaps a more comprehensive notion of who are “emergency managers,” but I would suggest that the population of emergency managers who are actually using HAZUS® is relatively small and often confined to output provided them by state, regional planning councils or engineering firms at best. The goal should be getting these methodologies in the hands planners and emergency managers in local communities, but we have a way to go to meet that goal. The open source nature of HAZUS® represents a strong step in that direction. The authors quite rightly suggest that a more “regional approach” might be beneficial for mitigation analysis. This might well be the case, however it also seems clear that, depending on the type of policy being considered,

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multiple levels of analysis – community, county, municipality, regional, state, etc. – might be important. In other words, given the nature of the mitigation strategy being considered, the unit of analysis could change and the loss estimation methodology should be flexible enough to generate meaningful analysis at the appropriate level of aggregation. Loss estimation methodologies must not only consider local physical conditions, but the policy environments in which they will be employed as well. In some states, counties are meaningful for driving mitigation policies, while in other states and for certain policies, incorporated municipalities are the meaningful unit of policy analysis. Hence our methodologies should also be flexible to handle mitigation analysis at various levels of aggregation. While I agree with many of the authors’ points in the response and recovery section, future researchers should consider alternative loss measures that might be of equal importance, particularly for long term recovery. For example, while dollar losses might be important in kick-starting federal and state responses, simply knowing the number of occupied and unoccupied dwelling units destroyed or left inhabitable – particularly at a relatively fine level of aggregation – would also be important, not only for estimating shelter and temporary housing, but also for long term housing recovery. These dimensions of loss estimation would be enhanced further by combining them with some understanding of losses to rental and owneroccupied housing (dwelling units). Since recovery policy generally favors owner-occupied housing, understanding the extent to which there is a significant loss in rental housing – particularly low income rental housing – can, could, or should be critical for driving the creative modification in post-event housing recovery strategies. Similarly estimating recovery times for various forms of housing might be important. Having those estimates mapped out can be critical. The Research Topics section: The authors offer a thoughtful list of potential research topics addressing such topics as inventory enhancements, creating web-based models and output to enhance public assess, and improved data collection protocols which will be critical for model development and verification. Their final topic concerns the need to better explain the results of loss estimation studies, how they can be used and their limitations. This is indeed important; however, I would rephrase this topic slightly to read as a need to research the policy environments and needs of potential users of loss estimation technologies so as to ensure a better fit between these technologies (or more specifically their outputs) and end user needs. Through our understanding of policy environments we an better shape the types of outputs needed, at appropriate levels of scale, as well as gain a more complete understanding of potential end-users of these tools. In addition by better understand end user needs, not simply what


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they think they need, but also their needs in terms of explanation, presentation, and abilities and capacities to interface with loss estimation technologies, we can expand the adoption and appropriate use of these technologies. As a social scientist I would have also liked to see statements regarding research needs with respect to social loss estimates such as dislocation/displacement, as well as the need to examine temporary shelter and housing needs. In addition, given the emergence of social vulnerability approaches in hazards research (Blaikie et al. 1994; Peacock, Morrow, and Gladwin 1997; Bolin and Stanford 1998; Mileti 1999), it may well be worth considering how social vulnerability approaches and issues might be integrated into loss estimation methodologies, tools and techniques.

7.3 Social and Economic Considerations in Loss Estimation Modeling Steven P. French Georgia Institute of Technology, Atlanta, Georgia, USA

Chapter six focused on describing the current state-of-the-art in risk analysis modeling. It documents the fact that today’s risk analysis models are now able to produce reasonably accurate estimates of physical damage to buildings and infrastructure systems. These models also produce reasonable estimates of the repair and replacement costs associated with this damage. We have identified several factors that limit current modeling efforts: 1. Poor quality/expensive inventory data 2. Inability to model casualties accurately 3. Inability to estimate length of disruptions in lifeline functions 4. Over estimation of losses for small events and underestimation for large events I would add several other significant limits with our existing models. The first, and most important, is the poor linkages between physical damage and resultant social and economic consequences. Our ability to model damage to nonstructural systems also needs significant improvement. Hurricane Katrina highlighted the fact that we do not currently have accurate

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models to support evacuation planning. While this capability is not central to seismic risk analysis, it is critical for hurricanes and flood risk models. An important extension of today’s risk models is improvement of our ability to model social and economic consequences. Why? Metrics that describe social and economic consequences are the most salient to elected officials and to the general public. Social and economic consequences are key to emergency response planning and design of effective mitigation strategies. Our current methods such as HAZUS® do not produce acceptable estimates of social and economic consequences. The following social and economic consequences are needed to support effective risk decision making: x x x x x x x x

Emergency shelter demand Long term housing recovery Evacuation demand Business and institutional interruption Changes in employment by industry Business failures by industry Short term casualty estimates Fiscal impacts

Additional focus is needed on the damage metrics that are produced by risk analysis models. The models need to produce metrics that are tailored to particular types of decision making. It has been suggested that the models should produce results “that a local homeowner can understand.” In some cases this is true, but the audience varies significantly across different decisions. The damage metrics should vary to match particular decisions. For example, the outputs needed are quite different for: x x x x x

public awareness/education emergency response planning post event logistics management evaluation of mitigation measures business continuity planning

Risk models need to produce outputs that are tailored to these various types of decisions. More research needs to be done to better understand the ground motiondamage relationship. This is the heart of any risk analysis model. Currently these relationships can be estimated using regression models on actual damage data or can be determined from experimental studies. We need to do more of both to decrease the uncertainty in these relationships. The corresponding relationships for other hazards such as depth damage


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relationships for flooding and wind intensity-damage relationships need to be similarly strengthened.

7.4 Strategic Directions for Loss Estimation Models and Metrics The previous chapter provided a broad overview of the development and current use of models to estimate losses from disasters and the appropriate loss measurements to be estimated. In addition, the authors described the methodology used in HAZUS® to calculate losses, along with its limitations. Eguchi and Seligson also described calibration studies in which they participated, and provided their recommendations for improving HAZUS® based on these studies. The responses in this chapter highlight the validation and development challenges posed by proprietary software, along with strategic directions for the development of public loss estimation models and metrics. Although Eguchi and Seligson focus on open-source models, others (Kunreuther et al. 2005; Whitman et al. 1997) have studied and compared output from proprietary insurance company models. A key challenge is the lack of funding available to collect post-disaster event data for validation and calibration of models. Many important losses from tears in our social fabric are not economic in nature. Loss estimation modeling could benefit greatly from input from sociologists and, if possible, from more rigorous modeling of society itself. The indirect costs of disruptions due to natural disasters in society can be enormous in terms of mental anguish, increases in daily inconvenience, and decreased social resilience. Network analysis is needed in HAZUS® because some disruptions are related to lifeline networks (roads, pipelines, electricity, water supply) being cut off. The HAZUS® network model was dropped after 1999. Even if the network models were built, is the data available to support them? Currently, HAZUS® does not include network models for lifelines or transportation. Since disruptions in lifelines and transportation can be a major source of losses in natural disasters, several discussants thought it would be worthwhile to include these modules in HAZUS® or to run them separately, but in conjunction with HAZUS® for loss estimation assessments. There was general agreement that the data to populate these modules may either not exist or may be inaccessible, and that it is important for funding for research in this area to expand. Other estimation models include infra-

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structure (network) components and could be coupled with or run separately from HAZUS®. As HAZUS® is the de facto standard for regional loss estimation for emergency planning, mitigation and response, the strategic direction of loss estimation models and metrics rests on its future development. While HAZUS® has improved considerably in recent years a number research needs remain, including new data collection protocols, model calibration and validation, and methods of presenting results. Together, these could significantly enhance HAZUS® and other loss estimation tools and modules. Two broad themes emerge: 1. Validation of loss estimation models will improve to the extent that it is based on enhanced empirical data gathered after disasters occur. 2. There is a need to prioritize losses, including social and economic disruptions Existing limitations of HAZUS® include an inability to measure regional social and economic disruptions that estimations of physical damage and loss alone do not sufficiently address. In order to support decisionmaking, loss assessments should better account for these neglected factors and model outputs should be customizable to appropriate audiences. Indirect and non-economic losses are difficult to assess, quantify and present. However, it is of vital importance to take steps to include them in loss assessment models. For loss estimates to be widely accepted and used, their methods should be defensible and transparent, and their results must be useful, reliable, and validated. This is a tall order, but these issues can be addressed through continued focus on developing open-source models and supplying them with pre- and post-disaster data to support calibration and validation studies. Finally, sociological modeling and network analysis are neglected components that must be developed to enhance the credibility and usefulness of HAZUS® and other loss estimation models. Clearly, there is a great deal of work to be done to improve HAZUS® and other models before they can be said to approach an accurate accounting of potential losses due to earthquakes and other natural hazards. Researchers, programmers, public officials and others will have to cross traditional boundaries to determine priorities for model development and to ensure that we can learn from disasters when they do occur.


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References Blaikie P, Cannon T, Davis I, Wisner B (1994) At Risk: Natural Hazards, People’s Vulnerability and Disasters. Routledge, London Bolin R, Stanford, L (1998) The Northridge Earthquake: Vulnerability and Disaster. Routledge, London and New York Chang SE (2000) Disasters and transport systems: Loss, recovery, and competition at the Port of Kobe after the 1995 earthquake. Journal of Transport Geography 8:53-65 Eguchi RT, Goltz JD, Taylor CE, Chang SE, Flores PJ, Johnson LA, Seligson HA, Blais NC (1998) Direct economic losses in the Northridge earthquake: A three-year post-event perspective. Earthquake Spectra 14:245–264. Kunreuther H, Grossi P, Patel CC (2005) Catastrophe Modeling: a new approach to managing risk. Springer Science+Business Media, Inc., New York Lindell MK, Perry RW (1992) Behavioral Foundations of Community Emergency Planning. Hemisphere Publishing Corporation, Washington Lindell MK, Perry RW (2000) Household adjustment to earthquake hazard, a review of research. Environment and behavior 32: 590–630 Lindell MK, Perry RW (2004) Communicating Environmental Risk in Multiethnic Communities. Sage, Thousand Oaks California Miles SB, Chang SE (2006) Modeling community recovery from earthquakes. Earthquake Spectra 22:439–458 Mileti DS (1999) Design for future disasters: A sustainable approach for hazards research and application in the United States. Joseph Henry Press, Washington, DC National Research Council (1999) The impacts of natural disasters: A framework for loss estimation. National Academy Press, Washington, DC National Research Council (2006) Facing hazards and disasters: Understanding human dimensions. National Academy Press, Washington, DC Okuyama Y, Chang SE (eds) (2004). Modeling spatial economic impacts of disasters. Springer-Verlag, Berlin Peacock, WG, Morrow BH, and Gladwin H (1997) Hurricane Andrew: Ethnicity Gender and the Sociology of Disaster. Routledge, London Tierney KJ, Lindell MK, Perry RW (2001) Facing the Unexpected: Disaster Preparedness and Response in the United States. Joseph Henry Press, Washington, DC Whitman RV, Anagnos T, Kircher CA, Lagorio HJ, Lawson RS, Philip Schneider P (1997) Development of a National Earthquake Loss Estimation Methodology. Earthquake Spectra 13: 643–661

8 Seismic Risk Mitigation Decisions Under Uncertainty

Robin K. McGuire Risk Engineering Inc., Boulder, Colorado, USA

8.1 Introduction Decisions about the level of resources appropriate for seismic risk mitigation must be made in the context of uncertainties about seismic risk. This is true if uncertainties are quantified and is also true if they are ignored, for ignoring uncertainties does not make them go away. Critical uncertainties are related to the level of seismic hazard (the amplitudes of ground motion and their frequencies of occurrence) and to the vulnerabilities of facilities to seismic shaking. With a complete description of these uncertainties, complete seismic risk curves (loss vs. annual frequency of occurrence) can be developed. These seismic risk curves can form the basis for informed decisions about seismic risk mitigation. In a broad context it would also be important to understand the cost of providing various levels of seismic safety, e.g. through lower or higher seismic design criteria, and to understand the cost of seismic failures. Then decisions about seismic risk mitigation could be made in the context of the mitigation of other risks to society, from other natural hazards and from other causes of loss (monetary loss, injury, and death). In this way, societal resources could be allocated in an optimal way to achieve minimal losses from all causes, given a finite amount of resources (at least in theory). There are, of course, substantial hurdles to comparing risks from different sectors and different sources (see, for example, Paté-Cornell 2002). Sufficient data and theories for all causes of loss are not available to

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achieve this optimization and likely will not be available in the foreseeable future. As a surrogate, we can use societal decisions in the past as approximate indicator of the average level of risk that is acceptable, e.g. from seismic causes, and we can use sophisticated models and data to discriminate among locations, facilities, and mitigation decisions. This allows us to identify and avoid the most risky combinations of high-hazard locations and high-risk facilities, without incurring extreme costs for all locations and all facilities. A good example is the regulation of seismic design motions for nuclear power plants. It should be stated that, as a general rule, if single measures of hazard and risk are required for decision making, the mean hazard and mean risk are the appropriate measures to use. For background on the reasons for this recommendation, please see McGuire, Cornell, and Toro (2005). However, we cannot estimate the mean without a description of the entire distribution. This implies that the characterization of uncertainties is critical, both for the description of hazard (annual frequencies of ground shaking) and of vulnerability (estimates of loss to facilities for various levels of ground shaking).

8.2 Seismic Risk Analysis for Nuclear Power Plants Probabilistic Seismic Hazard Analysis (PSHA) for nuclear power plants (NPPs) has provided the most detailed interpretations of earthquakes occurrences and associated ground motions for any engineered facilities. This is true in the U.S. as well as elsewhere in the world. Instrumental to the development of PSHA for NPPs has been the inclusion of uncertainty in the analysis. This aspect of PSHA was recognized to be so important and critical to future studies that a specific National Research Council panel (SSHAC 1997) was formed to make recommendations on how to included expert opinion in PSHA, given two major studies conducted in the 1980s (EPRI 1989 and LLNL 1989). The SSHAC panel recognized that uncertainty must be divided into aleatoric uncertainty (intrinsic, randomness) and epistemic uncertainty (subjective, professional). A PSHA integrates over aleatoric uncertainty, but shows explicit results as a function of epistemic uncertainty. For instance, fractile hazard curves can show the 0.15, 0.5, and 0.85 fractile hazards. This quantifies the degree of confidence in the basic PSHA, e.g. it gives a range of ground motions for a 475-year return period. This epistemic distribution is produced by alternative viable models of earthquake


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behavior that express the range of available informed scientific opinion. Much remains unknown about the sizes of future earthquakes, their rate of occurrence, and the associated ground motions, even in highly-seismic and well-instrumented regions such as California. Also, much remains unknown about how facilities (structures, dams, power plants) will behave when subjected to large ground motions. These unknowns translate into uncertainties about seismic hazards and seismic risks. In the 1970s, large studies of existing nuclear plants, called Probabilistic Risk Assessments (PRAs), developed probabilistic systems models (event trees and failure trees) to represent the system behavior of NPPs. These PRAs modeled seismic events as well as other accident initiators (e.g. tornadoes, hurricanes, floods, fires, and human errors) to identify and improve the most vulnerable failure mechanisms in NPPs. An important aspect of these PRAs was that they included epistemic uncertainty in component behavior and, through systems analysis, in plant-level behavior. Thus the plant vulnerability is characterized by a distribution of plantlevel performance, not just a single curve expressing (for example, for seismic events) the probability of failure for a given input ground motion.

8.3 Nuclear Power Plant Seismic Design The inventory of commercial nuclear power plants (NPPs) in the US consists of about 100 units that were designed and built in the 1960s and 1970s. The seismic designs of these units were determined by a deterministic procedure that specified the largest ground motion that had occurred or was likely to occur at the site. The ground motion was either estimated directly from an earthquake magnitude and distance on a fault, or through the Modified Mercalli intensity (MMI). This ground motion was typically quantified by a peak ground acceleration (PGA) and a standard response spectrum shape (e.g. U.S. NRC 1973). Theories about earthquake occurrences and their associated ground motions in the US have improved dramatically since the 1970s. Two major studies (EPRI 1989; LLNL 1989) completed in the 1980s demonstrated how diverse scientific opinion about earthquakes could be synthesized in a probabilistic format to develop composite expressions of seismic hazard (levels of ground shaking with associated frequencies of occurrence). As a result of these large (and expensive) PRA studies conducted in the 1970s and 1980s, estimates are available for the annual frequency with which earthquakes will damage the nuclear core in NPPs. This is called the Seismic Core Damage Frequency (SCDF) (see, for example, Kaplan et

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al. 1983). For each individual NPP, this is a quantitative measure of the seismic risk of that plant. It indicates how often an earthquake will occur that will be severe enough to cause damage at the NPP significant enough that the nuclear core cannot be brought to a cold-shutdown state without some level of damage. The calculation takes into account the number and severity of earthquakes that will occur in the vicinity of the NPP and how well the NPP is designed to resist earthquake shaking. The SCDF is a relevant measure because the US Nuclear Regulatory Commission (NRC) has stated that the overall Core Damage Frequency (CDF) from all causes should be judged in comparison to a mean core damage frequency goal of 1×10-4 per reactor year (U.S. NRC 2001). Figure 8.1 shows a summary of SCDF values from 25 existing NPPs from PRA studies conducted at these plants. These data are available in U.S. NRC (2002). These 25 plants were chosen because they were NPPs designed according to the most recent seismic design criterion (U.S. NRC, 1973). SCDFs at individual plants range from about 2×10–7 per year to 2.3×10–4 per year, with a mean value (shown on Figure 8.1 as a vertical line) of 2.5×10–5 per year.

Fig. 8.1. Cumulative plot of SCDFs from 25 existing NPPs


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Why are the results in Fig. 8.1 relevant to seismic design? Because they give us a target performance goal with which to make risk mitigation decisions, based on past practice. The procedure has 4 steps: 1. Define the specific performance and quantitative target performance goal. In this context the performance of a facility is binary, i.e. either it meets the performance criterion (success) or it doesn’t (failure). The target performance goal is the acceptable frequency with which we can fail to meet the specified performance, for an acceptable design. 2. Estimate, for a given level of seismic shaking, the distribution of facility performance relevant to that performance definition. In other words, if I design a facility to ground motion level a*, what is the probability of not meeting the performance criterion if a* occurs? If 2×a* occurs? If 3×a* occurs? 3. Derive a simple expression for the failure frequency given the site hazard and the facility-performance distribution developed in step 2:

P> failure@ Pf # ³ P> failure | A

a@ f >A

a @da

(8.1)

where A and a are measures of the ground motion, P[failure|A=a] is the probability of facility failure to meet the performance criterion given that the ground motion equals amplitude a, and f[A=a] is the frequency with which amplitude a occurs. This last frequency is obtained from the seismic hazard curve (it is actually the negative derivative of the seismic hazard curve). With some reasonable assumptions this integral can be calculated with the simple expression:

Pf # H a * f s

K

exp[ x p KE 1 2 KE ] 2

(8.2)

where the symbols are as follows: H(a*) is the seismic hazard at a nominal seismic design amplitude a*fs is the factor of safety used for design (the nominal capacity of the facility is actually a* × fs) K is the (negative) log-log slope of the hazard curve at a* xp is 2.326 when the design point corresponds to a 1% probability of failure ȕ is the logarithmic standard deviation for capacity.

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The derivation of Eq. (8.2) is given in McGuire (2004), and this equation allows a very simple calculation of the probability of failure given a few simple parameters of the seismic hazard curve and the facility capacity. Eq. (8.2) follows the design convention that the nominal capacity a* × fs is a conservative estimate of the capacity, and the degree of conservatism is such that there is a 1% probability of failure for a facility that experiences a ground motion equal to a* × fs. 1. Use Eq. (8.2) to determine the seismic design level a* necessary to meet the target performance goal Pf. Figure 8.2 illustrates some of the parameters in Equation (8.2) in a graphical way. Design amplitude a* is chosen to be consistent with a hazard goal H(a*), e.g. 1×10–4. The ground motion associated with hazard goal H(a*) is a*, but the capacity of the facility is significantly higher by a factor fs, because of safety factors inherent in design. Uncertainty in capacity is measured by logarithmic standard deviation ȕ.

Fig. 8.2. Graphical representation of hazard curve and facility capacity distribution


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Eq. (8.2) is an amazingly simple equation that shows that the seismic risk (the convolution of earthquake occurrences, the associated ground motions, and the facility seismic response to those ground motions) is controlled by only three critical parameters. These are (a) the factor of safety fs, (b) the slope of the hazard curve K, and (c) the uncertainty in the capacity distribution ȕ. Figure 8.3 illustrates how these three factors affect seismic risk: a) If the factor of safety fs is increased, the capacity distribution moves to the right, and the probability of failure pf decreases. b) If the slope K of the hazard curve decreases (i.e. the hazard curve becomes less steep), the probability of failure pf increases, because higher ground motion (above a*) are more frequent. c) If the capacity uncertainty ȕ increases (and the facility is still designed to a capacity a'), the probability of failure pf decreases, because it is more likely that the facility has a higher capacity.

Fig. 8.3. Illustration of the three factors that control seismic risk

The four steps described above have been applied to NPP seismic design, giving results as follows. We define performance as “no seismic core damage” and select the target performance goal as 1×10–5 per year (the approximate median in Fig. 8.1). We use the mean seismic hazard curve at

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the site and Eq. (8.2) to derive the required seismic design a*. In other words, we iteratively solve Eq. (8.2) using different values of a*, until the calculated Pf = 1×10–5. Then we use Eq. (8.1) to check the actual frequency of failure (the SCDF for that design at that site). In these calculations, the slope K of the seismic hazard curve for spectral accelerations between 100 Hz and 0.5 Hz is typically 2.5 to 4.0 in the eastern US. For SCDF, the factor of safety fs is taken to be 1.67, based on experience with existing plants (Kennedy 2005). The uncertainty in plant seismic capacity ȕ is typically 0.3 to 0.45 (Kennedy 2005). Figure 8.4 shows the calculated SCDF values for NPPs designed in this way, superimposed on the existing NPP SCDF data from Figure 1. The hypothetical plant design is slightly conservative in that it achieves an SCDF slightly less than the target of 1×10–5 per year for representative ȕ values of 0.3 and 0.4. Note that NPPs with ȕ = 0.4 have a slightly lower SCDF than NPPs with ȕ = 0.3. This follows from Fig. 3: the ȕ = 0.4 plant has the lower tail of its distribution fixed at a', but the median capacity is higher than the plant with ȕ = 0.3.

Fig. 8.4. SCDFs of a hypothetical NPP designed to achieve SCDF=1×10–5 for ȕ=0.3 and 0.4, compared to SCDF distribution of 25 existing NPPs

Note that following the steps outlined above will achieve a uniform SCDF at all locations by varying a*. Design amplitude a* will be high at highhazard sites, and will be low at low-hazard sites, but the SCDFs will be uniform at all locations. Existing plants with relatively high SCDF values


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(the top right part of the curve in Fig. 8.1 and 8.4), if they were to be redesigned with this procedure, would be required to use a higher design criterion than the existing (deterministically based) design. It is likely that a few NPPs would have a much lower SCDF than the target, because of the imposition of minimum seismic design requirements at all plants. These minimum seismic design requirements can be met without significant cost. A design procedure of this type has been recommended by a committee of the American Society of Civil Engineers (2005), called “ASCE 43-05,” to establish design criteria necessary to achieve a target performance goal. In this application the definition of seismic performance was that individual components (e.g. beams and columns, mechanical equipment, pipes, and containment structures) should not behave in a significant non-linear fashion during earthquake motion. Note that there is still a substantial seismic margin on component failure if, during an earthquake, a beam or column exhibits non-linearity. There is even more seismic margin on plant failure, because the failure of a single component does not necessarily mean a plant-level failure (e.g. core damage) because of redundancies designed into the plant. The target performance goal recommended by ASCE 43-05 is 1×10–5 per year. This target performance goal for individual components achieves a plant-level SCDF of about 5×10–6 per year. The component performance goal was chosen in ASCE 43-05 to achieve plant seismic designs that are more conservative than existing NPPs.

8.4 Recommendations for Seismic Risk Mitigation Decisions The experience with NPPs described above provides a model for how rational seismic risk mitigation decisions can be achieved in the face of significant uncertainties. Recent efforts in performance-based earthquake engineering (PBEE) for building structures support this model, because PBEE has, at its core, the evaluation of a building’s performance. Following the 4 steps outlined above in the context of PBEE would mean: 1. Define multiple performance objectives and the associated quantitative target performance goals. Following SEAOC (1995) these might be as follows: a) The structure must remain fully operational following earthquakes, and may fail this criterion with a frequency not to exceed 1/43 years.

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The structure must be operation following earthquakes, with perhaps some repairs required to non-structural components, and may fail this criterion with a frequency not to exceed 1/72 years. c) The structure must be safe with respect to occupant’s lives during earthquakes, and may fail this criterion with a frequency not to exceed 1/475 years. d) The structure must not collapse during earthquakes, and may fail this criterion with a frequency not to exceed 1/970 years. More stringent (a) and (b) criteria may be appropriate for facilities of high economic or societal value (e.g. hospitals and buildings containing expensive chip-fabrication equipment). 2. Conduct engineering analyses of existing buildings to determine, for a range of ground motions, the probabilities that they will fail each of the performance objectives in 1 above. Buildings must be segregated by structural type and age, multiple buildings must be examined, and account must be taken of the original seismic design level of each building. From these studies, for a given building type, age, and design level, we can determine the distribution (vs. ground motion) of facility performance relevant to each performance objective, as a function of design level. 3. Presuming that building responses can be modeled with a lognormal distribution (this should be confirmed and the appropriate values of xp and beta should be determined), use Equation (8.2) to represent the probability of failing to achieve each performance level for any site. 4. Derive from Equation (8.2) the design required at any site to meet each performance level. Different components may have their designs controlled by different performance objectives. For example, non-structural partitions would be controlled by criteria a) and b) above; structural columns might have deflections controlled by criteria a) and b), and strength controlled by criteria c) and d). b)

This gives a straightforward way in which to derive design criteria for buildings to meet defined performance objectives. Structures located in high seismic hazard zones, or structures vulnerable to earthquake shaking, will be identified as having high seismic design requirements. Other structures located in low seismic zones, or inherently safe structures, will have lower seismic design requirements.


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8.5 Importance of Uncertainties The unbiased quantification of uncertainties is crucial to making rational decisions for risk mitigation. In the context of the proposed procedure described above, the mean seismic hazard curve and the mean performance curve can be used to obtain the mean frequency of failure. However, the mean seismic hazard curve and the mean performance curve cannot be accurately estimated without considering epistemic uncertainties in earthquake occurrences and ground motions (for the hazard curve) or in building response and damage (for the performance curve). In particular, using best estimates of parameters will misrepresent the seismic hazard curve or performance curve, and ill-informed decisions on risk mitigation will result. Note that Eq. (8.1) above explicitly integrates over a range of ground motion levels, and Eq. (8.2) is a closed-form approximation to this integral. Thus, the use of Eqs. (8.1) and (8.2) account for the range of ground motions that might occur at a site during a facility’s lifetime. That is, they explicitly account for the aleatoric variability in seismic hazard and in facility capacity. With respect to epistemic uncertainties, several parts of the seismic hazard and vulnerability assessment modeling have particular importance and should be the focus of attention, because they can have an important contribution to uncertainty assessment (and hence to an unbiased assessment of the mean). These are as follows: 8.5.1 Seismic Hazard Assessment Ground motion models. Alternative ground motion models must be considered. Even in California, empirically based estimates differ, depending on the data that was selected, the form of the equation being used, and the parameters that are considered. Uncertainty associated with ground motion models is often the most important uncertainty to consider in developing a comprehensive estimate of seismic hazard. Ground motion sigma. Every ground motion equation must have an estimate of aleatoric uncertainty. There is also epistemic uncertainty on the value of the aleatoric uncertainty, which is rarely reported. Sometimes aleatoric uncertainties are estimates, because data are not available, and these estimates in particular should have epistemic uncertainties included. Magnitude of characteristic earthquakes. The magnitude (or magnitude distribution) of characteristic earthquakes on a fault will be important for

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hazard assessment, particularly for return periods of motion that are longer than the mean recurrence interval of the characteristic event. Mean recurrence interval. Alternative viable methods for estimating the mean recurrence interval for characteristic earthquakes should be considered. Site response. Uncertainties in how local site conditions respond to earthquake motion are critical. Non-linear models of soil behavior can lead to widely differing estimates of surficial motion, given the earthquake motion at the base of a soil column. Basin effects, non-vertical waves, and contributions of surface waves can also add uncertainties in ground motion at the surface. 8.5.2 Facility Vulnerability Site failure. If the site is vulnerable to liquefaction or landslides, these failures can cause failure of the most robust facility. Estimation of site failure is prone to substantial uncertainties, particularly if detailed site studies are not available. Facility conditions. Uncertainties in how a structure has been (or will be) built will lead to substantial uncertainties in how it will respond to motions that challenge the seismic design. These uncertainties may be caused by construction errors, on-site changes, and by structural and non-structural material properties being different from what was assumed during design. Failure modes. The largest uncertainties may come from failure modes that are not anticipated during the vulnerability assessment. As an example, many steel buildings experienced failure of welded beam-column connections during the 1992 Northridge earthquake. This failure mode was a surprise to engineers and caused substantial unanticipated losses. Loss causes. After structural and non-structural damage occur, large uncertainties exist in what will be the associated losses to owners. Apart from costs to repair damage to the structural and non-structural elements, an owner of a commercial facility may incur costs from loss to building contents, loss of employees, business interruption, and loss of market share. These are not losses that can be estimated using engineering models – estimates must come from data and experience with prior earthquakes and reasonable extrapolation. These loss estimates are subject to considerable uncertainties that should be included in any analysis.


