Applications of Physiological Ecology to Forest Management (Physiological Ecology)

Applications of Physiological Ecology to Forest Management

Applications of Physiological Ecology to Forest Management

This is a v o l u m e in the P H Y S I O L O G I C A L E C O L O G Y series E d i t e d by H a r o l d A. M o o n e y A complete list of books in this series appears at the end of the volume.

Applications of Physiological Ecology to Forest Management j.j. Landsberg CSIRO Australia Institute of Natural Resources and Environment Canberra, Australia

S. T. Gower University of Wisconsin Department of Forestry Madison, Wisconsin

Academic Press San Diego

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Front cover photograph: Canopy of a jack pine stand in Sayner, Wisconsin.

Photo courtesy of Jeff Martin/JMar Photo Werks.

This book is printed on acid-free paper. @ Copyright 9 1997 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Academic Press, Inc. 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.apnet.com Academic Press Limited 24-28 Oval Road, London NW1 7DX, UK http://www.hbuk.co.uk/ap/ Library of Congress Cataloging-in-Publication Data Applications of physiological ecology of tbrest management / edited by J.J. Landsberg. S.T. Gower. p. cm. -- (Physiological ecology) Includes bibliographical references and index. ISBN 0-12-435955-8 (alk. paper) 1. Trees--Ecophysiology. 2. Forest ecology. 3. Forest management. I. Landsberg, J. ]. II. Gower, S.T. Ill. Series. SD395.A66 1996 574.5' 2642--dc20 96-27735 CIP PRINTED IN THE UNITED STATES OF AMERICA 96 97 98 99 00 01 BB 9 8 7 6 5

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Contents

Preface

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1. Introduction: Forests in the Modern World I. Forest Management: Levels, Decisions, and Influences II. Overview 9

2. Forest Biomes of the World I. II. III. IV. V. VI.

Management 20 Plantation Forestry 21 Species Adaptations and Climatic Conditions 22 Forest Biomes of the World 33 Future Distribution and Extent of Forest Biomes 48 Concluding Remarks 49 R e c o m m e n d e d Reading 50

3. Canopy Architecture and Microclimate I. Canopy Architecture 53 II. Energy Balance and Interception of Visible (Photosynthetically Active) Radiation 63 III. Heat and Mass Transport 72 Effects of Topography on Microclimate 83 V. Concluding Remarks 87 R e c o m m e n d e d Reading 88

4. Forest Hydrology and Tree-Water Relations I. Hydrologic Balance 92 II. Catchment Hydrology 111 III. T r e e - W a t e r Relations and Their Effects on Growth

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IV. Concluding Remarks 122 R e c o m m e n d e d Reading 124

5. Carbon Balance of Forests I. II. III. IV. V. VI. VII. VIII.

Leaf Photosynthesis 128 Canopy Photosynthesis 133 Autotrophic Respiration 139 Net Primary Production 144 Growth Efficiency 154 Net Ecosystem Production 155 Forests in the Global Carbon Budget Concluding Remarks 158 R e c o m m e n d e d Reading 160

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6. Soil Organic Matter and Decomposition I. II. III. IV. V.

Soil Carbon Content and Accumulation 163 Sources of Soil Organic Matter 166 Litter Decomposition 170 Carbon Losses from Forest Ecosystems 176 Influence of Forest Management on Soil Carbon Dynamics 180 VI. Role of Forest Soils in the Global Carbon Budget VII. Concluding Remarks 184 R e c o m m e n d e d Reading 184

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7. Nutrient Distribution and Cycling I. The Essential Plant Nutrients and Ion-Exchange Capacity of Soils 187 II. Nutrient Distribution 191 III. Nutrient Cycling 194 IV. Impacts of Natural and Anthropogenic "Disturbances" on Nutrient Cycles 214 V. Concluding Remarks 226 R e c o m m e n d e d Reading 228

8. Changes in Ecosystem Structure and Function during Stand Development I. General Succession Theory 230 II. Changes in Species Composition

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III. IV. V. VI.

Stand Functional Characteristics 234 Forest and Ecosystem Productivity 236 Nutrient Cycling 242 Concluding Remarks 244 Recommended Reading 245

9. Ecosystem Process Models I. Forestry Models 249 II. Current Process-Based Models III. Concluding Remarks 272 Recommended Reading 276

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10. Applications of Modern Technology and Ecophysiology to Forest Management I. Geographical Information Systems 278 II. Remote Sensing 282 III. The Use of GIS, Remote Sensing, and Models as Management Tools 289 IV. Concluding Remarks 297 V. Peroration 299 Recommended Reading 300

Symbols and Definitions References Subject Index

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Preface

An i m p o r t a n t objective in writing this b o o k was to bridge the gap between f o r e s t e r s m w h e t h e r they be managers, decision-makers at various levels, or p o l i c y m a k e r s m a n d ecologists a n d ecophysiologists. Unfortunately, there is a d i c h o t o m y between the teaching, the practice a n d profession of forestry, a n d the m o r e basic science areas of forest ecology a n d ecophysiology. U n d e r g r a d u a t e forestry students learn a great deal a b o u t forest types a n d forest m a n a g e m e n t - - g e n e t i c s , silviculture a n d m e n s u r a tion, forest engineering, and forest e c o n o m i c s - - b u t are given only a brief i n t r o d u c t i o n to soils and plant sciences. T h e emphasis varies f r o m school to school a n d from c o u n t r y to country, b u t in general there is not m u c h space in forestry curricula for the basic disciplines. T h e d i c h o t o m y carries t h r o u g h to the world of forest m a n a g e r s a n d those who m a k e decisions a b o u t the use of forested lands. T h e r e are u n d o u b t e d l y various reasons for this p r o b l e m , b u t a m o n g t h e m is the p e r c e p t i o n that ecology a n d ecophysiology are n o t of m u c h relevance or i m p o r t a n c e in the practical world of m a n a g e m e n t a n d decision-making. T h e reasons for this p e r c e p t i o n may lie as m u c h in the failure of ecologists a n d physiologists to m a k e a convincing case for the relevance of their disciplines as in the lack of interest a m o n g foresters in what they perceive as esoteric " b a c k g r o u n d stuff." M a n a g e m e n t of any system, in any sphere of life, is a m a t t e r of m a k i n g decisions a n d taking actions that are i n t e n d e d to have p a r t i c u l a r e f f e c t s m t h e m a n a g e r is trying to control or m a n i p u l a t e the system. In c o m m e r c i a l forestry, the e n d results are usually economic, with ecology a n d sustainability often barely considered; decisions t e n d to be m a d e by accountants, n o t scientists. This is unavoidable, b u t it is i m p o r t a n t that the c o n s e q u e n c e s of those decisions are predictable. It is no less i m p o r t a n t w h e n forests are managed for o t h e r uses, for example, as r e c r e a t i o n a l areas, for the preservation of biodiversity, or as w a t e r - p r o d u c i n g areas. To predict the results of p a r t i c u l a r actions the m a n a g e r m u s t u n d e r stand how the system works. This is what ecology and ecophysiology have to offer. We have tried, t h r o u g h o u t this book, to show how the u n d e r standing a n d insights into the f u n c t i o n i n g of ecosystems offered by these disciplines are relevant a n d i n d e e d essential to ensure that the decisions

