Flood Warning, Forecasting and Emergency Response

Flood Warning, Forecasting and Emergency Response

Kevin Sene

Flood Warning, Forecasting and Emergency Response

Kevin Sene United Kingdom

ISBN 978-3-540-77852-3

e-ISBN 978-3-540-77853-0

Library of Congress Control Number: 2008927074 © 2008 Springer Science + Business Media B.V. No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

Preface

This book provides an introduction to recent developments in the area of flood warning, forecasting and emergency response. The topic spans a wide range of disciplines, including weather forecasting, meteorological, river and coastal detection systems, river and coastal flood forecasting models, flood warning dissemination systems, and emergency response procedures. The text deals mainly with general principles and concepts, but also includes references to a number of manuals, guidelines and papers which provide more detailed information on factors to consider in designing and implementing a flood warning system. Although informal flood warning systems have existed ever since people settled near to rivers and coastlines, improvements to communication and computer systems in recent years have opened up a range of possibilities in many aspects of the flood warning process. These include developments in remote sensing techniques, ensemble forecasting, automated flood warning systems and decision support systems. Some recent research and operational developments in these areas are discussed, although specific brands of equipment (software, instrumentation etc.) are not considered. The topics of performance monitoring, risk based design and prioritisation of investment are also considered in several chapters, with recent developments driven in part by rising public expectations, and by an increasing need for organisations to justify investments in new equipment and procedures. Early warning systems are often described in terms of the detection, warning dissemination, response, recovery and review stages. In many cases, a forecasting component will also be included, and preparedness is essential for an effective emergency response. This structure is also adopted here, although with only a short discussion of the recovery phase, since flood warning and forecasting has a less important role to play once flood levels start to recede, such as estimating when floodwaters will drain, or if any further flooding is imminent. By contrast, the warning aspect is discussed in several locations, including a chapter on the decision criteria used for issuing flood warnings (often called thresholds) and sections on decision support and decision-making under uncertainty. The book is presented in three main sections as follows: ●

Part I – Flood Warning, which discusses the topics of detection, thresholds and dissemination

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Preface

Part II – Flood Forecasting, which discusses general principles, specific types of river and coastal forecasting models, and examples of specific applications Part III – Emergency Response, which covers the topic of preparedness, response and review

The types of flooding which are discussed include river flooding, coastal surge, snowmelt, ice-jams, urban drainage, flash flooding, and geotechnical risks, such as Tsunami, dam breaks, and debris flows. The impacts of tropical cyclones, hurricanes and typhoons are also discussed from a flooding perspective, although the meteorological aspects are only considered briefly. Examples of operational systems are also provided from several countries, which in places has led to a need to decide on the most appropriate terminology to use. So, for example, the term catchment is used to describe what in some countries is known as a river basin or watershed, the term cell phone describes mobile or cellular phones, and the term flood defence is used in place of the terms levees or dikes. A glossary provides more detail on the terminology used. Although the book is primarily about real time flood warning, forecasting and emergency response, some of the techniques described have evolved from those used in other applications, such as flood simulation, water resources, hydrometeorology, and ocean modelling, and may be of wider interest. The main difference in flood warning applications is the requirement for rapid decision making, often with incomplete or uncertain information. Supporting tools, such as forecasting models, also need to operate sufficiently quickly and reliably to be of value in the process, again often with less input data than would be available in simulation modeling, although with the option of updating outputs in real time to help to correct for differences between observed and forecast values. There is also often a greater emphasis on the resilience of systems, and on documenting any design, operational and other decisions made during model operation. These differences all add an interesting dimension to this diverse and wide ranging subject.

Acknowledgements

This book has benefited from discussions with many people. Following several years working in fluid mechanics, I joined the Centre for Ecology and Hydrology in Wallingford (formerly the Institute of Hydrology) which provided the opportunity to work on a wide range of research and consultancy projects on flood-related, hydrometeorology, water resources and hydrometry topics in more than twenty countries. The many discussions with colleagues during that time provided a useful grounding for the topics discussed in this book. Subsequently, as part of a large engineering consultancy, I have had the benefit of many meetings, site visits and discussions with operational staff as part of flood warning and forecasting improvement projects and strategies, and on projects to develop best practice guidelines in flood forecasting for the Environment Agency and SEPA. In a rapidly developing field such as flood warning, forecasting and emergency response, much information can also be obtained from internet searches, and many organizations place conference proceedings, reports, manuals, and other useful documents in the public domain. In presenting figures, references and quotations from internet and published sources, both the publisher and myself have attempted to identify and provide citations to the appropriate sources, although we apologise if there have been any unintentional errors. Many people assisted with providing comments on short extracts from the draft text and providing figures, and I hope that I have included their comments accurately. Michael Robbins and Steve Jebson from the Met Office, and Ian Marshall and Hazel Phillips from the Environment Agency, were also very helpful with my requests to use a range of figures and tables in the book. I am also grateful to a number of colleagues for discussing aspects of the text, or providing figures, including Marc Huband, Nick Elderfield, Jayne Lamont, Tom Rouse, and Graham Clark. Also, Yiping Chen for many useful discussions on hydraulic modelling for real time forecasting applications, and Jonathan Wright for his general advice and support. Finally, from Springer, I would like to thank Robert Doe and Nina Bennink for their help and advice throughout the process of writing the book and bringing it to production.

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Acknowledgements

Additional organizations that I would like to thank include: ●

● ● ● ● ● ●

● ● ● ●

Environment Agency for Figures 3.5, 4.2, 8.7, 9.3 and Tables 3.1, 3.3, 5.1, 5.5, and 6.1 Federal Emergency Management Agency (FEMA) for Figure 10.1 Her Majesty’s Stationary Office for the text cited in Box 9.2 KNMI, Royal Netherlands Meteorological Institute for Figure 3.1 Met Office for Figures 2.1, 2.3 and 2.4 NOAA/National Weather Service for Figures 7.5 and 7.6 Proudman Oceanographic Laboratory/National Tidal and Sea Level Facility for Figures 7.3 and 7.4 Royal Meteorological Society for Figure 2.2 Scottish Hydraulics Study Group for Table 8.1 STOWA for Figures 10.4 and 10.5 World Meteorological Organisation for Figures 1.2, 1.3, 3.4, 3.6, 7.1, 7.2, 8.2, 8.5 and 11.1

Note that any text/material regarding TCP/WMO does not imply the expression/ endorsement of any opinion whatsoever on the WMO Secretariat concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Also, I would like to thank the following people for assisting with providing figures, or comments on draft text: ● ● ● ●

● ● ● ● ● ● ● ●

● ●

S. Baig, National Hurricane Centre, for comments on Box 7.3 C. Carron, Environment Agency, for comments on Box 8.2 P. Durrant for Figure 10.3 K. Horsburgh, Proudman Oceanographic Laboratory, for comments on Box 7.2 H. Lewis, North Cornwall District Council, for comments on Box 10.1 J. Nower, Environment Agency, for comments on Box 8.2 T. Peng, World Meteorological Organisation, for comments on Box 7.1 A. Richman, Virtual Environmental Planning, for Figure 9.4 B. Stewart, Bureau of Meteorology, for comments on Boxes 1.1, 7.1 and 8.1 K. Stewart, Urban Drainage and Flood Control District (UDFCD), for Figure 4.4 A. Tyagi, World Meteorological Organisation, for comments on Boxes 1.1 and 8.1 D. Vogelezang and colleagues, Royal Netherlands Meteorological Institute (KNMI), for comments on Box 3.1 and for providing Figure 3.1 H. Vreugdenhil and colleagues, STOWA, for comments on Box 10.2 D. Whitfield, Environment Agency, for comments on Boxes 4.1 and 5.1

Contents

Preface .............................................................................................................

v

Acknowledgements ........................................................................................

vii

1

1 1 8 8 9 13 15

Introduction .............................................................................................. 1.1 The Flood Warning Process .............................................................. 1.2 The Nature of Flood Risk ................................................................. 1.2.1 Flooding in Context .............................................................. 1.2.2 Assessing Flood Risk ............................................................ 1.3 Emergency Response ........................................................................ 1.4 The Role of Flood Forecasting .........................................................

Part I

Flood Warning

2

Detection ................................................................................................... 2.1 Meteorological Conditions ............................................................... 2.1.1 Site Specific Observations .................................................... 2.1.2 Remote Sensing .................................................................... 2.1.3 Weather Forecasting ............................................................. 2.2 River and Coastal Conditions ........................................................... 2.2.1 River/Tidal Level Monitoring ............................................... 2.2.2 River Flow Monitoring ......................................................... 2.2.3 Wave Monitoring .................................................................. 2.3 Instrumentation Networks................................................................. 2.3.1 Telemetry Systems ................................................................ 2.3.2 Network Design ....................................................................

21 21 22 28 33 36 37 39 42 44 44 47

3

Thresholds ................................................................................................ 3.1 Rainfall Thresholds ........................................................................... 3.2 River and Coastal Thresholds ........................................................... 3.2.1 Introduction ........................................................................... 3.2.2 Simple Forecasting Techniques ............................................ 3.3 Performance Monitoring ...................................................................

51 51 56 56 61 67

ix

x

4

Contents

Dissemination ........................................................................................... 4.1 Flood Warning Procedures ............................................................... 4.1.1 Introduction ........................................................................... 4.1.2 Flood Warning Areas ............................................................ 4.1.3 Organisational Issues ............................................................ 4.1.4 Control Rooms ...................................................................... 4.2 Dissemination Techniques ................................................................ 4.2.1 Introduction ........................................................................... 4.2.2 Role of Information Technology .......................................... 4.2.3 Warning Messages ................................................................ 4.3 Design and Implementation ..............................................................

Part II

71 71 71 73 75 77 79 79 81 84 87

Flood Forecasting

5

General Principles.................................................................................... 5.1 Model Design Considerations ........................................................... 5.2 Forecasting Systems ......................................................................... 5.3 Data Assimilation ............................................................................. 5.3.1 Error Prediction ..................................................................... 5.3.2 State and Parameter Updating ............................................... 5.3.3 Other Techniques .................................................................. 5.4 Model Calibration and Performance ................................................. 5.4.1 Basic Concepts ...................................................................... 5.4.2 Model Calibration ................................................................. 5.4.3 Performance Measures .......................................................... 5.5 Model Uncertainty ............................................................................

93 93 97 104 106 107 108 108 108 110 113 114

6

Rivers......................................................................................................... 6.1 Model Design .................................................................................... 6.1.1 Forecasting Requirement ...................................................... 6.1.2 Data Availability ................................................................... 6.1.3 Type of Model....................................................................... 6.2 Rainfall Runoff Models .................................................................... 6.2.1 Introduction ........................................................................... 6.2.2 Process-Based Models .......................................................... 6.2.3 Conceptual Models ............................................................... 6.2.4 Data-Based Methods ............................................................. 6.3 River Channel Models ...................................................................... 6.3.1 Introduction ........................................................................... 6.3.2 Process Based Models........................................................... 6.3.3 Conceptual Models ............................................................... 6.3.4 Data Based Methods .............................................................

123 123 124 126 128 132 132 135 137 139 141 141 142 145 146

7

Coasts ........................................................................................................ 7.1 Model Design Issues ......................................................................... 7.2 Process-Based Models ......................................................................

149 149 156

Contents

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7.2.1 Astronomical Tide Prediction ............................................... 7.2.2 Surge Forecasting.................................................................. 7.2.3 Wave Forecasting.................................................................. 7.2.4 Shoreline Processes ............................................................... 7.3 Data-Based Methods ......................................................................... 7.3.1 Artificial Neural Networks ................................................... 7.3.2 Other Techniques ..................................................................

156 157 165 167 169 169 171

Selected Applications ............................................................................... 8.1 Integrated Catchment Models ........................................................... 8.1.1 Introduction ........................................................................... 8.1.2 Modelling Approach ............................................................. 8.1.3 Ungauged Inflows ................................................................. 8.2 Flash Flood Forecasting .................................................................... 8.3 Snow and Ice ..................................................................................... 8.3.1 Snowmelt Forecasting ........................................................... 8.3.2 River Ice Forecasting ............................................................ 8.4 Control Structures ............................................................................. 8.4.1 Dams and Reservoirs ............................................................ 8.4.2 River Control Structures ....................................................... 8.4.3 Tidal Barriers ........................................................................ 8.5 Urban Drainage ................................................................................. 8.6 Geotechnical Risks ........................................................................... 8.6.1 Structural Risks ..................................................................... 8.6.2 Earth Movements ..................................................................

175 175 175 177 178 181 185 185 188 190 190 195 198 199 202 203 205

Part III

Emergency Response

9

Preparedness............................................................................................. 9.1 Flood Emergency Planning ............................................................... 9.1.1 General Principles ................................................................. 9.1.2 Risk Assessments .................................................................. 9.1.3 All-Hazard Approaches ........................................................ 9.1.4 Validation and Testing of Plans ............................................ 9.2 Resilience .......................................................................................... 9.2.1 Introduction ........................................................................... 9.2.2 Analysis Techniques ............................................................. 9.3 Role of Information Technology ...................................................... 9.3.1 Introduction ........................................................................... 9.3.2 Geographical Information Systems....................................... 9.3.3 Visualisation and Simulation ................................................

209 209 209 214 217 219 220 220 224 226 226 227 228

10

Response.................................................................................................. 10.1 Flood Event Management ............................................................. 10.1.1 Preparatory Actions ........................................................ 10.1.2 Timelines ........................................................................

231 231 231 234

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Contents

10.2 10.3

Decision Support Systems ............................................................ Dealing with Uncertainty ..............................................................

237 244

Review ..................................................................................................... 11.1 Performance Monitoring ............................................................... 11.2 Performance Improvements .......................................................... 11.2.1 Detection ......................................................................... 11.2.2 Thresholds ....................................................................... 11.2.3 Dissemination ................................................................. 11.2.4 Forecasting ...................................................................... 11.2.5 Preparedness ................................................................... 11.2.6 Response ......................................................................... 11.3 Prioritising Investment .................................................................. 11.3.1 Cost Benefit Analysis ..................................................... 11.3.2 Multi Criteria and Risk Based Analysis .........................

249 249 253 254 255 256 257 258 258 260 261 265

Glossary ..........................................................................................................

267

References .......................................................................................................

275

Index ................................................................................................................

299

11

Chapter 1

Introduction

Recent flood events have shown the devastating impact that flooding can have on people and property. Flood warning and forecasting systems can help to reduce the effects of flooding by allowing people to be evacuated from areas at risk, and to move vehicles and personal possessions to safety. With sufficient warning, temporary defences can also be installed, and river and tidal control structures operated to mitigate the effects of flooding. Many countries and local authorities now operate some form of flood warning system, and the underlying technology requires knowledge across a range of technical areas, including rainfall and tidal detection systems, river and coastal flood forecasting models, flood warning dissemination systems, and emergency response procedures. This introductory chapter provides a general overview of the flood warning process, approaches to flood forecasting and emergency response, and the nature of flood risk.