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8.6 Summary A straightforward, quantitative method exists to make seismic design decisions to mitigate risks to facilities. This method can achieve performance levels with a selected target performance goal, or with calibration to the performance goals implied by past decisions. The method has been adopted as a recommendation by a committee of the American Society of Civil Engineers for use with nuclear power plant design. It can be applied to commercial structures and other facilities to be consistent with performance-based engineering methods and the performance goals used by those methods. The unbiased implementation of this method requires a complete inclusion of uncertainties in seismic hazard and facility vulnerability assessment. Without an evaluation and inclusion of uncertainties, estimates of the mean seismic hazard and mean vulnerability are subject to considerable error.

References American Society of Civil Engineers (ASCE) (2005) Seismic design criteria for structures, systems, and components in nuclear facilities (ASCE Standard 4305) Reston, VA EPRI (1989) Probabilistic seismic hazard evaluations at nuclear plant sites in the central and eastern United States: Resolution of the Charleston earthquake issue (Report NP-6395-D). Electric Power Research Institute, Palo Alto, CA Kaplan S, Perla HF, Bley DC (1983) A methodology for seismic risk analysis of nuclear power plants. Risk Analysis 3:169–180 Kennedy RP (2005) Risk (performance-goal) based approach for establishing the SSE design response spectrum aimed at achieving a seismic core damage frequency less than a target goal for future nuclear power plants. Draft manuscript LLNL (1989) Seismic hazard characterization of 69 nuclear plant sites east of the Rocky Mountains (US Nuclear Regulatory Commission Report NUREG/CR5250). Lawrence Livermore National Lab, Livermore, CA McGuire RK (2004) Seismic hazard and risk analysis (Monograph MNO-10). Earthquake Engineering Research Institute, Oakland, CA McGuire RK, Cornell CA, Toro GR (2005) The case for using mean seismic hazard. Earthquake Spectra 21:879–886 Paté-Cornell E (2002) Risk and uncertainty analysis in government safety decisions. Risk Analysis 22:633–646 SSHAC (1997) Recommendations for probabilistic seismic hazard analysis: Guidance on uncertainty and use of experts (US Nuclear Regulatory Commission

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Report NUREG/CR-6372). Lawrence Livermore National Lab, Livermore, CA Structural Engineers Association of California (SEAOC) (1995) Vision 2000performance based seismic engineering of buildings, SEAOC, Sacramento, CA US Nuclear Regulatory Commission (USNRC) (1973) Design response spectra for seismic design of nuclear power plants, (Regulatory Guide 1.60), USNRC, Washington, DC US Nuclear Regulatory Commission (USNRC) (2001) Modified reactor safety goal policy statement (SECY-01-0009), USNRC Washington, DC US Nuclear Regulatory Commission (USNRC) (2002) Perspectives gained from the Individual Plant Examination of External Events (IPEEE) program (Report NUREG-1742), USNRC, Washington, DC

9 Perspectives on Seismic Risk Mitigation Decisions Under Uncertainty

Responses to McGuire’s paper “Seismic Risk Mitigation Decisions Under Uncertainty”

Introduction This chapter provides a dialogue between the risk engineering and risk analysis communities, particularly as it pertains to communicating risks to decision-makers. Clearly, there is a conflict between the ability of engineers and risk analysts to perform necessary quantitative analyses, and the ability or willingness of decision-makers and construction professionals to use these analyses in practice. Society faces significant tradeoffs when allocating scarce resources to mitigating risks, and the information required to generate well-informed risk analyses is costly. The first commentary on McGuire’s chapter provides a decision-making perspective, representing risk assessment researchers and practitioners, who are users of models such as the Probabilistic Seismic Hazard Analysis advocated here. Mitchell Small draws on his knowledge and experience with other types of hazards (floods, toxic contamination) to generalize the methodology. He highlights the importance of epistemic uncertainties and suggests that social sciences be included in seismic risk assessments. Bruce Ellingwood addresses in greater detail the state-of-the-art methods for reducing uncertainties inherent in risk assessment models. He readily acknowledges that such efforts will require a significant investment of resources and the results may be difficult to appropriately communicate to decision-makers. The final commentary in this set focuses on networked infrastructures, of which nuclear power plants are an important component (through the power generation grid). Leonardo Dueñas-Osorio argues that the methodology described in the paper can and should be considered a

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part of analyses of infrastructure reliability and vulnerability. He suggests several additional methods and modeling paradigms to deal with risk management using a network-oriented approach.

9.1 Perspectives from a Risk Assessment Practitioner Mitchell J. Small Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

9.1.1 Introduction McGuire’s chapter provides a clear and compelling argument for the use of Probabilistic Seismic Hazard Analysis for the assessment and risk-based design of buildings and other facilities subject to earthquake damage. As someone with principal experience in the application of probabilistic methods in the area of environmental health risk assessment, I found the chapter very clear and accessible. I structure my evaluation by highlighting the key elements of the chapter, offering comments and questions for each. The chapter advocates an integrated assessment of earthquake risks, damages, and costs of mitigation, noting the difficult challenges faced when making such an assessment, given the significant uncertainties in each phase of the analysis. I fully support this approach. The chapter goes on to offer that: “Then decisions about seismic risk mitigation could be made in the context of the mitigation of other risks to society, from other natural hazards and from other causes of loss (monetary loss, injury, and death). In this way, societal resources could be allocated in an optimal way to achieve minimal losses from all causes, given a finite amount of resources (at least in theory). Sufficient data and theories for all causes of loss are not available to achieve this optimization and likely will not be available in the foreseeable future.”

In addition to the appropriate caveat concerning the lack of sufficient data and theories, I would go on to caution that society and individuals view different risks in a very different manner, depending on other, often lessquantitative features of the risk-generating event or activity. Important


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factors include whether the risk is manmade or natural, whether peoples’ exposure to the risk is voluntary vs. involuntary, whether individuals have some degree of control over their exposure and mitigation efforts, and whether the processes that determine the distribution of risks and benefits are fair and equitable (i.e., National Research Council 1996). As such, caution is needed in making comparisons across different types of risks, and risk communicators are often cautioned against this practice. Nonetheless, I concur with the author that better knowledge of the magnitude and likelihood of different risks will help us to make more rational decisions in allocating our scarce resources for risk avoidance and protection. The paper argues in favor of “the mean hazard and mean risk” as appropriate measures to use for risk characterization and decision making. The cited reference, McGuire, Cornell, and Toro (2005), provides justification for this recommendation, noting that integrating over aleatory uncertainty (due to inherent variability) and epistemic uncertainty (due to our lack of knowledge) yields the mean probability of an event, which is the probability of the event. The authors do acknowledge that information on the frequency distribution of event magnitude (and its uncertainty), specific percentiles (and their uncertainties), etc., are still of interest, but argue that if a single measure is used for design, then the mean is the measure of choice. I concur with this recommendation, so long as the analysis is for a single facility and the aleatory uncertainty is temporal, representing (in this case) year to year variation in earthquake magnitude or peak ground acceleration. However, at times aleatory uncertainty representing spatial variability, or facility to facility variability in capacity, can be important and included in the model. For example, calculations may be made for the 20,000 apartment buildings in an affected area, each of which varies in the peak acceleration that it will experience from a seismic event (due to distance from epicenter, underlying soils, etc.) and in its capacity to withstand damage. An analyst may choose to characterize these variations using empirical or parametric frequency distributions. In this case the mean hazard and risk are still of interest, but the full distributions of hazard and risk across the facilities are still needed, especially if there are thresholds for damage, but also to characterize the distributional elements of the risk. The paper recognizes this by plotting the empirical distribution of the Seismic Core Damage Frequency (SCDF) across 25 nuclear power plants in Fig. 8.1. Nevertheless, the recommendation to focus primarily on the mean might cause others to lose sight of the importance of facility to facility variation. It is interesting from my perspective how the methods for probabilistic seismic hazard assessment and probabilistic flood hazard assessment have co-evolved over the past 50 years. Like early seismic design, flood as-

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sessment and protection was previously based on building to protect against a very large design storm or flood. In the case of flood protection, the design decisions involve the size of the flood protection reservoir, the depth of the stream or river channelization project, or the required flood protection for a structure in a given location in the flood plain. To avoid “designing for failure”, hydrologists attempted to identify a “probable maximum flood (PMF)” resulting from a probable maximum storm. The PMF was chosen using a variety of ad hoc methods and was usually quite large, but with unspecified return period. Recognition of the need for risk-based design led to the determination of a design flood with a given annual probability of exceedance, and subsequent consideration of the entire probability distribution of annual loss, the expected annual loss, and the net present value of the expected loss. Similar practice for seismic design is reflected in the author’s presentation as well as other recent papers, such as Porter et al. (2004), Malhotra (2006), and Montiel and Ruiz (2006). Further discussion of the Porter and Beck paper is provided later in this chapter. 9.1.2 Proposed Parametric Model The paper offers specific recommendations for a parametric seismic amplitude frequency distribution, and a parametric facility capacity distribution, to compute the probability of facility failure. The equations are subsequently used to select a design amplitude, a*, that yields a targeted probability of failure, e.g., 1 × 10–5. The actual design amplitude that yields this probability of failure, a’, is greater than a* by a factor of safety, fs (that is, a' = fsa*). Experience with existing nuclear power plants suggests that fs ~ 1.67. The parametric frequency distribution for the ground motion is given by the following logarithmic relationship: HA(a) = Koa–K

(9.1)

where HA(a) is the probability that the amplitude A is greater than the particular value a (i.e., HA(a) is the complementary cdf for annual occurrences of ground motion, also referred to as the annual exceedance frequency) and Ko and K are the two parameters of the distribution. In McGuire’s chapter, the parameter K is specified as the negative log-log slope of the hazard function (this can be seen by taking the log of the equation above, so that log[HA(a)] = log[Ko] + –K log[a]). The probability of failure is given in


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Eqs. (8.1) and (5.2). The derivation of Eq. (8.2) is given in McGuire (2004). In Eq. (8.2), the parameter K is the same as the parameter K in equation A1, while H(a*) in Eq. (8.2) and Ko in Eq. (9.1) are related as: Ko = H(a*) / [a* –K]. Typically a* is chosen so that H(a*) is ~ 1 × 10–4 per year. The (epistemic1) uncertainty distribution for facility capacity is assumed to be lognormal with logarithmic standard deviation E. The influences of the factors that control seismic risk are illustrated in Figure 8.3. The paper goes on to recommend the application of this procedure for general building performance–based earthquake engineering (PBEE) assessments, allowing for different probabilities of failure for different functions of the building, in increasing order of required protection (lower annual probability of failure): a) maintaining full operations following an earthquake; b) building remains operational, with some repairs to nonstructural components; c) building is safe for occupants’ lives; and d) building does not suffer total collapse. Some observations and questions: x

x

x

1

As the factor of safety fs increases, the probability of failure Pf decreases, as this is equivalent to shifting the capacity distribution upward by the amount (fs – 1)a*. However, if a preset value of Pf is used for design, will not a higher value of fs result in a lower value of a*, and subsequently the same value of a’ as before? Is the facility actually designed with a targeted capacity of a’, or is the targeted capacity the median of the capacity distribution curve? If this is the case, then the design would become more conservative as E increases, for a fixed Pf. What exactly is meant by the 1% probability of failure that corresponds to an xp value of 2.326 (albeit, 2.326 is the -Z value for an N(0,1) r.v. corresponding to 1% probability), and how is this failure probability related to the overall probability of failure, Pf, and the seismic hazard, H(a*)? Is 1% the probability of failure at the design value a’, which itself has a very low annual probability of

This implies that there is a fixed capacity for the facility, we just don’t know what it is. There may, however, be elements of aleatory uncertainty that contribute to this uncertainty. For example, for a given amplitude, a, the response of the facility may differ depending on the direction from which the wave approaches the building. Also, the variation in the response of many similar buildings to a similar amplitude of ground motion might be used to derive the epistemic uncertainty distribution of capacity for a single, given building.

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occurrence? Some additional discussion on how all these probabilities are related would be helpful. 9.1.3 Importance of Uncertainties Chapter eight concludes with a somewhat compressed, but still insightful discussion of epistemic uncertainties and their influence on: a) estimates of earthquake occurrence frequencies and ground motions (which combine to yield the hazard curve, HA(a)); and b) building response and damage (which determines the capacity distribution). Significant scientific understanding, data collection, and modeling are needed to characterize both best guess estimates for model parameters and their uncertainties, including use of ground motion models, characterization of ground motion spatial variability, the models for earthquake occurrence and magnitude, and the uncertainty in site response and facility vulnerability. A Similar Approach by Porter et al. (2004)

An approach that is similar to that of the current paper is undertaken by Porter et al. (2004), who focus on explicit loss functions for building damage, the calculation of Expected Annual Loss (EAL), and the relationship between the EAL and a Probable Frequent Loss, or PFL. The PFL is proposed as an easy to communicate, understandable replacement for the Probable Maximum Loss (PML), which, like the probable maximal flood in hydrology, has no standard quantitative definition. The PML has been computed as: “… the 90th percentile of loss given the occurrence of what building codes until recently called the design base earthquake, or DBE – an event producing a shaking intensity with a 10% exceedance probability in 50 years. Colloquially (and inexactly), this is the upper bound loss given the 500-year earthquake. More accurately, assuming Poisson arrivals of earthquakes, this shaking level has a mean occurrence rate of 0.00211 yr–1 and a mean recurrence time of 475 years. Because this PML is the 90th percentile loss given this level of shaking, the PML-level loss can have a much longer mean recurrence time.” (Porter et al. 2004, p 1240)

Porter et al. propose use of a PFL computed as “the mean loss associated with the shaking intensity that has 10% exceedance probability in 5 years, which corresponds to a return period of approximately 50 years (more accurately, 47.5 years, assuming Poisson arrivals of earthquakes).” The earthquake with this intensity is referred to as the economic basis earth-


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quake, or EBE. Porter et al. argue that this PFL speaks to the typical commercial investor’s time frame for financial consideration (~5 years)2, while the 10% exceedance used for the intensity speaks to a reasonable level of risk aversion – this combination for the PFL is thus argued to provide an effective basis for risk communication for commercial investors. Porter et al. further show that for a reasonable set of assumptions for the shape of the hazard function, HA(a) (similar to the assumptions in the previous chapter), and the shape of the damage function (linear between a lower threshold for damage, aL, and an upper limit for damage, aU), the EAL is directly proportional to the PFL, with the constant of proportionality a simple function of HA(aL) and HA(aEBE). The PFL, which is relatively easier to compute than the EAL, can thus be used as a surrogate for it. The authors also demonstrate that market risk is likely to dominate earthquake risk for most investment decisions made with risk aversion, with the implication being that the mean earthquake risk can suffice for characterizing the seismic loss distribution. Porter et al. (2004) discuss a number of the difficulties involved in deriving the hazard function (HA(a)) and the capacity distribution, invoking a number of the same challenges and uncertainties described in chapter eight: “Let us refer to the method of calculating EAL by Equation 9 as Method 1. It is not easy to perform. To determine G(s) (equivalent to HA(a) in our paper) requires an understanding of the local seismic environment: the distance to nearby earthquake faults, the expected rate at which they produce earthquakes of various magnitudes, and the attenuation relationships that give shaking intensity s as a function of magnitude, distance, and other geological parameters. This information is increasingly available. However, to determine y(s) requires either large quantities of empirical postearthquake survey data (which for various reasons do not exist in reliable form), or laborious engineering damage and loss analyses requiring a skill set beyond that of most engineers, or the exercise of expert opinion, which carries with it the stigma of unverifiability.” “Software such as HAZUS® (NIBS and FEMA 1999), USQUAKE (see, e.g., EQECAT 1999), and ST-RISK (Risk Engineering 2002) contain pre-evaluated vulnerability and hazard information and can calculate EAL. These programs are widely employed and produce useful information relatively quickly and inexpensively. They treat a wide variety of structure types, and some offer the ability to account for several configurations and 2

Such a short time horizon would certainly not be appropriate for other sensitive facilities, such as nuclear power plants and dams.

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Mitchell J. Small other characteristics that affect seismic performance. However, they rely to a significant extent on expert opinion and do not perform structural analysis on a buildingspecific basis. They are thus insensitive to many of the details that cause performance differences between distinct buildings of the same building type. So what can be done if one wishes to avoid reliance on expert opinion, account for details at the level of standard practice of design, and yet keep the analysis relatively simple? We explore the basis for such a simplified loss-estimation procedure in this paper.” (p. 1243)

A particular aspect of interest is the decomposition of HA(a) into the occurrence model for earthquakes and the ground response and attenuation mechanisms that translate an earthquake of a given magnitude and distance into ground motion amplitudes, a. Page and Carlson (2006) discuss alternative earthquake occurrence models that were used by the 2002 Working Group on California Earthquake Probabilities, and alternative methods for characterizing the uncertainties in the individual models and their combined forecast for the San Francisco area. The five models using by the Working Group included: a) a Poisson model using all historical data; b) a Poisson model with the rate estimated using only post-1906 seismicity; c) a Brownian Passage Time (BPT) model that can be fit to reflect some degree of periodicity in combination with the purely random elements of the Poisson model; d) a BPT-step model that incorporates the effects of stress interactions from events on nearby faults; and e) a Time-Predictable Model that “uses the slip in the last earthquake, coupled with the slip rate of the fault segment, to calculate the expected time of the next rupture (the fault is expected to rupture once all of the strain released in the last earthquake has reaccumulated on the fault).” Page and Carlson go on to show how simple averaging or even weighted averaging based on agreement with observed data can lead to systematic bias and large uncertainties in the final hazard estimate. Alternative methods are suggested using Bayesian approaches, but being sure to use all data to update all candidate models, and being sure to use a proper likelihood function when using sequential data that are dependent. They also recommend the use of copulas as a method for model aggregation. An earlier review of the bases for alternative event occurrence models is provided by Vere-Jones (1995), including implications for earthquake forecasting.


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9.1.4 Social Science Research in Support of Seismic Risk Assessment The social dimensions of seismic risk and risk perception are critical to determining how individuals and institutions will react both before, with protective measures, and after an earthquake, during emergency response and subsequent recovery and rebuilding. Integrated consideration of the physical and social-behavioral dimensions of earthquake science is needed to ensure the most effective design of protection, mitigation and response strategies. For example, in a common physically-based statistical model used to predict earthquake occurrence, the time-predictable model, the likelihood of an earthquake occurring increases with time since the last event. In contrast, people’s perception of earthquake likelihood decreases with time since the last event (e.g., Fischer et al. 1996; Chiodo et al. 2004). This leads to the “window of opportunity” following an event in which home and building owners, as well as community agencies responsible for infrastructure, become more likely to undertake mitigation and to purchase earthquake insurance. However, relative to cost-effective levels of protection, it may also lead to overinvestment during this early period and significant underinvestment as the time since the last event increases. A significant body of social science research has taken place addressing these and other risk perception and human behavioral issues important to earthquake risk management. A list of some of the important papers follows. Increased integration of work of this nature into the technical planning process for earthquake protection will undoubtedly yield benefits to both the research community and society at large. 9.1.5 Representative Papers on the Social Dimensions of Earthquake Risk Management Atwood LE, Major AM (2000) Optimism, pessimism, and communication behavior in response to an earthquake prediction. Public Understanding of Science 9(4): 417–431 Bostrom A, Turaga RMR, Ponomariov B (2006) Earthquake mitigation decisions and consequences. Earthquake Spectra 22(2): 313–327 Flynn J, Slovic P, Mertz CK, Carlisle C (1999) Public support for earthquake risk mitigation in Portland, Oregon. Risk Analysis 19(2): 205–216 Lindell MK Perry RW (2000) Household adjustment to earthquake hazard: A review of research. Environment and Behavior 32(4): 461–501 Lindell MK, Whitney DJ (2000) Correlates of household seismic hazard adjustment adoption. Risk Analysis 20(1): 13–26 May PJ (2001) Societal perspectives about earthquake performance: The fallacy of “acceptable risk”. Earthquake Spectra 17(4): 725–737

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Mileti DS, Darlington JD (1997) The role of searching in shaping reactions to earthquake risk information. Social Problems 44(1): 89–103 Mileti DS, Fitzpatrick C (1992) The causal sequence of risk communication in the Parkfield earthquake prediction experiment. Risk Analysis 12(3): 393–400 Palm R, Hodgson M (1992) Earthquake insurance: Mandated disclosure and homeowner response in California. Annals of the Association of American Geographers 82(2): 207–222 Whitney DJ, Lindell MK, Nguyen H-HD (2004) Earthquake beliefs and adoption of seismic hazard adjustments. Risk Analysis 24(1): 87–102

9.2 Seismic Risk Assessment and Mitigation: Current Issues Bruce R. Ellingwood Georgia Institute of Technology, Atlanta, Georgia, USA

9.2.1 Introduction Modern risk assessment in civil infrastructure has three general elements: probability (including uncertainty in hazard and response), consequences, and context (Elms 1992). Dr. McGuire’s excellent paper addresses the first two elements, the first directly. The paper suggests that seismic risk decisions, at the current state-of-the-art, can be guided by calibration to similar risk-informed decision-making processes that are considered acceptable in other contexts, and to this end reviews concepts of seismic risk analysis as practiced in the nuclear industry. Consequences are dealt with indirectly by this calibration process. The paper emphasizes design applications and illustrates how ground motions for design can be stipulated to achieve a performance goal stated in probabilistic terms. The importance of incorporating both aleatoric (inherent randomness) and epistemic (knowledge-based) uncertainties in risk-informed decision-making is stressed. The method leads to designs that yield geographically uniform risk (expressed in terms of annual frequency), but requires a geographi-


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cally non-uniform specification of design-basis hazard to achieve that risk.3 The concept has recently been adopted in ASCE Standard 43-05, Seismic design criteria for structures, systems and components in nuclear facilities and in a similar form in DOE Standard 1020-02, Natural phenomena hazards design and evaluation criteria for Department of Energy Facilities. 9.2.2 Discussion of Implications for Seismic Risk Management At the heart of risk-informed decision-making is the identification of dominant contributors to risk and, in particular, modeling and analysis of uncertainties. Dr. McGuire’s nuclear plant example is instructive because it is an application where the importance of both aleatoric and epistemic uncertainties and their roles in risk-informed decision-making have been recognized and tracked. The building and bridge community can learn from this experience. Some issues that require further consideration are discussed below. Modeling, Analysis and Display of Uncertainties

Understanding and quantification of various sources of uncertainty are essential to develop the probabilistic models of system behavior needed for seismic risk assessment. All sources of uncertainty – both aleatoric and epistemic – must be considered in the risk assessment process. The prediction, modeling and evaluation of low-probability, high-consequence natural events involves significant uncertainties arising from imperfect scientific and engineering modeling, simplifications, and limited databases. Accordingly, epistemic uncertainties play a significant role in any seismic risk assessment, especially when applied to civil infrastructure in regions of low-to-moderate seismicity, such as the Central and Eastern United States (CEUS). In contrast to aleatoric uncertainties, which are essentially irreducible at the customary scales of engineering analysis, epistemic uncertainties generally can be reduced, at the expense of more comprehensive (and costly) analysis. Deciding whether to invest in additional data or more complex modeling in the hope of improving the estimate of risk is nontrivial. 3

Current earthquake-resistant design practices are based on a uniform hazard (e.g., the maps in ASCE Standard 7-05 are based on a 2% probability of being exceeded in 50 years). A uniform hazard design basis leads to non-uniform risk (as measured by annual frequency) because the risk is dependent on the local characteristics of the seismic hazard as well as the uncertainty in the capacity of structures, systems and components to withstand earthquake ground motion.

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Useful results for risk-informed decision-making can be obtained by prescribing, a priori, specific probabilistic models of aleatoric uncertainty from reasonable physical arguments, supported to the extent possible by the available databases. Modern seismic risk analysis, beginning with the paper by Cornell (1968) and supported by later studies, has indicated that earthquake ground motion intensity (measured as peak ground motion or spectral ordinates) can be represented by the Cauchy-Pareto family of distributions. Similiarly, research conducted during the past decade (Wen, et al. 2003; Ellingwood and Wen 2005; Wen and Ellingwood 2005) has confirmed that the seismic fragility (conditional failure probability) can be modeled by a lognormal distribution. Using this normative approach to selecting the probability laws of seismic demand and capacity, one can derive a simple expression for the limit state probability4. The risk assessment then can concentrate on the necessary information-gathering to define the parameters of these distributions and on methods for communicating the risk so determined. Epistemic uncertainties are reflected by incomplete knowledge regarding the probability distributions and their parameters used to model the hazard and fragility. Relatively simple models of epistemic uncertainty can be developed through expert elicitation (Ellingwood and Wen 2005). Point vs. Internal Estimates of Risk (Probability)

In seismic risk assessment, the role of epistemic uncertainty can be visualized through either interval or point estimates of the limit state probability, P[LS], or other decision variable. When the hazard and fragility are modeled by families of distributions to display the epistemic uncertainty, the limit state probability is described by a frequency distribution. This frequency distribution then displays the epistemic uncertainty in the estimate of P[LS] in much the same way that a sampling distribution provides an interval estimate on an estimator in classical statistical inference. If the epistemic uncertainties in the risk assessment are small, the distribution will be centered on the point estimate P[LS] with small dispersion; conversely, if the epistemic uncertainties are large, the frequency distribution of P[LS] will be broad. One can, of course, compute a mean value of this frequency distribution using the customary analysis. Dr. McGuire asserts that this mean value is the “best” point estimate of P[LS], an assertion that is sup4 Equation (2) of Dr. McGuire’s paper is based on these assumed probabilistic models.


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ported by other decision-theoretic considerations and has been accepted in the nuclear industry.5 Whether to use a point or interval estimate of P[LS] depends on the nature of the decision at hand. The mean of P[LS] is an unambiguous metric of risk, and is a natural choice in performing minimum expected cost (or loss) analyses. On the other hand, it does not convey a sense of the confidence that the analyst has in his/her risk assessment. If the aleatoric and epistemic uncertainties are propagated separately in the risk assessment, it is possible to construct statements such as “I am 95% confident that the limit state probability is between p1 and p2” or “I am 90% confident that the limit state probability is less than p2.” Recent experience6 has indicated that many decision-makers would like to see such a statement of confidence accompany a risk estimate, particularly when the probabilities are very small. Calibration as a Basis for Risk-Informed Decision-Making

The process of setting probabilistically stated performance goals through calibration to past decisions establishes a degree of continuity between deterministic and probabilistic approaches. This approach was used in deriving the first-generation limit states (or LRFD) codes in the United States and Canada (Ellingwood 2001), but a note of caution is in order before applying it to set seismic risk targets. The calibration of limit states design to working stress design considered an inventory of building and bridge structural components and systems for which traditional design and construction practices had, in the main, been satisfactory. The limit state probabilities, averaged over these inventories, were approximately the same for LSD and WSD.7 Furthermore, the calculated probabilities can be validated, at least to some extent, by building or bridge performance over decades. The experience base is less complete for seismic risk, and approaches adopted by the NRC, or the Department of Energy (DOE, 2002) 5 In early seismic PRA and margin studies of nuclear plants conducted in the 1980’s, the aleatoric and epistemic uncertainties were tracked and propagated through the risk analysis separately. In recent years, however, the NRC has adopted the mean value as basis for regulatory decision-making. 6 The writer has been involved with the ATC-58 Project, “Guidelines for seismic performance assessment of buildings.” Stakeholder workshops held in connection with this project have conveyed this message clearly. 7 Some also have observed that calibration allows inconsistencies in an old method to carry over into the new, an observation that the writer has addressed elsewhere.

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may not transfer to ordinary building and bridge construction because of the differences in regulatory environments. The regulatory functions of the NRC are established by Federal law. The criteria are applied to a relatively narrow group of facilities. There is essentially one performance goal: to prevent core damage. The enforcement process is strict, with NRC assigning staff to each licensed nuclear plant. In contrast, Model Building Code organizations (International Code Council, National Fire Protection Administration) have no Federal or State legislative authorization, model code provisions are developed by a complex process involving diverse interests, and have no legal standing until formally adopted by a legislative body. Building code provisions apply to a more diverse group of facilities. Finally, all proposals for performance-based seismic engineering contain multiple performance goals. In the circumstances, assigning appropriate risk metrics for different performance levels may turn out to be an extraordinarily difficult exercise. Risk Metrics

First-generation limit states design criteria are based on a notional limit state probability (related to a “reliability index”). ASCE Standard 7-05 and DOE Standard 1020-02 take a similar approach, identifying target performance goals in terms of annual probability for SSC in different seismic design categories (SDC)8. Since the SDCs are identified with progressively more severe consequences of failure (severity of radiological or toxicological effects), the “consequences” element of risk (cf summary, p. 1) is addressed indirectly by reducing the target probability for the more critical SDCs. Taking a long-term view, this approach may not be sufficient for seismic risk assessment of general civil infrastructure. For one thing, most stakeholders are not trained in or comfortable with the tools of quantitative risk analysis, especially when the decision process involves rare events.9 Stating that the design-basis earthquake is one with a magnitude 7.0 occur8

In these standards, the seismic design category has a different meaning than in the BSSC/NEHRP Recommended Provisions or in ASCE Standard 7-05. 9 The concept of probabilistically specified loads was introduced in the forerunner of ASCE Standard 7 in its 1972 edition (ANSI A58.1-1972). Three decades later, the writer is still explaining to consulting structural engineers the meaning of a 50-year mean return period event. The notion of an earthquake hazard with a 2,475-yr mean return period is even more difficult for them to grasp. They interpret the return period specification as meaning that the earthquake will occur every 2,475 years; they find it confusing when informed that this is not the case, and this makes the entire risk analysis less credible.