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that will be m a d e by people responsible for the m a n a g e m e n t of forested lands are the best possible. We do not delude ourselves by thinking that we can bridge the gap with one book, but we believe that it will make a significant contribution and that it will stimulate forestry students to address the interesting ecophysiological questions that are relevant to the long-term sustainable m a n a g e m e n t of forests. This b o o k is aimed at u n d e r g r a d u a t e students in the final courses for degrees in forestry and forest ecology and at postgraduate students in the same fields. We also hope that the synthesis contains e n o u g h of interest to be of value to our professional colleagues in research and that practicing foresters will find it useful in explaining observations made in the forests. We have covered a wide range of topics, from the distribution of forest ecosystems, with some indication of the reasons for that distribution, to treatment of the basic disciplines of m i c r o m e t e o r o l o g y and hydrology, the factors affecting the carbon balance, organic matter decomposition, and nutrition. We have provided an introduction to the m e t h o d s of modern process-based modeling, illustrated by a review of some of the better known extant models, and in the last chapter we have illustrated how all this can be used by managers in c o n j u n c t i o n with the technologies of remote sensing and geographical information systems. Specialized textbooks are available for the areas covered by each chapter. We are aware that, by addressing such a wide field, our coverage will inevitably be patchy, and in parts inadequate, so that specialists in particular areas will find gaps and shortcomings. For these we apologize in advance, but we are p r e p a r e d to gamble that the benefits of the overall synthesis will more than compensate for our shortcomings in specialist disciplinary areas. It is a pleasure to acknowledge the help of friends and colleagues, experts in the fields covered by this book, who have generously given their time and expertise to read and criticize drafts of the various chapters and to keep us from straying too far from the paths of established scientific wisdom. As always, in these cases, we exonerate them from blame for omissions, mistakes, and failures--we did not always take their a d v i c e - - a n d thank them most sincerely for helping to keep such errors to (we hope) a reasonable m i n i m u m . We list them in no particular order: Ross McMurtrie and Roddy Dewar (School of Biological Sciences, University of New South Wales); Peter Sands (CSIRO Division of Forestry, Hobart); Tim Fahey (Cornell University); Dale J o h n s o n (Desert Research Institute, Reno, NV); Craig L o r i m e r ( D e p a r t m e n t of Forestry, University of Wisconsin, Madison); Neal Scott (Landcare Research, Palmerston North, New Zealand); J o h n Raison (CSIRO Division of Forestry, Canberra); Almut Arneth (Landcare Research, Lincoln, New Zealand); Peter Briggs (CSIRO Centre for Environmental Mechanics, Canberra); Ian Woodward (School of Biological Sciences, University of Sheffield); Frank Kelliher

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(Landcare Research, Lincoln, NZ); David W h i t e h e a d (Landcare Research, Lincoln, NZ); Rob Vertessy (CSIRO Division of Water Resources, Canberra); Jim Vose (Coweeta Hydrologic Laboratory). T h a n k you all very much. Jerry Melillo (Ecosystems Research Center, Woods Hole, MA) kindly gave permission for us to use a version of the world vegetation map (Fig. 2.2 in this book) that he and his colleagues developed as the basis for their analysis of terrestrial net primary productivity, published in Nature in 1993. David Kicklighter, part of that team, transmitted the (then) latest version of the map to us in digital form. Sune Linder (Swedish University of Agricultural Sciences, Uppsala), Douglas Malcolm (Institute of Ecology and Resource Management, University of E d i n b u r g h ) , and J o h n Bartle (Conservation and Land Management, Western Australia) provided comments about what managers in their parts of the world see as their needs and priorities. On the p r o d u c t i o n side, Greg Heath (CSIRO Centre for Environmental Mechanics) was patient and skillful in drawing and redrawing the f i g u r e s - - a n invaluable c o n t r i b u t i o n - - a n d Suzie Bubb not only typed the tables and entered a great many of the references into a data base but also taught one of us (j.j.L.) to use the thing with reasonable proficiency. Tara Stow ( D e p a r t m e n t of Forestry, University of Wisconsin, Madison) skillfully and cheerfully assisted with n u m e r o u s facets of the book, as well as with some of the day-to-day tasks that S.T.G. did not have time for while working on this project. Thanks Tara! Connie Gower patiently proofed the Reference section, located missing references, and typed the Subject Index. We are grateful to J o h n Finnigan, H e a d of the Centre for Environmental Mechanics, for allowing J.J.L. to spend a great deal of his time on this project and to enlist the help of lab support staff. We also acknowledge the support provided by Landcare Research, New Zealand, and by University of Wisconsin College of Agriculture and Life Sciences to S.T.G. for allowing him to put a great deal of time into p r e p a r i n g this book while on sabbatical in Australia and New Zealand. We acknowledge the support of NASA Grant NAGW-4181, which enabled j.j.L, to do some preliminary library work in the United States and S.T.G. to spend some time in Australia. Finally, I (S.T.G.) thank my family (Connie, Kristen, and Cathy) for their support and love during the p r e p a r a t i o n of this book. Joe Landsberg Stith T. Gower

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1 Introduction: Forestry in the Modern World

T h e objective of this b o o k is to p r o v i d e an u p - t o - d a t e t r e a t m e n t o f curr e n t scientific k n o w l e d g e a b o u t the effects o f the e n v i r o n m e n t , h u m a n s , a n d t h e i r i n t e r a c t i o n s o n the g r o w t h o f forests, a n d to show h o w this k n o w l e d g e can be u s e d in m a k i n g decisions a b o u t the m a n a g e m e n t o f forests. To u n d e r s t a n d how e n v i r o n m e n t a l factors, s u c h as i n c o m ing solar r a d i a t i o n , air t e m p e r a t u r e a n d h u m i d i t y , p r e c i p i t a t i o n , nutrients, a n d water b a l a n c e , affect f o r e s t growth, we n e e d to u n d e r s t a n d the way the p h y s i o l o g i c a l p r o c e s s e s that d e t e r m i n e t h e g r o w t h o f trees are i n f l u e n c e d by t h e s e factors. This r e q u i r e s an u n d e r s t a n d i n g o f p l a n t - e n v i r o n m e n t i n t e r a c t i o n s as physical p r o c e s s e s as well as o f the i n f l u e n c e o f e n v i r o n m e n t a l c o n d i t i o n s o n the p h y s i o l o g i c a l processes. T h e g e n e r a l a p p r o a c h a n d p h i l o s o p h y are e n c a p s u l a t e d in Fig. 1.1, w h i c h is a very simplified s c h e m a t i c o u t l i n e o f the m a j o r c o m p o n e n t s o f the f o r e s t / e n v i r o n m e n t interactive matrix: the key c o m p o n e n t s are water, n i t r o g e n 1 a n d c a r b o n . T h e d i a g r a m shows climate a c t i n g o n the p l a n t c o m m u n i t y t h r o u g h the foliage ( r a d i a t i o n i n t e r c e p t i o n , t e m p e r a ture, a n d h u m i d i t y ) , w h e r e C O 2 is a b s o r b e d a n d water v a p o r lost. T h e C O 2 a b s o r b e d is c o n v e r t e d to c a r b o h y d r a t e s t h r o u g h t h e p r o c e s s o f p h o tosynthesis; these are a l l o c a t e d to m a i n t e n a n c e a n d t h e g r o w t h o f foliage, stems, a n d roots. P r e c i p i t a t i o n is, o f c o u r s e , a m a j o r d e t e r m i n a n t o f the soil water b a l a n c e that, with t e m p e r a t u r e , affects t h e rate o f organic m a t t e r d e c o m p o s i t i o n a n d n i t r o g e n r e l e a s e a n d u p t a k e by t h e roots. Any m a n a g e m e n t a c t i o n in r e l a t i o n to forests will, directly or indirectly, i n f l u e n c e all c o m p o n e n t s o f t h e system. 1Note that the focus on nitrogen as the most important nutrient may be arguable, particularly in relation to areas of the world where phosphorus is known to be the major nutrient limiting plant growth. However, a great deal of modern research has focused on nitrogen that, because of its fundamental role in the carbon cycle and because it is inextricably linked to growth processes through that cycle, appears to be the key nutrient. Phosphorus availability is much more strongly controlled by geochemical processes. We provide a more general treatment of tree nutrition in Chapter 7.