1.1

The Flood Warning Process

Flood warning systems provide a well-established way to help to reduce risk to life, and to allow communities and the emergency services time to prepare for flooding and to protect possessions and property. Actions may also be taken to reduce or prevent flooding; for example, by operating river control structures, and floodfighting activities such as reinforcing flood defences, and installing temporary or demountable barriers. Informal flood warning systems have existed ever since people started to live and work near rivers and coastlines. Heavy rainfall, high river levels, unusual sea states and other cues, such as the sound of running water, all provide useful information on impending flooding, with traditional methods for providing warnings including word of mouth, messengers, and raising flags and storm cones. These approaches still have a valuable role to play, particularly where flooding develops rapidly, and communities must rely on their own resources for the initial response. For example, in remote parts of Australia, farmers may alert others further downstream if river levels are high or flooding has started (Emergency Management Australia 1999) and, following the December 2004 Tsunami, several community leaders were praised for recognising the abnormal sea conditions and issuing an alert in time to prevent major loss of life (e.g. UNESCO 2006). K. Sene, Flood Warning, Forecasting and Emergency Response, © Springer Science + Business Media B.V. 2008

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1 Introduction

The use of a more technological approach started to become widespread with the introduction of telegraph transmission of river levels in the mid to late 19th century in countries such as the USA, France and Italy (e.g. Smith and Ward 1998), followed by telephone and radio telemetry early in the 20th century, and accelerated in the 1950s and 1960s as the computer and electronic industries developed. Developments have included the introduction of operational computer models of the atmosphere (from the 1950s), weather radar and satellite based observations of rainfall (from the 1970s), and automated and internet based methods of warning dissemination (from the 1990s). The widespread ownership of televisions, radios and telephones and, more recently, cell (mobile) phones and computers, has increased the range of methods which can be used for issuing warnings, supplementing traditional door knocking, loud hailer, siren and other techniques. Many countries and local authorities now operate some form of flood warning system, and Box 1.1 summarises estimates by the World Meteorological Organisation for the status of flood warning and forecasting services worldwide. Flood warning is also increasingly considered as part of a multi-hazard response to natural, technological and other risks (e.g. United Nations 2006a). If the performance meets the required levels of accuracy, reliability and lead time, flood warning can also be one of a range or portfolio of non-structural measures which can be used to manage or reduce flood risk in river catchments or along coastlines, together with other measures such as land use planning, and tax and insurance incentives to limit development in flood prone areas. A flood warning system can include rainfall and tidal detection systems, river and coastal flood forecasting models, flood warning dissemination systems, and emergency response procedures. Each link in this chain is important, and the modern emphasis is on a Total Flood Warning System (Emergency Management Australia 1999) or people-centred approach, in which communities provide inputs to the design of flood warning systems, and help with their continuing operation (e.g. Parker 2003; ISDR 2006; Basher 2006; Martini and de Roo 2007). The various components considered in this book (Fig. 1.1) are shown in Table 1.1, although the recovery component is only discussed briefly, since flood warning and forecasting has a lesser role to play at this stage of a flood event (for example, advising on when flood waters will recede). Also, mitigation measures (e.g. land use planning, insurance) are not discussed. Of course, the terminology used varies between countries and organisations, and some aspects may overlap (e.g. Alexander 2002). For example, the US Army Corps of Engineers (1996) identifies the following stages in the flood warning process: Flood-Threat Recognition; Warning Dissemination; Emergency Response; Postflood Recovery; and Continued Plan Management whilst, for tropical cyclone forecasting (Holland 2007) the following ten phases are identified in a typical cyclone season: Pre-Season Check; Routine Monitoring (at least twice daily); Cyclone Information (about 48 hours from estimated landfall); Cyclone Watch or Alert (landfall within 36–48 hours), Cyclone Warning (landfall within at least 24 hours); Imminent-Landfall; Post-Landfall; Impact Assessment; Documentation; and System Review.

FLOOD WARNING Detection

EMERGENCY RESPONSE PREPAREDNESS For example Stakeholder meetings/consultations

FLOOD FORECASTING

Customer satisfaction surveys Public Awareness campaigns Media briefings

Thresholds

School/outreach campaigns Interagency coordination meetings Flood Hazard Mapping

Dissemination

Flood Emergency Plans Table-top and full scale exercises

Response

Business Continuity/Resilience assessments Staff Training Forecasting model improvements

Recovery

Review

System improvements (instrumentation, communications, dissemination etc) Inputs to flood mitigation projects

Fig. 1.1 Illustration of the components of a flood warning, forecasting and emergency response system Table 1.1 Typical components in the flood warning, forecasting and emergency response process Item Component Examples Flood warning

Flood forecasting Emergency response

Detection

Monitoring of meteorological, river and tidal conditions; and meteorological forecasting (e.g. nowcasting, numerical weather prediction) Thresholds The meteorological, river and coastal conditions under which decisions are taken to issue flood warnings (sometimes called triggers, criteria, warning levels or alarms) Dissemination Procedures and techniques for issuing warnings to the public, local authorities, emergency services, and others Rivers, coasts Conceptual, data based and process based models for forecasting future river and coastal conditions Response Emergency works, temporary barriers, flow control, evacuation, rescue, incident management, decision support Recovery Repairs, debris removal, reuniting families, emergency funding arrangements, providing shelter, food, water, medical care, counselling, support to businesses, restoration of services if interrupted Review Review of the performance of all components of the system, and recommendations for improvements Preparedness Emergency planning, public awareness campaigns, training, systems improvements, business continuity/resilience assessments, flood risk mitigation etc.

4

1 Introduction

The resilience of flood warning systems to failure is also an important consideration, and risk based techniques from other technical sectors and types of emergency are gradually being introduced to help to identify potential points of failure, and appropriate risk reduction measures. There is also much debate about the effectiveness of flood warnings (e.g. Drabek 2000; Handmer 2002; Parker 2003) and of computer models and information systems (e.g. Fortune 2006). Clearly, a warning is successful if it initiates action which prevents flooding which might otherwise have occurred in the absence of that warning; for example by triggering the closure of a tidal barrier, or installation of a temporary defence. However, research suggests that success with providing warnings to the public is mixed, although in some countries has improved markedly in recent years through a combination of using flood forecasting models to extend the lead time and accuracy of warnings, a better understanding of how to communicate warnings, and an increased emphasis on community participation and inter-agency collaboration. For example, one recommendation (Emergency Management Australia 1999) is that the flood warning task can be boiled down to providing appropriate responses to the following five questions: ● ● ● ●

●

How high will the flood reach, and when? Where will the water go at the predicted height? Who will be affected by the flooding? What information and advice do the people affected by the flooding need to respond effectively? How can the people affected by the flooding best be given the appropriate information?

A particular issue to consider is that of the requirements for warning lead time, which can range from a few minutes or less for people on a steep sloping river bank to reach higher ground, to many hours or days for some situations, such as raising temporary defences, evacuating large numbers of people, or drawing down a reservoir in advance of flooding. Similarly, the requirements for accuracy, and tolerance to false alarms, will vary between organisations and communities, and can be influenced by education and public awareness exercises. This topic is discussed in more detail in later chapters. One early success story is that of Bangladesh (World Meteorological Organisation 2006b) in which a programme of investment in education, early warning systems, establishing a volunteer network, and emergency planning has led to a significant reduction in the number of casualties from tropical cyclones, storm surges, and tidal and river floods. For example, in 1998, a major storm surge led to about 140 deaths but, in a storm of similar magnitude in 1991, approximately 130,000 people lost their lives. Flood forecasting and warning systems have also led to major reductions in casualties in China in recent years (e.g. Huaimin 2005). Similar improvements can also be cited in many other countries where, due to improvements in flood warning systems, the risk to loss of life from flooding has reduced markedly. Approaches to flood warning, forecasting and emergency response are constantly evolving as technical advances are made, lessons are learned from flood events, and

1.1 The Flood Warning Process

5

ideas are adapted from other technical disciplines. For example, technological developments in recent years have included the introduction of short range rainfall forecasting techniques (nowcasting) which typically combine weather radar observations with the outputs from Numerical Weather Prediction models, and of multimedia systems for issuing warnings. Much social and behavioural research has also been performed into public understanding of, and response to, flood warnings, in some cases building on research in other disciplines, such as health care and emergency response for other natural hazards. Improvements can also be driven by national legislation, rising public expectations, customer satisfaction surveys, performance monitoring, and the introduction of level of service targets (e.g. Andryszewski et al. 2005). Risk based and probabilistic approaches are also increasingly being evaluated and used operationally, building on ideas from meteorological forecasting and elsewhere; for example, in techniques for prioritising investment, and ensemble forecasting. Increasingly, improvements are performed within a framework of targets for flood warning performance at a national level. Chapters 2–4 discuss the topics of detection, threshold setting and dissemination for flood warnings, whilst Chapters 9–11 discuss the preparedness, response and review stages. The remaining chapters (Chapters 5–8) cover flood forecasting for rivers and coastlines.

Box 1.1 The WMO Flood Forecasting Initiative The WMO Flood Forecasting Initiative aims to improve the capacity of meteorological and hydrological services to jointly deliver more timely and accurate flood forecasting and warning products and services for use in emergency preparedness and response. The initiative was launched in April 2003, and the main expected outcomes are (World Meteorological Organisation 2006a): ●

●

●

●

Improved quantitative and qualitative weather forecasting products are available in such a way that these can be directly used for flood forecasting. Medium-range weather forecasting and climate prediction tools can be applied to extend warning times and produce pre-warning information. National Meteorological and Hydrological Services have improved their capacity to cooperate to jointly deliver timely and accurate flood forecasting information. Integrated weather, climate and hydrological forecasting information are available in a relevant format for use by civil organizations responsible for disaster preparedness and mitigation.

Between 2003 and 2006, a series of regional workshops on “Improved Meteorological and Hydrological Forecasting for Floods” was held in West, Central and South Africa, Latin America, Asia, Europe and the Mediterranean (continued)

6

1 Introduction

Box 1.1 (continued) basin countries. These meetings involved hydrologists and meteorologists from about 85 countries and a number of regional and river basin organisations, as well as universities and research institutions. These meetings helped identify the status of flood forecasting and warning in the countries which can be categorised as: (Fig. 1.2)

Fig. 1.2 Overall status of national flood forecasting and warning services (sample-86 countries) (Reproduced from the WMO Strategy and Action plan for the enhancement of cooperation between National Meteorological and Hydrological Services for improved flood forecasting, courtesy of WMO)

●

●

●

Level I – flood forecasting and warning services are limited or not operational, and a significant upgrading and strengthening of the basic data collection and transmission networks is required, together with improvements in the coordination between meteorological and hydrological services and in the dissemination of flood warnings. Level II – the basic infrastructure is in place for flood forecasting and warning services but improvements are needed in data management and flood forecasting modelling, with training in advanced modelling techniques, and some improvements in coordination between meteorological and hydrological services. Level III – well established flood forecasting and warning services using the latest observation and forecasting techniques, and with warnings generally communicated through various media to Government and Civil Protection Agencies, industry and the public. The main requirement identified here was for improved training and staff capacity in some cases.

These workshops were followed by an international conference in Geneva in November 2006 to identify gaps in current procedures and to establish and agree on a framework and action plan to improve national and regional capacities for flood forecasting. The action plan addresses flooding due to flash

1.1 The Flood Warning Process

Box 1.1 (continued) floods, riverine floods, coastal floods, snowmelt floods, ice-jams glacier, lake outburst floods, landslides and mud flows. The review of existing techniques showed a wide range of capabilities, ranging from well developed systems using the latest Numerical Weather Prediction, weather radar, and satellite and modelling technologies, through to some countries lacking the technical or institutional capacity to operate flood forecasting and warning systems (Fig. 1.3).

Fig. 1.3 Main symptoms of insufficient or non-existent national flood forecasting capability (sample-86 countries) (Reproduced from the WMO Strategy and Action Plan for the enhancement of cooperation between National Meteorological and Hydrological Services for improved flood forecasting, courtesy of WMO)

However, for some of the countries with limited capacity (14–16%), hydrological forecasts are provided by a regional transboundary river basin authority, and activities were underway in a further 28–33% of countries to improve and modernise existing monitoring and forecasting systems. More than half of countries surveyed (55%) identified a lack of monitoring equipment (automatic weather stations, weather radars, satellite imagery) as an issue, including some 27% of countries which required a significant upgrading of basic meteorological and hydrological networks and telemetry systems for flood forecasting applications. More general requirements which were identified included the need for improved coordination and cooperation between organisations and countries, guidance materials for a range of subjects including data exchange, warning dissemination, and forecast products, and improved training and capacity building.

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1.2 1.2.1

1 Introduction

The Nature of Flood Risk Flooding in Context

Flooding is a threat to many communities and businesses, and flood risk is increasing in some locations due to development on floodplains, migration to urban areas at risk from flooding, and artificial influences on flow regimes; for example, urban developments can sometimes increase flood risk through changes to runoff characteristics and the drainage paths of floodwater. Climate change may also be increasing the likelihood of flooding in some places through changes in the frequency and severity of storms, patterns of snowfall and snowmelt, and rising sea levels. Estimates by the Centre for Research on the Epidemiology of Disasters (CRED) suggest that, in the period 1974–2003, there were more than 200,000 victims of flooding, with many more people affected for every casualty. In that period, the July 1974 and December 1999 floods in Bangladesh and Venezuela each accounted for about 30,000 deaths, and flood events in India and China accounted for seven of the ten disasters which were identified as affecting more than 100,000,000 people (all figures from Guha-Sapir et al. 2004). During 2007, flooding due to heavy rainfall affected approximately half of all African countries, affecting more than 1,000,000 people with about 400 victims whilst, in India, Bangladesh and Nepal, the death toll from monsoon rains exceeded 2,000 and affected some 30,000,000 people. Compared to other types of natural disaster, floods account for approximately 20–40% of the events which are reported. Floods can also cause extensive damage to property, infrastructure and crops, and can cut across administrative and national boundaries. For example, the 1998 floods in China were estimated to have submerged more than 200,000 km2 of farmland (e.g. Kundzewicz and Jun 2004) whilst, for Hurricane Katrina in August 2005, in addition to causing more than 1,000 deaths, hundreds of thousands of people were evacuated, and billions of dollars of damage was caused to property, businesses and infrastructure, much of this flood related. Other examples include the Midwest floods of 1993 on the Missisippi and Missouri rivers in the USA, which affected more than 15% of the country, damaging or destroying some 50,000 homes, with approximately 54,000 people evacuated (Smith 2004) whilst, in Europe, the flood events of 2002, 2005 and 2006 affected thousands of people in central Europe, and caused more than 100 deaths. The causes of flooding are mainly atmospheric or geotechnical (Table 1.2). Atmospheric hazards include heavy rainfall, causing rivers to flood, sometimes linked to snowmelt and ice-jams in colder climates, and coastal and estuarine flooding due to surge, wave and wind effects, most notably in tropical cyclones, hurricanes and typhoons. Geotechnical factors such as landslides, debris flows and earthquakes can also lead to raised river levels causing inland flooding, and Tsunami waves resulting in coastal flooding. Secondary effects may include overtopping or breaches of river and sea defence structures, debris blockages at bridges and other structures, surcharging of drainage networks in urban areas, and dam failure or overtopping. Due to the short time available for people to react, fast

1.2 The Nature of Flood Risk

9

Table 1.2 Examples of flooding mechanisms Type

Example

Typical types of flooding

Atmospheric

Frontal depressions

Extensive river flooding, coastal surge and wave overtopping, estuary and delta flooding, urban and pluvial (surface water) flooding Fast response/flash flooding and urban and pluvial (surface water) flooding Extreme prolonged rainfall causing a range of river and urban flooding issues Coastal surge and wave overtopping, inland flooding, estuary and delta flooding Extensive river flooding Rapid rises in river levels Fast moving, deep river flows

Thunderstorms Monsoon Tropical cyclones

Geotechnical

Snowmelt Ice jams Glacial lake outburst flows Dam break Defence breach Tsunami Debris flow

Fast moving, deep river flows Extensive inundation of coastal or inland areas Extensive inundation of coastal margins Destructive flows with high mud and rock content

developing floods present a particular risk to life, including flash floods, dam or defence breaches, and some ice-jam and local surge and wave overtopping events. Tropical cyclones, hurricanes and typhoons are all forms of tropical storm, with the term tropical cyclone used in the Indian Ocean, hurricane in the Atlantic and Eastern Pacific Oceans, and typhoon in the Western Pacific. Frontal depressions are most common in mid-latitudes, and can cause prolonged rainfall, as can monsoons which are driven by seasonal variations in temperature between sea and land masses. Thunderstorms can occur at most latitudes, and can cause intense rainfall for periods of typically up to a few hours. Snow and ice related problems affect many high latitude regions on all continents, and high mountain ranges elsewhere. Dam and defence risks are possible anywhere that reservoirs or polders have been constructed, or dams built across lakes, as are breaches in river or coastal flood defences (often known as levees or dikes). Tsunami can affect all ocean basins, but are most prevalent in the Pacific Ocean and in South East Asia (although the December 2004 Tsunami was in the Indian Ocean). Debris flows are a major problem in Central Asia and the Caucasus and in parts of the USA.