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ring 30 km from the facility site is more understandable than stating that it has a 2% probability of being exceeded in 50 years. Unfortunately, such a statement requires that the earthquake hazard be disaggregated, a process which carries uncertainties of its own. Second, focusing on the probability without considering the consequences explicitly omits an important dimension of the assessment and decision process. Low-probability events can be exceedingly risky; it is difficult to see how, considering the diversity of civil infrastructure, it is possible to collapse all the consequences – mortality, direct and indirect economic losses – into a change of one or two orders of magnitude in the target probability. While it is no doubt difficult to quantify consequences, it is clear that this is what most building owners, engineers and regulatory officials in the civil arena want, and concurrent research is proceeding in this direction. Most of the people who control the investment resources for seismic risk mitigation are not expert in probabilistic risk analysis, and their needs (and the context of the decision process) should be given heavy weight in selecting appropriate metrics for risk-informed decision-making.

9.2.3 A Look to the Future Dr. McGuire recommends a framework for achieving seismic risk mitigation decisions in the face of uncertainties. It is a sensible recommendation, but a significant effort will be required to achieve a broad consensus on performance goals, appropriate annual probabilities for each goal, and commitment of resources to perform such assessments in a meaningful manner for the general inventory of buildings and bridges. Implementation of decision-making for earthquake risk assessment and mitigation poses a number of challenges, several of which have been alluded to in this chapter. Some that are particularly obvious are listed below to stimulate discussion. Many of the most significant challenges occur at the interface between seismology, engineering and social policy. x Ground motion modeling in regions of low-to-moderate seismicity x Relating damage states to losses or other decision variables – Losses to engineered systems – Losses to non-engineered facilities – Damage to building contents – Impact on social fabric

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x Validation of quantitative risk analysis methods x Risk deconstruction to identify dominant contributors and target investment in risk mitigation x Risk aggregation to assess losses to communities and regions x Risk communication – Expressing acceptable risk o Annual damage state probabilities o Expected annual losses o Lifetime losses o Morbidity and mortality – Establishing reliability benchmarks – Addressing differences in risk perception/aversion among stakeholders – Convincing stakeholders of added value – Management of investment of public/private resources for risk mitigation.

9.3 On the Importance of Network Considerations in Lifeline Risk Assessment Leonardo Dueñas-Osorio Rice University, Houston, Texas, USA

9.3.1 Introduction Chapter eight proposes a clear method to support decision making under uncertainty for seismic risk mitigation. The method requires definition of structure-specific performance criteria and target performance goals. The author suggests setting target performance goals that reflect past decisions and construction practices. The chapter also highlights the importance of systematic treatment of uncertainty. It claims that accurate development of seismic risk curves requires explicit accounting of uncertainty in seismic hazard and facility vulnerability. Thinking of critical facilities as constituent elements of civil infrastructures requires expansion of the paradigm proposed by the author from a structure-specific approach to a network-oriented approach. Taking the


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power industry as an example, power generation facilities (including nuclear power plants), and electrical substations (e.g., low-, medium-, and high-voltage systems), are constituent elements of a grander system or infrastructure: the national power grid. Hence, instead of starting with a target performance for individual elements, it would be instructive to start with a target functionality level for the entire infrastructure, or collections of interdependent infrastructures (Rinaldi et al. 2001; Dueñas-Osorio 2005; Newman et al. 2005; Dueñas-Osorio et al. 2007). For example, one can specify the duration of power service interruption not to exceed x number of days per year (not only to restore power service, but also not to delay operation of water, oil, gas and other lifelines). Then, the required retrofits and guidelines for new facilities are established to achieve predefined infrastructure functionality goals. This network functionality approach is consistent with current practices of the power industry (which try to minimize power interruption indices), and also with the paradigm illustrated by the author in the paper for individual facilities (where performance goals for structural members are specified to achieve a plant-level performance goal). One implication of this network approach is that decision makers require more information than the seismic hazard and the vulnerability of the facilities in question. This additional information is related to the role that each facility plays in maintaining flow within its own network, and the role in facilitating flow across networks (Dueñas-Osorio 2005). Another implication of the network approach is that in the establishment of functionality goals, information from already constructed infrastructures is not enough to establish a reference infrastructure functionality goal. Additional information is required regarding future trends on quality of flow supply, expansion areas, capacity increase, redundancy, etc. The reason is that lifelines are dynamic systems that are constantly growing and changing, and the reliability requirements also change with end-user evolving needs. However, the example of the author using nuclear power plants justifies extracting target performance based only on past practice, because nuclear power plants are static systems that were built more than 30 years ago. 9.3.2 Beyond Past Decisions McGuire indicates the need of understanding the cost of achieving different levels of seismic safety. The paper recognizes that decisions about seismic risk need to be made in the context of other competing risks. Also, sufficient data and analytical tools are not yet available for performing risk

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assessments to all possible hazards. For these reasons McGuire claims that a surrogate model for this comprehensive hazard approach is to use societal decisions in the past as indicators of acceptable risks. McGuire also highlights the adequacy of using mean hazard and mean risk as appropriate measures for decision making. Additionally, McGuire re-emphasizes the need for uncertainty treatment, even if single measures are used to characterize the problem. As modern infrastructures quickly grow larger, denser, and more interconnected, then societal decisions made in the past need to be adapted to reflect current acceptable risks. Continuing with the example of the power industry, the service reliability of the power grid of the central and eastern parts of the United States mainly focuses on frequent hazards such as lightning, animals, and severe winds. However, operators of these systems currently do not take into account risks coming from low-frequency/high consequence seismic events, and are falling behind in considering flow congestion and aging. Therefore establishing performance goals solely on existing infrastructure will miss the risk coming from infrequent hazards and accelerated deterioration. A better reference point should include combined effects from seismic hazards on existing systems and the trends in infrastructure changes to balance flow supply, demand, built capacity, and service reliability. 9.3.3 Seismic Risk Analysis for Nuclear Power Plants The paper by McGuire presents a brief overview of the relevance of uncertainty treatment in probabilistic seismic hazard analyses (PSHA) for nuclear power plants. The author differentiates between the aleatoric (inherent) and epistemic (knowledge-based) sources of uncertainty. He also illustrates how the epistemic uncertainty can be explicitly observed in the degree of confidence of basic PSHA’s. Finally, McGuire mentions how probabilistic risk assessments (PRA) have been helpful in probabilistic system models, which combine element performance to provide systemlevel performance. This concept of system-level reliability is entirely compatible to deal with the civil infrastructures that sustain social welfare. The probability that a generic infrastructure has of exceeding a predefined functionality level is simply a function of the probability of failure of its constituent elements. One of these components can be a power plant, which in turn has a probability of failure that depends on its constituent structural elements. Hence, a new layer (network-level) is being added to the existing well established risk assessment methodologies. This problem can be ap-


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proached via numerical simulation when the size of the systems makes intractable traditional failure analyses. Nuclear Power Plant Seismic Design

McGuire summarizes the current inventory of nuclear power plants (NPP’s). There are approximately 100 units that were designed and built in the 1960’s and 1970’s. The seismic design of these NPP’s relied upon deterministic approaches where the units were subjected to the largest ground motions expected at their respective sites. Probabilistic risk assessments were conducted in the 1970’s and 1980’s to estimate the annual frequency of seismic-induced damage to the power plant nuclear cores. A parameter to measure the associated seismic risk of each plant was introduced as the seismic core damage frequency (SCDF). Outcomes of these PRA’s can be used to characterize the mean core damage frequency of existing units, and use these values to set an annual mean core damage frequency goal of 1×10–4 per reactor. The SCDF’s for 25 of the most recent NPP’s has an annual mean value of 2.5×10–5. This kind of information, according to McGuire, can be used to establish performance goals for consequence minimization based on past practice; he cites several reports from the U.S. Nuclear Regulatory Commission relevant to power plant design and safety. Lessons learned from recent natural disasters and intentional disruptions to the built infrastructure should complement accepted past practices for establishing performance goals of engineered systems. Target annual mean values of damage frequency for generic facilities need to account for the resilience that most infrastructures are lacking after severe disruptions. In addition, end-users and their requirements for service quality are continuously growing. Therefore, the reliability of infrastructure systems cannot simply be based on more conservative values relative to the ability of existing systems to absorb disruptions. It would be advisable to include in the target performance goals the effect of infrastructure functionality requirements. These requirements are the ones that society expects for reducing bottlenecks in recovery and business reactivation. McGuire describes a four-step procedure to estimate target performance goals for risk mitigation. The steps are: (1) definition of performance measures and their associated quantitative performance goals; (2) estimation of facility performance given a level of seismic shaking (i.e., fragility relations); (3) assessment of the unconditional probability of facility failure; and (4) estimation of the seismic design level to achieve a target performance goal. Then, the author provides a sensitivity analysis of the probability of facility failure, Pf. Three parameters influence this probabil-

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ity: fs (design factor of safety), K (slope of hazard curve), and ȕ (lognormal capacity dispersion). If any of these parameters is increased, then smaller probabilities of failure, Pf, are achieved. The proposed four-step method is a straightforward tool to estimate seismic design levels to achieve uniform facility performance. However, it should be stressed that definition of facility performance goals needs to be made within a network-oriented approach. In synthesis, a network approach requires looking at the vulnerability of the facilities (infrastructure elements), the functional role of these facilitates to maintain and share flow within and across infrastructures (e.g., flow of goods and services), the resilience requirements expected from essential infrastructures in the event of service disruption, and the quality that end-users at their locations expect from utilities and service providers for their normal operation. Considering these factors will result in modified factors of safety and capacity dispersions. Regarding the effects of the capacity dispersion term ȕ on the calculation of the probability of failure, Pf, there is a need to explore an underlying assumption made in Chapter eight. If ȕ increases, then Pf decreases because of the assumption of facility design to identical seismic levels a*. However, if the uncertainty in facility capacity ȕ increases, then it is because estimating facility responses is now a less tractable task. This increased intractability is necessarily related to the complexity of the system; and a more complex entity has higher chances of experiencing unforeseen failure modes. This implies that if a system with increased uncertainty is designed for a 1% probability of failure, then there is no guarantee that unforeseen failure modes will not cause the structure to fail at lower seismic design levels. For example, if a simple structure is enhanced with an active control system, the capacity of the system, the dispersion of the capacity, and the complexity of the system all increase. This implies that a higher capacity can be achieved, but there is still a chance that the structure is adversely affected by the control system. This intractability is even stronger for networked systems, where the interaction between individual facilities can still lead to cascading failures, even if devices for protection are installed. Analytically, the intractability is reflected in the movable design hazard intensity for a predefined probability of failure. The same target Pf can yield different design hazard intensities for different levels of capacity dispersion. The problem is to assess adequate Elevels, when it is not yet understood how network complexity affects capacity and response uncertainties. Figure 9.1 adapted from the commissioned paper hypothetically illustrates these remarks.


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Fig. 9.1. Sensitivity of Pf to uncertainty in facility capacity ȕ, and system complexity. (Adapted from Figure 8.3)

McGuire also presents an illustrative example with an application of the four-step procedure for design of NPP’s facilities. First, performance measures and target performance goals are defined (based on existing facilities). Then the median hazard curve and the expression for estimating the unconditional probabilities of facility failure are iteratively used to estimate the required seismic design level a*. This level is chosen so that predefined performance goals are achieved. Two values of the capacity uncertainty ȕ are used, and both result in more conservative designs as compared to existing facilities. This method highlights the usefulness of uniform target performance goals across facilities of the same type at different locations. Seismic design criteria recommendations have been suggested by ASCE following uniform target performance goals for systems. These recommendations establish seismic performance goals for both the constituent components structural systems and entire facilities or systems. Hence, the system performance results higher than the performance of the individual pieces, due to safety margins in element behavior and system behavior. McGuire also explains the rationale of defining performance goals for individual components. However, in the context of infrastructure systems, it is necessary to define which would be the lowest-level elements of networked systems to prescribe performance objectives so that an infrastructure functionality level is achieved. There are several levels for starting failure analyses: parts (smallest identifiable components), units (function-

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ally related collections of parts), subsystems (array of units), systems (grouping of subsystems), infrastructures (collection of like systems) (Rinaldi et al. 2001), and interdependent infrastructures (built environment). One of these levels needs to be chosen as a practical starting point to define performance goals and propagate them to achieve expected infrastructure functionality. 9.3.4 Recommendations for Seismic Risk Mitigation Decisions McGuire’s paper highlights the similarities between design methods applied to design and construction of NPP’s, and current performance-based design strategies in building codes. The paper then provides a four-step design method adapted to the context of performance-based earthquake engineering (PBEE). First, building performance objectives and quantitative target values are established. Second, engineering analyses are performed to estimate the likelihood of the structures to exceed predefined performance goals. Here, McGuire emphasizes the need to break down the analysis in groups according to building type, age, and design level. Third, a distribution law is fitted to the structural responses so that the probability of failing to achieve a predefined performance level is analytically calculated. Finally, the seismic design level for each structure or component is established, so that it achieves the target performance goal. It should be clear that different components may have their designs controlled by different performance objectives. McGuire makes an interesting analogy between NPP design and performance-based earthquake engineering design. However, other paradigms such as the Mid-America Earthquake Center’s consequence-based risk management (CBRM) are also compatible with the NPP design model. A key difference between the PBEE and the CBRM models is that the later addresses regional consequences and the effects of loss of functionality of geographically distributed infrastructures to society. Hence, CBRM supports a network-oriented approach for definition of functionality targets. It also strengthens the notion that network functionality goals need to be defined in order to fulfill societal needs under multiple hazards. Also, as current risk analysis practices require buildings and bridges to be classified by structural type, networks can be classified according to their topological features, and flow patterns used to transmit, distribute and provide services. This classification can facilitate implementation of mitigation strategies tailored to specific networked systems.


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9.3.5 Importance of Uncertainties Chapter eight stresses the importance of accounting for aleatoric and epistemic uncertainties in the estimation of mean hazard and vulnerability functions. The aleatoric uncertainty in embedded in the calculation of the unconditional probabilities of failure, while the epistemic uncertainty is discussed for each element of the seismic hazard and the structural vulnerability. Among the epistemic uncertainties is the potential for unanticipated failure modes. This reinforces the relation between increased complexity of engineered systems and increased uncertainty in its capacity estimation. This also opens the possibility of accounting for failure modes of the systems under consideration triggered well below their seismic design level points. Finally, adopting an infrastructure-functionality approach to define performance goals of constituent network elements, allows better estimation of causes of loss. In other words, calculating network-oriented functionality measures, such as service flow reduction, or time to restore service, is more meaningful to estimate the impact of infrastructure disruption on specific subpopulations in a given region. The network focus provides a bridge between engineering responses and estimation of indirect effects on communities. 9.3.6 Closure McGuire’s paper presents an interesting and clear approach to achieve uniform performance levels across engineered systems. It uses the lessons learned from nuclear power risk assessments as the basis for proposing a design method consistent with modern performance- and consequencebased earthquake engineering strategies. The method proposed by the author is flexible and can accommodate additional features discussed in this chapter. One feature is the potential for adopting an infrastructure-oriented functionality approach to withdraw the performance goals for the constituent elements of networked systems. This network approach can simultaneously account for network element vulnerability, functional roles of network elements, infrastructure capacity to absorb disruptions, and enduser evolving expectations from lifeline service. At this level of analysis, network interdependencies and their potential loss-of-functionality effects on ever-expanding urban and rural systems still remains tractable.

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9.4 Strategic Directions for Seismic Risk Mitigation Decisions Under Uncertainty Full characterization of the risks and uncertainties of natural hazards can help society to best allocate scarce resources for mitigating losses from catastrophic events. The previous chapter used seismic risk analysis for nuclear power plant siting and design to illustrate a rational methodology for dealing with a low-probability, high-consequence hazard with significant uncertainties. The recommendations for seismic risk mitigation decisions based on using Probabilistic Risk Assessment focused in particular on developing design criteria to meet defined performance goals. The chapter concluded with a discussion on the importance of uncertainties and a brief outline of the aleatoric and epistemic uncertainties that are most frequently considered or are left out of seismic risk analyses. Decisions about mitigating seismic risk with potentially disastrous consequences (i.e., nuclear power plant design) should be based on quantitative analysis, but the uncertainties in the analyses are likely not fully appreciated by decision makers. A survey of earth scientists in the San Francisco Bay Area about the probability of a large earthquake in the area in the near future revealed a wide range of opinions about the quantitative terms implied by the terms “probability”, “large” and “near future”. There is a “pinch-point” in these analyses where design engineers require a single number they can use and more complex spectra are difficult for them to work with. Finally, a conflict exists between the ultimate goals of protecting society by mitigating risk and on-the-ground design and construction decisions. Risk modeling would benefit from analysis of many different building types and development of capacity building curves for each of them, although the benefits of this exercise might not outweigh the costs. Testing whole buildings on shake tables has been done for some building types (about a dozen, including wood frame and reinforced concrete buildings; e.g. Porter et al. 2001), but all of the model building types in HAZUS® could be tested this way. However, this type of study would be very expensive and might not be worth the investment. Resources might be better invested in studying or making improvements for natural hazards with higher frequency or greater annualized loss (Wiggins 1976; 1980). The rational, quantitative analysis of risk mitigation options applies broadly to all natural hazards. This chapter helps flesh out a framework for risk analysts to use. Specific recommendations for making rational mitigation decisions about potential hazards include analyzing multiple structural performance objectives, prioritizing performance goals and acquiring more


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detailed input data to improve the models. Additionally, in a theme repeated frequently throughout this volume, the importance of uncertainty in risk analysis is emphasized. The chapter outlines several critical needs for reducing uncertainty generally applicable but specified for seismic risks, including better physical hazard models (in this case, ground motion models) and site- or incident-specific information about the hazards and facility response. Strategic directions on a number of fronts are suggested by the commentaries in this section. As the National Research Council and other national committees have resoundingly endorsed, the risk perception and behavior of individuals, communities, and decision-makers must be considered in planning processes alongside quantitative models of risk. Indeed, the role of quantitative risk analysis in the broader context of decision-making is complicated by limited information in producing valid models, limited resources for acquiring needed information, and the difficulty of communicating the need for more informed models and model results to decisionmakers. However, such models can provide guidance for prioritizing investment in information needs as well as in mitigation strategies. Finally, adding to the already complex analysis of individual site or facility vulnerability, a network-oriented approach to risk analysis will more fully account for society’s dependence on infrastructure that is interdependent. Performance goals are not sufficient to ensure the integrity of lifelines, but including functionality goals (the role of a structure or facility within a network) in risk analysis may better help to reduce disruptions in critical systems. Quantitative risk analyses, particularly those with a high degree of uncertainty, limited information bases, and complex outputs, can only be useful if decision-makers understand their results. Improved information about structural responses to the physical stresses of natural hazards comes at a high price, so prioritizing information needs is essential in research design. In addition, risk analysts need to account for societal risk perceptions and provide information appropriate to end user needs.

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10 Modeling Seismic Mitigation Strategies

Patricia Grossi Risk Management Systems, Newark, California, USA

Over the past two decades, as the cost of natural disasters has skyrocketed in the United States, much emphasis has been placed on mitigating these hazards. From earthquakes to floods to hurricanes, agencies such as the Federal Emergency Management Agency (FEMA), the Institute for Business and Home Safety (IBHS), and the Association of Bay Area Governments (ABAG) have tried to increase public awareness of natural hazard risk and encourage those at risk to mitigate for the hazard. At the same time, advanced software tools have emerged to allow stakeholders to more accurately assess their hazard risk exposures. These include the proprietary catastrophe models used by the insurance and reinsurance industries and the federal government’s model, HAZUS®. While the insurance-based catastrophe models and the HAZUS® model are intended for different audiences and have diverged in application over time, each utilizes the same general methodology to analyze catastrophic earthquake losses. As a result, some comparisons can be made in the modeling of mitigation strategies utilizing HAZUS® and insurance-based models. This paper explores the modeling of mitigation strategies for seismic risk in HAZUS® and the insurance-based models. Comparisons between the two models are discussed, underscoring advantages and limitations of modeling mitigation strategies in the software. An illustrative example is presented and in conclusion, the implications for cost-benefit analyses of mitigation strategies are discussed and suggestions for improvements in modeling mitigation strategies are presented.

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Patricia Grossi

10.1 Introduction When introduced, the use of catastrophe models was not widespread. In 1989, two large-scale disasters occurred that instigated a flurry of activity in the advancement and use of these models. On September 21, 1989, Hurricane Hugo hit the coast of South Carolina, devastating the towns of Charleston and Myrtle Beach. Insured loss estimates totaled $6.6 billion (2005 dollars) before the storm moved through North Carolina the next day (Insurance Information Institute 2006). Less than a month later, on October 17, 1989, the Loma Prieta Earthquake occurred at the southern end of the San Francisco peninsula. Property damage to the surrounding Bay Area was estimated at $6 billion (Stover and Coffman 1993). These two disasters sent a warning signal to the insurance industry. On the heels of these two events, Hurricane Andrew made landfall in Southern Florida in August of 1992. It was not until months later that the final tally, $15.5 billion, was issued by ISO’s Property Claim Services (PCS). Nine insurers became insolvent as a result of their losses from Hurricane Andrew. Insurers and reinsurers realized that, in order to remain in business, they needed to estimate and manage their natural hazard risk more precisely. Many companies turned to the modelers of catastrophe risk for decision support. The modeling companies grew and catastrophe models increased in number, availability, and capability. The series of natural disasters in 1989 and 1992 also sent a warning signal to the public sector of the United States. The government recognized the need for an accurate assessment of the impact of disasters for mitigation and emergency planning purposes. In 1992, the Federal Emergency Management Agency (FEMA) funded a study to assess the latest loss estimation methodologies for earthquakes. The agency issued a report in 1994 on the results of this study entitled Assessment of the State of the Art Earthquake Loss Estimation Methodologies (FEMA 1994). This study convinced FEMA to fund the development of HAZUS® (i.e., ‘Hazards U.S.’), a catastrophe model in the public domain (hence it is often referred to as an “open source” model). From the outset, one of FEMA’s goals was to create a methodology that was the “standard national loss methodology for assessing losses from natural hazards” (FEMA 2002a). The first version of HAZUS® was released in 1997 (NIBS 1997), developed using a combination of public and private resources to estimate earthquake losses only. Since 1997, updates to the HAZUS® earthquake model have been in the form of data and software integration; methodologically, the software remains the same. In 2006, the latest HAZUS® multi-hazard methodology, relabeled HAZUS®MH, integrates the earthquake module with two new


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modules for estimating potential losses from wind and flood (riverine and coastal) hazards.

10.2 Structure of Catastrophe Models Catastrophe models utilize advances in Information Technology (IT) and Geographic Information Systems (GIS). With the ability to store and manage vast amounts of spatially referenced information, GIS is an ideal environment for conducting catastrophic hazard and loss studies for large regions. While it is true that catastrophe loss studies were done for twenty years prior to this time (e.g., earthquake loss estimates in Steinbrugge 1982), this advancement in computer technology has created an easier, more cost-effective way to perform these studies. The four basic components of a catastrophe model are: hazard, inventory, vulnerability, and loss as depicted in Fig. 10.1. First, the model characterizes the natural hazard phenomenon (e.g., earthquake) to which one is at risk. An earthquake hazard is characterized by its epicenter location and moment magnitude (Mw), among other parameters. The frequency of certain magnitudes or frequencies of events also describes the hazard in question.

Fig. 10.1. Structure of catastrophe models

Next, the inventory of properties at risk is described as accurately as possible. Arguably, the most important parameter used to characterize the inventory is the location of each property at risk. A process called geocoding is normally used to assign geographic coordinates (e.g., latitude and longitude) to a property based on its street address, ZIP code or another location descriptor. With a property’s location in spatial terms, other factors that could aid in estimating the vulnerability of a property are added to its char-

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acterization. For a building, these parameters include such features as its construction type, the number of stories in the structure and its age. These hazard and inventory modules enable one to calculate the vulnerability (i.e., damage) of the structures at risk. In essence, this step in the model quantifies the physical impact of the natural hazard phenomenon on the property at risk. This module uses damage curves or damage functions for the property, its contents and other types of loss such as business interruption (BI) losses. From this measure of vulnerability, the loss to the inventory is evaluated. In a catastrophe model, loss is characterized as direct or indirect in nature. Direct losses include the cost to repair and/or replace a structure. Indirect losses include BI loss and relocation costs of residents forced to evacuate their homes.

10.3 A Comparison of Models While the proprietary catastrophe models and the HAZUS® model are intended for different audiences and have diverged in application over time, each utilizes the same general methodology to analyze catastrophic earthquake losses. As a result, some comparisons can be made in the modeling of earthquake loss – and by extension mitigation strategies – utilizing HAZUS® and insurance-based proprietary models. It should be noted at the outset, however, that all models carry assumptions and have limitations. Understanding the assumptions and limitations allows for better use of these predictive models and avoids the misuse of the model output. Furthermore, only some aspects of the models are compared (to provide an exhaustive list – in terms of methodological and implementation assumptions – would be a large undertaking). In comparing some aspects of the models, it is not the intention here to state that one model is better than another. The intent is to outline assumptions in order to determine which model’s implementation is currently more conducive to analyzing mitigation strategies and to promote the use of risk models to assess these strategies (See Table 10.1). The first fundamental difference between HAZUS® and insurance-based models is the type of hazard analysis. HAZUS® is a scenario-based approach to modeling losses (i.e., deterministic or semi-probabilistic). Deterministic earthquake hazards are defined primarily by their magnitude and epicenter location and include historical events and user-defined events. One can additionally use the USGS probabilistic seismic hazard maps to analyze a return period loss (hence – the reference to ‘semi-


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probabilistic’). In contrast, proprietary models utilized by insurers to manage catastrophe risk are probabilistic in nature. In a Probabilistic Seismic Hazard Analysis (PSHA), all possible seismic sources’ locations and geometries are determined, the maximum magnitude (Mmax) expected from each source is estimated, and the recurrence model or frequency of earthquake events for each source is obtained. A probabilistic analysis allows one to consider a set of events that have not happened in the past but could happen in the future. Other differing aspects of the hazard module in the loss estimation process are in the ground motion attenuation and the geotechnical hazard data. While both models utilize all the available empirically-based attenuation relationships (e.g., Boore, Joyner and Fumal 1997), the HAZUS® model allows the user to pick the ground motion attenuation for analysis. In insurance-based models, an average of applicable attenuations are utilized or all attenuations could be used (i.e., for probabilistic analysis in building the stochastic event set). Additionally, there is no detailed soils information as part of the HAZUS® model; there is an assumption that soil class D (‘stiff soils’) is the underlying soil throughout all regions. Effort must be made on the part of the user to obtain higher resolution soils data. The insurance-based models have high resolution soils data, distinguishing at a 100meter resolution, for example, the change in soil class, liquefaction potential and landslide potential. The final differences lie in the inventory and vulnerability modules of a catastrophe model. HAZUS® can analyze losses to buildings, as well as transportation and utility networks, and the approach to estimating structural and nonstructural building damage is based on spectral displacement. With vulnerability curves driven by spectral displacement, the capacity spectrum method (Mahaney et al. 1993) is utilized to estimate the peak building response at the intersection of the building capacity curve and the response spectral displacement demand. Insurance-based models limit their analyses to building losses and the relevant losses to the building’s owner, insurer, and reinsurer (if applicable). The approach to building damage assessment is based on spectral acceleration at a location and the response of a structure to the ground motion based on its height, among other parameters (i.e., the spectral response-based approach to vulnerability modeling).

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Table 10.1. Comparison of catastrophe models Availability Type of Hazard Analysis

HAZUS® Public domain Deterministic (or semiprobabilistic)

Insurance-based models Proprietary Probabilistic

‘Average’ attenuation User choice of attenuation function or set of attenuafunction tion functions Default assumption: soil High resolution soils data Geotechnical Hazard Data class D (stiff soils) across modeled regions Building structures; lifeAnalyzed Inventory lines (transportation and Building structures utility) Capacity spectrum method Various methods mostly Vulnerability Curves to analyze building loss spectral response-based Ground Motion Attenuation

The differences outlined between the HAZUS® and insurance-based models are primarily driven by their intended usage. The private companies developed models for insurance companies to estimate their portfolio losses. Therefore, these models are marketed by their advantages over other models – in ‘high resolution’ geotechnical hazard data and ‘more advanced’ vulnerability assessment. There are many inherent assumptions in the model that are built in – for ease of use. The insurance-based models do not allow the user to choose an attenuation function, as the regulatory environment in the U.S. dictates that the model issues one estimate of risk and the choice of attenuation function can drastically change loss estimates. Moreover, a probabilistic analysis is a necessity for the insurance market, as the use of exceedance probability (EP) curves, specifying the probability that a certain level of loss will be exceeded over a given time period, is widespread throughout the insurance industry. On the other hand, the federal government developed a regional loss estimation model to estimate monetary losses as well as other types of losses (e.g., casualties and shelter requirements) from an earthquake event. Hence, HAZUS® is not a full probabilistic model and incorporates the analysis of lifeline networks, an important component of a region’s infrastructure that – if impacted – will significantly increase losses. The HAZUS® methodology for vulnerability assessment is outlined in the accompanying user’s guide and technical manual and the choice of attenuation relationship is allowed in order to perform sensitivity analyses and understand the range of expected losses and impacts. The assumption that soils are ‘stiff soils’ is conservative as a default assumption. In order to


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perform a meaningful analysis in HAZUS®, a high degree of user knowledge and expertise is required. Training on the use of HAZUS® occurs at regular intervals and is sponsored by FEMA.