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Figure 1.1 Schematic diagram showing the interactions between climate and forest ecosystems. The solid lines denote flows of energy or matter, the dotted line indicates the effects of environmental conditions on p r o c e s s - - i n this case, the influence of nutrient and water availability on carbohydrate allocation within the p l a n t s m a n d the boxes are pools of material (soil organic matter, foliage, stems, etc.). These interactions form the theme of this book. T h e aim of the b o o k is to describe the various processes that are involved in this system and, as far as possible, to provide a t r e a t m e n t that leads to, or at least provides the basis for, quantitative m o d e l s that allow us to calculate the c o n s e q u e n c e s of p a r t i c u l a r events or actions. This is n o t always possible because k n o w l e d g e of various processes is not adequate or the i n t e r a c t i o n s involved are too c o m p l e x for anything m o r e t h a n estimates of the likely effects of c h a n g e or d i s t u r b a n c e . However, the aim of quantitative t r e a t m e n t is i m p o r t a n t a n d s h o u l d be pursued; w i t h o u t it the c u r r e n t gap b e t w e e n e n v i r o n m e n t a l physiologists a n d the p e o p l e who m a n a g e forests is likely to r e m a i n u n b r i d g e d . At various points in the b o o k we use the word "sustainability," or sustainable m a n a g e m e n t . We define sustainable m a n a g e m e n t of forest ecosystems as m a n a g e m e n t in a way that is likely to m a i n t a i n or improve species diversity a n d structure, a n d the f u n c t i o n i n g a n d biological productivity of the ecosystem, for the f o r e s e e a b l e future. We do not a c c e p t that e c o n o m i c c o n s i d e r a t i o n s provide a legitimate basis for evaluating ecosystem sustainability per se, a l t h o u g h they may have to be taken into a c c o u n t w h e n evaluating w h e t h e r the ( h u m a n ) c o m m u n i t y considers the cost of m a n a g i n g for sustainability to be worthwhile. We note also, for use in later discussions in this c h a p t e r , the distinction b e t w e e n "conservation" a n d "preservation." C o n s e r v a t i o n of forests is taken to m e a n that s o m e utilization of the r e s o u r c e , u n d e r a suitable m a n a g e m e n t plan, is a c c e p t a b l e . P r e s e r v a t i o n does n o t allow for utilization.

L Forest Management: Levels, Decisions, and Influences

I. Forest Management: Levels, Decisions, and Influences M a n a g e m e n t involves m a k i n g decisions, a n d taking actions, with the int e n t i o n of achieving s o m e specified result or goal. T h e decisions t a k e n are m o r e likely to lead to the d e s i r e d results, or a c h i e v e m e n t of the goal, if they are b a s e d on s o u n d k n o w l e d g e a b o u t the ( e c o ) s y t e m b e i n g mana g e d a n d the way it will r e s p o n d to c h a n g e . M a n a g e m e n t actions t a k e n w i t h o u t the ability to p r e d i c t the way the system is likely to r e a c t to the action are n o t h i n g m o r e t h a n guesswork. A decision n o t to i n t e r v e n e in any way is as m u c h a p a r t of m a n a g e m e n t (e.g., in w i l d e r n e s s m a n a g e m e n t ) as a decision to do s o m e t h i n g to the forest. T h e "no a c t i o n " option will o f t e n o c c u r by default, b u t it may also be b a s e d on s o m e assessm e n t of the f u t u r e p a t t e r n of forest growth, a s s u m i n g e n v i r o n m e n t a l c o n d i t i o n s r e m a i n , on average, a b o u t the same as in the past. A simple e x a m p l e of m a n a g e m e n t a c t i o n in forestry is the p r a c t i c e of thinning. Stands are t h i n n e d b e c a u s e the forest m a n a g e r believes that t h i n n i n g will lead to s o m e desirable results, such as faster g r o w t h of the trees left b e h i n d or b e t t e r quality w o o d f r o m those trees. T h e r e will und o u b t e d l y be all sorts of c o n s e q u e n c e s as a result of t h i n n i n g . T h e s e will i n c l u d e effects on f u t u r e w o o d p r o d u c t i o n , with e c o n o m i c i m p l i c a t i o n s a n d c o n s e q u e n c e s for o t h e r stands, a n d a r a n g e of effects o n the ecology of the t h i n n e d stand a n d its hydrology. T h e i m p l i c a t i o n s may be wide r a n g i n g if the stand is in a w a t e r - p r o d u c i n g c a t c h m e n t . T h e r e f o r e , it is i m p o r t a n t that the m a n a g e r ' s assessment of the likely results of the decision to thin s h o u l d be as a c c u r a t e as possible, a n d that t h e r e s h o u l d be a s o u n d a p p r e c i a t i o n of the p r o b a b l e c o n s e q u e n c e s of the action. In this case, a n d in m a n y o t h e r cases that we will outline, s o u n d u n d e r s t a n d i n g of the effects of m a n a g e m e n t actions on f o r e s t / e n v i r o n m e n t i n t e r a c t i o n s at the physical level, a n d on the physiological processes a n d processes that affect physiology such as water relations a n d n u t r i e n t release a n d uptake, m u s t be of i m m e n s e value. Forest m a n a g e m e n t is c a r r i e d o u t at a n u m b e r of levels. We may r e g a r d the stand as the lowest level or u n i t subject to decision m a k i n g . T h e block, or g r o u p of c o n t i g u o u s b u t n o t necessarily similar stands, is the n e x t level. A r e g i o n c o n t a i n i n g various forest types is the next, the national forest estate the next, a n d the forests of the globe are the h i g h e s t level. Clearly, different p e o p l e are r e s p o n s i b l e for decisions a b o u t forests at these various levels, a n d the types of decision they m a k e will vary f r o m o p e r a t i o n a l decisions at the stand a n d block level to policy decisions at r e g i o n a l a n d n a t i o n a l level. Forest blocks may be o w n e d by, or leased to, forestry c o m p a n i e s , or m a n a g e d by the state. 2 In the case of c o m p a n i e s 2The word "state" is used here in a generic sense, meaning the institution (s) of government. It does not mean state as opposed to federal, as in the American or Australian contexts.

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c o n c e r n e d with w o o d p r o d u c t i o n , the p r i m a r y considerations will tend to be economic, a l t h o u g h these will be modified by g o v e r n m e n t policy and the extent to which the c o m p a n i e s are inclined to behave in a responsible m a n n e r in terms of ecological sustainability. This will vary from country to country and is often influenced by w h e t h e r the companies are local or international. M a n a g e m e n t policies at regional or national levels are likely to be d e t e r m i n e d by legislation or regulation arising from social and political considerations. At the global level, we can only h o p e for agreements that forests will be m a n a g e d on a sustainable basis in as many countries as possible. A person responsible for the m a n a g e m e n t of stands, which we define as m o d e r a t e l y h o m o g e n e o u s areas of forest of the same age and general characteristics, may be r e q u i r e d to make or advise on a series of practical decisions that include matters such as thinning, fertilization, clear-cutting and reestablishment procedures, and weed or pest control. Areas of forest that should n o t be harvested must be identified and access roads constructed and maintained. P l a n n n i n g wood supplies and cutting schedules requires i n f o r m a t i o n a b o u t how m u c h timber is in the forests and how fast it can be expected to regrow after harvesting. Decisions a b o u t the use of various parts of the forest obviously require knowledge a b o u t the type of vegetation in the forest, including the d o m i n a n t tree species, c o n d i t i o n and stage of stand development, knowledge a b o u t the soils and their characteristics, and a b o u t the way various plant communities and forest types are likely to r e s p o n d to disturbance. In the case of those who manage forests at the stand and block level, knowledge about what is in the forest tends to come from direct observation and experience, translated into local maps and charts; at higher levels, good data bases containing information about forest types and condition become essential m a n a g e m e n t tools (see C h a p t e r 10). Those who m a n a g e a large n u m b e r of forests for companies or gove r n m e n t s (state forests) make different kinds of decision. In a woodp r o d u c i n g context, they are c o n c e r n e d with wood f l o w s - - t h e steady supply of wood to mills or contracted customers that must be maintained over long periods. This requires knowledge of the standing crop in all the areas u n d e r their control and the d e v e l o p m e n t of harvesting schedules that will maintain supplies both in the short and long term. Ensuring long-term supply requires knowledge of projected growth rates on a range of sites, a central issue and p r o b l e m in p r o d u c t i o n forestry and one to which e n v i r o n m e n t a l physiology and the m o d e l i n g procedures arising from it have much to offer. We discuss this question in more detail in later chapters (see, particularly, C h a p t e r 9). In many parts of the world, m a n a g e m e n t for wood p r o d u c t i o n has consisted of deciding on areas to be clear-cut or selectively logged in the