1.2.2

Assessing Flood Risk

Flood risk is often expressed as the combination of two factors; probability (or hazard) and consequence (or impact). The probability expresses the likelihood of damaging flood levels or flows being reached, whilst the consequence can be expressed in terms of indicators such as the numbers of properties affected, loss of life, or economic damages.

10

1 Introduction

Estimates for the numbers of people at risk from flooding, and affected in individual events, are of course subject to many uncertainties, including the degree to which events are reported, the approach taken to flood risk assessments and, for international comparisons, differences in the datasets and recording methods which are used. However, some studies (e.g. Parker 2000; Smith 2004) suggest that the percentages of people at risk from flooding range from 3% to 5% of the population in the UK and France, to about 12% in the USA, 50% in the Netherlands, and 70–80% in Vietnam and Bangladesh. Estimates are also complicated by transient populations, which can include tourists, hikers, temporary workers, business travellers, and the homeless. Indeed, in some countries, such as the USA, one of the main risks to life from flooding is from people in cars and other vehicles being trapped or swept away by floodwater (e.g. Henson 2001). The link between flood risk and social, political and economic factors, particularly risk to life, is well documented, and can arise from issues such as a lack of public awareness of flooding issues, or controls on floodplain development, limited funds available for flood control and protection (e.g. river and sea defences), low resilience of buildings to flooding (e.g. temporary compared to permanent settlements), and a lack of investment in flood warning, forecasting and emergency response systems. Where these factors are significant, the numbers of people affected by a flood event can be much higher than equivalent events in locations without these problems. Measures of vulnerability to flooding are also increasingly considered in flood risk studies: for example, combining the following factors (e.g. Wade et al. 2005): ● ●

●

Flood hazard (depth, velocity, debris) Area Vulnerability (effectiveness of flood warning, speed of onset of flooding, and type of buildings e.g. low rise/high rise) People Vulnerability (ability to ensure own safety and that of dependents e.g. the elderly, infirm, children)

Of course, vulnerability to flooding can depend on a wide range of physical, environmental, social, economic, political, cultural and institutional factors, and can vary widely between individuals, households and communities; for example, the length of time that people have lived in the floodplain (or if they are visiting the area e.g. tourists), recent experience of flooding, and local institutional capacity to respond to flooding. Some alternative definitions (e.g. World Meteorological Organisation 2006c) express vulnerability in terms of physical, material, constitutional, organisational, motivational and attitudinal conditions or, for tropical cyclones (e.g. Holland 2007), include the availability of existing community level plans and organisational structures, the proportion of cyclone resistant property, the state of protective works (river and coastal defences etc.), and the likely protection from coastal forests and mangroves. When designing a flood warning scheme, a starting point is often to make an assessment of the locations and numbers of people and properties at risk from flooding. Vulnerability studies can also highlight where to target effort in public awareness campaigns, developing flood emergency plans, and in emergency response. Methods for assessing risk include interviews with people who know the

1.2 The Nature of Flood Risk

11

area well, examination of historical flood records (trash mark surveys, aerial and other photographs, newspaper reports, satellite images etc), and hydrodynamic and other modelling techniques. Interviews and historical records can provide useful information, although may give a false impression if any significant changes have occurred since the last major flood event in the level of flood risk or key flooding mechanisms (e.g. construction of flood defences, dredging, urban development). Also, people may not be aware of more serious flooding before they moved to the area. Ground survey and remote sensing techniques can also provide detailed maps of flooding extent, although not necessarily for the peak of the flood, and satellite observations are increasingly being used to monitor flood extents using both optical and microwave frequencies, and to build up databases of flood extent information. Models provide a more formal way of assessing flood risk, and can range from simple correlation and other methods for single locations, through to detailed hydraulic models for river and coastal processes. Some countries (e.g. the USA, Japan and various European countries) have programmes in place to systematically assess flood risk at a national scale through detailed hydraulic modelling of locations with a significant flood risk (Box 1.2).

Box 1.2 Flood risk modelling The national flood risk mapping programmes in many countries use a range of modelling techniques to estimate flood depths, velocities and extents. For rivers, for example, actual or synthetic rainfall events can be fed into a network of rainfall runoff models representing major sub-catchments, whose outputs provide the inputs to a model for the river network and significant features such as floodplains and reservoirs. In areas prone to flooding, the model detail may include all significant controls on river levels and flows, such as bridges, culverts, gates, defences and other features, as well as the main details of the floodplain, using construction and topographic information obtained from conventional survey and remote sensing techniques (e.g. Light Detection and Ranging LIDAR equipment, or Synthetic Aperture Radar SAR equipment). In increasing order of complexity (and, in principle, accuracy), process-based methods for modelling river levels, flows and, in some cases, velocities, on the floodplain can include: ●

●

●

One-dimensional models for the main river channel, with projection of levels onto the floodplain, or separate pathways for main channel and floodplain flows One-dimensional models including floodplain pathways represented via spill units, compartments and/or cells Two dimensional models of the floodplain using ‘bare earth’ digital terrain models based on mass conservation only, or including momentum effects as well (continued)

12

1 Introduction

Box 1.2 (continued) ● Fully two or three dimensional models of the floodplain incorporating features on the floodplain such as buildings, embankments, gulleys etc., and possibly urban drainage networks Hydrodynamic techniques can also be used for modelling inundation of coastal floodplains due to high tidal levels, wave action and surge. Maps may be developed either with or without flood defences, with the no defence case sometimes being used to study the worst case flood extent; for example, if a defence is breached, overtopped or bypassed. Later chapters show several examples of the results from flood risk mapping studies including plan view and virtual reality representations. Having estimated river flows and levels, and possibly depths and velocities, within flooded areas, the resulting flood outlines can then be intersected with information on property locations, and lists generated of properties at risk. The resulting extents can also be related to the gauge heights used for triggering flood warnings (see Chapter 3). These property lists then form the basis for deciding which properties need to receive flood warnings. Vulnerability maps can also be generated to assist in developing emergency plans, although this is performed much less frequently than mapping of flood extent. The resulting flood outlines may also be expressed in terms of probability or return periods, with presentations of maps at 1 in 50, 100, 200 and 1,000 year return periods perhaps the most widely selected. Some sources of uncertainty in flood risk mapping can include the accuracy of input data and high flow rating curves (for river modelling), the various modelling assumptions and parameters, survey data accuracy, local influences around structures, and other factors. Methods for assessing the uncertainty in flood extent estimates are increasingly being explored (e.g. Pappenberger and Beven 2006; Pappenberger et al. 2007), and can potentially feed into decision support and other systems used in preparing for and managing flood events. Probabilistic techniques are also increasingly being used to consider the risk from failures or overtopping at flood defences (e.g. Sayers et al. 2002).

However, whatever the technique used to assess flood risk, one problem is always to assess the extent of mobile and transient populations who may not appear in conventional property and census databases. Examples can include vehicle users, shopping centres, supermarkets, tourists, hikers, outdoor events, and locations such as caravan or mobile home parks, and camp sites. Local visits, and discussions with people who know the area well, may be the best way of determining the extent of this risk, and the options (if any) for providing warnings to these groups, or preventing access in time to minimise the flood risk. Some other problems which can arise with property databases are that they may omit some commercial properties with significant numbers of occupants during working hours, since the correspondence address is at another location (e.g. head

1.3 Emergency Response

13

office), and that some locations with many residents (e.g. apartment blocks) may appear as only a single property. Also, some high-risk locations may not be clearly identified, such as water treatment or industrial works and critical locations such as hospitals, power stations, telecommunications hubs etc. Again, local visits and discussions can help to resolve some of these issues.

1.3

Emergency Response

Emergency response is the process of responding to a flood event, ideally on the basis of a flood warning received. In many countries, there is a separation in responsibilities between the flood warning and forecasting service, and emergency responders such as the police, fire service and local authorities. However, the organisation of a flood warning service can vary widely, with warnings being issued by the meteorological service in some countries, and a range of river management, coastal and local authorities in others. Privately developed systems also operate in some locations, with applications ranging from community based warning systems through to systems operated by owners of major infrastructure such as railways and hydropower schemes. Sometimes warnings may also be restricted to specific types of flooding, such as river flooding or coastal flooding, and exclude other types, such as flooding in urban areas from drainage problems. A major flood event often requires a multi-agency response, involving local authorities, the emergency services, transport operators (road, rail etc.), utility operators (water, electricity, gas, telecommunications), the military, coastguard, medical services, voluntary services, humanitarian aid organizations, and others. The response can include closing transport routes, protection of key installations, such as power stations and water treatment works, reinforcing flood defences, providing rest centers and shelters for people evacuated from properties, and rescue of people and livestock stranded in flood waters. Difficult decisions may also need to be made on issues such as the need to evacuate hospitals and nursing homes (with the evacuation itself presenting risks), precautionary shutdown of power or water supplies, and ordering widespread evacuations of property. During a flood event, individual property owners can also take action to reduce the damage caused by flooding by moving (as appropriate) vehicles, furniture, electrical equipment, personal possessions, valuables, animals and livestock to safer locations, and using sandbags, flood boards and other flood resilience measures to protect their property (if available). For example, in a post event survey of flooding in parts of the Elbe and Danube catchments (Thieken et al. 2007), emergency measures which were reported by residents included: ● ● ● ● ● ●

Put moveable contents upstairs Drive vehicles to a flood-safe place Safeguard documents and valuables Protect the building against inflowing water Switch off gas/electricity Disconnect household appliances/white goods

14 ● ● ● ● ● ●

1 Introduction

Gas/electricity was switched off by public services Protect oil tanks Install water pumps Seal drainage/prevent backwater Safeguard domestic animals/pets Redirect water flow

Businesses can also take actions to reduce damage to stock, equipment and systems and, depending on the time of day, may also be able to advise employees not to come in to work, or to leave early, in order to minimise risk. Flood warnings can also assist river management and coastal authorities with the operation of structures and in other actions to help to reduce or prevent flooding and some examples (Fig. 1.4) include: ●

●

●

●

Flood barriers – installation or operation of temporary or demountable barriers to protect properties and infrastructure from flooding Flood gates – closing gates which at low to medium flows are normally kept open to allow for drainage, access, navigation etc. Flow diversion – diversion of river flows into off-line storage areas to reduce flows further downstream (e.g. washlands, flood retention areas) Pumping – use of high volume pumps to reduce water levels

Fig. 1.4 Examples of river and coastal flood defences and a flood gate for washland drainage

1.4 The Role of Flood Forecasting ●

●

●

●

15

Reservoirs – draw down of reservoir levels in advance of high inflows to provide flood storage to reduce flows further downstream Sandbags – placing sand bags to raise the level of flood defences, fill gaps in defences, or to protect properties Temporary works – emergency repairs to flood defences (levees and dikes) and other locations which might provide a flow route for flood water Tidal barriers – closing barriers or gates to reduce the risk of inland flooding due to surge or high tides

Temporary and demountable barriers are increasingly used for flood prevention, and consist of metal, plastic, rubber and other types of panels, bags or tubes which can be placed at locations where flooding is anticipated, if a flood warning is received in time. Chapters 9–11 describe emergency response in more detail, including the development of flood emergency plans, decision support systems, dealing with uncertainty, and performance monitoring.

1.4

The Role of Flood Forecasting

Although flood warnings can be issued on the basis of observed meteorological, river and coastal conditions alone, the development of flood events can often only be anticipated a short time into the future, and it can be difficult to translate what is observed into estimates of flooding extent. Interpretation can also be complicated by other effects, such as operations at river control structures, storm surges, and inflows from major tributaries. Flood forecasting models can help with these issues, and are increasingly used to improve the lead time and accuracy of warnings provided by a flood warning service. Typically, forecasts are based on observations of river levels and rainfall higher in a catchment (for river flooding), or of tidal levels, wave heights, wind speed and other parameters (for coastal flooding). Rainfall and surge forecasts from atmospheric and oceanographic models may also be used as inputs to further extend the lead time of flood forecasting models. Forecasts may also have wider applications in areas such as river navigation, hydropower generation, water resource management, and pollution incident control. ‘What if’ scenarios can also be performed; for example, using scenarios for future rainfall or snowmelt, or for operational actions such as closing a tidal barrier. Flood forecasts can also be used to automatically trigger the issuing of warnings, or the operation of flow control structures, although the decision to use an automated approach depends on confidence in the model outputs, policy, and other factors, and the vast majority of current systems still require interpretation of outputs by an experienced forecaster. There are many approaches to flood forecasting, ranging from simple empirically based methods to fully integrated catchment or coastal models which, increasingly, incorporate real time hydrodynamic modelling components. Different types of models, or model components, may also be developed for different flooding mechanisms;

16

1 Introduction

Railway

Dam Dam Overtopping

Major road

Wave

Village

Town Defence Town Overtopping

River Out of Bank

Surge, Tide, Wave

Town

Power Station River, tide, surge Farms Caravan Park

River Out of Bank

Chemical Factory Town Surge, Tide, Waves

Fig. 1.5 Illustration of flooding issues which might be included in a regional flood forecasting model

for example, for the situation shown in Fig. 1.5, a range of rainfall runoff, reservoir (dam), river, estuary (delta), and offshore, nearshore and wave overtopping models might be required, optimised to provide forecasts at towns, infrastructure, and transport routes where they are at risk from flooding. One distinguishing feature of forecasting models, compared to off-line simulation models, is the ability to use observed (telemetered) data to modify forecasts as they are generated. Thus, if the forecast at the present time is in error, it can be adjusted to account for the current observed values, and also into the future, based on assumptions about the cause of errors up to the present time (‘time now’), and likely future trends. This real time updating of forecasts (or data assimilation) can significantly improve the accuracy of model outputs, and many techniques have been developed, including error prediction methods and techniques which adjust the internal state of model components, or model parameters. One reason for needing updating techniques is the uncertainty in model outputs, which can arise from many sources. For example, outputs can be affected by errors and uncertainties in measurements of rainfall and levels and flows (for rivers), and wind speed, wind direction, wave height and tidal levels (for coastlines). If meteorological forecasts are used to extend warning lead times, additional uncertainties arise from that component of the system. These issues have long been recognised (e.g. World Meteorological Organisation 1994; Emergency Management Australia 1999; Beven 2008) and it is widely accepted that flood forecasts should be issued with an indication of confidence or uncertainty. The case for probabilistic forecasts in hydrology has been concisely summarised by Krzysztofowicz (2001), which is that:

1.4 The Role of Flood Forecasting ●

●

●

●

17

First, they are scientifically more ‘honest’ than deterministic forecasts: they allow the forecaster to admit the uncertainty and to express the degree of certitude Second, they enable an authority to set risk-based criteria for flood watches, flood warnings, and emergency response; and they enable the forecaster to issue watches and warnings with explicitly stated detection probabilities Third, they appraise the user of the uncertainty; and they provide information necessary for making rational decisions, enabling the user to take risk explicitly into account Fourth, they offer potential for additional economic benefits of forecasts to every rational decision maker and thereby to society as a whole