10.4 The Exceedance Probability Curve With an understanding of each model’s assumptions and limitations, either one could be used to assess seismic mitigation strategies. After all, a catastrophe model is employed to assess catastrophe risk and make risk management decisions. But, how does one accomplish this? Briefly, the model output is quantified and presented in a way that is useful to the user. Once these metrics are in hand, different risk management strategies (e.g. mitigation) can be assessed. When analyzing mitigation measures using catastrophe models, the key determination is the reduction in damage (and subsequent loss) to a structure by ‘shifting’ the structure’s vulnerability curve. Figure 10.2 shows a simple illustration of this concept with mean damage as a function of an earthquake’s intensity. This shift quantifies the benefits of mitigation by producing losses before and after mitigation strategies are in place. One measure of benefits (using a deterministic approach) is the reduction in loss as a result of a specific earthquake event (e.g., a repeat of the 1994 Northridge Earthquake). Using a probabilistic approach, other measures are the reduction in Average Annual Loss (AAL), which translates into an annual benefit, or the reduction in Probable Maximum Loss (PML), a subjective risk metric associated with a given return period.

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Fig. 10.2. Illustration of the shifting of a vulnerability curve before and after mitigation

A good representation of the findings for the AAL estimates and changes in PMLs with mitigation is shown via the exceedance probability curve. An EP curve is a graphical representation of the probability that a certain level of loss will be surpassed in a given time period. The area under the EP curve is the AAL, whereas the PML can be defined as a point on the ‘right hand tail’ of the EP curve, such as the 250-year loss corresponding to an exceedance probability of 0.4% (Fig. 10.3). In order to utilize an exceedance probability curve, however, one needs to perform a full probabilistic seismic hazard analysis. Therefore, an insurance-based model (in its current form) is more conducive to assessing mitigation strategies and is chosen to illustrate the modeling of seismic mitigation measures in this paper. It should be noted, however, that local governments would benefit from running earthquake scenarios using HAZUS® to analyze the impacts of mitigation and plan for response and recovery efforts. Additionally, creating an EP curve from HAZUS® output has been done in the past, as detailed in Grossi (2000) and outlined in Chapter 4 of Grossi and Kunreuther (2005).

Exceedance probability, p


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original EP curve

possible EP curve with mitigation measure AAL (with mitigation) 250-year loss

0.4%

Loss, L (in Dollars) Fig. 10.3. An exceedance probability curve, showing impacts of mitigation, as well as representation of AAL and PML

10.5 Choice of Seismic Mitigation Strategies With the choice of an insurance-based model to analyze mitigation measures, it is important to further understand which type(s) of seismic mitigation strategies can be analyzed and the potential benefits of the strategies. As previously mentioned, the goal in modeling seismic mitigation strategies is to assess risk and the cost-effectiveness of the mitigation strategy. Determining the cost-effectiveness of strategy, in turn, informs the decision of whether or not to implement the strategy or prioritize mitigation strategies. Prioritization of mitigation strategies is mandated by FEMA for state and local governments to receive Hazard Mitigation Grant Program (HMGP) funds, as described in FEMA 2002b and FEMA 2003. Cost-benefit analysis (CBA) is employed to evaluate seismic hazard mitigation for residential structures, commercial structures and lifeline networks. There have been studies that utilized the HAZUS® model to analyze the impact of structural mitigation techniques to residential structures in Northern California (Grossi 2000) and to commercial buildings in Los Angeles County (Dodo et al. 2005), subject to earthquake hazards. There is also a guideline that illustrates the use of HAZUS® for mitigation (FEMA 2002c). Other studies have utilized insurance-based models to analyze the

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impact of structural mitigation techniques on property owners and insurers, subject to both earthquake and hurricane hazards (Chapter 9 in Grossi and Kunreuther 2005). Of course, there have also been analyses performed outside HAZUS® and the insurance-based models: for the mitigation of lifeline systems and for the mitigation of structures and nonstructural elements in various regions of the world. Studies of lifeline mitigation to an electric power system in Shelby County, Tennessee and water distribution systems in Alameda and Contra Costa counties, California (Kunreuther et al. 2001) have been completed; studies of the impact of mitigation on the infrastructure system in Portland, Oregon (Chang 2003) and the water, wastewater and drainage utilities in California (Seligson et al. 2003) have also been completed. The benefits of structural mitigation to a typical apartment building in Turkey (Smyth et al. 2004) have been analyzed, as have the impacts of nonstructural mitigation to schools in the southern California region (Seligson et al. 1998). It should also be noted that in 2005, the Multihazard Mitigation Council (MMC) of the National Institute of Building Sciences (NIBS) published a comprehensive report on the impact of mitigation activities for wind, flood, and earthquake hazards (Multihazard Mitigation Council 2005), which looks at the costs and benefits of FEMA mitigation grants. Before these analyses, two FEMA publications, 294 and 331, describe case studies of corporations, utilities, and homeowners that have used mitigation strategies to protect themselves against catastrophic losses (FEMA 1997; FEMA 1998). Several businesses experienced events after mitigation measures were put into place and thus could compare potential losses against what actually occurred. For example, a seismic reinforcement to buildings and critical equipment at the Anheuser-Busch brewery in Los Angeles, California saved the company an estimated $300 million in direct and business interruption losses from the 1994 Northridge Earthquake. Nonstructural mitigation such as bracing of building contents prevented an estimated $1 million in damages to the Warner Bros. studio in Burbank, California after the Northridge Earthquake. Unfortunately, these examples do not present the entire picture as the savings result from the "best case" scenario in which the event occurs shortly after measures were taken. This is a deterministic result, whereas the average benefit should be determined through a probabilistic analysis. In this paper, to make matters relatively simple, the impact of structural mitigation techniques as applied to individual buildings are analyzed.


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10.6 Cost-Benefit Analysis Cost-benefit analysis (CBA) is a systematic procedure for evaluating decisions that have an impact on society. There are different ways to conduct a CBA, depending on the information one has and the nature of the problem at hand. In the case of seismic mitigation strategies, a comparison between the upfront investment costs of mitigation to the benefits of mitigation (e.g., reduction in loss, reduction in casualties, and reduction in lifeline system downtime) is completed. A simplified five-step procedure for a CBA for the mitigation of residential or commercial structures is illustrated in Fig. 10.4; a more comprehensive approach, which incorporates additional steps, is discussed in Boardman et al. (2001). The five-step procedure includes: defining the nature of the problem, including the alternative options and interested parties; determining the direct cost of the mitigation alternative(s); determining the benefits of mitigation, e.g., via the difference in loss with and without mitigation; calculating the attractiveness of the mitigation alternative(s); and, finally, choosing the best alternative.

Fig. 10.4. Steps of a benefit-cost analysis

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If the expected reduction in loss (i.e. the benefit) exceeds the cost of undertaking mitigation (e.g., step 4), then one can justify investing in it. The point at which it is beneficial to mitigate is established, for example, using a net present value (NPV) calculation (Eq. 10.1). T

NPV

C o ¦ t 1

B tM (1 r ) t

(10.1)

In this equation, the upfront cost of mitigation, Co, is compared to the annual benefits of mitigation, BtM, through an assumed discount rate, r, and the lifetime of the structure, T. Whenever the NPV is greater than or equal to zero, it will be beneficial to mitigate. Framing the analysis in this way, one can assess the robustness of a specific mitigation strategy.

10.7 A Mitigation Example Using an Insurance-Based Model In order to illustrate the use of catastrophe models in analyzing seismic mitigation strategies, an example is presented. The San Francisco Bay Area is chosen (as it is a high seismic hazard region) and two separate structures are analyzed using two common structural mitigation techniques. As shown in Fig. 10.5 and summarized in Table 10.2, a commercial unreinforced masonry (URM) structure in downtown San Francisco and a residential wood frame structure in the city of Belmont are shown. The wood frame home is valued at $850,000 and is located on stable soil with low liquefaction and landslide potential. It is a two-story structure built in 1938 and has unbraced cripple walls1 and inadequate connections at the foundation. Pre-1940 homes in California were often built this way, due to the lack of plywood available at the time. According to the Association of Bay Area Governments (ABAG), only 20% to 40% of the pre-1940 homes in the Bay Area have been mitigated to resist earthquake damage. Currently, there are no standards or mandatory requirements to adopt cripple wall retrofits. Therefore, the chosen mitigation measure for the house in Belmont is the bracing of the cripple wall and securing the structure to its foundation with additional anchor bolts – at a cost of $5,000 (See Grossi 1998).

1

The cripple wall is the wall/crawl space between the structure’s foundation and its first-floor diaphragm.


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Fig. 10.5. Map of San Francisco Bay Area with location of analyzed structures Table 10.2. Summary of property characteristics and mitigation measures Structure Location

Wood Frame Unreinforced Masonry Belmont, CA San Francisco, CA Rock to Soft Rock soils; Very Stiff to Soft soils; Moderate Low liquefaction susceptibil- liquefaction susceptibility; Geotechnical ity; Low landslide susceptibil- Very Low landslide susceptiity) bility Occupancy Single family residential Retail commercial $850,000 total with $8,000,000 total with Value $420,000 structure $4,000,000 structure Square Footage 2,200 square feet 5,000 square feet Number of Stories 2 5 Year Built 1938 1930 Bolting Sills to Foundation Complete Rehabilitation – Mitigation Measure and Bracing Cripple Walls – $250,000 $5,000

The five-story unreinforced masonry structure is utilized as retail space and worth $8 million. It is located on softer soils with moderate liquefaction susceptibly although very low landslide susceptibility. It is one of the last remaining unreinforced masonry (URM) structures in San Francisco. While no longer permitted by California building code, these are (generally) brick buildings constructed prior to 1933, when the Long Beach earthquake spurned the beginnings of modern earthquake-resistant construction. A large percentage of these structures have been upgraded due to

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the passage of the 1986 URM Law, which requires cities and counties located in Seismic Zone 4 (per the Uniform Building Code) to identify potentially dangerous URMs and adopt a plan to mitigate the hazards posed by these buildings. This legislation requires that cities and counties, at a minimum: (1) survey suspected URM buildings, (2) notify owners that their buildings might constitute a hazard, and (3) report a proposed mitigation program to the California Seismic Safety Commission by January 1, 1990. As the law does not require owners to strengthen their buildings, as of 2003, about 3,000 URMs in the San Francisco Bay Region still do not meet the minimum recommended construction standards (SSC 2003). Therefore, for the five-story URM in San Francisco, a complete rehabilitation is completed for $250,000, creating in essence a reinforced masonry structure. As seen in Table 10.3, there is a fairly significant reduction in AAL in both cases with a 35% reduction for the wood frame retrofit and a 40% reduction for the URM retrofit, as well as overall return period losses (Figure 10.6). If one calculates the net present value (as outlined earlier), within the 30-year lifetime of the structure, the net present value is greater than or equal to zero for r 0.08 for the wood frame retrofit (Fig. 10.6) and NPV 0 for r 0.11 for the URM retrofit. If the property owners are concerned with a potential catastrophic loss, these results suggest that they have an incentive to invest in mitigation. Table 10.3. Overview of losses (Return Period and AAL) before and after mitigation Return Period

Loss (Wood Frame before Mitigation)

Loss (Wood Loss (URM be- Loss (URM after Frame after Mitifore Mitigation) Mitigation) gation)

10,000 5,000 1,000 500 250 200 100 50 25 10 5 2 AAL

$596,720 $535,520 $336,255 $221,340 $100,175 $68,640 $16,195 $1,810 $12 $0 $0 $0 $1,360

$470,700 $407,120 $225,540 $136,095 $56,285 $38,440 $9,390 $1,050 $9 $0 $0 $0 $875

$7,454,855 $7,178,655 $6,132,015 $5,285,095 $4,090,670 $3,660,420 $2,329,940 $1,158,085 $317,875 $155 $0 $0 $73,790

$6,573,555 $6,075,230 $4,474,385 $3,592,920 $2,630,210 $2,313,055 $1,361,390 $581,110 $122,410 $85 $0 $0 $43,180


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Fig. 10.6. Exceedance probability curve of URM (before and after mitigation)

Fig. 10.7. Net Present Value over time for wood frame retrofit considering 2 discount rates (8% and 6%)

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10.8 Consideration of Decision Processes A basic point to recognize from these results is that whether particular mitigation measures will be viewed as worth adopting is not a foregone conclusion but requires a detailed assessment of the costs and benefits under various hazard scenarios. It is important to note as well that only the direct property losses are evaluated in this analysis. Mitigation could have additional real and perceived benefits in reducing the risk of fatalities and business interruption. There are four principal reasons why individuals do not appear to want to invest in mitigation measures: short time horizons, desire for a quick return on investment, budget constraints and lack of perception of added economic value. First, when considering the recoupment of their investment in a mitigation measure, in general, individuals consider relatively short time horizons. Even if the expected life of the house is 25 or 30 years, the person may only look at the potential benefits from the mitigation measure over the next 3 to 5 years. This may be based on their expected length of stay in the current residence. If one considers a 5 year time horizon, there is no benefit to the wood frame retrofit as illustrated in Figure 10.7 (i.e., the NPV is less than zero). A related reason why mitigation is often unattractive is that individuals expect a quick return on their investment. Financially this is consistent with using a lower discount rate for evaluating potential future payoffs (i.e., a positive NPV in a shorter time period), which is unrealistic. Third, many individuals perceive the probability of a disaster causing damage to their property as being so low that they consider an investment in protective measures as unnecessary. Even if there is some concern with the potential of a hazard, budget constraints lead individuals to place mitigation as a low priority item for the use of their funds. In addition, many residents in hazard-prone areas feel they simply cannot afford these measures. For these individuals, low cost measures with a high impact (e.g., bracing of nonstructural elements in a home, such as bookcases or computer equipment) is a good starting point to convince them that mitigation is important. Finally, individuals may have little interest in investing in protective measures if they believe that the measures will have limited added economic value to them. For example, they may not consider an investment to be cost-effective if they believe that it will not increase the resale value of their property. Moreover, if they are financially responsible for only a small portion of their losses should a disaster occur, the measure would be even less attractive.


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10.9 Improving the Modeling of Seismic Mitigation Strategies This simple example illustrates the basic aspects of modeling seismic mitigation strategies in order to determine the cost-effectiveness of such strategies. In order to perform more realistic and targeted cost-benefit analyses, there are several areas in which improvement can be made. Two areas are outlined here: the reduction of modeling uncertainty and a consistent costbenefit analysis framework. 10.9.1 Reduction of Modeling Uncertainty As noted earlier in this paper, all models carry assumptions and have limitations. One significant limitation in using HAZUS® and insurance-based models for the modeling of seismic mitigation strategies is the uncertainty in the models. Natural hazard, engineering and economic data are the foundation of catastrophe models. Given the limitations on these data sources and the probabilistic nature of the approach, the development of a catastrophe model is not unique. Limitations in data and assumptions about the model’s parameters, in the hazard, inventory, and vulnerability modules, affect the precision of a catastrophe model’s loss estimates. It is not uncommon, for example, to have an AAL estimate differ by a factor of two when analyzing a portfolio in an insurance-based model. There is a continuing need for the collaboration in research on earthquake engineering, so that collective knowledge can be applied to reducing the uncertainties in the loss estimation process. 10.9.2 Consistent Cost-Benefit Analyses When one considers mitigating a structure for earthquake risk, the assumed benefits and costs of the mitigation technique complicate the analysis. To date, limited published data exists on the vulnerability of structures for various levels of ground shaking, and quantitative benefit-cost studies on earthquake mitigation are minimal. While the implications of the analysis presented in this paper are encouraging, there is still the challenge of using a CBA framework to make an informed decision. The key is to present the analyses to decision makers so they are willing to defend the proposed recommendations because it makes economic sense to them while satisfying their political concerns. In other words, if key decision makers appreciate the role CBA plays in de-

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termining whether to implement specific mitigation measures, this methodology will have practical importance. Furthermore, the task of making CBA a useful methodology for evaluating seismic mitigation strategies is challenging due to its multi-disciplinary nature. CBA requires bringing together engineers with social scientists to analyze a problem. It requires one to articulate the nature of the uncertainties associated with the occurrence of disasters, as well as the confidence intervals surrounding the expected benefits and costs of mitigation strategies. In a nutshell, it requires the integration of science with policy. There is a unique opportunity, however, for this type of multi-disciplinary research. If the proper respect and understanding between engineers and social scientists is established, CBA can play an important role in evaluating the benefits of seismic mitigation strategies.

References Boardman A, Greenberg D, Vining A, Weimer D (2001) Cost-benefit analysis: Concepts and practice. Prentice Hall, Upper Saddle River, New Jersey Boore DM, Joyner WB, Fumal TE (1997) Equations for estimating horizontal response spectra and peak acceleration from western North American earthquakes: A summary of recent work. Seismological Research Letters 68:128153 Chang SE (2003) Evaluating disaster mitigations: Methodology for urban infrastructure systems. Natural Hazards Review 4:186–196 Dodo A, Xu N, Davidson R, Nozick LK (2005) Optimizing regional earthquake mitigation investment strategies. Earthquake Spectra 21:305–327 FEMA (1994) Assessment of the state of the art earthquake loss estimation methodologies (FEMA 249). Federal Emergency Management Agency, Washington, DC FEMA (1997) Report on costs and benefits of natural hazard mitigation (FEMA Publication 294). Federal Emergency Management Agency, Washington, DC FEMA (1998) Protecting business operations: Second report on costs and benefits of natural hazard mitigation (FEMA Publication 331). Federal Emergency Management Agency, Washington, DC FEMA (2002a) HAZUS®99 SR2 User's Manual. Federal Emergency Management Agency, Washington DC FEMA (2002b) State and local plan interim criteria under the disaster mitigation act of 2000. Federal Emergency Management Agency, Washington, DC FEMA (2002c) A guide to using HAZUS® for mitigation. Federal Emergency Management Agency, Washington DC FEMA (2003) State and local mitigation planning how to guide (Report No. 3863). Federal Emergency Management Agency, Washington, DC


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Grossi P (1998) Assessing the benefits and costs of earthquake mitigation (Working Paper 98-11-11). Risk Management and Decisions Processes Center, The Wharton School, Philadelphia, Pennsylvania Grossi P (2000) Quantifying the uncertainty in seismic risk and loss estimation. Doctoral Dissertation, University of Pennsylvania Grossi P, Kunreuther H (eds) (2005) Catastrophe modeling: A new approach to managing risk. Kluwer Academic Publishers, Norwell, MA Insurance Information Institute. (2006) Catastrophes [online]. Retrieved 10 June 2006, from http://www.iii.org/media/hottopics/additional/isofactsheet/ Kunreuther H, Cyr C, Grossi P, Tao W (2001) Using cost-benefit analysis to evaluate mitigation for lifeline systems. MCEER research projects and accomplishments: 2000–2001 Mahaney JA, Paret TE, Kehoe BE, Freeman SA (1993) The capacity spectrum method for evaluating structural response during the Loma Prieta earthquake. In: Proceedings of the 1993 United States National Earthquake Conference Multihazard Mitigation Council (2005) Natural hazard mitigation saves: An independent study to assess the future savings from mitigation activities. National Institute of Building Sciences, Washington, DC NIBS (1997) HAZUS®: Hazards U.S.: Earthquake loss estimation methodology (NIBS Document Number 5200). National Institute of Building Sciences, Washington, DC Seligson HA, Blais NC, Eguchi RT, Flores PJ, Bortugno E (1998) Regional benefit-cost analysis for earthquake hazard mitigation: Application to the Northridge earthquake. In: Proceedings of the Sixth U.S. National Conference on Earthquake Engineering. Seattle, Washington Seligson HA, Ballantyne DB, Huyck CK, Eguchi RT, Bucknam S, Bortugno E (2003) URAMP (utilities regional assessment of mitigation priorities) – a benefit-cost analysis tool for water, wastewater and drainage utilities: Methodology development. In: Proceedings of the Sixth U.S. Conference on Lifeline Earthquake Engineering, American Society of Civil Engineers, Technical Council on Lifeline Earthquake Engineering (ASCE TCLEE). Long Beach, California Smyth AW, et al (2004) Probabilistic benefit-cost analysis for earthquake damage mitigation: Evaluating measures for apartment houses in Turkey. Earthquake Spectra 20:171-203 SSC (2003) Status of the unreinforced masonry building law, 2003 report to the legislature (SSC 2003-03). Seismic Safety Commission, Sacramento, CA Steinbrugge KV (1982) Earthquakes, volcanoes, and tsunamis: An anatomy of hazards. Skandia America Group, New York, New York Stover CW, Coffman JL (1993) Seismicity of the United States, 1568–1989 (U.S. Geological Survey Professional Paper 1527). United States Government Printing Office, Washington, DC

11 Perspectives on Modeling Seismic Mitigation Strategies

Responses to Grossi’s Paper “Modeling Seismic Mitigation Strategies”

Introduction In this chapter, four commentaries from experts in planning, engineering and sociology who specialize in natural hazard planning and recovery provide additional depth and breadth to the topic of modeling seismic mitigation strategies. Laurie Johnson draws on her experience in the catastrophe modeling field to advocate for a more holistic approach to risk management, in which estimating risks and losses are part of a larger process of ongoing risk management and reduction with shared responsibility between public and private entities. Mary Beth Hueste brings a structural engineering perspective to mitigation techniques and points out the nonmonetary aspects of natural hazard losses. Rob Olshansky provides existential support for mitigation modeling as a tool to help decision-makers and the public to understand the benefits of investing in sometimes costly mitigation efforts. Finally, Yang Zhang develops a matrix of the potential interactions between technical analysis and stakeholder participation in making mitigation decisions and discusses the consequences of neglecting either aspect of these components of model development. Discussion on the topics presented in this chapter centers on areas that are frequently overlooked in natural hazard risk analyses. Homeowners do not have access – nor likely the capacity – to fully understand the information that large insurers provide to government and private entities. Therefore, their motivation and ability to make rational mitigation decisions is limited. Risk to renters is not managed well in the U.S. and the goal of

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mitigation in protecting people’s lives and livelihoods has yet to be fully achieved.

11.1 Catastrophe Modeling Paradigm Shift Laurie Johnson San Francisco, California, USA

Some additional commentary on the status of the Catastrophe Modeling (CM) field/industry is provided here. Within the insurance industry, a CM society has been formed by the Reinsurance Association of America; there is a growing analyst field and CM curricula are being developed for the analysts in the insurance industry that use CM. Model outputs and their decision purposes (i.e., the EP curve, both with HAZUS® and proprietary models) are important to note. There are decisions points within the EP curve that have to be designated and there are also different kinds of EP curves (i.e., Occurrence EP curves and Aggregate EP curves). These curves have very different endpoints and their use could certainly affect the decisions made by a local or state government in setting mitigation policy (Aggregate EP curves look at the probability of having more than one event occur in a given time frame, as opposed to the Occurrence EP, which is just looking at the probability of exceeding one loss estimate by one event). Other mitigation approaches (besides CBA) as well as some key mitigation studies, such as Memphis/NCEER’s electric disruption study (Chang et al. 1996), should be highlighted as well. Also, California’s Regional Assessment of Mitigation Priorities (RAMP) – a regional approach to looking at mitigation strategies and using modeling (Seligson et al. 2003) – was a landmark study that deserves mention. The use of CM in Disaster Management Act (DMA) of 2000 plans and DMA compliance should be noted. FEMA’s mitigation effectiveness study (Multihazard Mitigation Council 2005) was a landmark CM study in that it demonstrated the costeffectiveness of mitigation.


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11.1.2 Holistic “Risk Management” Approach A study (Johnson et al. 2005) funded by the Public Entity Risk Institute (PERI) and the American Planning Association (APA) took the idea of risk management to planners to broaden their perspective on their role risk management. Risk management provides a holistic, structured approach to evaluate the range of potential impacts of events, and formally apply policies, processes, and practices to address those impacts. It creates a common valuation system, which is generally financial. This approach to valuation is useful in quantifying, comparing, and making risk management decisions and helps provide a broader set of decision options for handling risks. Why Risk Management? “Governments have the most to learn about risk. Without a better grasp of the costs and benefits of the rules they create to control it, they can do more harm than good.” (Smutniak 2004). There are a number of risk management centers in the U.S. with which government risk management practices could better align: PERI (http://www.riskinstitute.org), Public Agency Risk Management Association (PARMA – http://www.parma.org), Public Risk Management Association (PRIMA – http://www.primacentral.org), Nonprofit Risk Management Center (NORMA – http://www.nonprofitrisk.org), and the Risk and Insurance Management Society (RIMS – http://www.rims.org). Felix Kloman, one of the founders of PERI, publishes Risk Management Reports (http://www.riskreports.com). Finally, other countries are doing this: Australia and New Zealand have their Standard AS 4360, as does Canada, that includes risk management requirements for public and private entities (http://www.riskmanagement.com.au and http://www.standards.com.au). So, in the U.S., we’re not cutting-edge, we’re actually kind of behind. The four phases in the risk management process are as follows (CM deals with the first two): 1. Identifying Risks a. Where could an event occur? How often? What size or strength? 2. Assessing and Quantifying Risks a. Probabilistic analyses, scenario analyses, and vulnerability characterization 3. Managing and Implementing Risk Management Decisions a. Avoidance or elimination b. Reduction or mitigation c. Sharing or transfer d. Retention (how much risk are public entities retaining?)

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4. Monitoring and Implementation Over Time (rarely done – CM tools could contribute a great deal) a. Continuous and ongoing b. Means of checking and ensuring that decisions are working There are a number of elements to risk (Table 11.1). Johnson et al. (2005) outline these risk elements with different options to show planners how they can handle different risks using their own toolkit of policies, regulations, and projects. Table 11.1. Risk Elements Capital Risks Damage to government buildings and essential facilities Damage to buildings, lifelines/utilities Damage to machinery/equipment, furnishings Environmental Risks Water and air pollution Loss of biodiversity Noise and light pollution Social/Cultural Risks Loss of life; injury and illness Loss of residence and demographic change Loss of cultural/historical resources Impacts on vulnerable populations Change in neighborhood character

Institutional and Policy Risks Government liability Staff turnover Damage to reputation; increased distrust of government Erosion of community vision and undermining of other policies Economic Risks Financial loss to governments Financial loss to business and residents Reduced tax income and business income Increased government expenditures Lack of affordable housing Loss of high paying jobs

We must broaden our understanding and quantification of vulnerability. For example, a number of states including Texas, Louisiana, and Alabama have neither land-use nor building codes (Burby 2005). This raises the question of why we worry so much about getting the right answer on building mitigation when we have no institutional capacity to actually implement mitigation in many places in our country. If we have jurisdictions that aren’t even willing to adopt building codes, how are they going to be-


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gin to implement a retrofit of units in their jurisdiction? There are some real challenges here. 11.1.3 Insurers Use of CMs in Risk Management Insurers use CMs in many ways – it isn’t just about insurance. Take this framework of four decision options: (1) Avoidance or Elimination, which includes underwriting guidelines and decisions; (2) Reduction or Mitigation, which includes limited risk mitigation, accumulation or portfolio management and credit ratings1 or rate filings; (3) Sharing or Transfer, which includes reinsurance, alternative risk transfer, and swaps; and (4) Retention, or capital reserving. 11.1.4 Closing We need to broaden our perspective on risk management. We need to broaden the application of CM, for example by developing a more robust decision-support system (HAZUS® or MAEViz or something else) that allows us to consider all four of the above-listed management options. We need to expand the analysis of vulnerability, risk and loss elements. Finally, we need to broaden the user group to include city managers, risk managers, and planners, as well as emergency responders and ‘mitigators’. Alliances between researchers, practitioners, and organizations such as PERI, RIMS or the International City/County Management Association (ICMA) might help.