L Forest Management: Levels, Decisions, and Influences

5

m o s t e c o n o m i c a l l y efficient m a n n e r . A r g u m e n t s a b o u t clear-cutting a n d its c o n s e q u e n c e s , a n d a b o u t the sustainable m a n a g e m e n t systems for natural forests, rage in countries such as the U n i t e d States, C a n a d a a n d Australia. I n c r e a s e d e n v i r o n m e n t a l awareness within large sectors of society in the so-called d e v e l o p e d countries has led to i n c r e a s e d pressures to reduce destructive clear-cutting a n d m a n a g e n a t u r a l forests in ways that maintain aesthetic a n d r e c r e a t i o n a l values as well as biodiversity. Many natural forests are now m a n a g e d for m u l t i p l e use. This may i n c l u d e access to forests for r e c r e a t i o n a l p u r p o s e s m f o r hiking, riding, c a m p i n g , and, in some cases, h u n t i n g . T h o s e who use the forests in this way dem a n d that they are (or at least a p p e a r to be) unspoilt. People seeking r e c r e a t i o n t e n d to react unfavorably to the o r d e r e d rows of plantations or to the spectacular d a m a g e associated with c l e a r - c u t t i n g m a t least as it appears a s h o r t time after the event. They want to see large old trees, diverse u n d e r s t o r y growth, a n d a r a n g e of a n i m a l a n d bird life. It is also r e c o g n i z e d that forested c a t c h m e n t s are sources of clean water, a l t h o u g h the water yield may n o t be as h i g h as if the c a t c h m e n t s were cleared or t h i n n e d (see C h a p t e r 4). Obviously, the simplest m e t h o d of m a n a g e m e n t to m e e t these r e q u i r e m e n t s is to leave the forests a l o n e m t o accept that they s h o u l d n o t be utilized for w o o d p r o d u c t i o n . This may have significant e c o n o m i c implications, at least locally, a n d decisions to preserve forests in this way have been, a n d are, the cause of m u c h debate a n d controversy in various parts of the world. Even w h e r e the decision is m a d e to preserve forests, controversy is likely to arise a b o u t t h e i r m a n a g e m e n t in relation to matters such as b u r n i n g policy. In Australia, t h e r e are great debates a b o u t w h e t h e r to b u r n deliberately, at intervals, to avoid the massive wildfires that o c c u r if fuel is left to a c c u m u l a t e for long periods, or w h e t h e r to accept the natural cycle of fire. T h e U n i t e d States is n o t imm u n e to similar debates; the c o n s e q u e n c e s of "no b u r n " policies were evident in the massive fires in Yellowstone N a t i o n a l Park in 1988. T h e r e s o l u t i o n of debates a b o u t m a n a g e m e n t systems for n a t u r a l forests d e p e n d s o n a m u l t i t u d e of biophysical, ecological, sociological, political, and e c o n o m i c factors, which vary f r o m place to place. We c a n n o t even begin to c o n s i d e r t h e m here. However, we n o t e that it is possible to m a n a g e forests for w o o d p r o d u c t i o n a n d still c o n s e r v e m o s t of the characteristics d e m a n d e d by those w h o see their p r i m a r y values as aesthetic a n d ecological. Practices that t e n d to m i n i m i z e the i m p a c t of w o o d ext r a c t i o n f r o m forests include careful selective logging, restricting clearcutting to small areas, avoiding logging on steep hillsides, a n d the dev e l o p m e n t of suitable, " e n v i r o n m e n t a l l y friendly" m a c h i n e r y . It is also essential to p r e s e r v e a d e q u a t e p r o t e c t i v e strips along stream banks a n d leave u n t o u c h e d the rich, biologically diverse areas that o c c u r in gulleys. Careful use of seed trees, sympathetic r o a d e n g i n e e r i n g , a n d careful con-

6

1. Introduction

sideration of the structure and diversity of the forest(s) as a whole are also important. Clearly, multiple-use m a n a g e m e n t is likely to be more complex than if wood p r o d u c t i o n were the sole objective, but the forest m a n a g e m e n t p r o c e d u r e s followed in Swedish and Finnish forests, which have a long history of being m a n i p u l a t e d by humans, show that ecologically sympathetic and economically viable multiple-use m a n a g e m e n t is perfectly feasible. Many of the principles discussed in this book are relevant to multiple-use forest m a n a g e m e n t and need to be u n d e r s t o o d if it is to be practiced successfully. In some countries, plantation forestry is being p r o m o t e d as a substitute for logging natural forests. This may not be an advantage if native forests are being cleared in o r d e r to establish the plantations, and it may also be u n p o p u l a r with long-established forest industries, because it is not only necessary to invest considerable capital to establish plantations but, in the case of hardwoods, plantation-grown trees tend to have wood properties different from those of the wood of old trees. This leads to the need for changes in the technologies used for wood p r o c e s s i n g m whether sawing, pulping, or the p r o d u c t i o n of products such as chip and fiber boards. In many u n d e v e l o p e d regions and countries, social pressures are different. The d e m a n d for high-quality h a r d w o o d is a major factor driving the exploitation of forests in southeast Asia and the Pacific region. There, and in South America and Africa, high and rapidly growing human populations lead to strong pressures for land so that forests are cleared piecemeal by peasants practicing slash and burn agriculture. This practice may not adversely affect forest ecosystems where the areas affected are relatively small and they are allowed adequate time to recover. However, rapidly increasing h u m a n populations in these regions have increased both the areas affected and the frequency with which they are revisited. Access to the forests is often facilitated by roads cut by logging companies that extract the large, high-value tropical hardwoods. The n u m b e r of trees removed may be relatively s m a l l - - p e r h a p s 10-20 per h e c t a r e m b u t there is a high potential for serious damage to the ecosystems unless considerable care is exercised, including care in road construction, the disposal of residues, and the use of machinery in the forests. Unfortunately, for a variety of reasons, ranging from official corruption and the d o m i n a n c e of the economic motive to the lack of adequate, or adequately enforced, regulations, this care is lacking in many cases. The voracious apetite of the m o d e r n world for wood products, particularly p a p e r and packaging and disposable products such as paper napkins, tissues, diapers, and geriatric hygeine products, as well as the more "traditional" uses such as wood for building and furniture making, ensures that the e c o n o m i c pressures for forest products will remain high,

I. Forest Management: Levels, Decisions, and Influences

7

and increase, in the foreseeable future. These d e m a n d s for wood products also ensure that clashes will continue between those c o n c e r n e d primarily with goals such as short-term profit and the current e c o n o m i c welfare of a c o m p a n y or even a society, and those c o n c e r n e d with the longer-term goals of maintaining the integrity of forests and their n o n c o m mercial (in the short term) values. Such clashes are i m p o r t a n t in the political arena, where forest policy and regulation are, eventually, d e t e r m i n e d . At political levels, forest policy and the regulations governing forest m a n a g e m e n t are d e t e r m i n e d by all the usual pressures b r o u g h t on politicians by a range of interest groups. Commercial interests have, particularly in the past, t e n d e d to favor exploitive m a n a g e m e n t - - w o o d extraction with very little effort to ensure that r e g e n e r a t i o n was successful or that cutting rates were sustainable. This a p p r o a c h was e n c o u r a g e d by the illusion that natural forests were so extensive that areas d a m a g e d or even destroyed would not make any significant difference or cause shortages of wood. It was also e n c o u r a g e d by the lack of knowledge of forest ecosystems and lack of appreciation of the consequences of major disturbances to forests. These views are now changing rapidly, a l t h o u g h there is still resistance in the commercial world to expensive m a n a g e m e n t practices designed to minimize damage and sustain and conserve the forest estate. This translates into political lobbying, made m o r e effective by the difficulty of obtaining g o o d quality data on forests: What area of particular types exists; what is the volume of wood and what species are present in any particular area; and how will the forest r e s p o n d to logging, fire, fertilization, etc.? It is always possible to interpret poor-quality data in different ways and use t h e m to s u p p o r t particular arguments. T h e r e f o r e , if lobby groups are so inclined they can often present a strong case for the commercial exploitation of forests, while arguing that the m a n a g e m e n t practices they intend to follow will not cause any long-term damage. Political pressure in the o t h e r direction comes from conservationist groups and those who are aware of the long-term d a m a g e and loss of biodiversity b r o u g h t a b o u t by practices such as clear-cutting large areas, cutting riparian forest, b u r n i n g the residues from logging, using heavy, steel-tracked m a c h i n e r y u n d e r wet conditions, and so on. The responses of governments to such lobby groups d e p e n d on the effectiveness of their arguments and the degree of public s u p p o r t the g r o u p s can muster. S u p p o r t for conservation and preservation is invariably h i g h e r in develo p e d than developing countries: Such matters tend to be of m o r e concern and interest to people for w h o m the essentials for a comfortable life are readily available. In countries where high populations and poorly structured and poorly developed economies make it difficult for large p r o p o r t i o n s of the p o p u l a t i o n to reach and maintain acceptable standards of living, forest conservation and preservation tend to be low on