One widely quoted example of the potential use of uncertainty information in decision making (e.g. Krzysztofowicz 2001) is for the 1997 flooding on the Red River in Grand Forks, North Dakota, which caused flooding to some 5,000 homes, and is described in more detail in Chapter 10. Post event analysis showed that the actual peak was higher than the forecast values, with the question arising that, if the uncertainty in the forecast been known, would sandbagging of levees have been continued to higher levels, avoiding the flooding which occurred? By contrast, an example of deriving economic benefits from ensemble forecasts is in the hydropower industry, where some operators in the USA and Canada gain significant savings from using probabilistic forecasts of seasonal flows (e.g. Howard 2004). In meteorology, probabilistic forecasting techniques have been used since the 1990s, and are nowadays seen as an indispensable tool in weather forecasting. The basis of the method is to adjust the initial conditions for the computer models used to forecast atmospheric and ocean conditions over a range reflecting the uncertainty in current conditions, and possibly model parameters. Stochastic methods, consisting of statistical sampling of inputs or outputs, can also be used. The resulting scenarios, or ensembles, are then used to guide forecasters in the information that they issue to the public and, in some cases (e.g. the Netherlands), the range of estimates is presented in some national television weather forecast bulletins. Similar techniques are also starting to be used in flood forecasting, perturbing the meteorological and other inputs to models (e.g. river flows), and possibly internal model parameters and other factors (e.g. the high flow ends of stage-discharge relationships). The issue of how to communicate the resulting information on uncertainty to decision makers, including the public, is also an active research area (e.g. Todini et al. 2005; National Research Council 2006; Pappenberger and Beven 2006). Chapters 5–8, and 10, describe these topics in more detail. Forecasting models usually also require a computer platform on which to operate, capable of data gathering, the scheduling and control of model runs, alarm handling, and post-processing of model outputs into a form which is useful to forecasters. During a widespread flood event, a purpose-made system may provide the only practicable way of operating and interpreting the output from large numbers of models, particularly if data assimilation is used. Modern systems increasingly make use of spatial techniques for analysing and presenting data and forecasts, and functionality can include map-based indicators (e.g. flashing symbols) of locations

18

1 Introduction

where flooding is expected, overlays of property locations, street maps, aerial photographs, terrain etc., and the facility to ‘drill down’ for additional information and detail at any location. The facility to perform real time inundation mapping during an event is also increasingly available. Although much of the focus nowadays is on automated techniques, simpler techniques still have an important role to play, particularly where budgets are limited, the level of flood risk does not justify investing in a complicated approach, or as a backup to a more sophisticated approach. Low cost community based systems are also widespread, and typically involve nominated members of the community monitoring raingauges, marker boards or river gauges, and issuing warnings by loud speaker, community billboards, and door knocking as appropriate. Information may also be passed to local experts to decide on the appropriate action to take, who may also have access to paper based or computerised forecasting models (e.g. FEMA 2005). Informal systems are also widely used in some countries (e.g. Parker 2003). Another example is the flood forecasting system that was trialled for the two major rivers in Somalia (the Jubba and Shebelli) between 1988 and 1990 (Institute of Hydrology). The system operated on a stand-alone personal computer, with data entered manually based on observations by government workers at some 20 locations along the two rivers, and transmitted verbally to Mogadishu over the government radio network. A low cost approach was used for model development, using a range of simple correlation, flow routing and overtopping models, and a more detailed model for an off-line storage reservoir. Real time updating was included using a simple interactive method which allowed operators to adjust forecasts visually to account for the trend in forecast errors over recent model runs. In operational use, information was received three times per day, and the model runs were used to provide forecasts of future river levels and flows up to 7 days ahead to farmers, irrigation scheme operators, and engineers engaged in river works, together with warnings of high flow conditions. Forecasts were also included in weekly agricultural situation reports. Chapters 5–8 describe forecasting techniques in more detail, including general principles (Chapter 5), river forecasting methods (Chapter 6), coastal forecasting methods (Chapter 7) and a range of applications (Chapter 8), including integrated catchment modeling, and forecasting for flash floods, the effects of snow and ice, control structures, urban flooding, and geotechnical risks such as dam break, defence breach, and tsunami.

Chapter 2

Detection

Most flood warning systems use near real time measurements of meteorological and river or coastal conditions to guide operational decision making. Depending on the application, this may include information on rainfall, wind speeds, sea state, tidal levels, river levels and other parameters, such as snow cover. Remote sensing techniques such as weather radar and satellite may also be used, together with the outputs from Numerical Weather Prediction models and nowcasting techniques. This chapter provides a general introduction to these and other techniques for monitoring meteorological, river and coastal conditions for flood warning applications. Telemetry systems are also discussed, together with approaches to designing telemetry networks for flood warning applications.

2.1

Meteorological Conditions

With only a few exceptions, such as geotechnical risks (see Chapter 8), most flooding problems are linked to atmospheric conditions, and observations or forecasts of rainfall and other parameters often provide the first indication of potential flooding. The main types of meteorological information which are useful in flood warning and forecasting applications include: ●

●

●

Site Specific (or Point) Observations – measurements at a specific location using rain gauges, automatic weather stations etc. Remote Sensing (or Areal) Observations – based on satellite observations, weather radar etc. Computer Model Outputs – from Numerical Weather Prediction (NWP) models, nowcasting techniques, and other approaches

Note that weather forecasting techniques are included in this chapter as a form of detection since, as with site specific and remote sensing techniques, the outputs represent another source of information for the operation of flood warning and forecasting systems. When considering these approaches, there are various trade-offs in terms of the spatial resolution, accuracy and lead times of each technique. For example, site K. Sene, Flood Warning, Forecasting and Emergency Response, © Springer Science + Business Media B.V. 2008

21

22

2 Detection

specific observations provide an indication of actual conditions at certain locations in a catchment or coastal reach, but may be unrepresentative of the overall conditions which lead to flooding. By contrast, remotely sensed data provide an overall picture of the distribution of the parameter being observed (e.g. rainfall, snow cover), but require some assumptions or a model to translate observations to conditions at the ground or sea surface. This introduces an additional source of uncertainty, and measurements are sometimes of too coarse a resolution to be useful. Weather forecasting techniques provide additional lead times, and usually also provide detailed spatial information for the parameters being forecast (rainfall, wind, soil moisture etc.), but obviously rely on the outputs from computer models, which again can introduce an additional source of uncertainty. Chapters 5–8 discuss some of these various trade-offs between lead time and accuracy, whilst other factors which need to be considered include the likely reliability during flood events, and the choice of an appropriate degree of instrumentation in relation to the level of flood risk; a topic which is discussed further in Section 2.3 and Chapter 11 when considering techniques for prioritising investment in flood warning schemes. For river flooding applications, rainfall is often a key parameter, although other meteorological parameters which may be required include observations or estimates for air temperature, wind speed, net and solar radiation, soil moisture, snow cover, river ice cover and ice jam locations, and reservoir and lake evaporation. For coastal flooding applications, information on atmospheric pressure, and wind speed and direction, is often a key input to surge and wave forecasting models and, for tropical cyclones (and hurricanes and typhoons), information on storm size, intensity, track and speed is also important. Table 2.1 summarises these and some other requirements for the various threshold based and forecasting techniques described in later chapters. The remainder of this section discusses the technological background to these various techniques.

2.1.1

Site Specific Observations

The techniques for observing meteorological parameters at specific locations are well established (e.g. World Meteorological Organisation 1994b, 2000; Strangeways 2007). For flood warning and forecasting applications, near real time measurements are usually required, and methods suitable for telemetry include tipping bucket raingauges, cup or ultrasonic anemometers (wind speed), wind vanes (wind direction), radiometers (solar, net and reflected radiation), hygrometers (humidity) and neutron or capacitance probes (soil moisture). Instruments can be installed on land, or on moored buoys, ships and other offshore locations. These and other devices can of course also be used if values are transmitted manually; for example, by voice, email, fax or telegraph. Evaporation can be measured directly using evaporation pans, or, less often, by turbulence monitoring techniques which integrate water vapour transport through a

2.1 Meteorological Conditions

23

Table 2.1 Some common requirements for meteorological data and forecasts in flood warning and forecasting applications Parameter

Category

Examples of techniques

Rainfall

Site specific observations Remote sensing

Raingauges, disdrometers Satellite, weather radar, microwave links Numerical Weather Prediction models, nowcasting Capacitance probes, neutron probes, lysimeters Satellite (e.g. Synthetic Aperture Radar) Numerical Weather Prediction models, nowcasting Ablation stakes, snow pillows, snow cores Satellite based optical or infrared channels Numerical Weather Prediction models, nowcasting Automatic Weather Stations Satellite based infrared sensors (temperature) Numerical Weather Prediction models, nowcasting Satellite, weather radar Storm scale/mesoscale Numerical Weather Prediction models, nowcasting Solar radiation, net radiation (or individual components), soil heat flux Evaporation pan, turbulence measuring devices (also indirect methods using wind speed, radiation and humidity) Numerical Weather Prediction models, nowcasting

Weather forecasting Soil moisture

Site specific observations Remote sensing Weather forecasting

Snow cover

Site specific observations Remote sensing Weather forecasting

Atmospheric conditions (air temperature, humidity, wind speed etc.)

Site specific observations Remote sensing Weather forecasting

Storm scale information

Remote sensing Weather forecasting

Radiation

Site specific observations

Evaporation

Site specific observations

Weather forecasting

fixed pathway between two sensors. Alternatively, methods such as the Penman equation are widely used for estimating open water evaporation from wind speed, temperature, humidity and (possibly) net radiation measurements, with the Penman Monteith approach used for estimating evapotranspiration from grass, vegetation etc. Often the various sensors can be combined into an automatic weather station, which may monitor some or all of the following parameters:

24

2 Detection

Fig. 2.1 Examples of inland and offshore automatic weather stations (Kevin Sene and © Crown Copyright 2007, the Met Office)

● ● ● ● ● ●

Rainfall Air temperature Humidity Wind speed and direction Solar and net radiation Soil or water temperature

Figure 2.1 shows two examples of automatic weather stations, consisting of a temporary installation above a tropical lake in Southeast Asia for a study into long term trends in lake evaporation (Sene et al. 1991) and a buoy mounted instrument being inspected off the coast of the UK. Of the various meteorological parameters which could be monitored, for flood warning and forecasting applications, perhaps the two of most interest are rainfall and snowmelt, and these are described in more detail below. 2.1.1.1

Rainfall

For measuring rainfall, tipping bucket raingauges are probably the most widely used method for flood warning and forecasting applications, and record rainfall when the depth reaches a sufficient amount (or weight) to cause a bucket mechanism to tip. Typical bucket sizes are equivalent to rainfall depths in the range 0.1–2.0 mm, with the choice of tip size often based on the maximum rainfall intensities

2.1 Meteorological Conditions

25

anticipated at the site. Each tip is recorded, together with the time of the tip, and can be reported by telemetry directly, or accumulated to fixed time intervals before transmission. Weighing raingauges, by contrast, use springs, vibrating wires or balance weights to record the weight, and hence depth, of rainfall, whilst drop-counting gauges (e.g. Stow et al. 1998) use optical techniques or electrodes to record individual drops of a fixed size released through a constriction. Depth type gauges accumulate rainfall and use an electrode (e.g. Oi and Opadevi 2006) or float mechanism, linked to a recording device, to record the depth of rainfall and hence the incremental changes in given time intervals. Disdrometers, which use a laser or ultrasound beam to detect falling rainfall, are a newer technique for recording rainfall. These devices work on the principle of detecting the passage of raindrops through a beam of light (e.g. Nemeth 2006) or ultrasound, with appropriate signal processing to estimate rainfall amounts. In principle this approach requires less maintenance than traditional raingauges since there is no capture of rainfall. Factors to consider include processing for a range of droplet sizes (and fall velocities), for different types of precipitation (rainfall, snow, hail etc.), and for wind driven effects as rain passes through the beam. Low cost (micro) vertically pointing precipitation profilers are also another recent development for single site measurements of rainfall. Manually operated (storage) raingauges can also be useful for providing rainfall information to assist with post event evaluations of flooding, and more generally to improve understanding of the rainfall distribution in a region or catchment when developing rainfall runoff forecasting models. Measurements are typically made on a daily or monthly basis. Best practice in the installation and use of raingauges, and the strengths and limitations of different designs, is well documented (e.g. World Meteorological Organisation 1994b, 2000) but some specific problems which can arise in high wind and rainfall conditions include: ● ● ● ● ●

Splashing – both into and out of the gauge Exposure – sheltering by obstacles such as trees or buildings Wind influences – from the airflow over the gauge and at the site Snowfall – blocking by snowfall (raingauges only) Flooding – submergence of the gauge if it is installed in a flood prone site

For flood warning or forecasting applications, the recording interval to use will depend on the capability of the equipment and associated electronics, but should ideally be sufficiently frequent to resolve the key features of events. Typically a 5 minute, 15 minute or hourly value is used. More generally, for all types of site specific measurement, the question arises of how representative the measurements are of overall catchment conditions, and of appropriate techniques to use for estimating catchment average rainfall for input to rainfall runoff forecasting models. Box 2.1 describes some techniques for estimating catchment average rainfall.

26

2 Detection

Box 2.1 Catchment rainfall estimation Raingauges give estimates of rainfall at a single point whereas, for many flood warning and forecasting applications, area averaged values are required; for example, for catchment average rainfall. For small catchments, or reasonably uniform rainfall, a single raingauge may be representative. However, usually a number of gauges within the catchment, and possibly from nearby catchments, will be used in the averaging process, and some techniques include: ●

●

●

●

●

Arithmetic mean – which simply takes the average value for all selected raingauges, giving equal weight to all gauges without considering their spacing or the rainfall distribution in the catchment. Thiessen polygons – in which polygons are derived by joining the mid points of the lines between adjacent raingauges, with the weights based on the proportion of the catchment area attributed to each gauge within the catchment, divided by the catchment area. Isohyetal method – which derives lines of equal rainfall based on the observed values, from which a catchment average value can be derived. Surface fitting methods – which include a range of automated techniques, such as multiquadratic, inverse distance, triangular planes (TIN) and polynomial methods. Geostatistical techniques – such as Kriging which also interpolate values but using functions for the dependence of values on distance between gauges for all combinations of gauges. Methods such as co-Kriging also bring in auxiliary variables such as elevation or aspect.

Additional factors such as topography, aspect, runoff coefficient, and soil type can also be brought into some of these weighting schemes. The methods are presented in approximately increasing order of complexity and accuracy and there have been numerous studies into the merits of the various approaches (e.g. Creutin and Obled 1982; Goovaerts 2000). In particular, elevation and rain shadow effects can be significant, as illustrated in Fig. 2.2 for average annual rainfall estimates for the Lesotho Highlands, whose peaks rise to approximately 3,500 m in places. Analyses of weather radar data and computer modelling can also assist with understanding storm characteristics, such as typical storm scales, preferred directions of travel, local topographic influences etc., and in developing appropriate catchment averaging schemes. Sometimes it is found that, where there are no anticipated major spatial variations in flood generating rainfall (e.g. frontal events in low lying areas), the simpler fixed weight methods can provide reasonable results. However, where spatial and topographic variations (continued)

2.1 Meteorological Conditions

27

Box 2.1 (continued)

Fig. 2.2 Annual rainfall distribution along a transect through the Lesotho Highlands (Royal Meteorological Society/Sene et al. 1998)

are significant, more complex methods may be required if real time systems and software are available to support this type of analysis. 2.1.1.2

Snow Cover

Information on snow depth, water equivalent and snow extent can be required for input to the snowmelt forecasting component of flood forecasting models, and also for more empirical techniques for estimating the consequences of snowmelt. The challenge in snow monitoring is that the depth and type of snow cover can vary significantly over small distances compared to typical catchment scales, so that only a limited sample of values can usually be obtained. Satellite monitoring can assist in assessing snow coverage, whilst observation techniques for estimating depth include snow (or ablation) stakes and snow cores. Traditional depth measuring techniques rely on an observer sending values by telephone, email, radio etc., but automated techniques have also been developed. For example, radio isotype methods can be used in which the water equivalent of snow is estimated from absorption of gamma radiation in the vertical or horizontal plane and, in principle, can provide real time estimates of water equivalent snowfall (e.g. World Meteorological Organisation 2000). Tipping bucket raingauges can also be used to measure the water equivalent snow depth if they are fitted with heaters, although may under-record the true amount of snowfall, and show a lag between

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snowfall and the recorded values. Measurements of air temperature may be used to help in interpreting the readings from raingauges when snowfall is thought to be a factor. Perhaps the most extensive ground based monitoring of snow depth and extent is by the SNOTEL (SNOwTELemetry) monitoring network in the USA (Schaefer and Paetzold 2000). Observations were started in the mid-1970s and the network consists of more than 700 automatic sensors installed in mid and high elevation areas of the western United States and Alaska to record snowpack, precipitation and temperature, typically at hourly intervals. For the snowpack component, a typical installation includes one or more snow pillows, consisting of a flat circular container filled with non-freezing fluid, and a pressure sensor to record the changes in hydrostatic pressure due to the weight of the snow layer. A downward looking sensor may also be used to monitor snow depth. Some sites also include automatic weather stations, soil moisture and soil temperature sensors. Data transmission is by meteorburst telemetry (see Section 2.3) and power is from battery packs and solar panels. A snow pillow network is also used in Norway, supplemented by satellite and manual observations, to monitor snow conditions for flood forecasting and other applications (Rohr and Husebye 2005).