1 A.M. Best Co. (2006) currently estimates PML for 100-yr. wind & 250-yr. quake to determine capital adequacy.

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11.2 A Structural Engineering Perspective on Modeling Seismic Mitigation Strategies Mary Beth Hueste Texas A&M University, College Station, Texas, USA

The decision to mitigate is impacted by a number of different factors. In the previous chapter, Grossi highlights several of these issues. Some of these are reiterated here, along with several additional considerations. Useful loss estimations must include direct losses due to structural damage, non-structural damage, and contents damage. In addition, indirect losses due to business disruption and possible relocation are of critical importance. Other factors can also affect the decision to mitigate. For example, seismic mitigation may be more economical during other improvements to a structure, such as renovations to upgrade the HVAC system or other nonstructural improvements. One also should consider the importance of the structure to the owner and general public. Some structures cannot be replaced, such as historic structures, and the benefit of protecting them from seismic damage is difficult to quantify. Essential facilities must be functional following an earthquake to protect the community’s population and resources. From a structural assessment point of view, a basic safety objective could be used (as in FEMA 356 (American Society of Civil Engineers 2000)), rather than monetary losses, to ensure that structural performance meets a minimum requirement that is set based on the recurrence interval of the earthquake (for example, life safety performance for a 10% in 50 year event and collapse prevention performance for a 2% in 50 year event). In the final analysis, the protection of human life is of utmost importance and one cannot place a dollar value on this objective. Inputs into loss assessment can have a large degree of uncertainty. The uncertainty associated with the hazard, inventory, and vulnerability modules is a significant limitation in using current loss estimation models. In addition, the results are subject to modeling decisions and selection of parameters that represent “typical” structure types. This gives rise to the question of how appropriate current vulnerability and damage functions are for modeling a particular building stock. It also points to the need to document the basis for these relationships so that future users are informed on appropriate uses of this information. With respect to developing vulnerability functions for structures, it is recognized that although some nonlinear structural models are quite good, there can still be limitations in captur-


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ing all failure modes (such as shear) and duplicating actual damage. After developing fragility curves based on structural behavior, it is necessary to convert the structural performance to damage and monetary losses. Each step in model selection, analysis, and conversion to damage and monetary losses includes uncertainties that should be quantified. This is often difficult, due to lack of real data. There is a continuing need to collect information following an earthquake and to evaluate current loss estimation models. Model calibration and refinement based on actual data provides an opportunity to improve existing loss estimation tools. Grossi describes loss estimation models in general, providing a useful overview of the process of modeling mitigating strategies with the purpose of weighing the costs and benefits. Two major model types that are currently available are presented: insurance-based models and HAZUS®. There is an additional probabilistic model for regional seismic loss assessment that is under development through the Mid-America Earthquake Center called MAEViz. This model is being developed to track uncertainty through the computational process. In addition, the model stores metadata for each vulnerability function (fragility curve) to maintain information on the basis of its development and to allow a determination of when its application is appropriate. As a structural engineer involved in developing vulnerability relationships for building structures through academic research, I bring a more focused perspective to the issue of modeling seismic mitigation strategies. I value the big picture and informative discussion provided by Dr. Grossi. The chapter helps to emphasize the many different components that must come together for a complete loss estimation approach. At the same time, there is a need to appreciate that each of those components can influence the final analysis to some degree and, as such, having the most accurate relationships possible to describe the hazard, inventory, vulnerability, and loss, along with their uncertainties, is a worthy goal. As noted by Dr. Grossi, such advances will require a multi-disciplinary approach, presenting a unique opportunity for geoscientists, social scientists, and engineers to collaborate.

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11.3 The Role of Risk Modeling in Mitigation Robert B. Olshansky University of Illinois at Urbana-Champaign, Champaign, Illinois, USA

There are at least two appropriate roles for this topic in the overall volume: 1. Argue for and promote the need for using risk models to assess mitigation strategies. There will be a continuing need to justify mitigation in general and mitigation actions in particular because of the Disaster Mitigation Act, Hazard Mitigation Grant Program, and other related programs. FEMA is starting to look at emergency management activities more critically and to make strategic funding decisions. This field won’t advance very quickly if we must wait for an earthquake to occur to provide examples in order to show how retrofitting one building might have reduced its damages. To look at mitigation in a broad sense, one also needs to look at the times and places where earthquakes didn’t happen so we can have a more complete perspective. The only way to measure what didn’t happen is with models such as this one. Thus, we need risk models in order to assess mitigation from a systematic point of view. 2. Describing how different users face mitigation decisions. Doing so can help in conceptualizing development and application of these models for mitigation purposes. There are four reasons individuals do not invest in mitigation, which can be illustrated using diagrams: a. Individuals consider short time horizons (see Fig. 6.7). Individuals with differing discount rate curves will mitigate differently over different time horizons (15 years vs. 30 years). b. Individuals expect a quick return on their investment (equivalent to having a high discount rate). c. Individuals perceive the disaster probability as low, and their resources as being limited. d. Individuals believe that the benefits of the investment will not accrue to them (i.e., might not add to the resale value of the property or their insurance policy will pay for any anticipated damage).


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11.3.1 Use of HAZUS® for Mitigation One broadly held view is that models should be used as analytic support for mitigation. But another value of HAZUS® is that its wide use can serve as an attention-getter for local governments. We should appreciate this value. Most users find HAZUS® useful for preparedness and for creating scenarios for response exercises. But it also has value in promoting mitigation, especially in seismic parts of the country that have not had a damaging earthquake in a long time. In such places, such as Mid-America, “windows of opportunity” to promote mitigation after earthquake events are infrequent. Thus, the best mitigation opportunities may be after creating “fake earthquakes” using HAZUS® – when the thought might occur to users that they can take action to avoid some of the negative outcomes. The experience of these pretend earthquakes can help to catalyze mitigation actions. We don’t always know which mitigation actions are most cost-effective. The reality is that although we would ideally always like to make rational decisions, we have to make some decisions before the next earthquake arrives, so we can’t always wait for complete analytic confirmation before doing mitigation. Even though complete analysis is the ideal, an important role for these models, HAZUS® in particular, is to serve as attentiongetting devices to instigate mitigation actions and improve the state of the built environment before a disaster occurs.

11.4 Adopting Mitigation Yang Zhang Texas A&M University, College Station, Texas, USA

Adopting seismic mitigation for existing structures has proven problematic in the United States. Currently, earthquake risk problems are still often times approached with a technical solution focus. Other non-technical factors such as risk perception, risk communication, economic capability, social context, and community political environment are systematically overlooked in modeling seismic mitigation strategies. Mitigation advocates tend to oversimplify the decision process. Cost-benefit analysis is arguably good for economic feasibility analysis. However, it certainly is not a tool to address other facets of the mitigation decision process. Only

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through stakeholder participation can non-technical decision factors be addressed. Modeling seismic mitigation strategies should have both a technical component (technical analysis) and a non-technical component (stakeholder participation) (Fig. 11.1). A high degree of technical analysis with a low level of stakeholder participation can only result in limited mitigation adoption, because factors such as risk perception, risk communication, social context, and community political environment are not fully addressed. Our goal should be an integrated mitigation approach. Mitigation Strategies based on integration of technical analysis and stakeholder participation will increase the chances of successful mitigation adoption.

Fig. 11.1 Components of modeling risk mitigation strategies

11.5 Strategic Directions for Modeling Seismic Mitigation Strategies Standardized models for estimating potential losses and techniques for analyzing alternatives for mitigating these losses grew out of necessity. Major advances in computing technology coincided temporally with several particularly expensive natural disasters in the U.S., following which the federal government responded with the release of HAZUS® (as discussed in the earlier chapter on HAZUS® development). While the U.S.


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government sponsors and endorses the free use of HAZUS® for public service, insurance companies have developed proprietary models based on similar methodology. Output from these models can be compared and verified, keeping in mind their differing assumptions and intended use. The previous chapter presented an overview of the comparability of the proprietary and public catastrophe models, as well as a framework for conducting an assessment of potential mitigation strategies based on the output of these models. An example of using catastrophe models to analyze seismic mitigation strategies was presented, along with a discussion of needed improvements for modeling mitigation strategies. Several additional points merit mention here. Could vulnerability analysis be made an on-the-fly function in HAZUS®? How sensitive are the models to fragility curves? It would be difficult, and perhaps unwise to build these in because of the high level of uncertainty. Differences in attenuation override sensitivity to fragility curves (in Grossi’s analysis) by far. HAZUS® models are not very sensitive to fragility. Many public mitigation decisions are made on little or no analysis, while private clients and large corporations often employ engineers for such analyses. Housing is an important and overlooked area in mitigation. Homeowners don’t have access to resources and information that public or commercial entities have in making mitigation decisions. There is a need for more accessible information about the benefits of mitigation for homeowners and renters. Homeowners have little access to this type of information and the information that is available is often written for technical audiences, and does not directly address the decisions homeowners face. There have been attempts to generate “mitigation for dummies” guides, but they have not been printed or widely distributed. Even if these guides were widely distributed, they might not be used nor heeded widely unless they were packaged appropriately. In order to bridge the gap between the modeling perspective and real decision processes, we need to start with the user. What are their objectives, what are their budget constraints? We may be able to start bridging this gap by making the models more useful for a broader set of decision makers and contexts. Renters are another important group whose risk may not be sufficiently accounted for because they don’t own their own risk and have limited mitigation options. The risk to renters may not be in the value of the loss of property but in the loss of housing services, disruption in their lives, and other social consequences that are not accounted for in typical mitigation analyses. Other countries (i.e., Japan) assign a certain level of rights to occupants to occupy a similar space after a disaster, and not just financial rights to property owners.

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The issue of deaths and valuing loss of life is important. Most loss of life in the U.S. does not occur in housing, but in other countries it does. The fact that loss of life in housing in the U.S. is minimal is an indication that mitigation efforts here have been successful, but there is still room for improvement. Certain types of buildings in the U.S., such as unreinforced masonry, may be unsafe. The risk of high costs from natural disasters will continue to rise as development pressure in vulnerable areas increase. Insurers and other risk holders can best manage this risk through accurate modeling based on well-grounded theoretical frameworks and comprehensive empirical data. In the quest to mitigate potential losses from natural hazards, proprietary insurance-based models and HAZUS® offer different advantages and limitations depending on their intended use. Grossi’s paper offers a framework for maximizing returns on investment in mitigation based on full CostBenefit Analysis as an aid to decision-making. In order for CBA to be most useful, however, the modeling on which it is based must be as precise and accurate as possible – difficult to achieve, but approachable through research on natural hazards, structural engineering, and better inventories of economic data. Catastrophe modeling is taking on a larger role in the management of natural hazards and risk. As part of a larger framework for natural hazard risk management, quantitative modeling and cost-benefit analysis can provide a powerful tool for public and private entities, which can use modeling and CBA along with regulations and asset management to reduce overall risk. Decisions to mitigate may be based on factors that are difficult to quantify, but procedures exist for meeting community goals (such as safety objectives) in addition to protecting monetary value. While uncertainty cannot be eliminated, it can and should be tracked and presented so that decision-makers can include it in their decision processes. Finally, it is essential to recognize that individuals in society will receive and respond to the results of quantitative natural hazard modeling in different ways depending on their unique perspective and level of knowledge. The technical component of mitigation strategies alone is insufficient to achieve optimal planning – the goal should be an integrated mitigation approach based on an informed process of decision-making that includes stakeholder values and participation. Some issues deserve more attention from risk modelers. Making information about the benefits of mitigation more accessible and understandable to homeowners (who frequently are the ultimate decision-makers about whether to mitigate) could improve the rates of mitigation in vulnerable areas. Additionally, renters in the U.S. are a particularly vulnerable group. Not only do they not always have access to information about their


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risk, they frequently do not have pre-assigned rights to housing as they do in other countries such as Japan. Finally, some non-monetary risk, such as potential loss of life, has been managed well in the U.S., but there is still room for improvement in terms of safety.

References A.M. Best Co. (2006) Catastrophe analysis in A.M. Best ratings. Oldwick, NJ Alesch DJ, Petak WJ (1986) The politics and economics of earthquake hazard mitigation. Natural Hazard Research and Application Center, University of Colorado, Boulder, CO Alesch DJ, Petak WJ (2001) Overcoming obstacles to implementing earthquake hazard mitigation policies: Stage 1 report. Multidisciplinary Center for Earthquake Engineering Research, State University of New York at Buffalo, Buffalo, NY American Society of Civil Engineers (ASCE) (2000) Prestandard and commentary for the seismic rehabilitation of buildings (FEMA 356). Federal Emergency Management Agency, Washington, DC Burby RJ (2005) Have state comprehensive planning mandates reduced insured losses from natural disasters? Natural Hazards Review 6:67–81 Burby RJ, May PJ, Paterson RC (1998) Improving compliance with regulations: Choices and outcomes for local government. Journal of American Planning Association 64:324–334 Chang SE, Seligson HA, Eguchi RT (1996) Estimation of the economic impact of multiple lifeline disruption: Memphis light, gas and water division case study (Technical Report NCEER-96-0011). National Center for Earthquake Engineering Research, Buffalo, NY Dodo A, Xu N, Davidson RA, Nozick LK (2005) Optimizing regional earthquake mitigation investment strategies. Earthquake Spectra 21:305–327 Fischhoff B, Bostrom A, Quadrel MJ (1993) Risk perception and communication. Annual Review of Public Health 14:183–203 Johnson L, Samant LD, Frew S (2005) Planning for the unexpected: Land-use development and risk (531). American Planning Association, Chicago, IL Katz D, Kahn RL (1966) The social psychology of organizations. John Wiley & Sons, Inc., New York, NY Lindell MK, Alesch D, Bolton PA, Greene MR, Larson LA, Lopes R, May PJ, Mulilis J-P, Nathe S, Nigg JM, Palm R, Pate P, Perry RW, Pine J, Tubbesing SK, Whitney DJ (1997) Adoption and implementation of hazard adjustments. International Journal of Mass Emergencies and Disasters Special Issue 15:327–453 Lindell MK, Perry RW (2000) Household adjustment to earthquake hazard: A review of research. Environment & Behavior 32:590–630

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Lindell MK, Prater CS (2000) Household adoption of seismic hazard adjustments: A comparison of residents in two states. International Journal of Mass Emergencies and Disasters 18:317–338 Lindell MK, Prater CS (in press) Risk area residents’ perceptions and adoption of seismic hazard adjustments. Journal of Applied Social Psychology Lindell MK, Whitney DJ (2000) Correlates of seismic hazard adjustment adoption. Risk Analysis 20:13–25 March JG, Olsen JP (1976) Ambiguity and choice in organizations. Universitetsforlaget, Bergen Multihazard Mitigation Council (2005) Natural hazard mitigation saves: An independent study to assess the future savings from mitigation activities. National Institute of Building Sciences, Washington, DC Petak WJ, Atkisson AA (1982) Natural hazard risk assessment and public policy: Anticipating the unexpected. Springer-Verlag, New York, NY Seligson HA, Ballantyne DB, Huyck CK, Eguchi RT, Bucknam S, Bortugno E (2003) URAMP (Utilities Regional Assessment of Mitigation Priorities) - a benefit-cost analysis tool for water, wastewater and drainage utilities: Methodology development. In: Proceedings of the Earthquake Engineering 2003: 6th U.S. Conference and Workshop on Lifeline Earthquake Engineering. Long Beach, California, USA Smutniak J (2004) Survey of risk. The Economist. January 24, 2004 Tillinghast and Reinsurance (2005) Hurricane Katrina: Analysis of the impact on the insurance industry. Towers Perrin

12 Visualizing Uncertainty in Natural Hazards

Alex Pang Computer Science Department, University of California at Santa Cruz, CA

12.1 Introduction “Presenting data without error rate is misleading”. This is a quote from the O.J. Simpson defense team regarding the presentation of DNA evidence without valid error rate statistics. Taken more generally, this practice is a prevalent shortcoming in the scientific and information visualization communities where data are visualized without any indication of their associated uncertainties. While it is widely acknowledged that incorporating auxiliary information about data, i.e. data quality or uncertainty, is important, the relative amount of work in this area is small. On the other hand, developments by the geographic, cartographic, and GIS communities in this regard is much more concerted. Some of the early efforts were spearheaded by the participating members of the National Center for Geographic Information and Analysis (NCGIA) initiatives (Beard et al. 1991; Goodchild et al. 1994), where different methods of displaying and animating data with uncertainty were proposed. An excellent summary of this body of work can be found in (MacEachren et al. 2005). Combining these works with those from the information and scientific visualization communities, a typology for uncertainty visualization was presented which tries to map data, uncertainty, and tasks with the appropriate visual presentation (Thomson et al. 2005). Specifically, the typology for uncertainty visualization would give the user some guidance about the visual representations for the different types of uncertainty. There are some key differences in the approaches between the first (geographers, cartographers) and the second (information and scientific visualization researchers). On one hand, the first group focuses on the identify-

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ing and characterizing the type, nature, source, and characteristics of uncertainty; as well as map-based uncertainty visualizations. On the other hand, the second group focuses on the task of visually mapping the different facets of uncertainty, and extending the techniques to higher dimensional data sets as well. Clearly, we need to bridge this gap in order to provide an end-to-end solution to the users. Good visualizations need to target the results to the needs of the users. This means that not only do we need to identify who the users are, we also need to identify the particular task they’re trying to do with the given data at that particular moment. Thus, the same data set can be presented in a number of ways – perhaps at various levels of detail, emphasizing or deemphasizing different regions and features, and employing different visualization techniques to best present the message. However, there are also occasions were the users’ goals are not known. In such situations, it is not uncommon to see visualization systems that try to provide interactive exploration of the data sets, or a flexible framework for specifying and emphasizing different aspects of the data set. Frequently, the tradeoff for having a flexible system is that the users need to specify more parameters before they can get their visualizations. Likewise, interactivity usually comes at a cost, both in terms of increased computational resources, but also in the quality of the renderings (e.g. tradeoff in quality versus speed (Ma et al. 2003)). Hence, the exploratory process needs to be followed by a refinement stage where feature extraction may be encoded as the users get a better grasp and can better define what the important features are, and the visualizations and user interfaces are streamlined so that the desired visualizations can be obtained with minimal effort. The description above is also reflective of the multidisciplinary nature of the visualization researcher in combining engineering, science, and art. Engineering in that the visualizations are problem driven with users trying to understand or look for features in their data sets and the visualization researchers specifying the best practice approach. Science in that the visualization researcher also needs to draw upon various established fields such as perceptual and cognitive psychology, mathematical and physical analyses, etc. Visualization is also an art in that the results need to be tailored to the particular task, needs, and occasion. What was just described is true whether uncertainty is taken into account or not. Uncertainty surely does not simplify matters. It adds to the computational task of handling and presenting them, but also to the cognitive task of the users to understand them. This paper focuses on visualizing uncertainty in data sets, particularly those found in geospatial applications.


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12.2 From Concepts to Representations Uncertainty is a multi-faceted concept and includes such concepts as imprecision, imperfect knowledge, inaccuracy, inconsistency, missing information, noise, ambiguity, lack of reliability, etc. Its many definitions are quite rich and reflect different properties of uncertainty, but at the same time provide no clear no consensus or universally preferred meaning (Pang 2001). This has resulted in different ways of quantifying uncertainty (Klir and Wierman 1999), and included such measures as statistical variations or spread, minimum-maximum range values, data quality or reliability, likelihood and probabilistic estimates, etc. Likewise, numerical simulations involving uncertainties (Kloeden and Platen 1995) and propagation and reasoning in the presence of uncertainty (Pearl 1996) provide alternative means of manipulating the different flavors of uncertainty. In this section, we quickly review the concepts of uncertainty, how they are represented numerically, and how we can go from there towards visualizing them. Note that there are much more comprehensive papers that describe the different concepts and facets of uncertainty such as those by Klir and Wierman (1999) and Thomson et al. (2005). It is not the purpose of this paper to expand on those different concepts, but rather to focus on how the different concepts of uncertainty are ultimately represented numerically. The numerical representation is an important step in the visualization process as ultimately, these numerical representations need to be mapped to visual parameters. In a typical visualization pipeline, we see a data acquisition stage where data are collected from measurements, field observations, and numerical models; these data then undergo a transformation stage where measurements may undergo unit conversions, new variables are derived from available ones, data may be refined or summarized, or features may be extracted from them; the results of the data transformation stage are then fed to the actual visualization step where the derived quantities are finally mapped to visual parameters. This pipeline is illustrated in Fig. 12.1. We note that uncertainty can be introduced at any stage in the pipeline, including the final stage where the user may also misinterpret, or misuse the resulting visualization.

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Fig. 12.1. This visualization pipeline shows the introduction of uncertainty (U) from models and measurements, uncertainty from transformation processes, and uncertainty from the visualization process itself

Uncertainty in acquisition:

Data are collected through observations and measurements as well as numerical simulations. In both cases, uncertainty can be introduced in various forms such as miscalibration or drifts in instruments, bias, noisy or missing data, and over simplification of mathematical models. These can be characterized as statistical variations (Chatfield 1983). With instruments, there is an experimental variability whether the measurements are taken by a machine or by a scientist. The more times the measurement is taken, the more confident the measurement. But there will be a statistical variation in these measurements. In numerical modeling, the model and its parameters have been decided by a domain specialist, and are inherently a simplification (e.g. linearization of a nonlinear system) of the system being modeled. Aside from model simplification and sensitivity of these models to input parameters, numerical calculations performed on these models also introduce errors due to the choice of integration algorithms and the limited precision of the computing machinery. Likewise, there is variability in human observations both in terms of difference in perception among individuals and also to slight differences when asked to perform a task repeatedly.


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Uncertainty in transformation:

Often times, raw data are not rendered directly but are first subjected to further transformations with or without the knowledge of the person doing the visualization task. These data transformation operations may be as simple as conversion from one unit of measure to another, or may involve some algorithm to fuse one or more types of data together to derive a new data type. Data transformation operations may occur as early as the data acquisition stage or later in the visualization stage. Likewise, data may be rescaled, resampled, quantized, etc. either prior to, or as part of, the visualization stage. The key point is that these transformations alter the data from its original form, and have the potential of introducing some form of systematic uncertainty. Uncertainty in visualization:

What is also interesting is that uncertainty is also introduced in the visualization stage itself. For instance, in global illumination of 3D scenes, radiosity algorithms use approximations for calculating form factors. Similarly, there are different approaches to direct volume rendering of 3D data sets (Kim et al. 2001) resulting in discernable differences in renderings of the same data set. Uncertainty introduced in the visualization process is not limited to radiosity and volume rendering, but are also present in more routine operations. For example, the use of interpolation is quite prevalent in taking slices through data sets, in contouring, as well as isosurface algorithms (Lorensen and Cline 1987; Van Gelder and Wilhelms 1994). Similarly, in flow visualization methods, different integration methods, step sizes, orders, and seeding strategies lead to noticeably different flow visualization results. Effects of uncertainty are more pronounced in the vicinity of or on critical points in the flow field. These differences may at times result in drastically different flow visualizations. Animation allows visualization to include an additional parameter, usually time. Again, there are several opportunities for uncertainty to be introduced. The process of in-betweening to fill in frames between key frames is analogous to surface interpolation, and while there is no single preferred method, there are many methods available, and all of them will result in some slight variations. Hopefully, this short list of potential pitfalls illustrates the numerous ways in which uncertainty can be introduced into the visualization pipeline. Users should also be wary of blindly using visualization methods

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without fully understanding the limitations and assumptions of each method. Comparing these different sources of uncertainty with the different concepts of uncertainty, one can also see that while most of the uncertainty may be introduced upstream during the data acquisition stage (e.g. missing information, noise, imprecision, inaccuracy), other forms for uncertainties may also be introduced in later stages of the pipeline (e.g. inconsistency in data handling procedures, lack of precision in numerical calculations, smoothing, fuzziness or vagueness from filtering operations, loss of information from sub-sampling, etc.). What is critical for the uncertainty visualization task is to know how the uncertainty is numerically represented. That is, is uncertainty represented by a single scalar value that represents e.g. standard deviation or data quality? Or is it represented by a pair of scalar values that represents e.g. a minimum-maximum pair? Or is it represented by a whole range of values from measurements or simulations, or possibly by a probability density function? The different ways in which uncertainty is represented, coupled with whether this representation is available at individual spatial locations dictates to a large degree how uncertainty visualization can proceed. Combined uncertainty and data representation:

We now describe a multi-value data type as a means of representing both data and its uncertainty. Note that this representation is not a cure all. In fact, it would not be able to represent missing data, for example. A multivalue is a data type which is simply a collection of n values about a single variable at a location p and time t: M = [vi], where i =1..n (see Fig. 12.2). It is useful for representing uncertainty as multiple estimates such as those found in situations where there is variability in the measurement process or the physical phenomenon itself. A simple example is the measurements of temperature at a particular location using a variety of devices. The measurement from a single device is a single-valued data – which would also be a special case of a multi-value where n = 1. The collection of temperature values from all devices is a multi-valued temperature data for that location and time. Likewise, ocean and weather ensemble forecasts are generated from multiple runs of a set of models or parameters, and have multiple values for each physical field e.g. pressure, wind, current, temperature, at every spatial location and forecast period. Multi-valued data sets offer a richer representation of the variable nature of some data and can be used to represent uncertainty as well.


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“Multi-valuedness” is a concept orthogonal to multidimensional or multivariate data. That is, a data set can be multidimensional, multivariate and multi-valued. Multidimensional data refers to the spatial dimensionality e.g. 0D, 1D, 2D, 3D, of the data, and often also includes time as another dimension. Multivariate data, on the other hand, refers to the different variables represented at each location. These variables are usually scalars e.g. temperature, but may also be vectors such as ocean currents, or tensors such as velocity gradients, and so on. Of course, a data set can be both multivariate and multidimensional: these properties are truly orthogonal. For example, a weather forecast may be 3D, time-varying and contain information about temperature, humidity, and pressure at each location. In practice, such data may be stored in a 5D array: three for space, one for time, and the last one for the different variables. A notable visualization system that carries this name is Vis5D (Hibbard and Santek 1990). Note that in this context, the term “dimension” includes the spatial, temporal and multivariate properties of the data. Multi-valued data adds an extra “dimension” in that we also need to represent the collection of values for each variable at each location and time. Multidimensional, multivariate and multi-valued data are clearly different, as illustrated in Fig. 12.2. One can have scalar multi-valued data where each member of the collection is a scalar (last row, first column), or one can have multivariate multi-valued data where each member of the collection is a multivariate vector (last row, last column). Clearly, the multivalued data representation offers a much richer data representation compared to the single-value representation in use today. In the next section, we highlight the uncertainty visualization techniques leading up to multivalued representations, and different approaches to visualizing data with such representations.

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Fig. 12.2. Multidimensionality (space and time), multivariateness, and multivaluedness are orthogonal data properties. (a) illustrates the classic visualization of a scalar data, from a single value to a histogram for the multi-values. The next three rows illustrate the multidimension, multivariate and multi-valued properties. (b) illustrates the different spatial dimensions, while (c) illustrates the number of variables in a multivariate data. (d) shows the number of values in a singlevariable multi-valued data. To make the representation explicit, the multi-values are surrounded by [ ]. (e) illustrates a multi-valued data set with an increasing number of variables. Note that the graphical representations from (1c-1e) are for a single position in time and space. A multidimensional multivariate multi-valued data (not illustrated, this is a research challenge) would be represented as in the last row, last column, but replicated at every spatial location


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12.3 Uncertainty Visualization From the visualization point of view, we need to map data, including uncertainty, to visual parameters such as color, texture, transparency, geometry, glyphs, animation, etc. To make effective visualizations, we need to also ensure that these visualizations are designed to answer the particular questions or needs of the user. In this section, we look at the state of the art in uncertainty visualization. The latter goal of creating effective visualizations is a bit more difficult to generalize amongst the various types of user tasks without a more formal study. Instead, we will look at examples of how uncertainty visualizations can be used in visualizing natural hazards in the next section. In the following discussion of uncertainty visualization, we organize the techniques according to the manner that uncertainty is represented. 12.3.1 Scalar Uncertainty The majority of the uncertainty visualization research so far has focused on uncertainties that are represented as single scalar values. To illustrate this point, let us look at a number of examples from the geographic/cartographic communities as well as the information/scientific visualization communities. Information about the driving application behind the visualizations is provided as needed. Additional details about the visualizations are available through the referenced works. The idea of using blurriness, lack of crispness, or fuzziness to indicate degree of uncertainty is probably the most intuitive and widely used technique (see Fig. 12.3). This method is probably best for data over a 2D spatial domain. Applying this technique to 3D (see of Fig. 12.5a) creates some ambiguity arising from the selected viewpoint. Furthermore, this technique also has the drawback of not being able to depict slowly changing degrees of uncertainty i.e. it has relatively poor resolution in terms of distinguishing amounts of uncertainty.