8

1. Introduction

the a g e n d a of most people, unless they h a p p e n to be d e p e n d e n t on the forests for their livelihood, as is the case with some groups of native peoples in South America a n d southeast Asia. It is this lack of public concern, c o u p l e d with e c o n o m i c incentives, that leads to c o r r u p t i o n and exploitation by c o m m e r c i a l interests, often based outside the countries that contain the forests. Politicians can be induced, by bribery and payments of doubtful legality, to provide licenses and concessions for the extraction of high-value wood from large tracts of forest without concern for the m a n n e r of extraction and the c o n s e q u e n c e s of the resulting damage. This is h a p p e n i n g in a n u m b e r of countries of southeast Asia, the Pacific island nations, the C o m m o n w e a l t h of I n d e p e n d e n t States (previously the Soviet Union), and in South America. O n e of the m a j o r sources of a r g u m e n t a n d c o n c e r n relating to forests, as well as to a range of o t h e r facets of h u m a n life, is the question of global climate change. Increased emissions of g r e e n h o u s e gases (CO 2, NOx, CH4, etc.) are p r e d i c t e d to increase the average global air temperature by 1.5-4.5~ a l t h o u g h the increase is not e x p e c t e d to be uniform. High-latitude regions are e x p e c t e d to e x p e r i e n c e the greatest change in t e m p e r a t u r e , whereas the tropics may e x p e r i e n c e little or no change (IPCC, 1995). Precipitation patterns are also e x p e c t e d to change, but predictions a b o u t these are less certain than those relating to temperature. Forests are a significant c o m p o n e n t of the global carbon balance (see C h a p t e r 5), both because they store large quantities of carbon and because actively growing forests are significant sinks for c a r b o n - - t h e y absorb and store more carbon than they emit, and therefore are considered, by some, to be i m p o r t a n t as a m e a n s of mitigating or even halting the rise in a t m o s p h e r i c CO~. In fact, the area of new forests that would be n e e d e d to make a significant difference to the rising atmospheric CO 2 concentrations would be e n o r m o u s and completely impractical as a solution to the p r o b l e m . However, the increasing CO~ concentrations may result in m o r e rapid growth of young trees if o t h e r resources, particularly nutrients, are not limiting. We do not claim that knowledge of the effects of the e n v i r o n m e n t on the physiological processes that govern the growth of trees, and hence the growth a n d responses of forests, and of the interactions and feedbacks between forests and the e n v i r o n m e n t is likely to be used directly in developing policies a b o u t forest m a n a g e m e n t or in making decisions a b o u t how to m a n a g e forests. The previous discussion leads to the conclusion that social and e c o n o m i c considerations are the p r i m a r y drivers in these areas. However, we are convinced that better policy and decision making, a n d i m p r o v e d ability to develop sustainable m a n a g e m e n t systems and to evaluate the sustainability of existing sytems, will come from an improved, and steadily improving, capability to predict the productiv-

II. Overview

9

ity of forests in contrasting environments. Progress will c o m e f r o m the i m p r o v e d ability of scientists to provide reliable, supportable, a n d relevant i n f o r m a t i o n to bureaucrats a n d politicians, a n d f r o m i m p r o v e d ability to m o d e l and p r e d i c t the results of particular strategies or to assess the realism of m a n a g e m e n t goals. We recognize that j u d g m e n t s a b o u t the relative value of short-term e c o n o m i c benefits a n d long-term forest sustainability a n d unquantifiable aesthetic values will, unavoidably, rem a i n subjective. However, if scientists can evaluate in quantitative terms the likely impacts of disputed practices, they can provide a rational r a t h e r than an e m o t i o n a l basis for settling a r g u m e n t s a b o u t those practices. T h e r e m a i n d e r of this c h a p t e r provides an overview of the subject matter covered in this book, with c o m m e n t s on the relevance to m a n a g e m e n t of the various subject areas. T h e subsections are not c h a p t e r summaries but are i n t e n d e d to indicate the m a i n ideas u n d e r l y i n g the detailed treatments in the chapters.

II. O v e r v i e w A. Chapter 2: Forest B i o m e s of the World T h e b r o a d e s t vegetation classification system involves vegetation units of similar p h y s i o g n o m y or a p p e a r a n c e known as biomes. C o m m o n biomes include deserts, grasslands, tropical forests, etc. T h e n u m b e r of forest biomes in the world d e p e n d s on the criteria used to identify ecosystems. Within any particular biome, t h e r e is likely to be considerable variation in ecosystems, b r o u g h t a b o u t by differences in topography, soils, a n d local differences in precipitation a m o u n t s a n d patterns, so that any "lumped," or large-scale g r o u p i n g will e n c o m p a s s a n u m b e r of smaller ecosystems that are c o h e r e n t a n d readily identifiable b o t h in terms of their structure a n d function. Nevertheless, it is necessary to decide on some sort of classification that allows general statements a b o u t the properties of forest ecosystems, the f u n c t i o n i n g of i m p o r t a n t processes in different forest ecosystems, and the way they are likely to react to disturbance. We have t h e r e f o r e used a classification based on leaf habit (everg r e e n or d e c i d u o u s ) , leaf m o r p h o l o g y (needle leaved or b r o a d leaved), a n d climate (boreal, t e m p e r a t e , or tropical). T h e g e n e r a l characteristics of these forest biomes, a n d the climatic patterns characteristic of each region, are o u t l i n e d in C h a p t e r 2, which is i n t e n d e d to provide b a c k g r o u n d a n d a framework for the rest of the book, where principles a n d processes are discussed in general terms. T h e principles a n d processes apply to all forests, but their i m p o r t a n c e will vary a m o n g forest b i o m e s and ecosystems within a biome. Figure 2.2 is a m a p of the global distribution of biorues and Table 2.1 contains estimates of annual net p r i m a r y productivity

10

1. Introduction

(see C h a p t e r 5) for the major forest biomes. Clearly, the application of the principles of e n v i r o n m e n t a l physiological analysis to m a n a g e m e n t p r o b l e m s in any forest ecosystem will d e p e n d on m o r e detailed knowledge a b o u t the area of c o n c e r n than can be provided here, but these data give an indication of what we can expect, and some basis for considering p r o b l e m s at the level of the global c a r b o n cycle, and global estimates of forest productivity.