2.1.2

Remote Sensing

2.1.2.1

Weather Radar

Ground based weather radars use electromagnetic waves to detect precipitation in the form of raindrops, snowflakes and hail (often called hydrometeors). A typical installation consists of a radome, a tower, and buildings housing the computer and generator equipment needed to operate the device. Figure 2.3 shows a typical radar installation from the United Kingdom. There are many books, review papers and guidelines describing the principles of weather radar operation (e.g. Collier 1996; World Meteorological Organisation 2000; Cluckie and Rico-Ramirez 2004; Meischner 2005) and only a few details are provided here. For most types of radar, the beam is rotated about a vertical axis and the type and quantity of precipitation is inferred from the power of the back-scattered energy, whilst the location (distance) is inferred from the time of travel of the signal (the beam is pulsed to provide an interval in which to detect the returned signal). Rainfall intensity is estimated using relationships between drop size density and the power of the received signal. Many different hardware options are available including: ●

Wavelength – attenuation by rainfall reduces with increasing wavelength, but longer wavelength radars require a larger dish and are usually more expensive. In order of increasing wavelength are X-band (3 cm), C-band (5.5 cm) and S-band (10 cm) radars, where typical wavelengths are shown in brackets. Most weather radars use C or S bands, although X-band radars have been used operationally in some applications.

2.1 Meteorological Conditions

29

Fig. 2.3 View of the internal workings of a weather radar, and the Chenies Radar in the UK (© Crown Copyright 2007, the Met Office)

●

●

●

Dual polarisation – use of horizontal and vertical polarisation to help with identifying hydrometeor shapes and hence types (e.g. with larger drops showing more deformation between planes). Multiple beams – to detect vertical variations in reflectivity to assist in correcting radar outputs for gradient and other effects, including the option of multiple level scans. Doppler – to detect the direction of motion of hydrometeors to help in filtering out ground clutter and estimating wind speed and direction.

The power of the reflected signal decreases with the range of the precipitation from the radar due to attenuation by droplets, dust and other factors, and the spread of the beam. The beam may also be transmitted at a positive angle to the horizontal, and may overshoot precipitation at lower levels, including orographic growth of rainfall in hilly regions and evaporation and wind drift/dispersion at low levels. The use of a slight negative beam angle is also used for some weather radars in mountainous regions. However, the beam will eventually either overshoot rainfall due to the curvature of the earth, or encounter terrain causing anomalous reflections. The accuracy of a weather radar therefore decreases with range, so a regional or national radar network is often designed to achieve an acceptable coverage at a reasonable cost, perhaps focused on areas with the highest rainfall or flood related risks. For flood warning and forecasting applications, another option is to use a denser network of low cost short range radars to infill gaps in the main radar network in areas of interest such as major population centres, or as the main component

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of the radar network; for example, for the Local Area Weather Radar network in Denmark (Pedersen et al. 2007). In addition to considerations of range and beam angle, the outputs from a weather radar may in addition be affected by meteorological factors (e.g. Collier 1996) such as: ● ●

●

●

●

Bright band – due to the beam intersecting the melting layer increasing reflectivity Anaprop – caused by distortion of the beam in strong temperature or humidity gradients, causing the beam to intersect the ground surface causing false returns Ground clutter – intersection of the beam with hills, mountains and other obstacles (e.g. buildings, masts) Hail – causing an increase in the strength of the reflected signal compared to rainfall Drop size distribution – assumptions about typical distributions may be less valid in certain conditions, such as drizzle

Many of these factors can be reduced by post processing of the received signal, including linking into other sources of information such as the outputs from Numerical Weather Prediction models. Areas of research include making use of real time information on rainfall from vertically pointing radars, microwave communication links and disdrometer installations, and using Digital Terrain Models to help in identifying sources of ground clutter. The signals from individual radars are also often combined to produce a so-called composite or mosaic image. Measurements are usually presented on a gridded basis, after being transformed from the original polar coordinates. Many radar systems have a range of sophisticated visualisation and analysis software, for example allowing rainfall estimates to be accumulated at catchment level, sequences of radar images to be animated, and values to be sent to other systems (e.g. flood forecasting systems). Examples of composite images at a continental scale include the outputs from the NEXRAD system of radars in the USA, and the OPERA project, which combines more than 150 radar outputs for countries across Europe (e.g. Harrison et al. 2006). If the raingauge network is of sufficient density and quality, radar estimates of rainfall may also be adjusted to take account of raingauge measurements of rainfall, in an attempt to correct for low level and other effects missed by the radar. The methods used include multi-quadratic, Bayesian and other techniques (e.g. Moore et al. 2004; Todini 2001). In applying these techniques, of course, there are also uncertainties in the accuracy of the raingauge measurements, and in particular how representative they are of the spatial distribution of rainfall. In addition to using raingauges, the radar outputs can also be improved using outputs from Numerical Weather Prediction models, satellite imagery, wind profiles and other sources of information (e.g. lightning detectors), and this type of nowcasting product is described later. The resolution at which radar data is provided depends on the type of signal processing algorithms used, and the distance from the radar. In the UK, for example, values are available at grid lengths of 1, 2 and 5 km, and 5 or 15 minute time intervals, depending on the distance from the radar (the corresponding ranges are up to 50, 100 and 250 km). Figure 2.4 illustrates the appearance of images at these scales for a heavy rainfall event in the south of England.

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31

Fig. 2.4 Illustration of weather radar images at a resolution of 1, 2 and 5 km (© Crown Copyright 2007, the Met Office)

In the figure, the distance between the towns of Oxford and Watford is approximately 60 km. The figure shows that the degree to which a weather radar can resolve the spatial distribution of rainfall depends on the grid resolution, which in turn depends on the distance of the catchment from the nearest radar. Signal quality may also be influenced by topographic and other effects, particularly in mountainous regions.

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Radar coverage maps, typically estimated using digital terrain models, and line of sight calculations, can give an indication of the likely resolution and coverage of radar for a given location or catchment.

2.1.2.2

Satellite

Satellite observations offer the potential to provide estimates of rainfall and other parameters (e.g. sea state) for input to river and coastal flood forecasting models, and are routinely assimilated into Numerical Weather Prediction and nowcasting models. Images of cloud cover are also widely used by flood warning services, although the quantitative use of information, such as estimates of rainfall rate, is much less widespread, in part due to the relatively coarse resolution of measurements compared to the scale of some smaller catchments. Some examples of geostationary satellite systems which have been used in flood forecasting applications include: ●

●

Meteosat – operated by Eumetsat and the European Space Agency for weather forecasting including infrared, visible and radiation budget sensors Geostationary Operational Environmental Satellites (GOES) – operated by the National Oceanic and Atmospheric Administration (NOAA) for weather forecasting with multiple sensors

Geostationary satellites maintain a fixed position relative to the earth’s surface, whilst polar orbiting satellites are at lower altitudes (hence with better resolutions) although may only pass overhead a given location every few days. The sensors used vary depending on how long ago the satellite was built, and the primary applications. However, in general, most meteorological and oceanographic satellites are able to monitor cloud cover, the radiation budget (radiation, reflected energy), surface and cloud top temperatures, snow cover and other parameters. The GOES and Meteosat systems form part of the World Meteorological Organisation (WMO) World Weather Watch (WWW) Global Observing System (GOS) programme (World Meteorological Organisation 2003) which also includes a number of other satellites launched as part of national programmes for environmental and meteorological monitoring (e.g. GMS-Japan, METEOR and GOMS – Russian Federation; FY-1 and FY-2 – China). For flood forecasting applications, one technique of interest is the estimation of rainfall intensities from cloud top temperatures, with cooler temperatures indicating cloud tops at greater altitudes, and therefore possibly with greater depths. Observations of cloud temperature relative to surrounding regions, and of cloud morphology, can also be used to help to discriminate between clouds with similar cloud top temperatures but different rainfall producing potential, such as cirrus and cumulonimbus clouds (e.g. Golding 2000; World Meteorological Organisation 2000; Grimes et al. 2003). Methods may use single images (cloud indexing methods), or sequences of images (life history methods) to assess cloud development and movement. These techniques have also been used to estimate rainfall for input to rainfall runoff forecasting models (e.g. Grimes and Diop 2003)

2.1 Meteorological Conditions

33

Passive and active microwave measurements also show potential in estimating rainfall intensity and the soil moisture at land surfaces (e.g. Crow et al. 2004; Love 2006), although the algorithms which are used need to interpret the signals from different types of land surface including open areas, forest, water, ice, snow and urban areas. Rainfall rates at the surface may also be inferred from the radiation received from sources such as liquid water droplets or suspended ice particles. For active systems, similar principles are used to ground based weather radar, and are actively being developed as part of NASA’s Tropical Rainfall Measuring Mission (TRMM) and the planned international Global Precipitation Measurement Mission. Satellites can also be used for monitoring snow cover, and the formation and break up of ice in rivers and lakes, to provide advance warning of likely flooding problems. 2.1.2.3

Other Techniques

Some other remote sensing techniques which have been considered for rainfall detection in flood forecasting applications include: ● ●

Microwave techniques – use of horizontally transmitted beams to detect rainfall Lightning detection – inferring rainfall amounts from lightning activity

Microwave techniques estimate the path averaged rainfall rate from the attenuation in the signal, and could potentially make use of the extensive transmitter networks used by cell phone operators (e.g. Leijnse et al. 2008). For example, as part of the MANTISSA project (Rahimi et al. 2003; Holt et al. 2005), experiments were performed using dual frequency microwave links with path lengths from 9 to 23 km for a catchment in the northwest of England, and results compared with raingauge and weather radar estimates of rainfall. Uncertainties can arise from unknowns such as the drop shape, temperature and size distributions. Lightning detection methods (e.g. Price et al. 2007) aim to provide forecasts from the short term up to a few hours ahead for heavy rainfall linked to thunderstorms. Lightning activity can be monitored remotely at a global level using space and ground based observations, and in principle can be used to track the progression of thunderstorms. Historical rainfall-lightning relationships can be established from past records, and used together with satellite observations and Numerical Weather Prediction models to estimate rainfall intensity in real time. Lightning data is also assimilated into some forms of nowcasting model as described in the next section.

2.1.3

Weather Forecasting

The topic of weather forecasting covers a wide range of numerical, empirical, observational and other techniques, and in operational forecasting the final decisions on the forecasts to issue are often taken based on a combination of these approaches. For flood forecasting and warning applications, the following two approaches are of particular interest:

34 ●

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2 Detection

Numerical Weather Prediction models – primarily for rainfall forecasts (Quantitative Precipitation Forecasts) for input to rainfall runoff models, and wind fields for input to coastal models, but possibly also for a range of other surface variables which may be calculated (e.g. soil moisture, air temperature). Typical maximum useful lead times can be 3–5 days or more for deterministic forecasts, and up to 10–15 days for ensemble forecasts, although can be considerably less for events such as thunderstorms. Nowcasting systems – which provide short term forecasts based on a combination of weather radar, satellite and other observations and, increasingly, the outputs from Numerical Weather Prediction models.

The distinction between these two approaches is not clear cut, since Numerical Weather Prediction models also make extensive use of observed data from the sea, ground, air and space to initialise model runs via a process called data assimilation. A simple definition here is that a nowcast is a short term forecast, based primarily on weather radar, typically for times of up to 3–6 hours ahead. Seasonal forecasting systems, combining statistical and other modelling approaches, are also increasingly being used, and have been used in forecasting snowmelt, for example (see Chapter 8).

2.1.3.1

Numerical Weather Prediction

Numerical Weather Prediction models form the basis of the forecasting service offered by many meteorological services, and solve approximations to the equations describing mass, momentum and energy transfer in the atmosphere (e.g. World Meteorological Organisation 2000). The equations may be solved over a global domain, or domains limited by horizontal extent. The boundary conditions for the limited area models are then derived from the larger scale models (i.e. the models are nested). The equations are usually solved on a layered grid, with typical horizontal scales of 10–100 km for global scale operational models, and 1–10 km for local models, and up to 100 layers representing vertical development in the atmosphere. Local models may be called local area, mesoscale or storm-scale models, depending on the type and spatial extent of modelling approach adopted. Sub-models may be included for a range of processes, including cloud development and decay, energy and water transfer at the ocean and land surfaces, and interactions with topography and other obstacles. As noted earlier, models are initialised using a process called data assimilation which can be a major undertaking, using measurements taken from raingauges, weather stations, weather radar, lightning detectors, aircraft, ships, wave buoys, radiosondes, satellites, and other sources. Models typically run on a 1, 6 or 12 hourly timestep, and the data assimilation component can often take a significant proportion of the time between model runs.

2.1 Meteorological Conditions

35

Model outputs can include the wind field, rainfall, potential temperatures, specific humidity, surface pressure, evapotranspiration, snow depth, surface and soil temperatures, soil moisture, cloud water and ice, and other variables. Other outputs can include the convective cloud base and cloud top elevations, sea surface roughness, vertical velocities, and other parameters. Results are usually processed further into specific ‘products’ which vary from country to country but may include a general outlook, synoptic charts, surge forecasts, daily forecasts, strong wind warnings, heavy rainfall warnings, flash alerts, and other forms of output tailored to meet each user’s requirements. Other types of output which may be useful in hydrological applications include estimates for soil moisture conditions and snow cover. Due to the intrinsic uncertainties in both the models, and the data assimilation process, it is now standard practice in many meteorological services to use an ensemble forecasting approach, in which the initial model state is perturbed and multiple realisations of model runs are performed to provide an indication of the uncertainty in the forecasts. With current computing power, typically of the order of 10–100 ensemble runs are performed at each time step. In some countries (e.g. the Netherlands), the ensemble outputs may be presented as part of national weather forecasts on television in the form of an estimated range of values for parameters such as air temperature or rainfall, or as probabilities of occurrence. Multi-model techniques are also used, in which the outputs from several models are displayed in a common format to see the variability between different formulations (e.g. Garcia Moya et al. 2006; Rotach et al. 2007). Probabilistic and ensemble forecasts are also increasingly being introduced into flood forecasting applications, and are discussed further in Chapters 5 and 8. For river flood forecasting applications, a key requirement is often to translate the meteorological model outputs to a scale more appropriate to hydrological modelling. Both statistical and dynamical techniques are used (e.g. Rebora et al. 2006; Schaake et al. 2005). Statistical techniques can include multi-fractal cascades, nonlinear autoregressive models, and processes based on the superposition of rainfall cells at different scales (cluster models). Dynamical techniques can include nesting of higher resolution atmospheric models for the catchment or region of interest within models with a coarser resolution but wider spatial extent (e.g. Environment Agency 2007). For large catchments, upscaling may also be required to help to preserve hydrological spatial characteristics over large distances, particularly where there are significant topographic or climatic variations. 2.1.3.2

Nowcasting

The term Nowcasting covers a range of techniques which use spatial extrapolation of current observations of rainfall from weather radar, sometimes guided by or combined with the outputs from Numerical Weather Prediction models. For short

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lead times, these techniques can perform better than Numerical Weather Prediction models, and their relative simplicity allows more frequent model runs (e.g. every few minutes) and higher model grid resolutions (e.g. 1–10 km). The maximum lead times provided can be several hours, although values of 3–6 hours are often quoted. Nowcasting methods often use the assumption that, if the speed, size and direction of travel of a storm is known at the present time, then the future development can be estimated by extrapolation, at least at short time scales. Methods range from simple extrapolation of current conditions, neglecting possible growth or decay, to techniques using a wide range of sources of information to help to estimate the future evolution of a rainfall event or thunderstorm or tropical cyclone (e.g. Franklin et al. 2003). A more sophisticated approach is to use the outputs from Numerical Weather Prediction models. Forecasts can then be generated by extrapolating the motion of areas of rainfall, using the wind fields and other forecast outputs from these models to guide this response, including allowance for the development and dissipation of rainfall (e.g. Golding 2000; Wilson 2004). Sub-models may be included to forecast the development of convective cells (thunderstorms) using conceptual (life-cycle) models and probabilistic techniques. As with Numerical Weather Prediction models, ensemble and probabilistic approaches are increasingly being used in Nowcasting, with research also considering how seamless ensembles can be generated covering a range of timescales, from nowcasting through to Numerical Weather Prediction and seasonal forecasting. For example, the Short Term Ensemble Prediction System STEPS (e.g. Bowler et al. 2006) recognises the inherent uncertainty in forecasts over a wide range of scales, including the fact that smaller scales are shorter lived and less predictable, and blends extrapolation, stochastic noise and Numerical Weather Prediction model outputs on a hierarchy of scales. The system generates 50 member ensembles of rain rate and accumulation at a 2 km grid, 5 minute resolution to provide forecasts at lead times of typically up to 6 hours ahead.