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Fig. 12.3. Uncertainty mapped to blurriness (reprinted with permission from Ben Schneiderman, University of Maryland at College Park)

Fig. 12.4. (This figure also appears in the color insert) Uncertainty mapped to transparency. Remnants from architectural excavations reveal the locations of building foundations, coupled with information about the architectural style of the era, allowed the researchers to create a virtual reconstruction of the buildings. More opaque regions are more certain. Reprinted with permission from Strothotte (1999)


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Fig. 12.5. (This figure also appears in the color insert) (a) positional uncertainty is mapped to fuzzy surface. The data captures the atomic thermal motion as well as uncertainty of molecules using a Gaussian distribution (reprinted with permission from Lee and Varshney 2002). (b) positional uncertainty is shown using point clouds. The data is from a CAT scan of human kidneys with tumor formations. Arrows indicate positions of tumors. Red arrows are places where the uncertainty is higher (reprinted with permission from Grigoryan and Rheingans 2004)

Fig. 12.6. (This figure also appears in the color insert) Probability that a magnitude 5.0 or greater earthquake will hit the Boulder area in the next 10 years is mapped to a discrete color scale. Image produced by the USGS Earthquake Probability Mapping at http://eqint.cr.usgs.gov/eq-men/html/neweqprob-06.html

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Fig. 12.7. (This figure also appears in the color insert) Left images show the 95% confidence interval where darker regions have higher values. Users can then progressively adjust the focus to only show those regions (reprinted with permission from MacEachren et al 1993). The data is about the confidence levels of the dissolved inorganic nitrogen in the Chesapeake Bay

Fig. 12.8. (This figure also appears in the color insert) (a) A contour is overlain on top of the data map. The contours show uncertainty and grow thicker as the uncertainty rises (reprinted with permission from MacEachren et al. 1993). The data set is the same one as in Figure 12.12. (b) A similar idea is used where the reliability of watershed boundaries is inversely mapped to the width of the boundary lines (reprinted with permission from Miller and Morrice 1996)


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Fig. 12.9. (This figure also appears in the color insert) Yet another variation on how uncertainty can be depicted on contour lines. The contours show uncertainty and appear more broken as the uncertainty rises. Reprinted with permission from Djurcilov and Pang (2000). Color of the contour lines is available for mapping to another variable

Fig. 12.10. (This figure also appears in the color insert) A grid is superimposed over the 2D domain. The data values are mapped to color, while the grid is modified according to uncertainty values. On the left, it is mapped to amplitude, while on the right, it is mapped to noise (reprinted with permission from Cedilnik and Rheingans 2000). The data set shows the total ozone measurements made by the Nimbus-7 satellite on September 16, 1979. Onboard sensors are subject to drop outs hence leading to uncertainty in portions of the data set

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Fig. 12.11. (This figure also appears in the color insert) Angular uncertainty represented as angular spread is mapped to the width of the arrow head (reprinted with permission from Wittenbrink et al. 1995). Data is derived from correlating radial vectors from two different sensor locations. The derived 2D vector field has higher accuracy closer to the sensor locations and drop off with distance as well as the angular difference between the two sensor locations. That is, if the radial components from the two sensors have less angular difference, the derived vector also has less accuracy. Wittenbrink et al. (1995) also describe methods for showing uncertainty in velocity magnitudes in the derived vector field

The effects of blurriness can be achieved by other means aside from a low pass filtering operation. For example, uncertainty can be mapped to transparency (see Fig. 12.4) or noise (see Fig. 12.5b). Comparing these two examples, the point based approach seems to have the advantage over the transparency approach particularly for 3D scenes where occlusion can be an issue in the interpretation of the amount of transparency. A more drastic approach is to map uncertainty directly to a color map. The color map can either be continuous or discrete (see Fig. 12.6), While this approach has the distinct advantage of allowing the user to easily gauge the degree of uncertainty, and can be used directly on a 3D scene, it uses one of the key visual parameters for mapping data values --color. Hence, if data plus the uncertainty about the data need to be simultaneously visualized, data would have to be visually mapped to a less prominent visual parameter. Rather than mapping uncertainty to color, it can instead be mapped to saturation or value (using an HSV color model). Figure 12.7 illustrates this. In addition, the user can adjust a threshold below which the mapping simply grays out the uncertainty values. This mechanism allows the user to focus on the more “interesting" regions. Drawing contour lines is one of the basic visualization techniques often applied to 2D data sets. Just as color can be used to indicate uncertainty, contour lines can also be used to indicate uncertainty (rather than data). An example of such use is in car-


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tography where maps are generated with contour lines showing different amounts of angular or area distortions. Contour lines can be embellished so that it can depict the underlying data, while the embellishments depict the uncertainty about the data. Fig. 12.8 and 12.9 illustrate three such embellishments: (a) varying the thickness of the contour lines, (b) varying the brightness of the contour lines, (c) varying the connectedness of the contour lines. Other variations such as using noise, color, texture or other visual parameters can also be applied to contour lines. Another approach proposed by Cedilnik and Rheingans (2000) is to overlay the domain with a grid, and apply modifications to the grid proportional to the amount of uncertainty (see Fig. 12.10). Conceptually, this technique should be extendible to data sets covering 2D manifolds in 3D space as well. One caveat is that the grid (or whatever texture) can potentially obscure the underlying data as well. The examples shown above are for scalar uncertainty values. Positional uncertainty using Euclidean distance, confidence level, and probability are all scalar terms. In the next example (see Fig. 12.11), we look at a 2D vector field where the angular uncertainty is used to alter the traditional arrow glyph. In addition to angular uncertainty, magnitude uncertainty can also be encoded into these uncertainty glyphs. Care must be taken in designing and using uncertainty glyphs. For example, it is less confusing to map the velocity magnitude to the area of the glyph rather than the usual mapping to length of the arrow (Wittenbrink et al. 1995). In summary, most of the uncertainty visualization research has focused on situations where the uncertainty is represented as a scalar term. Several techniques are now available for including scalar uncertainty in the visualization of scalar 2D and 3D data sets. In the last example, we have shown an example where uncertainty in a 2D vector field is visualized. Research is also ongoing in showing uncertainty in tensor fields such as those found in diffusion tensor imaging (Basser and Pajevic 2003) and in geomechanics simulations. Omitted from the examples above are animation techniques where uncertainty is mapped to different animation parameters (Fisher 1994). But the applications where such techniques have been applied usually have scalar uncertainty terms. In the next two sections, we look at examples where the uncertainty is represented by more than a single number. 12.3.2 Vector Uncertainty Here, the term vector refers to a feature vector, or a set of numbers that characterize the uncertainty at a particular location. A common example is

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to use a pair of numbers (i.e. minimum-maximum value pairs) to represent the bounds of an interval where the actual data value may lie. This is illustrated in Fig. 12.12 where the min-max pairs define the extent of the bar glyphs that are drawn over the mesh surface. This technique works best on 1D data sets. Extending it to 2D data sets such as the one shown in Fig. 12.12 can potentially obscure how much above or below the surface the min-max pair is, especially if the surface is rendered e.g. transparently. Drawing bars to represent the pairs does not extend very well into 3D volumetric data sets. In situations where multiple statistics, or a vector of features is available, then multivariate visualization techniques may be applicable. An example of this is to map (up to 3) values to an HSV color model (Luo et al. 2003). Figure 12.13 shows such as example. Since the mapping is to a color model, the technique can be applied to 2D and 3D data sets. However, where surface shading is used, it may cause some ambiguity with the value mapping. We do not find as many examples where uncertainty is represented as a feature vector. But in cases where they appear, glyphs, color mappings, and other multivariate visualization techniques are applicable.

Fig. 12.12. (This figure also appears in the color insert) Minimum and maximum pairs rendered as bars below and above the wireframe surface respectively. The data indicates future water balance change predictions (reprinted with permission from Cliburn 2002)


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Fig. 12.13. (This figure also appears in the color insert) HSV color mapping of three variables. Hue is mapped to the mean of the data values, saturation is mapped to skewness, and value is mapped to standard deviation. Dark regions represent areas of high standard deviation or uncertainty. Brighter regions represent places where the data values are skewed to the left – in this particular data set, those regions correspond to places where ground truth measurements were taken (reprinted with permission from Luo et al. 2003). The data set is from conditional simulations of “forest coverage” over a small region in Netherlands (Kao et al. 2001). The visualization highlights locations with ground truth readings, areas of higher uncertainty (standard deviation), as well as mean characteristics of the conditional simulations at each spatial location

12.3.3 Multi-value Uncertainty When data comes in the form of a multi-value at every point, we have information about the data values as well as the ability to derive additional information about the collection. For example, given a single multi-value datum, we can calculate some parametric statistics about that datum such as mean, standard deviation, minimum, maximum, interquartiles, etc. These data descriptors can then be visualized with, for example, a box plot. Alternatively, the data could be binned and rendered as a histogram. This is illustrated in Fig. 12.14. An obvious limitation is that this method, as well as the box plot method, does

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Fig. 12.14. Multi-value rendered as a histogram. While this approach works for individual data points or low spatial resolution data, this approach does not scale well with spatial resolution and dimension

not scale well with spatial resolution; neither does it scale with higher dimensional data. An alternative approach is to display the properties of the multi-value on a separate location or geometry (see Fig. 12.15). Examining the left and right walls, one can observe that the distribution of values on the left portion of the left wall and the right portion of the right wall (which corresponds to points in the vicinity of the corner in the foreground) have a bimodal characteristic. Looking at the left wall also reveals that there is a shift in the mean value about halfway across, with a corresponding increase in spread of values and reduction in peak height. While such visualizations can offer more insight into the properties of this 2D multi-value scalar field, there are 2 drawbacks: (a) It does not show the entire 2D multi-value field at once. Instead the user has to interactively explore the space by selecting different transects or slices through the data space. (b) This approach does not scale with spatial dimensionality and therefore does not work well with 3D multi-value scalar fields.


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Fig. 12.15. (This figure also appears in the color insert) The multi-values corresponding to those found on the right line are displayed as a colored surface on the left wall, while those found on the left line are displayed on the right wall. The height of the walls corresponds to the range of values in each multi-value, while the color on the walls represents the frequency of values. The color on the flat surface corresponds to the mean of the multi-value at each location. The data is the same as in Fig. 12.13. This visualization shows more detail about the characteristics of the multi-value at each location e.g. modality, shifts in mean, etc; however, it is done at the expense of looking at only part of the data at a time rather than at the entire 2D field simultaneously (reprinted with permission from Kao et al. 2001)

Fig. 12.16. (This figure also appears in the color insert) The bottom plane is the mean field colored from non-forest (cyan) to forest (red). The upper plane is generated from three fields: the bumps on the surface is from the standard deviation field and colored by the interquartile range; the heights of the vertical bars are from the absolute value of the difference between the mean and median fields colored according to the mean field on the lower plane. Only difference values exceeding 3 are displayed as bars to reduce clutter. The data is the same as in Fig. 12.13. This approach uses parametric statistics and assumes the multi-values can be adequately described by these statistics. However, for ill-behaved distributions, or higher dimensional data sets, this thematic layered approach is inadequate (reprinted with permission from Kao et al. 2001)

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12.17. (This figure also appears in the color insert) Contour lines showing where the multi-values in the 2D field are similar to the distribution shown on the right. The data is from multi-return LIDAR surveys of an Alaskan island (reprinted with permission from Kao et al. 2005). Each location of this 2D multi-value field shows the tree heights within a 10 x 10 square meter stand of trees. The distribution of tree heights at each location sheds important information about the trees e.g. old growth stand, storm damage and regrowth, etc. Using this visualization, the scientist found all locations on the map where the tree heights correspond to those found in areas recovering from a weather disturbance event

Fig. 12.18. (This figure also appears in the color insert) An isosurface using a reference temperature multi-value is shown on the right. The surface represents regions in the data where the multi-values are very similar to the reference multivalue. Color of the surface shows the standard deviation of the multi-value at each location. The data set is from an ocean circulation model of the Middle Atlantic Bight shelfbreak which is about 100 km wide and extends from Cape Hatteras to Canada. Both measurement data and ocean dynamics were combined to produce a 4D field that contains a time evolution of a 3D volume including variables such as temperature, salinity, and sound speed (reprinted with permission from Luo, Kao, and Pang 2003). An ensemble of 600 Monte-Carlo forecasts of each field was produced


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Fig. 12.19. (This figure also appears in the color insert) Streamline visualization of ensemble weather forecasts. Reprinted with permission from Luo et al. (2003). It is rendered with overlapping transparent circles. The effect is similar to using blurriness to depict uncertainty. This data set is courtesy of NOAA's operational forecasts (http://wwwt.emc.ncep.noaa.gov/mmb/SREF/SREF.html). The ensemble was created from two different models: ETA and RSM, with 5 different initial and boundary conditions each producing an ensemble or collection of 10 members at each location where the two models overlap. Unfortunately, the two models are not co-registered and have different projections and spatial resolutions. Thus, for the purpose of this paper, we just used the five member ensemble from the RSM model. The resolution of the RSM model is 185 x 129 and has 254 physical variables at each location. Velocity is available at every location in the model. However, only horizontal wind components are recorded and that is what is shown in this figure

Using multivariate visualization or the GIS layered thematic approach has similar limitations as illustrated in Fig. 12.16. This approach also suffers from inability to scale beyond 2D data sets. A more general approach to dealing with multi-value data sets is an operator based approach (Luo et al. 2003). Here, mathematically and procedurally defined operators work directly with multi-values as a data type. Using this approach, traditional visualization techniques such as contour lines, isosurfaces, streamlines, etc. can be modified to work directly with multi-values. Figures 12.17 to 12.19 illustrate this. Multi-values are much richer representations of data and their associated uncertainty. Visualizing multidimensional, multivariate, multi-values is quite challenging, although the operator based approach does provide a means for attacking this challenge. Because of the large degree of freedom on how operators are defined, the subsequent interpretation of the visualizations must be done with care. Users must also be educated on how to interpret such visualizations.

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12.4 Task-Oriented Visual Mappings In the previous section, we looked at various ways in which uncertainty information can be mapped to visual parameters. In the case of multi-values, one of the key problems was how to pack as much information into the display. In this section, we relax the criterion a bit and instead we first decide what subset or feature of the multi-value (or uncertainty) information needs to be presented. We should also preface this section that there has been some notable work on the more theoretical aspects of effective visualization, particularly from the point of view of perception. Bertin (1983) described 8 variables (plane – 2 variables in a 2D domain, size, value, color, grain, orientation, shape) and identifies whether they are selective, associative, ordered, or quantitative. Tufte (1983) advanced the idea of graphical excellence where a visualization gives the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. Ware (2004) brings the physiological and cognitive psychological views such as pre-attentive processing, gestalt laws, memory, eye movement patterns, etc. to bear on designing effective visualizations. Zuk and Carpendale (2006) used components from Bertin, Tufte, and Ware to analyze different uncertainty visualization methods. Another measure of effective visualization can be taken from the viewpoint of how well the visualization aided a particular task. In this regard, the key issue that Thomson et al. (2005) are addressing is how to map different types of uncertainties (e.g. accuracy/error, precision, consistency, lineage, currency/timing, credibility, subjectivity, interrelatedness) to visual metaphors. As an initial step, they have identified the underlying models for the different types of uncertainties and are in the process of finding the appropriate visual metaphors for the different models. While it is important to be aware of perceptual issues in designing the visualizations, it is equally important to be aware of the cognitive issues in terms of how viewers internalize and understand the visualizations and how the visualizations influence their decisions and actions. Hence, we include a few examples from cognitive psychology research that the visualization community can learn from in terms of right and wrong ways of presenting information. First of all, decision makers are able to process and use only a limited number of variables particularly when under time pressure. Therefore, it is foremost that presentations are kept simple and reserved for the most critical information in their decision process. In a recent study, Peters et al. (2006) found evidence that suggests a tradeoff between the completeness of data provided versus its comprehensibility –


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that in fact increasing the completeness of information can decrease comprehension and use of information in decisions. This suggests that information presentation should not overload the cognitive tasks, and selective preprocessing such as feature extraction, can alleviate the cognitive load by helping the users focus on the important aspects of the data. Secondly, the manner in which the information is presented can influence how decisions are made. For example, Slovic et al. (2000) found that a risk factor of “20 out of every 100 persons similar to Mr. Jones are estimated to commit an act of violence” was perceived as being riskier than “persons similar to Mr. Jones are estimated to have a 20% chance of committing an act of violence.” Slovic et al. (2002) also conducted a study that suggests individuals will “image the numerator” and “neglect the denominator.” Hence, information presented as “115 out of 10,000” and “2 out of 10,000” are more likely to be comprehended and used as opposed to the equivalent information presented as “23 out of 2,000” and “1 out of 5,000” respectively. These results suggest that visualization designers should also be careful in choosing the numeric scales and manner (e.g. frequentist) in which data is presented because the visualization can influence the decisions of the users in unintended ways. Another important finding is that we tend to conceptually simplify spatial entities (Tversky 2000). For example, curves are often remembered as straighter as they actually are, angles of intersections are schematized to 90 degrees, and areas of regions are diminished in memory. This concept of the mind simplifying spatial entities has been used successfully in maps (Monmonier 1996). The visualization community can likewise take advantage of this trait in presenting complex information. In the context of hazard communication, there are many stakeholders that need the information e.g. planners, emergency response teams, media, public, etc. Obviously, different stakeholders different needs and uses for such information. A “one-size-fits-all” approach in hazard visualization may therefore not be the right approach. As an alternative, one can identify classes of users, types of tasks, complexity of data they are dealing with, and then attempt to find a framework that matches one or more visualization methods that has been shown to be effective from best practices. The success of this approach depends on how well each category of users, tasks, and data is representative of the particular instance at hand. That is, how good is the resolution within each category, and how well does each element represent members of that category. An initial coarse categorization can be as follows. Types of users: 1. Scientists, Engineers, Doctors. This group of users are experts and familiar with the data sets they are working with. They are usually

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looking for a known feature in the data set e.g. location and extents of weather fronts or shockwaves; maximal stress points in structural design; and existence, presence, size of tumors. 2. Policy Makers, Decision Makers, Court Cases. This group of users may not be very familiar with the data set, but need to get a high level understanding and the potential impacts provided by the data set. 3. Operational Users. This group of users have a fairly well defined set of visualization products that they need. 4. Casual Users. This group of users would generally have the lowest technical expertise about the data. They are similar to the second group above, but their use of the visualization may be for educational/informational purposes, or the decisions they make based on the visualizations do not have as large a consequence. Types of tasks: 1. Analysis. The types of tasks that fall under this category may include feature extraction, identification, quantification, comparison, etc. 2. Monitoring. Usually, the task itself is passive, and is event-driven e.g. out of the ordinary trigger events. There is also some degree of assessment to gauge the severity of the event and to decide if an alert needs to be issued. 3. Exploration. Data Mining. Here, the user does not necessarily know what features to look for. They are looking for “interesting” aspects or perhaps hidden relationships in the data set. The tasks are therefore investigative by nature, and may even include “What-if” type questions. 4. Persuasion. Communication. The results or messages are known apriori, and the user simply wants an effective way of conveying them. Types of data: 1. Data dimensionality. This refers to the spatial and temporal dimensionality of the data. Related to this is mathematical manifold e.g. a 1D data could reside in 3D space as a curve. 2. Data type. This refers to whether the data is a scalar, vector, or higher order tensor quantity. Usually, visualization will focus on showing derived quantities from higher order tensors. 3. Multivariate data. This refers to how many separate variables are available at each physical location, or sampling/measurement event. 4. Multi-value. This is as described in the earlier section of this paper. 5. Ordinal, Cardinal, Categorical. This mainly refers to whether the data can be ordered or grouped.


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Given the categories above, the next task is to create a framework where different visualization methods can be used to match the needs of a particular user, task, data combination. This framework would be applicable whether uncertainty is a concern or not. Thus, if uncertainty is the primary concern, then the data category corresponds to the underlying model classification in (Thomson et al. 2005). On the other hand, if data and uncertainty both need to be visualized, the previous section illustrated some of the available techniques. In the next section, we will examine a few specific cases of hazard visualization and how they may relate to this framework.

12.5 Hazards Visualization Natural (and man made) hazards occur at different time scales. Environmental effects from pollution, logging practices, etc. take longer before the impacts are evident. On the other hand, fire, hurricane, flooding, earthquake, terrorist threats require a more urgent response. Different types of users will be interested in these hazards in different ways. The technical professionals would be interested from the point of view of modeling and prediction of these phenomena; the decision makers would be interested to know the likelihood of when, where, how significant or catastrophic the event might be; the operational users would like to know if there's sufficient basis to recommend an alert or emergency procedure; while the casual user may be more interested in safety of their loved ones, or perhaps evacuation routes from approaching hazards. With regards to tasks, examples related to hazards visualization may include: forecast and track the trajectory of a hurricane, determine the likelihood of an earthquake occurring at some region within a certain time frame and certain magnitude, study the cause and effect relationships of rise in sea surface temperature to migratory patterns of birds, monitor computer server traffic patterns for denial of service threats, etc. We look at how visualization and uncertainty play a role in two applications. 12.5.1 Seismic Application Seismic activities can trigger a chain of events that require immediate action. Aside from the immediate damage from a strong earthquake, it can also cause subsequent damage in the form of fires and tsunamis. Liquefaction causes much destruction in earthquakes, and their characterization is subject to uncertainties in determining the relevant properties of natural

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soil mass (Earthquake Engineering Research Institute 2003). The visualizations needed for seismic applications can vary from modeling/analyses of movements on fault lines, soil liquefaction and impact on surrounding structures, etc., to planning the best evacuation route in case of flooding, maps showing how to best deliver disaster relief services, etc. Some of the visualizations available on the web include the following: (i) Fig. 12.20 shows the 3D structure of underground and underwater faults, (ii) Fig. 12.21 shows frames from an animation of seismic wave propagation towards the surface, (iii) Fig. 12.22 shows a USGS hazard map. As illustrated in the figures, earthquake visualization spans the range of ground motion estimation in 2D and 3D, and characterization of fault structures in 3D. What has been omitted is the large body of work aimed at preventing or mitigating disasters by designing structures to withstand severe ground movements. In this regard, the data type of interest are usually 2nd order symmetric tensors that represent stress and strain, as well as 4th order constitutive relationships (Hashash et al. 2003) that describe how materials respond to stress over time. With the exception of Figure 12.6, the seismic related visualizations do not really portray uncertainty. And even with Figure 12.6, the visualization is straightforward in the sense that the data is 2D, the uncertainty is a scalar term, and no other underlying data need to be presented simultaneously. This type of visualization seems to be representative of those found in www.HAZUS®.org as well. Perhaps the reasons for the lack of uncertainty visualization in this field are because uncertainty is difficulty to quantify in a meaningful way (lack of data, lack of knowledge, etc.); the scientists and engineers are still grappling with how to visualize 3D second order tensors, not to mention 4th order tensors. Nevertheless, there is a need to focus on developing uncertainty visualization techniques in this area as probabilistic models are being employed to study elasto-plasticity (Jeremiü et al. 2005) and the simulations are generating essentially multi-value at each location. In short, if we try to examine the needs of the different users within the seismic application related communities, we find that the current data acquisition (modeling and measurements), and particularly the visualization tools and techniques are still quite rudimentary. We obviously still have quite a few hurdles to overcome before meeting their needs.


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Fig. 12.20. (This figure also appears in the color insert) On the left, fault block diagram showing 9 of the 11 major faults in the Santa Clara Valley. Reprinted with permission from Jachens et al. (2001). On the right, the physical structure of a North Sea oil field derived from 3D seismic data. Reprinted with permission from the Earthquake Engineering Research Institute (2003)

Fig. 12.21. (This figure also appears in the color insert) Four direct volume rendered frames from an earthquake-induced ground motion simulation. Reprinted with permission from Ma (2003)

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Fig. 12.22. (This figure also appears in the color insert) Hazard map showing regions with the same 10% probability of exceedance in 50 years, but with different degrees of ground motion (Bostrom and Anselin 2005). Warmer regions do not necessarily indicate areas that are more likely to experience an earthquake, just that if it does happen it would be more severe

12.5.2 Weather Application The spectacular force of nature is usually felt in severe weather disturbances like hurricanes. These hazards happen on a relatively periodic and predictable pattern, and with significant consequences to lives and property. Because the time scales is such that advance warnings can greatly save lives, there is much more research and advancement in this area. Output from weather forecasting models, grouped together into an ensemble forecast, coupled with in-situ and remote satellite measurements, are assimilated and used to refine nested models, to produce fairly accurate weather forecasts. However, the degree of accuracy may vary quite a bit depending on a number of factors such as the typical climate of the region, micro-climates, size and intensity of the disturbance, and how far out in time the forecast is for. While accuracy of a weather forecast is broadly used these days, for example, weather report stating 30% chance of shower, or the projected path of Hurricane Katrina (see Figure 12.23), the accuracy, or alternatively, the uncertainty, is presented at a fairly coarse level. 30% chance of shower within which smaller geographic region, or narrower time window?; or in the case of Figure 12.23 - is the hurricane track equally probable within the white region, or is it higher in the middle of the path? How about the accu-


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racy of the strength and estimated arrival time of the storm? Such information is important for decisions such as whether one should bring an umbrella, or to go to the beach, or to initiate evacuation procedures, etc. Uncertainty visualization for the weather application is in better shape compared to the seismic application. However, there is room for improvement. For example, the process of generating Fig. 12.23 could be automated. One way to do this would be with multi-value streamline integration as shown in Fig. 12.19. While winds from a hurricane can cause a lot of damage, the associated flooding and landslides can just be as deadly. It would be beneficial to integrate the modeling of these events and couple them with topographic and levee system information.

Fig. 12.23. (This figure also appears in the color insert) Projected path of Hurricane Katrina

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12.6 Challenges Decisions can only be as good as the quality of information decisionmakers have to work with. However, when dealing with real world data, and even with simulation data, data uncertainty is a fact of life. The presence of uncertainty should not only be acknowledged, but we also need to have a concerted effort to account for them, quantify, represent, track, and operate directly on them. In our driving science applications related to natural hazards such as, geomechanics, oceanography, and weather forecasting, we need to deal with spatial dimensionality of 2 to 4 (space and time) and with highly multivariate interactions. Adding uncertainty, especially in the form of multivalues, can be quite challenging from the visualization point of view. Furthermore, multidimensional multivariate multi-valued data sets are inherently much larger and hence present a computational and informatics challenge in itself. Johnson and Sanderson (2003) mentioned the development of new uncertainty visualization techniques as one of the key challenges. Dealing with multi-valued data certainly falls under this category. Griethe and Schumann (2005) cite the lack of uncertainty visualization techniques for abstract data as another research challenge. This is certainly true as higher level abstractions need to be presented from these scientific based applications in order to facilitate the task of decision makers. In summary, the theoretical and computational capabilities of current visualization techniques need to be extended. In particular, the research agenda should include: 1. A formal and theoretical framework for uncertainty visualization as proposed by Thompson et al. (2005) but one that is also cognizant of the users needs and tasks as well as the properties of the data that they are dealing with. 2. Research on uncertainty representation that captures its multi-faceted nature e.g. data are from multiple sources with varying degrees of reliability, etc. This includes visualization techniques that incorporate multi-values. In the earlier examples, we showed some visualization techniques that work with multi-values. However, there is still a lot more research needed. For example, we still don't know how to do direct volume rendering on such data sets, nor do we know how to do critical point analyses on such data sets. 3. Research on data analysis techniques that take advantage of such representations. This may also include feature analyses and extraction as a means of condensing the important aspects from large data sets.


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4. For the general area of seismic applications, we need to advance basic visualization techniques for 3D 2nd tensors fields, 4th order tensor fields, as well as probabilistic tensor fields. 5. A broader research into the perceptual and cognitive processes in how we digest and act upon visualizations, particularly those that contain uncertainty information. Uncertainty is an integral part of the data that we look at on a daily basis. The tools that we develop should reflect the nature of such data, and allow us to explicitly see them and factor their influence in our decision making.

12.7 Acknowledgments I would like to thank Ann Bostrom, Susan Cutter, and Ellen Peters for their review and comments on the earlier version of this paper, as well as Georgia Tech, the Mid-America Earthquake Center, and the NSF for making the highly interdisciplinary and timely Workshop on Strategic Directions for Seismic Risk Modeling and Decision Support possible.

References Basser PJ, Pajevic S (2003) Dealing with uncertainty in diffusion tensor MR data. Israel Journal of Chemistry 43:129–144 Beard MK, Buttenfield BP, Clapham SB (1991) NCGIA research initiative 7: Visualization of spatial data quality (Technical Paper 91-26). National Center for Geographic Information and Analysis, Santa Barbara, CA Bertin J (1983) Semiology of graphics. The University of Wisconsin Press, Madison Bostrom A, Anselin L (2005) Visualizing seismic risk and uncertainty. In: Proceedings of the MAEViz Meeting. St. Louis Cedilnik A, Rheingans P (2000) Procedural annotation of uncertain information. In: Proceedings of Visualization ’00, pp 77–84 Chatfield C (1983) Statistics for technology, a course in applied statistics, third edition. Chapman and Hall, Boca Raton Cliburn DC, Feddema JJ, Miller JR, Slocum TA (2002) Design and evaluation of a decision support system in a water balance application. Computers & Graphics 26:931–949 Djurcilov S, Pang A (2000) Visualizing sparse gridded datasets. IEEE Computer Graphics and Applications 20:52–57 Earthquake Engineering Research Institute. (2003). Securing society against catastrophic earthquake losses [Electronic Version] from http://www.eeri.org/cds/ publications/securing_society.pdf.