B. Chapter 3: Canopy Architecture and Micro climate This c h a p t e r on a r c h i t e c t u r e and microclimate is essential b a c k g r o u n d for the rest of the book. T h e way a stand interacts with its e n v i r o n m e n t is d e t e r m i n e d by the a m o u n t of foliage (leaf area) it carries and by the stand a r c h i t e c t u r e - - t h e way the b r a n c h e s and foliage are arranged and displayed. These p a r a m e t e r s d e t e r m i n e the absorption of radiant energy and m o m e n t u m . The physical principles a n d the p r o c e d u r e s discussed in C h a p t e r 3 provide the basis for calculations of CO 2 u p t a k e by canopies, a n d h e n c e for all the carbon balance calculations, and for calculation of the rates of water loss from canopies, a n d h e n c e for all the calculations necessary to evaluate the hydrological implications of m a n a g e m e n t actions or climatic conditions. T h e c a r b o n balance models presented later in the book ( C h a p t e r 9) all d e p e n d on some form of radiation absorption calculation, discussed in this chapter. Knowledge of the energy balance and e x c h a n g e processes is essential for calculating water use rates by stands, and h e n c e stand water balance and the hydrological implications of stand structure. Knowledge of radiation p e n e t r a t i o n and air flow patterns is valuable for assessing the effects of practices such as thinning and the p r o b a b l e influence of gaps on the ecology of forest stands, including u n d e r s t o r e y dynamics. Few m a n a g e r s will use the principles and equations in this c h a p t e r directly, but it is i m p o r t a n t that professional foresters and forest ecologists be familiar with t h e m because they provide the u n d e r p i n n i n g for so many models that m a n a g e r s may use or that may be used in the g e n e r a t i o n of m a n a g e m e n t advice.

C. Chapter 4: Forest Hydrology and Tree-Water Relations Water is essential for tree growth and it is also a m o n g the i m p o r t a n t commodities o b t a i n e d from forests. Anyone c o n c e r n e d with forest managem e n t on large scales is likely to be c o n c e r n e d with the water yields of catchments, which are often a source of debate and dissension between c o m p e t i n g interests. The c h a r a c t e r of the debate will d e p e n d on the priorities placed on a d e q u a t e water and the cleanliness of the water supply. T h e idea of crystal streams from forested catchments is e m b e d d e d in the folklore of western peoples and tends to be synonymous with the concept of u n s p o i l e d environments. It is i n d e e d true that streams and rivers

II. Overview

11

from forested catchments are usually clean, but it is less generally appreciated that the water yields from such catchments are, in most cases, lower than the water yields from, for example, catchments u n d e r grassland for the same a m o u n t of precipitation. The hydrological consequences of thinning or clear-cutting tree stands on catchments are therefore likely to be increased water yield, but this may be a c c o m p a n i e d by increased soil erosion and increased quantities of nutrients in the streams. The actual effects in any particular case will d e p e n d on the biogeochemical characteristics of the area and the rates of plant regrowth. The principles of catchment hydrology and stand water balance calculations are developed, in this chapter, from the basic hydrological (mass balance) equation, with relatively detailed treatments of canopy interception, soil water-holding characteristics, and transpiration losses. Results from catchment hydrology experiments are reviewed, and the last part of the chapter deals with tree water relations and their effects on growth. Forest managers at the s t a n d / b l o c k level, c o n c e r n e d with wood production, will be aware of the i m p o r t a n c e of the soil water balance for two reasons: It is generally inadvisable to carry out forest operations w h e n soil is wet, and p r o l o n g e d periods of d r o u g h t will reduce the growth of trees. In neither case, it might be argued, are managers likely to calculate water balances; they will generally be g u i d e d by experience and, in any case, there is n o t h i n g that can be d o n e a b o u t p r o l o n g e d dry periods. In fact, research in at least one Australian state (and probably elsewhere) has resulted in guidelines for the soil wetness safe for forest operations. These are issued in terms of rainfall within a p e r i o d for particular soil types: If there is more that a specified a m o u n t t h e n the soil will be too wet for safe (in terms of damage, c o m p a c t i o n , etc.) operations and work with machines in the forest is prohibited. A l t h o u g h expressed in crude "rule of t h u m b " terms, these guidelines were derived from studies in which soil water balances were p r o p e r l y calculated. T h e r e is n o t h i n g that can be d o n e a b o u t d r o u g h t in relation to established trees, but it is i m p o r t a n t to have some appreciation of d r o u g h t probabilities when new plantations are being established and in relation to the probability of success of reestablishment of stands after harvest or fire. Few managers (or scientists) calculate d r o u g h t probabilities, which d e p e n d on precipitation patterns and probabilities, soil water-holding characteristics, and rates of water loss by evapotranspiration. The procedures can be derived directly from the principles described in C h a p t e r 4. Very briefly, if the soil can hold (0 9z) m m of water within the r o o t zone (where 0 is volumetric water content a n d z is the rooting d e p t h ) , and the average rate of evapotranspiration over a p e r i o d At is E t m m / d a y , t h e n the time scale for adequate water for growth is a b o u t (0 9z ) / E t days. T h e probability of d r o u g h t is derived from analysis of long-term precipitation

12

1. Introduction

records, from which the cumulative probability of periods longer than (0. z)let, without significant precipitation, can be calculated. Rooting depths will vary with age and type of stand as well as soil type; clearly, the rooting d e p t h used for calculations relating to seedling establishment will be very different from that used for mature trees. Decisions about what constitutes "significant" rainfall will be made by the analyst. The probability of d r o u g h t will (presumably) influence m a n a g e m e n t decisions about plantation establishment and the timing of intervention in stand reestablishment. D. Chapter 5: Carbon Balance of Forests

The carbon balance of forests at any time is the net result of carbon uptake by photosynthesis and losses by respiration. Standing biomass is the net result of photosynthesis, autotrophic respiration, litterfall, fine root turnover, and coarse root mortality (if any). It is i m p o r t a n t to distinguish between net primary productivity ( N P P ) - - g e n e r a l l y taken as the difference between photosynthesis and autotrophic r e s p i r a t i o n m a n d net ecosystem productivity, which includes h e t e r o t r o p h i c respiration. Chapter 5 deals with the process of photosynthesis at leaf and canopy (stand) level, with respiration, carbon balance, and carbon allocation, and includes m o d e r n data on net carbon fluxes to (and from) a range of forest ecosystems. Forest m a n a g e m e n t for wood p r o d u c t i o n is essentially a matter of manipulating stands to optimize the harvestable yield, which entails maximizing the p r o d u c t i o n of carbon and its allocation to usefifl product, i.e., tree stems. O u r ability to calculate the carbon balance of stands is improving rapidly (see C h a p t e r 9), but our ability to predict carbon allocation within trees (to stems, branches, leaves, and roots) is still hamp e r e d by incomplete u n d e r s t a n d i n g of physiological controls on carbohydrate allocation. One of the areas in which most progress has been made in recent years has been in our u n d e r s t a n d i n g of the interactions between carbon fixation and nutrient dynamics. Laboratory measurements of leaf photosynthesis rates generally indicate that m a x i m u m leaf photosynthesis rates are strongly related to leaf nitrogen (N) concentrations. This has led to the a s s u m p t i o n m s u p p o r t e d by model analysismthat growth, in terms of dry mass p r o d u c t i o n , will be related to leaf N concentrations. (In fact, this has proved difficult to demonstrate in field experiments on conifer forests.) Nitrogen availability has a major effect on foliage mass and area and influences carbon fixed because increased foliage area results in greater radiation absorption and hence greater carbon fixation. However, it also results in m o r e transpiration and hence tends to lead to adverse water balance, which appears to cause greater allocation of carbo-

II. Overview

13

hydrates to roots. T h e r e is strong f e e d b a c k b e t w e e n the n i t r o g e n in foliage, the rate of d e c o m p o s i t i o n of litter, and the release a n d availability of N in the soil for u p t a k e by the trees. This may be a m a t t e r of considerable i m p o r t a n c e in relation to the long-term c a r b o n balance of forests, particularly in relation to rising a t m o s p h e r i c CO 2 concentrations; some m o d e l analyses indicate that the long-term effect of these rises on growth may be small because of limitations i m p o s e d by the availability of N. F r o m the m a n a g e m e n t point of view, the n e e d to be able to p r e d i c t the c a r b o n balance of stands, and the p a t t e r n of allocation of the carbon, r e m a i n s a central p r o b l e m , w h e t h e r the m a n a g e r is c o n c e r n e d with stand productivity or the carbon balance of ecosystems or is a policy maker, politician, or b u r e a c r a t c o n c e r n e d with c a r b o n fixation by forests a n d p r e d i c t i o n of the global c a r b o n balance. T h e p r o b l e m can only be solved by ecophysiological research.