2.2

River and Coastal Conditions

Near real time measurements of river and tidal levels, wave conditions, and river flows are important in many flood warning and forecasting applications. There is much in common between the techniques used for river and coastal monitoring, although river gauges may be affected by debris and sediment loads, whilst tidal gauges may experience a harsher environment in terms of salinity and wind and wave loading. Various types of instruments are also deployed in the open oceans although are not described here, including free drifting floats, ocean gliders, and ship-borne measurements. For river monitoring, depending on the nature of the catchment, information may also be required on levels in reservoirs and off-line storage reservoirs, on flow depths on floodplains, and for ice conditions, pump settings or flows, borehole levels,

2.2 River and Coastal Conditions

37

and other parameters. For both river and coastal monitoring, additional information may also be required on gate settings (e.g. at reservoirs, or tidal barriers), and the condition of river and sea defences and other key assets, particularly if there is a suspected risk of breaching or overtopping. Some monitoring techniques for these applications are discussed briefly in Chapter 8. The techniques used for river and coastal monitoring are well established (e.g. World Meteorological Organization 1980, 1998; Hershey 1999; Intergovernmental Oceanographic Commission 1994) and only a few key points are presented here, together with some recently developed techniques. It is convenient to categorise techniques as follows: ●

●

● ●

River/tidal level monitoring – shaft encoder (float), pressure transducer, bubbler gauges, downward looking devices, sensor networks, satellite altimetry River flow monitoring – ultrasonic and electromagnetic devices, gauging structures, particle imaging velocimetry Wave monitoring – recording of wave heights, periods etc. Position monitoring – applicable to gates, ice monitoring, flood defences etc.

Position monitoring devices are not described in detail but include shaft encoders, ice motion detectors (e.g. doppler radar, or instruments linked by wire to plates anchored in the ice), and strain gauges. Fixed or panning CCTV and webcams for visible light, low light or infrared are also increasingly used for monitoring locations prone to blockages, ice formation, or other problems, and for estimating parameters such as wave overtopping rates at sea defences. For flood forecasting and warning applications, unless manual observations are used, all devices require a means of translating movement into an electrical signal for data logging, and onward transmission by telemetry. Instruments should also be installed with electronics above the highest likely flood levels. Only instruments suitable for telemetry are described in the following sections.

2.2.1

River/Tidal Level Monitoring

Level monitoring devices record water levels using a range of techniques including: ●

●

Float recorders – a float contained in a stilling well, installed either in a downpipe within the water body, or set into the ground and connected by a horizontal pipe to the river, reservoir or sea. The float moves up and down with water levels, causing the pulley from which it is suspended to rotate, and the rotation is detected electronically by a shaft encoder. Pressure transducers – are typically submerged at the end of a downpipe which acts as a protective conduit for the wire connecting the device to the data logger. The pressure which is recorded depends on the depth of water above the sensor. Pressure sensors have also been used in urban areas to detect flooding on roads, for example.

38 ●

●

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2 Detection

Bubbler gauges – typically release bubbles of inert gas (e.g. nitrogen) or air from an orifice and are supplied from a gas canister or compressor. The pressure required to displace water from the submerged orifice depends on the water pressure and hence the depth of water above the device. Such devices also include a pressure sensor, and are sometimes called pneumatic gauges. Downwards looking devices – include radar, ultrasound and acoustic devices suspended above the water surface by a purpose-made frame, or from existing structures (e.g. bridges, piers), which estimate levels based on the time of travel of transmitted and reflected signals. Although acoustic devices can be operated in the open, usually they are contained within a narrow sounding tube contained within a stilling well (e.g. for tidal monitoring), whilst radar and ultrasound devices do not normally require a protective conduit for the beam. Sensor networks – are a newer technology (pervasive or grid computing) consisting of networks of small low power pressure sensor devices with integral microprocessors and transmitters (e.g. radio or microwave), programmed to collaborate to form networks which can reconfigure automatically if any one sensor fails, and are much cheaper to install and operate than conventional instruments (e.g. Hughes et al. 2006). Satellite altimetry – is widely used for monitoring ocean levels, and shows potential for monitoring of river levels, particularly on large rivers (e.g. Beneviste and Berry 2004; Xu et al. 2004; Zakharova et al. 2005).

For river applications, the time interval for measurements can be set at a value based on the expected rate of rise and fall of river flow hydrographs or reservoir levels, and ideally would provide several values on the rising limb in a flood warning application (although this may not always be practicable in a fast responding river). Figure 2.5 shows a float in stilling well and a pressure transducer installation. The examples are for a river float in stilling well device, and a reservoir pressure transducer installation with radio mast and a staff gauge for manual observations. Each method has its own strengths and limitations. Devices installed below the water surface face the risk of damage by debris during a flood event, or blockage of the equipment by sediment or ice. In some countries, heaters or other forms of protection may be required to ensure operation in ice conditions. For tidal applications, and to a lesser extent reservoirs and lakes, the gauge output may also be affected by wave action. Individual sensor types of course have their own limitations, and may require corrections for drift, temperature effects, density effects and other factors. In rivers, downward looking devices may return ambiguous signals when there is significant debris floating on the water surface (trees etc.). For all types of device, data recording and transmission may be affected by floodwater if the data logger and telemetry electronics are installed at too low a level or in a location prone to erosion or impact by debris. Also, datum values need to be established and regularly checked so that water level measurements are consistent over time and can be related to national datum values. Tidal gauges may also incorporate a datum probe or switch which operates at a known sea level so that datum offsets or errors in the tidal record can be identified.

2.2 River and Coastal Conditions

39

Fig. 2.5 Examples of water level recording devices

Instruments may also need to be able to record over considerable ranges in levels; for example, ranges of 10 m or more are not uncommon in some tidal and river monitoring locations. Also, if water levels can fall below the height of the sensor (e.g. in low flow periods), then the instrument needs to be able to cope with dry conditions, and possibly high air temperatures, and blowing sand. However, in tidal applications, sometimes a gauge is designed to become exposed to the air at low tides to allow the datum or instrument to be checked at regular intervals.

2.2.2

River Flow Monitoring

For flood warning applications, measurements of levels may sometimes be all that is required. Indeed, compact self contained units are available commercially combining a pressure transducer or float recorder with a solar power or battery supply and a direct connection or telemetry link to a warning device (e.g. a bell, siren or cell phone) which is triggered if one or more preset levels is exceeded (see Chapter 4). However, for many river monitoring applications, an estimate of flow is required and, unless a purpose made flow monitoring gauge is installed (see later), values must be obtained by calibration of a stage-discharge relationship or

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rating curve established based on concurrent measurements of levels and flows or discharge (i.e. spot gaugings). Most hydrometric services have programmes in place for regular gaugings, and often also for high flow measurements during flood events. The technique for measuring flows typically involves taking a series of measurements of depths and velocities across the river, using one or more velocity measurements at each location, and integrating values to estimate the flow. Velocity measuring devices include propeller meters (current meters), and Acoustic Doppler Current Profilers (ADCP). Except at low flows, when wading can be used, current meters must normally be suspended from a bridge, boat or cableway, whilst ADCP devices can be floated across the river surface from the river banks or using towed, manned or radio-controlled floats and boats. On a large river, a single measurement of flow can be a time consuming operation, with potential health and safety and access issues during a flood event, particularly for measurements taken at night and in fast flowing, debris laden water, although remotely controlled winch systems (e.g. Park et al. 2006) are a possible way of helping to avoid these risks. Other less widely used methods include dilution gauging, in which the change in concentration of a tracer such as salt water or dye is recorded along a river reach, and slope-area methods, in which the change in water surface elevation is measured along a reach and the flow estimated from hydraulic formulae. The procedures for establishing a stage discharge relationship are well established (e.g. International Standards Organisation 1996, 1998) and Fig. 2.6 shows a simple example. Stage discharge relationships are often represented by power law equations with parameters obtained from a least squares fit regression to observed values. Look up tables and polynomial functions are also used, although to a lesser extent.

Stage (metres)

10

1

0.1 0.1

1

10 Discharge (cumecs)

Fig. 2.6 A simple example of a stage discharge relationship

100

1000

2.2 River and Coastal Conditions

41

The interpretation of stage discharge relationships can be complicated by a number of factors including: ● Weed growth – seasonal or intermittent growth of weeds causing channel constrictions ● Backwater influences – influences from downstream of the site such as from gate operations, high flows in tributaries etc. ● Tidal influences – tidal influences affecting water levels at the gauge, perhaps only for exceptional tides ● Channel profile changes – changes in the channel bed profile at or near the site due to erosion, scour, sedimentation, dredging etc., and changes in the river cross section as levels rise (e.g. flows going out of bank onto a floodplain or bypassing the station) ● Ice cover – formation of ice constricting river flows to varying degrees at certain times of the year, causing backwater effects for ice downstream, throttling flows for ice cover next to the instrument, and with a range of effects for ice cover upstream ● Hysteresis – differences in flow values for rising river levels and falling river levels These and other factors can cause curves to change with river depth, season and over time, leading to multiple equations valid only for given periods or seasons. Also, errors in the high flow end of stage-discharge relationships are perhaps one of the main sources of uncertainty in flood forecasting models. However, the stage discharge approach is probably the most widely used method internationally for estimating flows in rivers. Some techniques for extending curves include hydraulic modeling (1D, 2D or 3D), slope-area methods, based on peak water levels estimated from photographs, maximum level recorders, or from trash left after flood events, and velocity-area methods, based on direct survey of the river cross section area, and extrapolation or estimates of velocity. Given the uncertainties in estimating river flows from levels alone, various techniques have also been developed to provide a more direct measurement of flows, although are often significantly more expensive in terms of initial capital costs. The main types of device include: ●

●

Ultrasonic devices – which measure flows at one or more depths by the travel times of ultrasound waves between senders and receivers set at angles (typically in the range 30–60 degrees) to the main river flow. The average velocity at each depth can be estimated from the difference in time of travel between pulses with an upstream and downstream component, and can be integrated to provide an estimate of overall flow. Electromagnetic devices – which record the electromotive force induced by water flowing over a coil buried in the river bed, which is notionally proportional to the water velocity. The signal is detected by electrodes in the river banks, and additional probes may be required to allow corrections to be made from other electromagnetic sources near to the station.

42 ●

2 Detection

Gauging structures – purpose made structures designed or adapted for flow measurements, including various types of weirs, flumes and other structures, with estimates of flow obtained from levels recorded at one or more prescribed locations in the structure and a corresponding theoretical formula.

For all three techniques, it is usually necessary to make a number of spot gauging measurements when the instrument is first installed to check or establish the calibration. Also, for gauging stations in particular, but also for ultrasonic and electromagnetic devices, occasional spot gaugings are often made during routine operation to check for any drift in the calibration. All three types of device can be affected by the problems of ice, algae, weed growth, sediment and damage from debris, although electromagnetic instruments are less affected by weed growth. In addition, for gauging structures, the usual assumption is that the depth at the structure is controlled at the structure, and is independent of levels downstream. However, under high flow conditions, structures may drown out, so that the theoretical relationship no longer applies. One approach to estimating flows at structures where this occurs is to install a second level recorder downstream and to use theoretical or modelled relationships to estimate flows in these conditions. To provide better sensitivity to changes in depth, some structures also include changes in channel cross section, with additional channels becoming effective at higher flows, and only a narrow channel in operation at low flows. V notch weirs also achieve a similar effect. In flood warning and forecasting applications, another option is sometimes to calibrate an existing structure, built for other applications (e.g. navigation, irrigation), if it provides a stable control on water levels. One newer technique which shows some promise is the use of automated computer analysis of video camera images of existing tracers (e.g. foam, flotsam) on the water surface (e.g. Creutin et al. 2003). The technique, Particle Image Velocimetry, gives an estimate of surface flow velocities, which can be related to overall flows either using standard formulae or previous current meter measurements. The method relies upon suitable tracers being present on the water surface, and can be affected by shadows and reflections; however, it offers the promise of being able to estimate flows at a low cost from remote locations. Similar trials have also been performed using hand held or bridge mounted radar devices.

2.2.3

Wave Monitoring

In flood warning applications, estimates of wave height and direction are useful to assist in deciding whether to issue coastal flood warnings, and for input to coastal flood forecasting models. Typical wave periods might only be a few seconds, so the information is usually recorded over intervals of a few minutes or more and expressed in terms of the spectral properties of the wave field, from which key statistics such as significant and maximum wave heights, dominant and average wave period and direction, wave spread, mean water level, wind speed and direction, and wave spectra, can be estimated. The output across a number of wave monitoring locations provides a spatial picture of wave distributions.

2.2 River and Coastal Conditions

43

The main techniques for monitoring waves include (Massel 1996; World Meteorological Organisation 1998): ● ● ●

Measurements from below the sea surface Measurements at the sea surface Measurements from above the sea surface

For sub-sea devices, the signal can be transmitted by cable to the shore, or to a nearby buoy to be transmitted by radio or satellite. Pressure transducers are the most widely used method, with the pressure at the instrument varying with wave height. The resulting spectrum is then corrected for hydrodynamic attenuation with depth. However, depth corrections start to be of a similar magnitude to typical wave pressure signals for water depths much beyond 10–15 m, and also tend to filter out higher frequency signals, limiting the depths at which pressure transducers can be used (e.g. World Meteorological Organisation 1998). Vertically pointing echo sounders can also be used, although the signal may be affected by bubbles from breaking waves. Measurements at the sea surface are typically made from wave buoys, in which the vertical acceleration is measured using an accelerometer mounted on a gyroscopically stabilised platform, although solid state techniques are increasingly used. Wave heights can then be inferred from the acceleration terms. Motion can also be monitored in the two horizontal planes (roll and pitch) to provide spectral estimates of wave direction. Some devices also use Global Positioning Systems (GPS), and solid state inertial motion sensors which provide combined values for surge, sway, heave, roll, pitch, yaw and heading. Telemetry can consist of radio or satellite links. Lightships can also be used, in which the accelerometer output is combined with pressure sensor outputs to detect horizontal motions. A ship provides a more stable platform, although is less sensitive to smaller waves, whilst a buoy needs to be carefully installed so that the mooring does not influence the motion significantly. These methods are more appropriate for deep water, and shallow water techniques include capacitance probes and resistance probes, which can be mounted on structures such as piers or platforms. These devices consist of a series of sensors along a board (wave staff), where the signal depends on the depth of wave immersion, although can be affected by breaking waves. Devices which use ultrasonic or electromagnetic velocity meters can also be used to measure the two horizontal components of wave orbital velocity which, in conjunction with a pressure recorder or capacitance or resistance probe, can provide useful directional information. Downward looking devices of the types described earlier, such as laser, infrared, and acoustic range finding devices, can also be used to monitor waves if a suitable platform is available, although can be affected by reflections and other influences from the structure. For model calibration, satellite based estimates of long term wave state can be derived using synthetic aperture radar and other spaceborne instruments, although for polar orbiting satellites observations at a given location are only made once every orbit. Shore based high frequency radar also provides a method for monitoring wave states and sea surface currents over large areas.