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Fisher P (1994) On animation and sound for the visualization of uncertain spatial information. In: Hearnshaw HM, Unwin DJ (eds), Visualization in geographical information systems. Wiley, Hoboken, pp 181–185 Goodchild M, Buttenfield B, Wood J (1994) Introduction to visualizing data validity. In: Hearnshaw HM, Unwin DJ (eds), Visualization in geographical information systems. Wiley, Hoboken, pp 141–149 Griethe H, Schumann H (2005) Visualizing uncertainty for improved decision making. In: Proceedings of the 4th International Conference on Business Informatics Research Grigoryan G, Rheingans P (2004) Point-based probabilistic surfaces to show surface uncertainty. IEEE Transactions on Visualization and Computer Graphics 10:564–573 Hashash YMA, Yao JI-C, Wotring DC (2003) Glyph and hyperstreamline representation of stress and strain tensors and material constitutive response. International Journal for Numerical and Analytical Methods. Geomechanics 27:603–626 Hibbard W, Santek D (1990) The VIS-5D system for easy interactive visualization. In: Proceedings of Visualization ’90, pp 28–35 Jachens RC, Wentworth CM, Gautier DL, Pack S (2001) 3D geologic maps and visualization: A new approach to the geology of the Santa Clara (Silicon) Valley, California (Technical Report Open-File Report 01-223). USGS Jeremié B, Sett K, Kavvas ML (2005 (In Review)) Probabilistic elasto-plasticity: Formulation of evolution of probability density function. ASCE Journal of Engineering Mechanics Johnson CR, Sanderson AR (2003) A next step: Visualizing errors and uncertainty. IEEE Computer Graphics and Applications 23:6-10 Kao D, Dungan J, Pang A (2001) Visualizing 2D probability distributions from EOS satellite image-derived data sets: A case study. In: Proceedings of Visualization ’01, pp 457- 460 Kao D, Kramer M, Luo A, Dungan J, Pang A (2005) Visualizing distributions from multi-return LIDAR data to understand forest structure. The Cartographic Journal 42:1–14 Kim K, Wittenbrink C, Pang A (2001) Extended specifications and test data sets for data level comparisons of direct volume-rendering algorithms. Transactions on Visualization and Computer Graphics 7:299–317 Klir G, Wierman M (1999) Uncertainty-based information: Elements of generalized information theory, 2nd edn Physica-Verlag, Heidelberg Kloeden PE, Platen E (1995) Numerical solution of stochastic differential equations. Springer-Verlag, Heidelberg Lee CH, Varshney A (2002) Representing thermal vibrations and uncertainty in molecular surfaces. In: Proceedings of the SPIE Visual Data Exploration and Analysis ’02, pp 80–90 Lermusiaux PFJ (1999) Data assimilation via error subspace statistical estimation, part II: Middle Atlantic Bight shelfbreak front simulations and ESSE validation. Monthly Weather Review 127:1408–1432


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Lorensen WE, Cline HE (1987) Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics 21:163–169 Luo A, Kao D, Pang A (2003) Visualizing spatial distribution data sets. In: Proceedings of VisSym’03, pp 29–38 Ma K-L, Stompel A, Bielak J, Ghattas O, Kim EJ (2003) Visualizing large-scale earthquake simulations. In: Proceedings of Supercomputing MacEachren AM, Howard D, Von Wyss M, Askov D, Taormino T (1993) Visualizing the health of Chesapeake Bay: An uncertain endeavor. In: Proceedings of GIS/LIS '93 Proc. v. 1. Minneapolis, MN, pp 449–458 MacEachren AM, Robinson A, Hopper S, Gardner S, Murray R, Gahegan M, Hetzler E (2005) Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science 32:139–160 Miller DR, Morrice JG (1996) Assessing uncertainty in catchment boundary delimitation. In: Proceedings of the 3rd International conference on GIS and Environmental Modelling Monmonier M (1996) How to Lie with Maps, 2nd edn. University of Chicago Press Pang A (2001) Visualizing uncertainty in geo-spatial data, prepared for the national academies committee of the computer science and telecommunications board. In: Proceedings of the Workshop on the Intersections between Geospatial Information and Information Technology Pearl J (1996) Decision making under uncertainty. ACM Computing Surveys: ACM 50th Anniversary Symposium 28:89–92 Peters E, Dieckmann N, Dixon A, Hibbard JH, Mertz CK (2007) Less is more in presenting quality information to consumers. Medical Care Research & Review 64(2):169–90 Slovic P, Finucane ML, Peters E, MacGregor DG (2002) The affect heuristic. In: Gilovich T, Griffin D, Kahneman D (eds), Heuristics and biases: The psychology of intuitive judgment. Cambridge University Press, Cambridge, pp 397–420 Slovic P, Monahan J, MacGregor DG (2000) Violence risk assessment and risk communication: The effects of using actual cases, providing instruction, and employing probability versus frequency formats. Journal of Law and Human Behavior 24:271–296 Strothotte T, Puhle M, Masuch M, Freudenberg B, Kreiker S, Ludowici B (1999) Visualizing uncertainty in virtual reconstructions. In: Proceedings of the EVA Europe ’99: Electronic Imaging & the Visual Arts. Florence, Italy, pp 16–18 Thomson J, Hetzler B, MacEachren A, Gahegan MN, Pavel M (2005) Typology for visualizing uncertainty. In: Visualization and data analysis volume 5669, pp 146–157 Tufte ER (1983) The visual display of quantitative information. Graphics Press, Cheshire, CT Tversky B (2000) Levels and structures of cognitive mapping. In: Kitchin R, Freundshuh SM (eds), Cognitive mapping: Past, present and future. Routledge, New York, pp 24–43

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Van Gelder A, Wilhelms J (1994) Topological considerations in isosurface generation. ACM Transactions on Graphics 13:337–375 Ware C (2004) Information visualization: Perception for design. Morgan Kaufmann Series in Interactive Technologies, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco Wittenbrink CM, Pang AT, Lodha SK (1996) Glyphs for visualizing uncertainty in vector fields. IEEE Transactions on Visualization and Computer Graphics 2:266–279 Zuk T, Carpendale S (2006) Theoretical analysis of uncertainty visualizations. In: Erbacher RF, Roberts JC, Grohn MT, Borner K (eds), Visualization and data analysis, Proceedings of SPIE-IS&T Electronic Imaging, SPIE 6060

13 Perspectives on Visualizing Uncertainty in Natural Hazards

Responses to Pang’s Paper “Visualizing Uncertainty in Natural Hazards”

Introduction This chapter focuses on the perspective of consumers of visual information about natural hazards – particularly decision makers and individuals who must make difficult personal choices in the face of vast quantities of data that have been produced for other purposes. Ellen Peters draws on empirical studies to show that “Less is More” in the presentation of information. She also argues that visualizations should be tailored to specific decision needs by choosing precision levels and visual cues appropriate to the evaluation context. Ann Bostrom expands the discussion on the definitions of risk and uncertainty and discusses how spatial information in particular is perceived by users. She emphasizes the particular visual tools that have been shown to be effective in affecting individuals’ judgments about certain risks. Finally Susan Cutter discusses the strengths and weaknesses of various visual approaches to representing uncertainty, and recommends keeping the overall presentation simple. Increasing use and acceptance of spatial representations opens new potential for expanding into the 3D realm to help users understand HAZUS® outputs. But even in two dimensions, there remain significant technological, legal, social, and cognitive issues that the hazard community must address in order to effectively communicate the risks natural hazards pose to society.

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13.1 Preferred Data Visualization Techniques May Not Lead to Comprehension and Use of Hazard Information Ellen Peters Decision Research, Eugene, Oregon, USA

Risk reduction planning involves complex data about physical, social, and economic systems. Analyzing this data requires decision makers to understand the data and interpret risky and uncertain issues (Derby and Keeney 1981). When there is confusion about the data and its interpretation, the myriad stakeholders will have difficulty understanding the implications for their concerns and the concerns of others. Effective risk reduction requires all participants to have access to information and its meaning so that the entire group can interpret the information, incorporate it into their decision making, and support a group decision (if necessary) to the outside world. The success of data visualization techniques rests, therefore, on their ability to assist various users in interpreting and incorporating information into informed choices. These users (scientists/engineers/doctors, policy makers/decision makers/court cases, operational users, casual users) differ substantially in their levels of expertise and familiarity with the types of data being visualized, and also in their uses of the data. As a result, the same visualization technique may not fit all needs for all users. Complex data concerning natural hazards and weather are available increasingly to experts and the public. The data are intended to be useful and to make lives better through improved risk management. To a degree never before possible, individuals are in a position to understand natural disasters and their likelihood of occurrence and, in the process, increase control over their lives. The evidence demonstrates, however, that having an abundance of information does not always translate into it being used to inform choices. The challenge is not merely to communicate accurate information to users, but also to present that information so that it is understood and used in decision making. Information about natural hazards often includes uncertainty. At least four kinds of uncertainty information exist in natural hazard settings – 1) uncertainty about the strength of evidence, 2) uncertainty about the likelihood of risks, 3) uncertainty about the severity of possible risks, and 4) uncertainty about how to weigh multiple risks or multiple time points of a changing risk when making a choice. In Alex Pang’s paper “Visualizing uncertainty in natural hazards,” he does an excellent job considering the


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first type of uncertainty above (uncertainty about the strength of evidence). He also includes at least one example examining the second type, namely uncertainty about the likelihood of risks (his Fig. 12.6). In the present paper, I review some findings in the judgment and decision making literature that offer a somewhat different viewpoint on the first two types of uncertainty above. Although we often assume decision makers are well informed, far sighted, and rational, psychologists who study judgment and decision making assume that individuals are “boundedly rational,” and may or may not have the cognitive resources or motivation available to process information at the moment of a decision. In other words, although we are capable of great feats of intellect, our intellectual capacity is nonetheless limited. Decision makers are able to process and use only a limited number of variables in any one choice. As the number of options and information increases, the ability to use all of it in choice declines. Although our market economy assumes that more information is better, evidence from decisionmaking research demonstrates that more information does not always improve decision-making, and, in fact, can undermine it (Gabardino and Edell 1997; Iyengar and Lepper 2000; Peters et al., in review; Slovic 1982; Tversky and Shafir 1992). Evidence suggests that a tradeoff exists, for example, between the completeness of provided data and its comprehensibility such that increasing the completeness of information through auxiliary information can decrease comprehension and use of information in decisions (see Fig. 13.1). Although tempting to provide more complete information in order to assist decision makers, in two experimental studies, we demonstrate that more complete information can lead to decreased comprehension and quality of choices, particularly for individuals with lower skills (Peters et al. 2007b). In Study 1, when given less information (retaining only the more important information and deleting the less important information), consumers were better able to comprehend more information about available hospital options, and they were more likely to choose a higher quality hospital. In Study 2, making only the more important measure easier to evaluate (rather than doing so for the important and a less important measure) led to more choices of higher quality hospitals. Results of both studies were particularly strong for those lower in numeracy who had higher comprehension and made better choices when the information-presentation format was designed to ease the cognitive burden and highlight the meaning of only the important information. We conclude that “Less can be More” when presenting numerical information to individuals who differ in ability, motivation, and time.

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Fig. 13.1. Tradeoff between completeness of information and its comprehensibility and use

13.1.1 Presenting Auxiliary Hazard Information About Data Quality The “Less is More” concept may apply to presentation of hazard information. Pang suggests that “it is widely acknowledged that incorporating auxiliary information about data, i.e. data quality or uncertainty, is important” (p. 1). Although he does not mention reasons for its importance, a reasonable assumption is that an understanding of data quality can lead a decision maker to weigh higher quality information more than lower quality. Although a laudable goal, the human mind has difficulty coping with complicated probabilistic relationships so that people tend to reduce the burden of processing such information by using simplifying strategies. In processing information of uncertain accuracy or reliability, for example, individuals will tend to accept the information fully or reject the information fully, without adjusting for data quality. This “best guess” strategy (Gettys et al. 1973; Schum and DuCharme 1971) simplifies information integration at the expense of ignoring some of the uncertainty. A data visualization technique that makes data quality salient could help the decision maker adjust more for data quality or may simply lead to greater acceptance of all information (and equal weighing of all information regardless of data quality). It may also compromise comprehension of key information. Providing minimum-maximum range estimates rather than point estimates is another way to provide more complete information, but again, we should consider not only the benefits of more complete information but also its costs. In this case, research by Hsee (1995) suggests that incorporating range information about an attribute (such as “there is a 25%–45% chance of hurricane damage” rather than “there is a 35% chance of hurri-


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cane damage”) allows decision makers to weigh that attribute less in a decision and to bring other less relevant factors to bear on the decision. In one of his experiments, subjects were shown samples of two different files. They were to choose a file to edit for a fixed number of pages. One file was more interesting but would earn them less money; the other file was somewhat boring but would pay better. A manipulation making the pay rate more elastic through the use of a minimum-maximum range estimate resulted in subjects choosing the more interesting, lower paying file, presumably because it allowed them to weigh the expected pay rate information less. Providing range information about hazards increases the completeness of the information but at the same time may decrease its use in decisions. Simple experimental tests of data visualization techniques in hazard management could help to determine the impact of providing more information about the quality of data. The benefits of representing systematic uncertainty in data may sometimes be outweighed by the costs of decreased comprehension and use of key information. 13.1.2 Presenting Risk Information Figure 12.6 of Pang’s paper contains a depiction of the probability of an earthquake in various areas of Colorado. The probabilities range from 0.000 to 0.060 in increments of .005. From the perspective of research in numeracy and health, two questions arise about using this information. First, are all the levels necessary for decision making? The information provider had to make choices about what level of detail to provide. Although the figure provides information in more detail, that same detail may result in less comprehension and use of the information in decisions. Second, risk information can be provided in a variety of formats (e.g., decimals = .05, percentages = 5%, and frequencies = 5 out of 100). Frequentistic information has been shown to be more affective and to be used more readily in decisions (e.g., Hoffrage et al. 2000). For example, Slovic et al. (2000) conducted a series of studies in which experienced forensic psychologists and psychiatrists were asked to judge the likelihood that a mental patient would commit an act of violence within 6 months after being discharged from the hospital. An important finding was that when clinicians were told that “20 out of every 100 patients similar to Mr. Jones are estimated to commit an act of violence,” 41% refused to discharge the patient. But when another group of clinicians was given the risk as “patients similar to Mr. Jones are estimated to have a 20% chance of committing an act of violence,” only 21% refused to discharge the patient. Similar

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results have been found by Yamagishi (1997), whose judges rated a disease that kills 1,286 people out of every 10,000 as more dangerous than one that kills 24.14% of the population. Unpublished follow-up studies by Slovic showed that representations of risk in the form of individual probabilities of 10% or 20% led to relatively benign images of the patient, unlikely to harm anyone, whereas the “equivalent” frequentistic representations created frightening images of violent patients (e.g., “Some guy going crazy and killing someone”). These affect-laden images likely induced greater perceptions of risk in response to the frequency frame. Peters et al. (2006) demonstrated that low-numerate and not high-numerate individuals are more likely to show this effect; the highly numerate when given one numerical frame (e.g., 10%) appear more likely to transform it into the alternative frame (10 out of 100), making both formats accessible for decision making. Research in a multitude of domains suggests that different numerical frames of hazard information will influence decisions. A different type of framing concerns the choice of denominator when presenting risk information. Research on the affect heuristic suggests that individuals will “image the numerator” and “neglect the denominator” (Slovic et al. 2002). As a result, information presented in the form of “23 out of 2000” and “1 out of 5,000” will be less differentiated, comprehended, and used than the equivalent information presented as “115 out of 10,000” and “2 out of 10,000” (see Table 13.1). Table 13.1. Keeping the denominator constant and using frequencies aids in understanding the concept that “Older people taking NSAIDs have higher risk of stomach bleeding” Original Format of Data For people age 16–44 1 in 2,100 chance of a serious GI bleed 1 in 12,300 chance of death due to GI bleed For people age 45–64 1 in 646 chance of GI bleed 1 in 3,800 chance of death due to GI bleed For people age 65–74 1 in 570 chance of GI bleed 1 in 3,300 chance of death due to GI bleed

Better format of data For people age 16–44 10 out of 20,000 people will have a serious bleed 2 out of 20,000 people will die due to a bleed For people age 45–64 31 out of 20,000 people will have a serious bleed 5 out of 20,000 people will die due to a bleed For people age 65–74 35 out of 20,000 people will have a serious bleed 6 out of 20,000 people will die due to a bleed


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13.1.3 Evaluability and Choice Recent research with younger adults on the evaluability principle (e.g., Hsee et al. 1999) has demonstrated that people evaluate options differently and exhibit preference reversals between modes of joint evaluation (i.e., two or more options are presented simultaneously and are evaluated comparatively) and of separate evaluation (i.e., options are presented in isolation and evaluated separately). This work suggests that the weight given to a stimulus attribute is proportional to the ease or precision with which the value of that attribute can be mapped onto an evaluative or affective (good/bad) scale. For example, in separate evaluation, a used music dictionary with 20,000 entries and a torn cover may be evaluated as less good than another used music dictionary with 10,000 entries that appears likenew because the number of entries is difficult to evaluate on its own while the torn cover is easy to evaluate. In joint evaluation, however, the ability to compare the number of entries makes this information more evaluable, and the dictionary with 20,000 entries is evaluated as better. Evaluability is used to explain an anomalous finding by Slovic (reported by Slovic et al. 2002). One group of subjects was asked to rate the attractiveness of a simple gamble (7 chances out of 36 to win $9; otherwise nothing) on a 0–20 scale where 0 = Not at all an attractive bet and 20 = Extremely attractive bet. A second group was asked to rate a similar gamble with a small loss (7/36, win $9; 29/36, lose 5¢) on the same scale. The data were anomalous from the perspective of economic theory, but expected from the perspective of affect and evaluability. The mean response to the first gamble was 9.4. When a loss of 5¢ was added, the mean attractiveness jumped to 14.9; there was almost no overlap between the distributions of responses around the two means. These curious findings can be explained by reference to Hsee’s evaluability concept and the notion of affective mapping. According to this view, a probability maps relatively precisely onto the attractiveness scale, because it has an upper and lower bound and people know where a given value falls within that range. In contrast, the mapping of a dollar outcome (e.g., $9) onto the scale is diffuse, reflecting a failure to know whether $9 is good or bad, attractive or unattractive. Thus, the impression formed by the gamble offering $9 to win with no losing payoff is dominated by the rather unattractive impression produced by the 7/36 probability of winning. However, adding a very small loss to the payoff dimension puts the $9 payoff in perspective (i.e., makes it evaluable) and thus gives it meaning. The combination of a possible $9 gain and a 5¢ loss is a very attractive win/lose ratio, leading to a relatively precise mapping onto the upper part of the scale. Whereas the imprecise mapping of the $9 carries little

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weight in the averaging process, the more precise and now favorable impression of ($9: –5¢) carries more weight, thus leading to an increase in the overall favorability of the gamble. Paradoxically, it is those individuals with greater number ability who are more likely to make the numerical comparisons and show these anomalous results (Peters et al., 2006). Another way to make information more evaluable, and hence more usable, is to present plans in an ordered display (e.g., from high to low quality. Russo et al. (1975) demonstrated the value of this technique in their classic study demonstrating how display format affects the use of unit price information in grocery stores. Russo et al. contended that mere availability of information is insufficient. They distinguished between information that was available versus information that could be processed and showed that before consumers can effectively use unit-price information, a convenient useable display of that information is necessary. They found that posting a list that rank ordered products with respect to their unit prices significantly increased the selection of items with lower unit prices. Hibbard et al. (2002) extended this “evaluability-by-ordering” hypothesis into health-plan choices. Consumers were more likely to choose higherquality health plans in the same cost strata when the health plans were presented in a list ordered by quality within cost strata. Although most of the research on evaluability has not been done with environmental risks, the conceptual model predicts that evaluability will have a greater impact on more difficult or complex judgments and choices such as those faced by decision makers involved in natural hazard risk reduction. To illustrate how the evaluability of hazard information might be enhanced, Nathan Wood, Rich Margerum, Andre LeDuc, and Ellen Peters worked with output from the US Geological Survey Land Use Portfolio Modeler (LUPM) using data modified by Nathan Wood from Dinitz et al. (2003). The LUPM is a decision analysis tool that allows decision makers to analyze and compare multiple choices using economic and noneconomic attributes (Dinitz et al. 2003). Figure 13.2 shows a typical output from the Modeler based on data from Southern Santa Cruz County, California. The figure allows users to define their own risk preferences to determine which policy is “best.”


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Fig. 13.2. Output from USGS Land Use Portfolio Modeler (modified from Dinitz et al. 2003)

In an initial analysis, the team identified several difficulties for decision makers. The output requires decision makers to weigh several different policies against several different factors, and research has shown that integrating different types of information into a decision is a very difficult decision process (Payne et al. 1993; Slovic 1995). Furthermore, the policies are difficult to evaluate against a variable such as community wealth without some enhancement of their meaning – such as their relationship to existing community wealth. As noted above, research on evaluability has demonstrated that when people do not understand how to evaluate information on a good/bad scale, they are unable to weigh the information properly in decision making (Hsee 1998; Slovic et al. 2002). To improve the evaluability, the research team modified the presentation of the data (see Figs. 13.3 and 13.4). First, the complex output of the modeler was taken apart into more simplified figures that allow decision makers to draw meaning from each component and to minimize the inferential processes needed (Shah et al. 1999). Second, data categories were ordered to ease comparisons of different policies (Russo et al. 1986). Finally, baseline comparisons were added to allow decision makers to compare policies to the pre-disaster conditions and the effects of no action (no mitigation policy) (Hsee et al. 1999).

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Fig. 13.3. Expected community wealth of various hazard mitigation strategies (more community wealth and therefore higher numbers, is better)

Fig. 13.4. Uncertainty in various mitigation strategies (less uncertainty, and therefore lower numbers, is better)

This example illustrates some of the changes that could be made to transform the outputs of the USGS Modeler outputs into a format that would allow decision makers to comprehend the data and use the information in policy decision making.


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13.1.4 Conclusions Hibbard and Peters (2003) proposed three processes (lowering cognitive effort, helping people to understand the experience of choice, and highlighting the affective meaning of information) as design principles in the creation of consumer information products to enhance the successful use of information in choice. Users of hazard information are going to differ in their ability, time, and motivation to handle different types and quantities of information. Knowing this and understanding the elements that underlie the usefulness of particular strategies should help information providers design the key ingredients necessary for useable information. Three factors should be considered in selecting information presentation strategies: 1) the complexity and amount of the information; 3) the experience, skill, and motivation of the users; and, 3) the nature of the choice (e.g. the degree to which there is a right or “best” option). These factors can be used to determine which combination of strategies is going to best facilitate decision making. These attempts, by definition, bring up important ethical considerations. Because the way hazard information is presented is very likely to influence how it is used, information producers have a responsibility to be conscious of that influence and direct it in productive and defensible ways. The alternative is to manipulate people in ways that are unknown, are not thought out or are not defensible, but are no less manipulative. Testing of hazard information products with appropriate audiences thus becomes the next key ingredient. This testing should focus on three levels: the degree to which the information is understood; the degree to which the process goals are achieved (reduce cognitive effort, highlight information; help understand the experience of the choice); and the degree to which information is actually used in choice. This is likely to be a departure from current testing methods. Most consumer information material, if tested at all, assesses consumer preferences for how information is presented. However, consumer preferences for presentation format may not actually support the use of that information in choice. The conscious use of information presentation strategies to support choice represents a critical departure from how most information producers see their role (i.e., most view their role as providing complete, objective, and accurate information.) To acknowledge that the way information is presented can influence choice is to accept a new level of responsibility. For example, choosing how to display information puts a greater burden on the information provider to summarize or to add “meaning” to the information. However, if the information is going to be weighted and used in choice, information producers must aim beyond providing more and ac-

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curate information to providing information in a way that supports decisions. As the preceding discussion makes clear, supporting decisions will require more strategic and sophisticated efforts. While the quantity of information can be voluminous and complex, there are strategies that will increase the likelihood that it will actually get used in judgment and choice.

13.2 Putting Seismic Risk and Uncertainty on the Map Ann Bostrom Georgia Institute of Technology, Atlanta, Georgia

Pang’s paper moves us toward the creation of a toolbox of uncertainty visualization techniques and their related considerations, for use with HAZUS® and similar risk and loss assessment software packages, by creating a general structure for the design of the toolbox, and summarizing the current state of the art. Applying Pang’s framework to the visualization of risk information and the uncertainty around risk creates additional challenges. Pang’s definitions of uncertainty range from statistical variability to ambiguity and missing information. Thomson et al (2006) discuss several (complex) taxonomies of uncertainty, including Pang’s, but conclude that different types and sources of uncertainty may be preserved as underlying models, but represented as a scalar, based on a linear model. They do not, however, really address users’ potential needs. An issue with this kind of combined representation is the extent to which viewers can discern the provenance of the visualization, or in other words, how transparent the underlying model is. Another is whether even scalar visualizations of uncertainty will be useful to different classes of users, and what attributes of visualizations can increase their usefulness.1 A general framework for thinking about these issues is presented by Ellen Peters in the previous sec1

In risk research uncertainty is often divided into aleatory (from the inherent randomness of a phenomenon) and epistemic uncertainty. However, in the context of seismic hazards, some experts have concluded that there is fundamentally only epistemic uncertainty, stating that “leading philosophers of science and uncertainty (e.g. de Finetti 1974; de Groot 1988) believe that, conceptually, there is only one kind of uncertainty; namely, that which stems from lack of knowledge.” (NRC 1997, p 32-33).


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tion (see also Hibbard and Peters, 2003), in terms of the evaluability of information. To parallel the discussion of uncertainty definitions provided by Pang, considering how risk is defined can provide some insights useful for visualization choices. Formal conceptions of risk include, but are not limited to: probability of loss, size of credible loss, expected loss (probability multiplied by the size of loss), and the variance of or probability distribution over all possible consequences (Vlek and Cvetkovich 1989). Some hazard researchers, such as Collins (1998), define risk as the probability of hazard, and define hazard in terms often used by risk researchers to define risk. Radke et al. (2000) and others define “risk” as “the potential or likelihood of an emergency to occur,” but “hazard” as the “physical characteristics that may cause an emergency.” Risk is generally defined as incorporating the probability of realizing the potential adverse consequences or harm, as well as the magnitude of these (NRC 1989, Appendix E), and potentially other characteristics of consequences as well (Slovic 2000). Both the definitions and the process of defining risk can be controversial (for further discussion of risk definitions, see Fischhoff et al. 1984). Simpler numerical definitions of risk may be more amenable to visualization in a cartographic context, especially if uncertainty about risk is also visualized. One of the lowest dimensional definitions is the probability of an adverse consequence (e.g., building collapse, or death). Graphical representations of probability have been shown to have stronger effects than numerical representations (Lipkus and Hollands 1999; Pavio 1969; Stone et al. 2003), and increase the accuracy of perceived changes in risk (Waters et al. 2006). However, visualization of probability and of more complex quantitative spatial information poses new challenges, which are likely to multiply with the inclusion of qualitative or categorical information about risk and uncertainty, such as information about causal processes or knowledge gaps. Earthquakes, floods, mudslides and avalanches, hurricanes, toxic spills or explosions, transportation of dangerous goods, and hazardous waste management are all spatially distributed problems. Additionally, evidence suggests that the construction of risk in the mind of the perceiver depends on the representation of the hazard being addressed (e.g., Collins 1998), and such representations generally have some spatial aspect. Spatial thinking and metaphors pervade human cognition (Lakoff and Johnson 1999; Lakoff and Nunez 2001; Tversky et al. 1981). Barbara Tversky shows that there is no tenable distinction between the mind’s perceptual and conceptual actions, as the mind necessarily schematizes and distorts the world in order to perceive it (Tversky 2000a, 2000b). The main thrust of her work, as it is relevant to the research questions at

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hand, is that the mind imposes a necessary conceptual structure on the world in order to understand it. As Monmonier (1996) points out, falsification through simplification is one of the most fundamental ways that maps organize space. Tversky (2000a, p. 6) provides an overview of how spatial entities are simplified: curves are often remembered as straighter than they actually are; angles of intersections are schematized to 90 degrees; and the estimated areas of regions are diminished in memory. Tversky theorizes that all conceptual schematizations of space (and perhaps all conceptual schematizations, including schematizations of risk) are based upon more fundamental interactions between the human body and space. For Tversky the axes of the body are primary to conceptual understanding, or perhaps we are meant to understand that conceptual understandings were evolutionary and functionally bootstrapped onto a prior understanding (Lakoff would add ‘embodied’ understanding) of the body in space. Mental representations are the cognitive, structured counterpart of visual representations, derive from direct perceptions and other cognitive processes; mental representations and direct visual perceptions activate the same parts of the brain (Shah and Miyake 2005). As such, mental representations of hazards are the visual element of mental models of the underlying hazard (i.e., how people understand and think about the hazard, their causal beliefs), and likely mediate between visual percepts and related mental models. It follows that mental representations of hazards and risks, perhaps as much or more than the underlying hazard, are a key part of risk perceptions. So spatial visualizations should have a strong potential to change risk perceptions, judgments and decision making. The evidence on this is, however, limited to date (see Collins 1998, for an exception). Prior research on the visualization of risk and uncertainty has focused primarily on simple statistical graphics such as risk ladders, confidence intervals, pie charts, and icons such as stick figures (Lipkus and Holland 1999). The following summarizes a few of these findings, beginning with univariate, multi-valued risk ladders. A risk ladder is a vector using a single metric to compare risks. Risk ladders have been used most extensively to describe environmental hazards (e.g., radon or asbestos (United States Environmental Protection Agency 1992; Smith et al. 1985; Covello 1991; Sandman et al. 1994; Smith et al. 1990; Sandman et al. 1994; Smith et al. 1995; Smith et al. 1990)). Typically, the risk ladder displays a range of risk magnitudes such that increasing risk is portrayed higher up on the ladder. The risk ladder effectively helps people ‘anchor’ a risk to upper- and lower-bound reference points, and provides a mapping as well as a metric for comparing risks. Perceived risk is influenced by the location of risk perhaps more so that the actual numbers (Sandman et al. 1994). The effectiveness of risk ladders to


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promote behavior change, or improve understanding of personal risk, can be enhanced by the addition of an action standard and advice relevant to different risk levels (Sandman et al. 1994). Although action standards and related advice may influence significantly whether any actions to advert the risk are taken (Smith at al. 1995), questions about their use remain (Sandman et al. 1994). Accurate statistical information can be presented graphically in numerous formats, some of which are better than others in conveying specific information or inducing inferential thinking (Shah et al. 1999). Viewers’ graph comprehension involves: (1) viewing the graph and identify salient features of the graph; (2) recognizing the quantitative information intended to be conveyed by these graphic features; and (3) associating the quantitative information from the graph with the variables presented on the graph (Shah and Carpenter 1995). In a study of the second of these phases, Shah et al. (1999) find that after seeing different graphs that contain the same accurate information but present different graphic interpretation cues, viewers’ grasp of the information can differ substantially. For example, line-linked-dots graphs convey trend information better than sticks and histograms, because line-linked-dots graphs contain interpretation cues that help viewers construct the idea of a trend in their mind, which histograms do not (Shah et al. 1999). Line graphs effectively communicate trends in data (Shamo et al. 1996; Hollands and Spence 1992; Meyer at al. 1997; Schutz 1961). Histograms have been used to convey smoking-related diseases in cessation manuals, such as the American Cancer Society’s Pathways to Freedom: Wining the War against Tobacco. This cessation manual portrayed the leading causes of death for African-Americans (i.e., homicide, car crashes, smoking-related diseases, drug abuse, and acquired immunodeficiency syndrome) in a histogram. Although the research linking histograms with perceptions of risk is sparse (but growing), it appears that people readily understand and find histograms helpful and that they may induce risk aversion compared with numbers alone (e.g., Chua et al. 2006). In research findings to date, willingness-to-pay for risk-abatement is higher for participants given graphical risk information in histograms than for those given risk information in numerical form (Stone at al. 1997). A few studies have tested the effects of using a field of dots to communicating probabilities of disease (Kaplan et al. 1985; Sandman et al. 1994; Stone et al. 1997; Weinstein et al. 1994). These studies test the effects of dot visualizations in conveying low-probability health risks. Baty et al. (1997) used marbles to visualize breast cancer risk differences between women with or without BRCA1 mutations, and report (without experimental testing) that the visualizations seem to have a stronger impact than nu-

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merical or verbal presentations of probability information. Kaplan et al (1985) report that using dot visualization to show the probability of having negative effects from taking vaccine can increase participants’ willingness to be vaccinated. This is consistent with Stone et al.’s (1997) findings. In their analysis of visual displays of risk, (Stone et al. 1997) examined how well stick and other dot-like visuals such as asterisks communicated lowprobability adverse events (e.g., tire blowouts and serious gum disease). Willingness-to-pay for risk-abatement was higher for participants given graphical risk information in stick figures (a version of dot plots) (Stone et al. 1997). These and other results from experiments using photographs (e.g., Stephenson and Witte 1998; see also Keller et al.2006) suggest again that visual displays of comparative risk increase risk aversion relative to presenting numbers alone. Explanations for these effects include increasing foreground:background salience (Stone et al. 2003), and evoking a stronger affective response (Chua et al. 2006; Keller et al. 2006). These effects could either counteract or exacerbate the general tendency for perceived risk to be compressed (display less variability) in systematic ways, relative to statistical measures of those risks (Fischhoff et al. 1997). As Pang emphasizes, a single visualization will not perform optimally in all situations. This holds for statistical graphs as well as maps. Pang points out several factors that influence the effectiveness of visualizations, including the task (i.e., purpose), display characteristics (e.g., use of colors, width of lines, or type and space of legends), and data complexity (e.g., number of data points or configuration of the display). The effectiveness of a display is also affected by other conditions of presentation (e.g., lighting or time pressure), and user characteristics as well as use characteristics (e.g., cognitive styles, use of heuristics) (Gillanet al. 1998). And as mentioned above, some performance characteristics of visualizations are likely to be in conflict, (e.g., speed of performance may be traded off for accuracy). Few studies have examined the effects of visualizing uncertainty about risk estimates on risk judgments or decision making. In one exception, Johnson and Slovic (1995) demonstrated that showing confidence intervals for risk estimates influenced attributions regarding the honesty and trustworthiness of the organization producing the estimates. Much remains to learn about how different users respond to more complex visualizations of risk and uncertainty, and use them in their judgment and decision making.