E. Chapter 6: Soil Organic Matter and Decomposition Soil organic matter, its structure a n d rate of d e c o m p o s i t i o n , exerts strong control over the availability of some nutrients for u p t a k e by plants that, in turn, affects the health and sustainability of the forest ecosystem. Sustainable productivity d e p e n d s on the m a i n t e n a n c e of the soil organic m a t t e r and the balance between rates of d e c o m p o s i t i o n a n d n u t r i e n t supply and forest growth. T h e quality a n d q u a n t i t y of organic m a t t e r dep e n d on the type of l i t t e r f a l l - - w h i c h is affected by n u m e r o u s ecological f a c t o r s m a n d the m a n a g e m e n t of the forest. T h e rate of d e c o m p o s i t i o n of the material d e p e n d s on t e m p e r a t u r e a n d m o i s t u r e conditions. As a general rule, large material d e c o m p o s e s m o r e slowly t h a n fine material and, quite obviously, organic material does n o t d e c o m p o s e w h e n it is frozen or very dry. In some systems, such as boreal forests, the rate of organic matter a c c u m u l a t i o n in the soil exceeds the rate of decomposition so that over long periods there is a c c u m u l a t i o n in the soil: T h e organic matter layer in the boreal forest regions is e s t i m a t e d to contain billions of tons of carbon. In o t h e r forest biomes, notably lowland tropical forests, where high t e m p e r a t u r e s a n d continuously wet soil cause high rates of d e c o m p o s i t i o n , there is very little, if any, organic m a t t e r accumulation. T h e rate at which nutrients b e c o m e available to plant roots dep e n d s on the rate of organic m a t t e r d e c o m p o s i t i o n . M a n a g e m e n t can have direct effects on soil organic matter. Any harvesting operation, w h e t h e r t h i n n i n g or clear-cutting, results in the addition of large a m o u n t s of organic m a t t e r to the soil, including material that may take a long time to d e c o m p o s e . T h e d i s t u r b a n c e of the soil may cause increased aeration or c o m p a c t i o n . T e m p e r a t u r e a n d soil water relations are altered. T h e r e have b e e n cases in which site-preparation practices have h a d such massive effects on soil organic m a t t e r that the sus-

14

1. Introduction

tainability of the forestry enterprise was threatened: one of these was in the state of South Australia where, for m a n y years t h r o u g h the 1950s, 1960s, a n d 1970s, the s t a n d a r d practice, after harvesting P i n u s r a d i a t a plantations, was to push the debris into windrows and b u r n it. The soils in the region are sandy a n d low in organic matter and the result was declining growth and yield (Keeves, 1966; Woods, 1976) until the p r o b l e m was r e c o g n i z e d a n d rectified. W h e r e fire is used as a forest m a n a g e m e n t tool, it can affect organic m a t t e r content a n d distribution.

F. Chapter 7: Nutrient Distribution and Cycling T h e objective of this c h a p t e r is to present an outline of current knowledge a b o u t forest nutrition. It deals with the macro- and micronutrients and their occurrence, availability, and role in the growth of trees. Man i p u l a t i o n of nutrients has been, for m a n y years, a major target of forest m a n a g e r s at the stand level, particularly in the m a n a g e m e n t of plantation forests. T h e r e was a strongly held view, despite the paucity of convincing evidence to s u p p o r t it, that the nutrient status of trees could be d e t e r m i n e d and fertilizer regimes d e v e l o p e d on the basis of the concentration of nutrients in foliage. A great deal of research was carried out to try to d e t e r m i n e the o p t i m u m sampling and analysis p r o c e d u r e s and to try to establish correlations between foliage nutrient concentrations and growth. T h e p r o b l e m with this now largely discredited a p p r o a c h was that it i g n o r e d the fact that nutrients a n d the nutrient cycle are highly dynamic, b o t h in terms of the rates of release of nutrients from organic m a t t e r a n d the soil (by weathering) and the uptake and utilization of nutrients by the trees. T h e r e is significant internal cycling of nutrients, which vary in their mobility within the plant; the rates of m o v e m e n t are also d e p e n d e n t on the stage of stand d e v e l o p m e n t , the condition of the trees, a n d the season. Detritus p r o d u c t i o n and d e c o m p o s i t i o n represent a major pathway of nutrient supply for trees. T h e quality a n d quantity of detritus p r o d u c t i o n are affected by forest type, climate, soil fertility, and events such as storms and d r o u g h t . T h e r e is a c c u m u l a t i n g information d e m o n s t r a t i n g that changes in species composition, even removal of the understory by herbicides, alter detritus p r o d u c t i o n a n d nutrient cycling. H u m a n s can have t r e m e n d o u s i m p a c t on a n u m b e r of the nutrient cycling processes. Nutrients are lost by direct removal when forests are harvested as well as by leaching and in fires, and the c o m b i n a t i o n of unusual climatic events with such actions can lead to major changes in the nutrient balance of stands. It is i m p o r t a n t to be able to calculate nutrient budgets a n d to have some knowledge of rates of uptake and r e p l a c e m e n t so that m a n a g e m e n t practices are not only g e a r e d toward o p t i m u m productivity b u t also result in sustainable forest systems. Drastic changes in

II. Overview

15

the nutritional status of a stand are likely to lead not only to changes in the growth rates a n d productivity of species of interest for wood p r o d u c tion but may also lead to changes in species c o m p o s i t i o n and shifts in d o m i n a n c e patterns. Typically, w h e n we discuss nutrient cycling we are c o n c e r n e d a b o u t nutrient d e p l e t i o n . A t m o s p h e r i c d e p o s i t i o n of n i t r o g e n may initially cause increased forest growth rates, as we note in the outline of C h a p t e r 9. However, t h e r e is increasing evidence that excessive a m o u n t s of nutrients, in the f o r m of a t m o s p h e r i c deposition, can saturate the d e m a n d by m i c r o r g a n i s m s a n d vegetation, causing imbalances a n d system malfunction. Relatively high concentrations of nitrates, in particular, are prod u c e d from industrial effluents and d e p o s i t e d in precipitation: T h e adaptation of species to h i g h soil n i t r o g e n may vary considerably, and if the balance between available n i t r o g e n a n d o t h e r nutrients is heavily biased then we can expect to see changes in the c o m p o s i t i o n , structure, a n d function of forest ecosystems. Abiotic m i n e r a l i z a t i o n of n i t r o g e n is likely to be i m p o r t a n t in forests subject to large additions of n i t r o g e n in the form of fertilizer or as a result of a t m o s p h e r i c deposition.

G. Chapter 8: Changes in Ecosystem Structure and Function during Stand Development The species c o m p o s i t i o n and structure of forests are subject to continual change. Catastrophic natural disturbances, such as wildfires or hurricanes, or h u m a n - i n d u c e d disturbances such as clear-cutting, destroy forests so the process of d e v e l o p m e n t m u s t start f r o m the beginning. During the d e v e l o p m e n t cycle there may be changes in species composition a n d stand functional characteristics. T h e patterns of c a r b o n a n d nutrient flow c h a n g e with the changes in forest structure. T h e r e is no g u a r a n t e e that regrowth forest will have the same species c o m p o s i t i o n as a forest that has b e e n destroyed. For e x a m p l e , the seeds of some Australian Eucalyptus species will n o t g e r m i n a t e unless they have b e e n subjected to fire, so it may h a p p e n that, if there is a large reservoir of such seeds in the soil of an area that has n o t b e e n b u r n t for m a n y years and is t h e n burnt, there will be massive g e r m i n a t i o n of the species in question. This can result in a virtually monospecific stand that is m u c h less diverse t h a n its predecessor. Serious insect d a m a g e , or disease, will also lead to alterations in forest succession cycles. In a d d i t i o n to e x o g e n o u s factors, there are e n d o g e n o u s factors associated with stand d e v e l o p m e n t a n d succession that alter species composition. Changes in stand structure affect forest microlimate, which in t u r n can affect n u t r i e n t a n d c a r b o n cycling a n d allocation. Most notable is the well-established decline in above g r o u n d n e t p r i m a r y p r o d u c t i o n with stand age. This may cause forest m a n a g e r s to harvest forests on s h o r t e r

16

1. Introduction

rotations than m i g h t otherwise be the case, leading to m o r e frequent site disturbance and possibly greater d e p l e t i o n of nutrients from the soil. Successional patterns d e p e n d on stand structure and the life cycles and growth patterns of the species in stands, as well as on such matters as nutrient cycling. These factors are reviewed in C h a p t e r 8.