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2 Detection

2.3

Instrumentation Networks

Flood warning and forecasting systems usually rely on a network of meteorological, river and/or coastal instruments. Individual types of instrumentation may also be combined; for example, an automatic weather station may be installed on a wave buoy, or a raingauge at a river gauging station. Monitoring networks can also serve a range of purposes in addition to flood warning and forecasting, such as water resources monitoring, marine forecasting, and climate change monitoring, requiring a compromise between these different applications. For example, a water resources gauge may be installed close to a river confluence to monitor the entire runoff from a catchment but, at high flows, suffer from backwater influences from the main river, possibly making it unsuitable for use in a flood forecasting application. For new sites, issues of site permissions, power supply, access for installation and maintenance, and other factors may lead to gauges being installed in locations that are not ideal. The requirements for telemetry connections may also influence the locations at which gauges are installed. The following sections discuss some options available for telemetry of real time information, and give a brief introduction to the design of networks for flood warning applications. Chapter 11 also discusses some of the economic considerations in network design, and in choosing an appropriate solution tailored to the level of flood risk.

2.3.1

Telemetry Systems

For telemetry of real time data, the following options are widely used in flood warning and forecasting applications (e.g. World Meteorological Organisation 1994b): ●

●

●

●

●

●

Telephone lines (PSTN) – connections via land-lines using the public switched telephone network. Each instrument has a unique telephone number which can be dialled to retrieve data or check the condition of the instrument. Mobile telephone (GSM, GRPS) – similar to PSTN lines but using cell phone technology. Radio – Ultra High Frequency (UHF) or Very High Frequency (VHF) communication links. Satellite – transmission from the instrument to an orbiting or geostationary satellite for relay to a ground station. Meteorburst – use of naturally occurring ionisation in the atmosphere left by meteor trails to reflect radio waves between a base station and outstation. Meteor impacts are sufficiently frequent that reasonable data transfer rates can be achieved. Internet – broadband, Ethernet and wireless connections.

Each approach has advantages and limitations and Table 2.2 provides some examples of these considerations. Other considerations can include power consumption, licence requirements, and purchase and installation costs. Some generic examples of use of these techniques include:

2.3 Instrumentation Networks

45

Table 2.2 Examples of strengths and possible limitations in telemetry methods Method

Strengths

Possible limitations

Telephone, broadband, local wireless ethernet

Uses an existing network

May incur connection and usage charges Requires a reliable public network Land lines and exchanges can be damaged by flooding and high winds etc. if not designed to avoid these problems May incur connection and usage charges Possible data drop-outs in heavy rainfall Networks can be affected by power cuts during flood events User needs to establish and maintain the network (equipment, permissions, licences etc.)

Simple to set up and operate

Cell phone

Uses an existing network Simple to set up and operate

Radio

Probably no connection charges other than radio licence fees once the network is established User retains full control of the network

Satellite

Instruments can be installed anywhere visible to the satellite No requirement to establish a network

Meteorburst

No requirement to establish a network Signals can be transmitted over long ranges

●

●

Line of sight required for transmission possibly requiring repeater stations, or limiting the range for coastal applications May be affected by interference May incur data transmission charges Possibly no suitable satellite visible Transmission may be restricted to the time of overpass (for orbiting satellites) or transmission time slots determined by the operator Relatively high power transmitter required Possible delays whilst waiting for suitable transmission conditions

WHYCOS – a World Meteorological Organisation initiative to install river and climate monitoring stations on the main rivers worldwide, which is being developed as a series of regional projects. Instruments typically use Data Collection Platforms (DCPs) transmitting via the Meteosat satellite system and other systems (World Meteorological Organisation 2005). ALERT (Automated Local Evaluation in Real Time) – a set of radio based communication protocols, sensing technologies and data formats which is widely used in the USA and elsewhere for locally operated flood warning systems incorporating raingauges, river level and other sensors (NOAA/NWS 1997).

There are also many national and regional examples of applications of these techniques; for example, in the United Kingdom, the public switched telephone network

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(PSTN) is used almost exclusively for data links to river and raingauge instrumentation whilst, in the USA, the Meteorburst system is used for transmitting data from the snow monitoring SNOTEL network described in Section 2.1. Manual systems, in which levels are relayed by telephone or radio, are still widely used in some countries. For the limitations which are listed, many potential solutions have been devised by suppliers, and can work well in some situations. Also, careful design can eliminate some problems. Networks consisting of more than one type of telemetry link are also an option if no single method is appropriate, or if backup transmission routes are required at each instrument in case of failure of any one method (e.g. radio backed up by cell phone). Interfaces may also be required to locally operated systems, such as the SCADA (Supervisory Control and Data Acquisition) systems which are sometimes used at reservoirs, hydropower schemes and other control structures. The connections to individual instruments typically consist of a data logger, to keep a record of values which can be downloaded at each visit, and a modem, to translate the signal into a form suitable for transmission by telemetry. The logger and data link may allow for multiple sensors, as with an automatic weather station, for example. Additional channels may also be used for sensors internal to the instrument or the logger/modem housing to monitor the status and environmental conditions of the instrument; for example, battery or solar panel condition, air temperature, and humidity, and sometimes a GPS unit for time and location information (e.g. for satellite telemetry). Telemetry connections can be bi-directional or one way only. Simplex connections are links in which the instrument sends packets of data at predefined times, or when a critical threshold is exceeded, whilst duplex connections allow downloading of data on demand. A duplex system provides the flexibility to increase the sampling (polling) rate of instruments when required (e.g. as a flood starts to develop) and also allows the operational status of the instrument to be checked remotely. By contrast, simplex systems are cheaper to install and operate, although with the risk of communications clashing between instruments if they transmit at the same time. Overall control of a telemetry network is typically from one or more central computers which will periodically poll, or update, values from the network. Modern data gathering systems typically include a wide range of functionality including: ● ●

● ● ● ● ●

●

Interfaces to a range of data sources and systems Map based displays of instrument locations and data values, and spatial data (e.g. weather radar data) Summary presentations of instrument status and data returned Data validation facilities (possibly) Report and graph generation facilities Alarm Handling options with transmission of alerts by email, SMS, fax etc. A wide range of options for onward transfer of data (e.g. to a flood forecasting model, automated dialling system, or permanent database system) Database options for short term on-line storage of data, and longer term off-line storage of data

2.3 Instrumentation Networks

47

Alarms can include rainfall depth duration values, river level thresholds, tidal level thresholds, and other types of threshold (see Chapter 3). Some systems may also be programmable, so that simple flood forecasting models such as level to level correlations can be operated on the telemetry system as a back up to the main forecasting system. Also, multicriteria alarms and rules might be included (e.g. IF X > Y AND Z > A THEN…). In control rooms, large wall mounted ‘mimic panels’ can help with providing an overview of current system status against a backdrop of key information, such as reservoir locations, towns and catchment boundaries, although are increasingly being replaced by computer displays. A hydrometeorological database is usually either an integral component of the system, or may be operated alongside the system as a long term repository for the near real time data. Many such systems are available commercially or have been developed by national hydrological and meteorological services, and the functionality might typically include: ● ●

● ●

●

●

Database summary options including key metadata for individual stations Statistical reporting functionality (e.g. hydrological year books, extreme value statistics) Data validation tools for checking, correcting and infilling erroneous data values A wide range of map based, reporting and graphical options for display and printing of data A range of data conversion options (e.g. from river levels to flows, or hourly values to daily values) Possibly a range of data analysis options (e.g. for stage discharge relationships, flood frequency analysis)

For database and telemetry systems, most modern systems provide options to facilitate the exchange of spatial and time series data through agreed data formats (e.g. XML) and metadata standards.

2.3.2

Network Design

The topic of network design for river flood warning and forecasting applications is covered in a number of guidelines, manuals and papers (for example, World Meteorological Organisation 1994a, 1998; USACE 1996; NOAA/NWS 1997; Environment Agency 2002, 2004; Sene et al. 2006), although recommendations can be specific to local meteorological conditions (desert, mountain, tropical etc.), the types of flooding mechanisms experienced, and other catchment and coastline specific factors. Some general issues to consider in network design include: ● ●

The accuracy, reliability and lead time requirements for flood warnings The likely performance of any new or existing instrumentation under flooding conditions

48 ●

●

2 Detection

The reliability of any existing or proposed telemetry links under flooding conditions The level of flood risk at the location, or locations, for which flood warnings are required

Here, flooding conditions can include high river or tidal levels, and the associated high winds and heavy rainfall which often accompany flood events, and an assessment of likely performance under these conditions usually forms part of the design study (e.g. is the instrument range sufficient to monitor all likely conditions, and are the electronics above likely maximum flooding thresholds). Backup power units and lightning conductors may also be needed and, in cold climates, heaters may be needed to ensure operation in snow or ice. The lead time requirement for flood warning can also influence network design. For example, for river monitoring sites, to assess local conditions, ideally an instrument would be installed at or near the location for which flood warnings are required. However, typical rates of rise of river levels in flood events may be so fast that the flood warning threshold level would have to be set to a low value to achieve a useful lead time, causing too many false alarms. Some ways of extending the lead time would therefore be to install a gauge further upstream, or to develop a forecasting model to the original proposed gauge location. Both methods introduce some uncertainty into the flood warning process, and both approaches might be used to help to reduce that uncertainty, possibly also using data assimilation and a probabilistic approach for the forecasting component, as described in later chapters. Similarly, for coastal locations, a tide gauge may be available at or near the location of interest, but if, for example, several hours of advance warning are needed to evacuate properties or to operate a tidal barrier, then locations further afield would need to be considered (or additional instruments installed), probably combined with use of surge forecasting models. Offshore monitoring also provides early warning of deep swell and Tsunami events not linked to local storms. For locations with complex wave and surge patterns (e.g. some harbours, and coastal reaches), on site monitoring is often the only way to resolve these effects. For raingauges, the flood warning or forecasting requirement may be simply to give an idea of rainfall in the general area, or to provide estimates of catchment rainfall or rainfall distribution for lumped or distributed rainfall runoff models. If the raingauge density is insufficient, then additional raingauges might be installed, including gauges in nearby river catchments. Existing alternatives, such as weather radar, might also be considered, if the coverage and accuracy is sufficient in the locations of interest. Given that major operational decisions may be taken based on the data provided, the issue of reliability (or resilience) is also important, and often one or more backup instruments may be identified in case of failure of any one instrument during a flood event. For a river monitoring gauge, that might be a gauge further upstream, whilst for a tide gauge another gauge might be selected from the same coastal reach. Backup instruments might also be installed at the same site or nearby locations, particularly in high risk locations (e.g. city centres).

2.3 Instrumentation Networks

49

A number of techniques can assist with network design including: ●

●

●

●

Digital terrain models – for radio path or line of sight studies, for estimating catchment characteristics (area, slopes, elevations etc.), and for viewing potential instrument locations against a backdrop of topography, flood risk locations, and other factors Hydraulic and hydrological analyses – to study the likely response of the catchment or coastal reach at the proposed instrument locations (e.g. rate of rise of levels for typical events, typical depth-duration values for rainfall, times of travel of flood or surge waves from distant locations, possible backwater and confluence influences etc.) Meteorological analyses – using historical raingauge data, weather radar data and possibly Numerical Weather Prediction model outputs to help in developing an understanding of flood generating conditions, with the likely scale, speed, and direction of storms all being important factors in deciding on appropriate raingauge locations Temporary gauges – installation of temporary gauges, maybe without telemetry, to investigate river or coastal characteristics at potential sites, and to check site security and feasibility (e.g. risk of vandalism, objections from nearby residents etc.)

More generally, it is often worthwhile considering other current or planned applications of the data; for example, for other purposes (e.g. water resources monitoring, ocean climate monitoring, port and harbour operations), or for providing flood warnings to additional locations. For example, considerable cost savings can sometimes be realised by considering opportunities to share data between departments or organisations, or by finding alternative nearby site locations which would serve more than one purpose. Another consideration, particularly for flood forecasting applications, is the level of uncertainty which can be tolerated. For example, for river forecasting models, it is often not economically feasible to place raingauges and river gauges in all major subcatchments, with the result that some inflows to the model (lateral inflows) will need to be estimated, introducing a source of uncertainty into the process. Also, it might be desirable to install more raingauges to obtain a better idea of rainfall distribution in and around the catchment, and to upgrade gauging stations so that they are better able to record accurate values at high flows. These various trade-offs and compromises are all part of the process of network design, and in part are one of the motivations for the increasing interest in using probabilistic and ensemble techniques to help to quantify the uncertainty (see Chapters 5–8 for examples).

Chapter 3

Thresholds

Flood warning thresholds define the meteorological, river and coastal conditions at which decisions are taken to issue flood warnings, whilst flooding thresholds are the values at which flooding occurs. Normally, a flood warning threshold will be set to achieve an acceptable lead time before the flooding threshold is reached, or may be time based (as with tropical cyclones, for example). Alternative names for flood warning thresholds include triggers, criteria, warning levels, critical conditions, alert levels and alarms, and sometimes a range of values will be required as warnings are escalated from advisories (or watches, or pre-warnings) through to full warnings. Threshold values may be set based upon experience or analysis of historical data, or using conceptual, data based or process based modelling studies. Values may be fixed (static) for all flood events, or dynamic, varying depending on how each event unfolds. This chapter describes a range of techniques ranging from simple fixed flood warning thresholds through to probabilistic approaches, together with several examples of approaches to performance monitoring of thresholds.