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13.3 Keep Representations Simple for Effective Communication Susan Cutter University of South Carolina, Columbia, South Carolina, USA

The primary role of geospatial visualization in the context of hazards research is to communicate the following to stakeholders: 1) the probability of an event, or risk; 2) the potential for harm to human, built environment, and physical systems, or vulnerability; 3) the impact of an event on people, the built environment, and physical environment; and 4) the capacity to recover from such an impact, or resilience. The stakeholders are the emergency management and response practitioner community, at all levels of government – local to federal, to non-governmental organizations, the faith-based organizations and everything in between. Stakeholders also include the public, mass media, and researchers. Given the diversity and range of stakeholders, such visualizations must be clear, simple, readily interpretable, and truthfully represent the data upon which they rest. There are many ways to cartographically represent data about hazards. We can use symbology to represent the location of point phenomena such as tornadoes; we can use lines to approximate an earthquake fault; or polygons to illustrate plume pathways. We can represent quantities at point locations such as the number of fatalities using graduated circles; or we can use color or line width to differentiate intensity along a tornado’s path; or use color to represent high and low values of social vulnerability at county scales. Each approach has its utility; but they also have strengths and weaknesses. Once the attribute has been graphically represented, adding the confidence or uncertainty inherent in the underlying measurement or forecast is the next step. In the spirit of keeping it simple, this could range from an elementary symbol for confidence in the data (low, medium, high) to more elegant solutions such as error bars or some of those initially suggested in the paper. But, they must be tied to the attribute that is being displayed. There are two areas where strategic investments in research and practice will yield the best results. The first is the appropriate use of color and color ramping. Much of the work on the use of color in cartographic visualization has been conducted by Cynthia Brewer (2003, 2005). GIS software has enabled novel users to create visualizations at will, with little understanding of the cartographic conventions that underpin them. For

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example, the use of color ramping (same color but hue differences) is best used for choropleth mapping where values are ordered from high to low. Darker hues represent higher values; lighter hues represent lower values. Using 5 different colors to show data classes is confusing and difficult to interpret. The use of color can be used to represent bivariate interactions as well. A second fruitful area for strategic investments is to improve our understanding of cognition and cognitive processes. Specifically, we need to move risk visualization research beyond the technical development of widgets into more cognitive approaches aimed at deciphering how individuals understand and cognitively process not only spatial information, but also probability and uncertainty. Certainly the work of Tversky, Slovic, Fischhoff and colleagues has great relevance here. Perhaps more importantly is the need to understand the potential differences in the cognitive processing and understanding of risk visualizations based on race, class, gender, culture, experience, educational training, etc. This line of research has the most relevance to the decision making community who must rely on visualizations of risk to communicate warnings to prompt individuals and communities to engage in protective action behaviors. A poor visualization of risk, such as the DHS threat alert system, does not garner trust in the information provider (oftentimes quite the opposite). It may even be worse than no risk visualization at all. There continues to be a deep crevasse between the IT, GIS science, and emergency management and response practitioner communities. The advancements of methods and models are important, but they need to be geared for the users and tested for their application in real-time disaster contexts. At the same time, the end users need to ramp up their IT sophistication. In order to maximize the effectiveness and use of risk visualizations, I suggest that the developers KEEP IT SIMPLE and make it intuitive.


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13.4 Strategic Directions for Visualizing Uncertainty in Natural Hazards Visualizing complex information is a difficult and computationallyintensive task, but an effective visualization can be a key ingredient to conveying scientific messages. This task is further complicated by the introduction of uncertainty, which enters at various stages of data acquisition, analysis, and visualization. The previous chapter provided an overview of the methods used by computer scientists to help other researchers visually present their multidimensional, multivariate, and multi-valued data and summarized the state-of-the-art in uncertainty visualization. Natural hazard data present a particular challenge, especially when the information needs to be conveyed to a non-technical audience that may be making real-time, real-life decisions based on its interpretation of the presentation. Specific examples from oceanographic, seismic, and weather applications in the previous chapter amply illustrate current techniques and potential future directions for visualizing complex information with a certain degree of uncertainty to appropriate audiences with specific needs. Linking GIS better with HAZUS® could enable presentation of outputs that are much more useable. The general public is becoming accustomed to understanding information spatially. There needs to be some exploration of using 3D technologies to create effective visualizations of HAZUS® output. There is work going on at Georgia Institute of Technology on using more realistic representations of place such as orthophotos, but there is some creative conflict with regard to multivariate analyses, because the displays become complicated. The Southern California Earthquake Center has developed some effective visualizations that are available on their website (http://epicenter.usc.edu-/cmeportal/viz.html). Detailed information (i.e. city block-level data) often cannot be presented to the public for legal reasons, in addtion to the difficulty of representing them visually. Block-level analyses for estimating damage in San Francisco using HAZUS® have been developed that cannot be presented to the public due to legal liability. City block-level analyses have such small units (5000 city blocks); it is difficult to come up with a visualization that works at this level. Additionally, due to the high level of uncertainty in the analysis, it is potentially misleading to present data at such a detailed level. Future developments in the visualization of hazards and risk, especially for situations with a high degree of uncertainty, will require the marriage of science, technology and cognitive psychology. As computational processing capabilities increase the possibilities for displaying ever more complex information, use will not necessarily keep pace, unless designs assess

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the needs and abilities of decision makers. Visualizations that employ symbologies and levels of accuracy appropriate to the needs of specific users for a specific purpose will be most effective in supporting informed decision-making about hazard mitigation and response. Developing a user-sensitive theoretical framework for uncertainty visualization is a complex research challenge. In particular, representing multi-valued factors, each with its own sources and levels of uncertainty, is an open problem from a purely technological standpoint. Additionally, scientists and software developers need to tap into (and participate in) ongoing research on the cognitive aspects of risk perception and the ability of decision-makers to integrate information in various forms to make rational decisions about social responses to natural hazards. Robust visualizations in multi-dimensional space may eventually be technologically possible but may not necessarily convey the most relevant information in a way that is understandable or useful to end-users. HAZUS® in particular may benefit from an increasingly spatiallyenabled output presentation and users who are becoming more familiar with map-based displays. Nevertheless, the development of 3D or even 4D visualizations of hazard forecasting tools will be a wasted effort unless it is done with an awareness and associated study of users’ cognitive proficiency and decision-making needs. At the same time, ethical and legal considerations must guide the development of visualizations to avoid manipulation (intentional or unintentional) and other undesirable consequences.

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14 Conclusion

Ann Bostrom1, Steven P. French2, Sara J. Gottlieb3 1,2,3

Georgia Institute of Technology, Atlanta, Georgia, USA

The analysis and mitigation of the risk posed by natural and other hazards has been developed over several hundred years. The last 20 years have seen a significant leap forward in our ability to model the effects of earthquakes on the built environment. This rapid progress in risk modeling is based upon significant advances in engineering, and information and sensor technologies that have resulted from significant research investments by the National Science Foundation in the United States and sister agencies in Japan and Europe. Currently available risk models enable planning and mitigation of natural hazards in ways that were not previously possible. The U.S. federally funded multi-hazard HAZUS® model is the most widely known example of this history. HAZUS® and similar risk modeling tools are poised to play an increasingly important role in catastrophe management and risk mitigation. The papers and discussions at the Boulder workshop highlighted several strategic directions that are emerging in the field of risk analysis and modeling. How these directions develop will determine the shape of risk modeling over the next decade. Strategic investment in specific areas can reinforce and accelerate one or more of these directions. Over the past decade there has been a strong trend toward a multihazard approach to risk analysis. While owners, insurers and public officials are interested in seismic hazards, most must confront a variety of hazards. A rational decision-making approach requires evaluation of the full array of hazards, preferable within a standard framework using comparable methods. This trend toward a multi-hazard risk analysis approach is already becoming a reality, as reflected in the extension of HAZUS® from an earthquake risk analysis model to address flood and wind hazards. This trend toward a multi-hazard approach will require communication and collaboration across hazard-specific boundaries in both science and engineer-

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ing. Common language and analytic perspectives will need to be developed and refined. Difficult compromises may be required to forge a common paradigm to address the full range of natural and manmade hazards consistently. In parallel to this multi-hazard trend, we are beginning to see the extension of risk modeling beyond traditional engineering measures of loss to include estimates of the social and economic consequences that result from earthquakes and other natural hazards. This extension represents a significant challenge because it requires rigorous models of social and economic systems that are sensitive enough to respond to the perturbations caused by a hazard event. Most social and economic models are not designed to incorporate the impacts of such events. Significant additional work will be required to build such models. This effort will also require close collaboration between disparate disciplines including structural engineering, economics, sociology and others. Models that can accurately predict the social and economic consequences of hazard events do, however, offer great promise because they will produce results that will be directly useful in emergency response planning. They will also be more meaningful and salient to decision makers. Another key direction that is only in the initial stages is the ability to model the effects of alternative mitigation actions. Being able to use risk analysis to help decision makers trade off the costs and benefits of alternative mitigation strategies will bring risk modeling fully into the policy process. Knowing the amount and location of damage that will occur is useful; understanding the social and economic consequences of that damage is a strong motivator for decision makers and is very useful in emergency response planning; but the ability to rigorously evaluate investments in alternative mitigation measures is key to effectively addressing risk and rationally evaluating potential mitigation actions. Similarly we see an important role for psychology and risk communication in shaping the output of risk models to better communicate risk and uncertainly to both technical and non-technical audiences. There have been significant advances in visualization in the past decade. These have largely not been integrated into the current generation of risk analysis modeling software. These techniques can improve the capability of risk models to be understood by decision makers. Again significant research remains to be done and collaboration between disciples that have not traditionally worked together will be necessary. One of the key advances in recent years is the advent of experimentally and analytically based fragility curves that have replaced the expert opinion based relationships used a generation ago (e.g. those in ATC-13). We can expect our understanding of the way structures respond to seismic

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forces to continue to advance incrementally as this research is continually refined. While we have developed a reasonable ability to predict the response of structures to earthquakes, our ability to predict the behavior of the nonstructural components of buildings has lagged significantly. The non-structural components (e.g. ceilings, piping, HVAC and electrical systems) have accounted for 70–80 percent of the economic damage to buildings in recent earthquakes and are key to the continued functioning of a building after an event. To be really useful the next generation of risk models must include a more rigorous analysis of the non-structural damage. This will require developing fragility curves for these non-structural systems that are as sound as those we now have for structural systems. A key to achieving risk mitigation is community-based development of risk tools and wide dissemination of new risk analysis capabilities in an open and transparent manner. The next generation of risk modeling software is moving away from the standalone, proprietary computing platform toward open source software implemented over a distributed network. Network implementation allows users to combine data and models from a variety of sources and will require significantly less maintenance by users because individual software components are maintained separately. The National Earthquake Engineering Simulation (NEES) network has already established this distributed computing model in the earthquake engineering field. It is only natural that it should be extended to the risk analysis domain. The MAEviz model developed by the Mid-America Earthquake Center is an example of this next generation risk modeling approach. Institutional and policy changes that enable risk analysis tools to reflect social values and are designed to support planning and policy making are also prerequisites to realizing the benefits of these new capabilities. Open source software, internet access for users, transparency regarding the sources and quality of data, data acquisition through sensors that are linked to adaptive data analysis and visualization techniques is the future of risk modeling that emerges from this volume.

Index

A affect heuristic, 288 affective mapping, 289 Algermissen, S.T., 24, 26 Ambraseys, Nicholas, 26 Americans with Disabilities Act (U.S.), 108 Anderson, John, 13 Assembly Based Vulnerability, 38 assets, 6, 7, 9, 44 average annual loss, 221

cognitive processes, 300 cognitive psychology, 270 Comite Euro-internationale de Beton (CEB), 94 consequence-based risk management, 208 Core Damage Frequency, 176 Cornell, C. Allin, 24, 25, 26, 28, 29, 45, 55, 56 cost-benefit analysis, 22, 44, 151, 223, 225, 231, 243 D

B Ballore Comte Montessus de, 26 Biot, Maurice, 17 Blue Book, 20 Blume, John, 20 Boore, David M, 28 C calibration, 199 California Earthquake Authority, 39 California Seismic Safety Commission, 35, 97 Calvi, Gian, 95 Capacity Spectrum, 39 Carson, Rachel, 22 casualties, 137 catastrophe model, 217, 231, 236 catastrophe planning, 101, 104, 106, 127 Central U.S. Earthquake Consortium, 125

damage, 8 Damage Probability Matrix, 36 Danusso, Arturo, 92 Davenport, A.G., 24, 26 Davison, Charles, 26 Degenkolb, Henry, 20 dendrochronology, 27 Disaster Management Act of 2000 (U.S.), 125 Dynes, Russell, 23 E earthquake, 7 Earthquake Engineering Research Institute, 17 earthquake hazard, 6 earthquake magnitude, 27 Earthquake Research Institute (Japan), 91 earthquake visualization, 274 Earthquakes

324

Index

Alaska (1964), 97 Caracas Venezuela (1967), 35 Charleston (1866), 18 ChiChi (1999), 27 Coalinga (1983), 148 Ft. Tejon (1857), 27 Fukui (1948), 33 Gifu (1891), 91 Kanto (1923), 36, 91, 93 Kern County (1952), 33, 35 Kobe (1995), 42 Lisbon (All Saints Day) (1755), 6, 92 Loma Prieta (1989), 40, 148, 216 Long Beach (1933), 14, 33 Managua (1972), 6 Marmara (Turkey) (1999), 43 Messina (1908), 12, 92, 93 Mexico City (1985), 41 Miyagiken-oki (1978), 38 Nahanni (1985), 16 Napa/Yountville (2000), 135, 148 Niigata (1964), 18, 41, 42 Nisqually (2001), 124, 136, 137 Nobi (1891), 12 Northridge (1994), 37, 42, 44, 137, 143, 148 Prince William Sound (1964), 33 San Fernando (1971), 14, 28, 35, 36, 38, 39, 50, 97 San Fernando (1971), 143 San Francisco (1906), 33, 42, 92 San Simeon (2003), 124, 136 Santa Barbara (1925), 13 Taiwan (1999), 137 Tokachi-oki (1968), 36 Tokyo (1923), 13, 33, 42 Turkey (1999), 137 Whittier (1987), 148 economic basis earthquake, 193 Electric Power Research Institute, 37 Emergency Operations Plans, 104 emergency planning, 216

Equivalent Lateral Force, 13 essential facilities, 119, 128 Esteva, Luis, 24 Eurocode8, 94 evaluability, 289, 290 Ewing, James Alfred, 12, 90 exceedance probability, 220, 221, 222 Expected Annual Loss, 192 F fault maps, 27 feature vector, 263 Federal Emergency Management Agency (FEMA), 94 Field Act of 1933 (California), 14 Field, Ned, 28 Finite Element Method, 31 fire following earthquake, 40, 42, 137, 142 first-generation limit states, 199 Forel, François-Alphonse, 12 fragility, 8 fragility function, 9, 30 Freeman, John Ripley, 13 Friedman, Don, 22 FRISK, 28 G Geographic Information Systems, 22, 135, 217 Gramm-Leach-Bliley Financial Services Modernization Act of 1999 (U.S.), 52 Gray, Thomas, 12, 90 grid analysis, 127 Gutenberg-Richter law, 14, 29 H hazard, 9 hazardous materials, 42

Index hazards visualization, 273 HAZUS®, 28, 37, 39, 41, 42, 44, 45, 46, 53, 84-86, 88, 101-107, 109114, 118-124, 126, 127, 135, 136, 139, 142, 145, 146, 149, 160, 163, 216, 218-220, 222, 223, 243 Hooke, Robert, 90 housing, 245 Housner, George, 5, 11, 17, 18, 24, 89, 90, 95 hurricane, 277 Hurricane Andrew, 216 Hurricane Hugo, 216 Hurricane Katrina, 42, 53, 104, 276 Hydrologic Engineering Center, 22 I Imperial Earthquake Investigation Committee, 12, 91 Incident Command System, 105 Infrastructure Security Partnership, 111 injuries, 41 insurance, 12, 13, 22, 39, 42, 44, 47, 48, 51, 53, 239 insurance-based model, 218, 219, 222 International Association of Earthquake Engineering, 94 inventory, 45, 46, 87, 109, 112, 114, 121, 128, 137, 144, 153, 217, 219 isoseismals, 11, 26 J Japan National Fire Research Institute, 42 Japanese Building Law, 94 Jishin Kogaku, 92 K Kawasumi, H., 18, 24, 25 Krishna, Jai, 94

325

L lateral force resisting system, 31 LessLoss, 95 lifeline disruption costs, 142 lifelines, 9, 32, 39, 86, 106, 128, 138, 145, 161, 203 limit state probability, 198, 200 liquefaction, 18, 41 logistics, 128 loss, 8 direct, 138 indirect, 138 loss calibration, 145 loss estimation, 102, 135, 136, 139, 143, 150, 216, 219 loss of life, 246 M MAEViz, 241 Mallet, Robert, 26, 90 Malone, Thomas F., 22 Maximum Foreseeable Loss, 47 McGuire, Robin K., 28 McHarg, Ian, 22 mental models, 296 Mercalli, Giuseppe, 12 metrics, 168 Mid-America Earthquake Center, 31, 95 Millikan, Robert, 13 Milne, John, 12, 90, 91 Milne, W.G., 24, 26 mitigation, 118, 120, 125, 151, 216, 221, 222, 223, 230, 242, 243, 244 Modified Mercalli Intensity, 137 moment magnitude scale, 26 Multidisciplinary Center for Earthquake Engineering Research, 118 Multihazard Mitigation Council, 111, 124, 224

326

Index

multi-valued data sets, 254 multivariate, 255 N Nader, Ralph, 23 Naito, Tachu, 13 National Earthquake Hazard Reduction Program (NEHRP), 94 National Flood Insurance Program, 52 National Incident Management System, 105 National Response Plan, 105, 119 Navigation Act of 1936 (U.S.), 22 net present value, 226 network, 129 network functionality, 203 New Madrid Seismic Zone, 107, 108, 119, 125 New Zealand Fire Service, 42 nuclear power plant, 22, 23, 174, 175 numeracy, 285 numerical modeling, 252 Nuttli, Otto W., 28 O Omori, Fusakichi, 12, 91 OpenSHA, 28

Pinto, Artur, 95 portfolio, 32 power grid, 203 preparedness, 118 Probabilistic Risk Assessment, 175 Probabilistic Seismic Hazard Analysis, 28, 174, 188, 219 Probable Frequent Loss, 192 Probable Maximum Loss, 47, 48, 49, 52, 192, 221 aggregate, 49 building class, 48 Q Quarantelli, Enrico L., 23 R recovery, 118 Reid, Harry F., 12 resilience, 118 response, 118 Richter, Charles, 13, 14 Riley Act of 1933 (California), 14 risk analysis, 8, 47 risk assessment, 8, 84, 88, 96 risk evaluation, 8 risk ladder, 296 risk management, 8, 237 risk perception, 195 Rosenblueth, Emilio, 24

P Pacific Earthquake Engineering Center, 31 Packard, Vance, 22 paleoseismology, 27 Palmieri, Luigi, 92 Panetti, Modesto, 92 Paulay, Thomas, 94 peak ground acceleration, 137, 175 performance-based earthquake engineering, 181, 191

S San Francisco (1906), 12 Sano, Toshiki, 12, 13 Sarbanes-Oxley Act of 2002 (U.S.), 52 Schnabel, Per B., 25 Seattle Fault scenario, 84, 85, 87 Seed, H. Bolton, 25 Seismic Core Damage Frequency, 175, 189

Index Seismic Design Decision Analysis, 36, 38, 49, 56 seismic hazard, 8 seismic intensity, 8 Seismic Qualifications Utility Group, 37 seismic risk, 6, 8 seismic risk assessment, 5, 6, 7, 10, 50, 51, 52, 53, 55 seismicity, 14, 27 seismograph, 14 Seismological Society of America, 12 Seismological Society of Japan, 12, 90 Sekiya, Seikei, 91 Sieh, Kerry, 27 Social Vulnerability Index, 97 stakeholder participation, 244 Steinbrugge, Karl V., 35 Stepp, Carl, 26 strong motion, 14, 28 strong motion network, 27 Suyehiro, Kyoji, 12, 91

327

uncertainty, 122, 142, 160, 178, 183, 197, 231, 240, 250, 251, 252, 284, 294 aleatoric, 29, 174, 183, 189, 198, 199 epistemic, 29, 174, 183, 189, 198, 199 Uniform Building Code, 13, 19, 94 US National Seismic Hazard Maps, 28 user interface, 250 V Vision 2000, 51 visualization, 250, 251 vulnerability, 6, 8, 9, 10, 30, 40, 217, 218, 219, 220, 238 vulnerability function, 30, 32, 33 W

Technical Council on Lifeline Earthquake Engineering, 39

Wadati, Kiyoo, 14 weather forecasting, 276 Whitman, Robert V., 36 Willis, Bailey, 13 Wood, Harry, 13 World Conference of Earthquake Engineering, 20, 24

U

Y

U.S. Nuclear Regulatory Commission, 28, 51

Yarar, Refaat, 94 Young, Thomas, 90

T

List of Contributing Authors

Bostrom, Ann, Associate Professor and Associate Dean of Research University of Washington, Daniel J. Evans School of Public Affairs 327 Parrington Hall, Box 353055, Seattle, WA 98195-3055 USA Chang, Stephanie, Associate Professor University of British Columbia, Institute for Resources, Environment and Sustainability, Centre for Human Settlements 242-1933 West Mall, Vancouver, BC V6T1Z2 Canada Cutter, Susan, Carolina Distinguished Professor University of South Carolina, Department of Geography Callcott, Room 312, Columbia, SC 29208 USA Davidson, Rachel, Associate Professor University of Delaware, Department of Civil & Environmental Engineering 360B DuPont Hall, Newark, DE 19716-3120 USA Dueñas-Osorio, Leonardo, Assistant Professor Rice University, Department of Civil and Environmental Engineering MS-318, 6100 Main St., Houston, TX 77005 USA Durham, Thomas PBS&J, Inc. 3859 Centerview Drive, Suite 160, Chantilly, VA 20151-3232 USA Eguchi, Ron, President & CEO ImageCat, Inc. Union Bank of California Building, 400 Oceangate, Suite 1050, Long Beach, CA 90802 USA

330


Ellingwood, Bruce, Professor Georgia Institute of Technology, Department of Civil and Environmental Engineering 790 Atlantic Drive NW, Atlanta, GA 30332-0355 USA Elnashai, Amr, Professor & Mid-America Earthquake Center Director University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, 1245 Newmark Lab, Urbana, IL 61801 USA French, Steve, FAICP, Professor & Center for Geographic Information Systems Director Georgia Institute of Technology, City Planning Program 276 Fifth St., NW, Atlanta, GA 30332-0695 USA Gottlieb, Sara, Research Scientist Georgia Institute of Technology, Center for Geographic Information Systems 276 Fifth St., NW, Atlanta, GA 30332-0695 USA Grossi, Patricia, Earthquake Model Manager Risk Management Systems 7015 Gateway Blvd, Newark, CA 94560 USA Hueste, Mary Beth, E. B. Snead II Associate Professor and Assistant Division Head Texas A&M University, Zachry Department of Civil Engineering, Construction, Geotechnical and Structures Division 808U CE/TTI Building 3136 TAMU, College Station, TX 77843-3136 USA Johari, Pushpendra, Program Manager PBS&J, Inc. 5665 New Northside Drive, Suite 400, Atlanta, GA 30328-4617 USA Johnson, Laurie, AICP Consultant 2550 Gough St. #6, San Francisco, CA 94123 USA


331

La Fore, Susan, Doctoral Student University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering 1108 Newmark Civil Engineering Laboratory, MC-250, Urbana, IL 61801 USA McGuire, Robin, President Risk Engineering, Inc. 4155 Darley Ave., Suite A, Boulder, CO 80353-6536 USA Mileti, Dennis, Professor Emeritus University of Colorado, Department of Sociology, Institute of Behavioral Science 219 Ketchum Hall, UCB 327, Boulder, CO USA Muthukumar, Subrahmanyam, Doctoral Student & Research Scientist Georgia Institute of Technology, Center for Geographic Information Systems 276 Fifth St., NW, Atlanta, GA 30332-0695 USA Olshansky, Robert B., Professor University of Illinois Urbana-Champaign, Department of Urban and Regional Planning Room 111 Temple Buell Hall 611 Taft Drive, Champaign, IL 61820 USA Pang, Alex, Professor University of California at Santa Cruz, Computer Science Department Santa Cruz, CA 95064 USA Peacock, Walter, Professor & Hazard Reduction and Recovery Center Director Texas A&M University, Department of Landscape Architecture and Urban Planning 3137 TAMU, College Station, TX 77843-3137 USA Peters, Ellen, Senior Research Scientist Decision Research 1201 Oak Street, Suite 200, Eugene, OR 97401 USA

332


Scawthorn, Charles, Professor Kyoto University, Department Of Urban Management Department of Urban Management, Kyoto, 606-8501 Japan Schneider, Philip, Multihazard Loss Estimation Program Director National Institute of Building Sciences 1090 Vermont Avenue, NW, Suite 700, Washington, DC 20005-4905 USA Seligson, Hope, Technical Manager ABS Consulting, Inc. 300 Commerce Drive, Suite 200, Irvine, CA 92602 USA Small, Mitchell, Professor Carnegie Mellon University, Department of Civil and Environmental Engineering & Department of Engineering and Public Policy Porter Hall 123D, Pittsburgh, PA 15213 USA Tierney, Kathleen, Professor University of Colorado at Boulder, Department of Sociology Institute of Behavioral Science #6, Boulder, CO 80309-0482 USA Whitman, Robert V., Professor Emeritus Massachusetts Institute of Technology, Department of Civil and Environmental Engineering 1010 Waltham St #C557, Lexington, MA 02421 USA Zhang, Yang, Doctoral Student Texas A&M University, Department of Landscape Architecture & Urban Planning 503 B Langford Building A, College Station, TX 77843 USA

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