3.1

Rainfall Thresholds

Observations or forecasts of heavy rainfall often provide the first indication of likely river flooding. Some typical uses of rainfall thresholds are for the initial mobilisation of staff (e.g. opening an incident room), and moving to an increased frequency of monitoring river conditions and operation of flood forecasting models. Rainfall values can be obtained from observations (e.g. raingauges, weather radar, satellite) or forecasts (e.g. nowcasts, Numerical Weather Prediction models), with observed values usually providing higher accuracy, but with a shorter lead time before the onset of flooding. Best practice is to calibrate methods directly to the type of input data (or forecasts) to be used operationally, to account for any systematic or other differences between rainfall measurement and estimation techniques. Rainfall amounts can also be used directly to initiate flood warnings although, due to the various uncertainties in how rainfall translates into river flows (see later), this approach is used much less widely, with a greater risk of a high false alarm rate compared to warnings based on river levels. K. Sene, Flood Warning, Forecasting and Emergency Response, © Springer Science + Business Media B.V. 2008

51

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For some types of rainfall inputs, such as weather radar observations, or rainfall forecasts, rainfall values will usually be available on a gridded basis, so that the criteria for raising or displaying an alarm might apply to a single grid square, or to the average value across a region or in a river catchment. Information on rainfall amounts and accumulations can also be presented spatially; for example, as maps of rainfall amounts with overlays of catchment boundaries, rivers, topography and flood risk locations, and in terms of probabilities of exceedance (if using ensemble rainfall forecasts). Spatial estimates for rainfall distribution can also be derived for raingauges, if required, using the techniques described in Chapter 2. Rainfall threshold (or alarm) criteria are often expressed in terms of the quantity (depth) of rainfall in a given period (duration) which has the potential to cause flooding. A range of depth-duration values may be used; for example, an alarm might be raised if rainfall is forecast to exceed 25 mm in any 3 hour period, or 40 mm in any 6 hour period. Alternatively, thresholds may be expressed in rainfall frequency terms calculated from a statistical analysis of historical records, such as the 1% or 10% exceedance probability, or the 1 in 100 year or 1 in 10 year return period. Values can be tested by analysis of long term historical rainfall records; for example, by comparing the number of alarms which would have been raised compared to the number of actual flooding events (or near misses), and estimating the number of false alarms which would have occurred (see Section 3.3). Of course, rainfall values alone do not provide a full indication of flooding potential, since the catchment characteristics (topography, land use etc.), current catchment state (e.g. soil moisture, snow cover) and other factors (e.g. reservoir levels) may also influence the magnitude and timing of flooding. Rainfall thresholds are therefore often combined with indicators of catchment response and the current catchment state. Table 3.1 illustrates a simple approach of this type, and is adapted from one of several examples in Environment Agency (2002). Here, the codes and locations are for three hypothetical Flood Warning Areas, and the Forecast Rainfall values could be either best estimates, or worst-case scenarios. The depth/duration pairs (e.g. 20/6) are in units of mm of rainfall, and hours, and Table 3.1 Illustration of rainfall alarm criteria (Adapted from Environment Agency 2002; © Environment Agency copyright and/or database right 2008. All rights reserved) Flood Watch Criteria based on SMD

Forecast Rainfall (mm) Code and location

6 12 18 hours hours hours

24 hours

SMD (mm) 40

25/6 30/12 28/6 35/12 28/6 35/12

30/6 40/12 32/6 40/12 32/6 40/12

30/6 45/18 35/6 45/18 35/6 45/18

3.1 Rainfall Thresholds

53

catchment conditions are expressed in terms of the soil moisture deficit (SMD), which is the depth of rainfall which would be required to bring the catchment to saturated conditions (i.e. the amount of water required to bring the soil to field capacity). Some other possible indicators for catchment conditions (e.g. World Meteorological Organisation 1994; USACE 1996) include recent rainfall, current river flows, Catchment Wetness Index, Base Flow Index, Antecedent Precipitation Index, and borehole levels. Where, as is often the case, direct observations are not available, values are often computed from the soil moisture accounting component of rainfall runoff models (see Chapter 6) or as a secondary (diagnostic) output from the land-atmosphere component of Numerical Weather Prediction models (e.g. Cox et al. 1999). Satellite based methods also show potential for remote sensing of soil moisture. Another approach to setting thresholds is to use a catchment rainfall runoff and flow routing model to explore the rainfall amounts required to achieve flooding for a range of durations and catchment initial conditions. One approach is to first derive a typical storm profile from historical data, describing the variation in rainfall during the course of an event. These values are then scaled by magnitude and duration, and the resulting synthetic storms used as input to the catchment model. For each duration, the depth required to reach flooding thresholds is noted, perhaps for a range of catchment conditions, and the resulting table of values can then be used as the basis for estimating the rainfall thresholds for that location. Other factors, such as reservoir drawdown at the start of an event, or the depth of water in off-line storage areas, might also be considered in setting thresholds. Some possible criteria for flooding thresholds include bank full flows, peak river levels exceeding a threshold at which flooding commences, or flood flows of a given probability (return period). The latter method is often used for ungauged catchments and in ensemble forecasting approaches (see Chapter 5). For example, these types of method form the basis of the Flash Flood Guidance concept (FFG) developed by the National Weather Service in the USA (Sweeney 1992). Flash Flood Guidance is defined as the amount of rainfall of a given duration over a small basin needed to create minor flooding (bank full) conditions at the outlet of the basin. The approach has been used operationally since the 1970s and was integrated into a system called the Flash Flood Guidance System in 1992, and has more recently been considered for providing early alerts for debris flows (NOAA-USGS 2005). Chapter 8.2 provides some examples of international initiatives using this approach. In the original version of the method, threshold values of runoff were estimated based on the outputs from a lumped rainfall runoff modelling approach. More recent developments (National Weather Service 2003) have included improvements to the method for areas of the country where rainfall intensity and land characteristics have more influence on flash flooding than soil moisture (e.g. some desert regions), and the introduction of a distributed (grid based) hydrological modelling approach for estimating thresholds and for real time soil moisture accounting. Operationally, estimates of rainfall depths and durations (e.g. 1, 3, 6, 12 and 24 hours) are compared with the threshold values appropriate to the estimated soil moisture conditions. The resulting exceedance over threshold values can then be mapped.

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Spatial analysis tools are also available to examine rainfall accumulations, rainfall intensities and guidance values at point or catchment scale. Some other developments in the area of rainfall threshold based approaches (e.g. Martina et al. 2006; Georgakakos 2005, 2006; Reed et al. 2006; Collier 2007; Fouchier et al. 2007) have considered or implemented systems which include various permutations of the following techniques: ●

●

●

●

●

Bayesian techniques requiring optimisation of a utility function combining the perception of stakeholders, historical losses, and perhaps the losses from false alarms Alternative soil accounting approaches using a variety of conceptual and processbased catchment models Artificial neural network methods which improve forecasting skill by ‘learning’ from meteorological and streamflow response Thresholds based on the return periods/recurrence intervals of model flows based on long term simulations using historical or synthetic rainfall data Development of indicators of flash flood potential which can be searched in real time including soil moisture, channel constriction/debris risks, storm depthduration, direction and velocity

Ensemble approaches are also increasingly being used, in which the probability of rainfall is displayed on graphs, maps and tables and compared to probability based thresholds. Risk-based approaches, combining probability and consequence, can also be used, and Box 3.1 provides an example of an operational system in the Netherlands which uses ensemble forecasts of rainfall to provide rainfall alarms to assist with water management operations in polder regions. Chapter 5 provides further information on ensemble forecasting techniques and Chapter 10 gives more background on risk-based and cost loss approaches to decision making. In addition to the use of rainfall thresholds, various other meteorological indicators have been considered for use in providing early warning of flooding, with an emphasis on probabilistic techniques. One of the earliest methods was a combined deterministic/stochastic approach which was developed for application in the Mediterranean areas of France (Obled and Datin 1997; Bernard 2004). Observations and forecasts of rainfall and other parameters at lead times of 2–3 days or more are linked to an archive of rainfall and other parameters for past events; for example, geopotential or temperatures. The technique can also be used at shorter lead times, using stochastic modelling to link observations up to time now with likely future scenarios (again based on an historical archive), with the option of conditioning forecasts on nowcasts and likely limits on daily rainfall for the catchment for the type of storm being observed. Another approach is to use operational mesoscale and other Numerical Weather Prediction models to monitor parameters which are thought to be good precursors of flooding, including: ● Potential vorticity – as an indicator of atmospheric stability ● Convective Available Potential Energy (CAPE) – an indicator of the energy available for a storm to develop

3.1 Rainfall Thresholds

55

Box 3.1 The KNMI precipitation alert system The Netherlands is a low lying country with extensive areas of reclaimed land, known as polders, and more than half of the population lives in areas below sea level. The main flood risk in the polder areas arises from heavy rainfall falling directly on the polders, often combined with high river levels or sea levels (due to surge, wind and wave action), which may limit the ability to remove excess water. A network of pumping and drainage systems is used to manage water levels in polders, and rainfall observations and forecasts play an important role in optimising these operations. Rainfall alarms are used to mobilise staff, alert third parties, obtain emergency pumping equipment (if required), and to help in deciding when to start pumping operations. Since 2003, as part of a collaborative project with the Union of Water Boards, the Royal Netherlands Meteorological Institute (KNMI) has been issuing probabilistic rainfall alarms to selected Water Boards based on ensemble forecasts and observed rainfall data. For lead times up to 36 hours ahead, deterministic forecasts are used from the national HIRLAM Numerical Weather Prediction model whilst, at longer lead times, 50 member ensemble outputs are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Values for rainfall depth and duration are estimated for each polder based on weather radar observations of rainfall in the previous 5 days, and rainfall forecasts for the next 9 days (Fig. 3.1). Critical rainfall depth-duration values are defined by each Water Board on the basis of historical rainfall records and flooding histories and can depend on soil type, the proportion of urban areas, storage capacity, pumping capacity, the time of year, and other more subjective

Fig. 3.1 Example of a 9 day probabilistic rainfall forecast (KNMI)

(continued)

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Box 3.1 (continued) factors. The probabilities at which alarms are issued are calculated from a risk assessment which compares the cost of mitigating actions (pumping etc.) with the estimated losses (damages) if no action is taken. The so-called cost loss ratio gives an indication of the appropriate probability thresholds to use for each polder, which can be refined based on experience. Thus the criteria for issuing warnings are defined in terms of a risk profile for each polder consisting of a range of depth, duration and probability combinations (e.g. 50 mm in 72 hours with a 33% exceedance probability). Over a number of events, consistent use of these profiles should help to minimise the economic impacts from flooding. The criteria are checked automatically on an hourly basis and, when individual values are exceeded, the relevant Water Board managers receive an email alert or text message, and the alert is also published on a secure website. A user group meets at regular intervals to share experience on use of the system, and in particular to discuss approaches to the setting and verification of appropriate risk profiles, since this is a new and developing application for ensemble forecasting. Planned developments include use of radar rainfall nowcasts for shorter term rainfall forecasts, and use of wind and tide forecasts, since wind effects can add to the flood risk in larger polders via wave and local surge effects. Reference: Kok C J, Vogelezang D H P, Wichers Schreur B, Holleman I Description and use of the automated warning system for the Dutch water boards, KNMI.

● ●

Precipitable water – or precipitable water vapour Intensity and direction of the low level flow

These indicators can be calculated from wind, moisture, pressure and other fields (e.g. Collier et al. 2005; Environment Agency 2007). These developments are often linked to the general trend to develop higher resolution (storm scale) models better able to forecast the development of convective and other events. Lightning activity has also been considered as a possible indicator of the likelihood of heavy rainfall and flash flooding (e.g. Price et al. 2007).

3.2 3.2.1

River and Coastal Thresholds Introduction

River and tidal thresholds (or triggers) are a key component in many flood warning systems, and define the levels and possibly other variables (e.g. wind speed and direction) at which the decision to issue a flood warning should be taken, or other actions initiated

3.2 River and Coastal Thresholds

57

(e.g. mobilisation of staff, more frequent monitoring). They are sometimes called Action Thresholds. Some systems may also automatically issue a warning at these levels without any human intervention (e.g. using email, sirens or pagers) although there are many issues to consider in taking an automated approach of this type; for example, the likely false alarm rate, and the possibility of missed warnings (see Chapter 4). Other types of thresholds can include parameters such as ice motion and river flows. Time based criteria may also be used in some situations; for example, the time before landfall for hurricanes, typhoons and tropical cyclones (see Chapter 9 for further discussion of this topic). Observations are normally made by telemetry but, where this is not available, or extra safeguards are required, observers and patrols may be deployed on site, or other methods such as CCTV or webcams used. Community representatives may also monitor conditions in some flood warning schemes. On site observations can be particularly useful where site specific flood risks can occur, such as defences breaching, waves overtopping at sea defences, or bridges being blocked by debris, and as additional backup for high risk locations such as town centres. Threshold values are normally defined based on a combination of experience, analysis of historical data, and possibly detailed hydraulic and other modelling of river or coastal response. Values are usually chosen to achieve the required warning lead time, without causing an unacceptable number of false alarms and, for instruments at the location of flooding, are set in relation to the flooding threshold, as illustrated in Fig. 3.2 for the case of a river level threshold. Here, the flooding threshold is the gauge reading at which flooding impacts begin (and for which a warning is required), such as property flooding, or flooding of roads, or overtopping of flood defences (levees), and is sometimes called a Result Threshold. In practice, the actual warning lead time will be less than the potential

River Level Flooding Threshold

Flood Warning Threshold

Warning Lead Time

Time

Fig. 3.2 Illustration of a flood warning threshold for an at site gauge

58

3 Thresholds

value indicated in the figure since factors such as decision times, and flood warning dissemination times, must be accounted for, as described in Chapters 4, 5 and 10. Also, it is advisable to include some allowance (contingency) in the setting of values to allow for uncertainty in data, models and event specific factors. The terminology and approaches used vary between organisations and countries, but some typical types of threshold (or trigger) include: ●

●

●

At site or local values – where the flood warning is issued based on values at or near the location for which the flood warning is required Upstream or remote values – where the flood warning is issued based on values at a site further upstream in the river network, or further offshore or around the coast in the case of coastal triggers, to provide additional lead time at the site of interest Forecast values – where the flood warning is issued based on the output from a river or coastal forecasting model for the site or other location of interest

For each type of threshold, there is a trade off between the accuracy, reliability and timeliness which can be achieved; for example, if a threshold is lowered, this normally increases lead time, but may also increase false alarm rates (e.g. USACE 1996), whilst forecast values may be set at a higher threshold (e.g. a flooding or result threshold) than for at site values due to the additional lead time available from model outputs. To provide additional lead time and resilience, a site may have more than one type of threshold, with warnings being issued on the basis of exceedance of any one value, or other permutations. Values may also be nominated as the primary, secondary (or backup) or failsafe threshold, with the choice depending on the relative performance of each type of threshold. Within each category of threshold, there may also be a range of values for different operational and warning conditions. For example, a site might have standby or alarm values which are set at a low level for early warning of possible events, and mobilisation of staff, and a range of flood warning values to escalate the severity of the warning as river or sea levels rise. Also, as flood hazard mapping techniques improve, and flood warning dissemination systems become more sophisticated, it is increasingly becoming possible to target warnings to smaller areas, or even to individual properties, with the advantages of reducing the number of false alarms experienced by property owners, and allowing for a more phased approach to warning and evacuation of properties. If this approach is used, then each zone or sub-area will have its own warning threshold level, both for the At-Site gauge (if available) and for any Remote gauges or Forecast values. Of course, the terminology and formats used for flood warning procedures, and the criteria for escalating and downgrading alerts, differ widely between organisations but the general principle of escalation of warnings, followed by confirmation that the threat has passed, is widespread. Figure 3.3 provides an example of this general approach for a river flood warning application. In this hypothetical example, the Flood Warning Area at Newtown is divided into four sub-areas or zones, identified by codes FW001 to FW004. The corresponding warning threshold levels are shown for the At-Site gauge, and the Remote gauge would also have its own set of values (not shown). If a forecasting model output is available, that too would have a set of values based on a consideration of flooding thresholds,

3.2 River and Coastal Thresholds

59

FW004 FW003 FW002 FW001

R

IV

ER

Remote Gauge

At Site Gauge

FW001 – Riverside Paths

FW002 – Riverfront Apartments FW003 – Town Centre FW004 – Power Station

To Bridgeham Fig. 3.3 Illustration of at site and upstream thresholds (not to scale)

model lead times, and other factors. Table 3.2 provides a simplified illustration of how these values might be implemented into a set of operational Flood Warning Procedures for the At-Site gauge (sometimes called an Action Table or Flood Intelligence Card), although it is important to note that the details of warning messages and operational responsibilities differ widely between countries and organisations. Values are expressed in terms of gauge readings, but absolute values might also be included, relative to a national datum level. Other thresholds (sometimes called Information Thresholds) might also be included to indicate other useful information, such as the highest level recorded at the site, and peak levels for historical flood events. A similar table would also be produced for the Remote Gauge, with a separate set of values. As illustrated, the gauge at Newtown might also be a Remote gauge for another Flood Warning Area further downstream, and an example is included for the town of Bridgeham. Following the initial standby alarm, a series of flood warnings is issued as the event escalates, and operational instructions are also issued where this requires direct contact with other organisations or the public. As noted in Chapter 4, this allows an audit trail of actions and decisions to be maintained during the event, including any departures from the agreed approach as each warning level is exceeded (for example, based on other information which may be available, such

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3 Thresholds

Table 3.2 Illustration of flood warning thresholds for the example in Fig. 3.3 Observed level (m) Forecast level (m) Action required 3.2

>3.8

3.5

>4.0

3.8

>4.2

4.0

>4.4

4.2

>4.5

3.1