H. Chapter 9: Ecosystem Process Models The complexity of forest ecosystems and the interactions with which we are c o n c e r n e d make it essential that we use models to describe, analyze, and u n d e r s t a n d them. It is possible to envisage qualitatively the flows and interactions of mass and energy in a forest ecosystem, but it is n o t possible to describe t h e m with any precision, to predict responses not observed, or to analyze observed responses to disturbance or change w i t h o u t using models. We are c o n c e r n e d primarily with process-based models, i.e., models that describe the system(s) u n d e r study in terms of the biophysical processes involved and the way those processes are affected by and interact with external conditions. Most of the work on such models in recent years has been c o n c e r n e d with water and carbon balance, b u t there is now an increasing t r e n d toward the development of models in which water, carbon, nutrients, and their interactions are central to the models. The focus in forest m o d e l i n g of the type with which we are c o n c e r n e d has been on the d e v e l o p m e n t of models as research tools, aimed at explaining e x p e r i m e n t a l observations or m e a s u r e m e n t s of forest processes such as the CO~ and water vapor fluxes that can be m e a s u r e d with modern e q u i p m e n t and that provide estimates of the net uptake of carbon, total water loss by forests, or nutrient removal. T h e r e have been few, if any, serious efforts to use process-based models as m a n a g e m e n t tools; they are usually too c o m p l e x and too highly p a r a m e t e r i z e d , the scientists who develop t h e m never have the time to provide the backup necessary to deal with the p r o b l e m s that will arise in practical use, and forest managers, if they are aware of them at all, consider t h e m too esoteric and impractical. In C h a p t e r 9, we consider the empirical models managers use, based on the d e v e l o p m e n t of growth curves from site indices, and review t h e m briefly. Present-day empirical growth models will u n d o u b t e d l y become less accurate as the c o n d i t i o n s u n d e r which they were developed change with the climate, with atmospheric pollution, and with the impact of forest m a n a g e m e n t practices on soil nutrient status. T h e r e are already cases in which forest m a n a g e r s have n o t e d that growth and yield tables routinely overestimate forest growth of young plantations; the discrepancy has been attributed to ozone. In Europe, the opposite seems to be happening, apparently as a result of n i t r o g e n "fertilization" by atmospheric de-

II. Overview

17

position and increasing atmospheric CO 2 concentrations. We consider that the concept of site index could be supplanted by m o d e r n models utilizing the principles of environmental physiology. We review in more detail some of the more important and widely used process-based models, grouping them in terms of the time scales across which they operate and the number and type of parameters they need. Several examples are provided demonstrating how process-based models can and should be used in ecosystem management.

I. Chapter 10: Applications of Modern Technology and Ecophysiology to Forest Management This chapter focuses on three areas of technology: models (discussed in Chapter 9), remote sensing, and Geographical Information Systems (GIS). All are already used in forestry to some extent and in various ways. The chapter includes a basic outline of remote sensing, with emphasis on satellite measurements and the information that can be obtained from these measurements. This includes mapping and stand classification, with information about canopy architecture and leaf area index. It also includes the possibility of obtaining estimates of the radiant energy absorbed by forest canopies and using these with models of the type we advocate to make estimates of forest growth rates. GIS is obviously an essential tool in this e x e r c i s e - - s u p e r i m p o s i n g radiation absorption on stands m a p p e d by a combination of remote sensing and g r o u n d mapping or aerial photography, and using weather data to drive and modify the model calculations, will yield spatial estimates of productivity and water relations that can be varied on the basis of sequential series of satellite measurements. The idea is not new, having been p i o n e e r e d by Steven Running (Running et al., 1989) who has emphasized global carbon balance and NPP modeling, but the approach has not been used at the level of stand and block m a n a g e m e n t and there remains great scope for rapid progress and further developments. We explore the possibilities and implications of the use of these techniques.

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2 Forest B i o m e s o f the World

In this chapter, we provide an overview of the m a i n forest biomes in the world, their characteristics and distribution, as b a c k g r o u n d a n d context for the m o r e detailed treatments of various aspects of f o r e s t / e n v i r o n m e n t interaction in the following chapters. Forest ecosystems are usually c o n s i d e r e d in terms of natural forests, and there is a t e n d e n c y to assume that these are u n d i s t u r b e d systems in equilibrium with their e n v i r o n m e n t s (Melillo et al., 1993). In fact, there are now few such forests in the world. Most have b e e n d i s t u r b e d by hum a n s - s o m e very s e v e r e l y m a n d the area of "pristine" forest of any type is relatively small. T h e largest areas of such forests p r o b a b l y exist in the boreal regions, a l t h o u g h even there logging a n d m i n i n g have affected significant sections of the forest ecosystems. Tropical f o r e s t s - - t h e largest b i o m e in the w o r l d m h a v e been, and are being, grossly disturbed. The reasons for forest disturbance vary. W o o d e x t r a c t i o n has always b e e n a p r i m a r y factor, but the major cause of deforestation in the tropics is p o p u l a t i o n pressure a n d growth. Myers (1996) provides data to show that farmers are responsible for a b o u t half the forest felling in T h i r d World countries, whereas the l u m b e r industry accounts for a b o u t oneq u a r t e r of forest d e s t r u c t i o n or degradation. H a l t i n g these processes is therefore largely a s o c i o e c o n o m i c p r o b l e m r a t h e r t h a n a technical one. Clearing for f a r m i n g has always b e e n a m a j o r cause of forest d e s t r u c t i o n in the t e m p e r a t e areas; the forests of E u r o p e were largely cleared in the Middle Ages. In N o r t h America, vast areas of t e m p e r a t e m i x e d a n d deciduous forests have b e e n cleared for f a r m l a n d (Delcourt a n d Harris, 1980) and there are few areas of t e m p e r a t e coniferous forest that have n o t b e e n exploited for wood p r o d u c t i o n . Tropical d e c i d u o u s forests, as we note later, have all b u t d i s a p p e a r e d from m u c h of their range because of burning, g r a z i n g - - w i t h its a c c o m p a n y i n g effects of seedling a n d understory destruction a n d associated e f f e c t s - - a n d clearing for farming. Any consideration of forest ecophysiology must, therefore, recognize the fact of disturbance a n d the fact that m a n y forests are m a n a g e d in some sense. The principles that we discuss in later chapters are applicable to any for19

20

2. Forest Biomes of the World

est ecosystem, but because we are c o n c e r n e d with their application in m a n a g e m e n t we provide here some general ideas about management and the consequences of manipulation and note the major differences between natural forests and plantations in terms of the actions taken in the course of managing them.

I. Management Management implies manipulation in some sense, so a managed forest may be defined as any forest that is, or has been, subject to some form of m a n a g e m e n t action, i.e., manipulation aimed at achieving some specified objective. Managed forests cover the range from plantations, where m a n a g e m e n t includes all silvicultural activities, through natural forests managed for sustainable wood production, to natural forests exploited for timber with little or no long-term perspective (Landsberg et al., 1995). Exploitation for timber without any attempt to manage the system in a sustainable way is arguably not management, but the line between such exploitation and constructive m a n a g e m e n t is often very fine. For example, natural forests in Australia have been logged for timber since settlement by Europeans in the 18th century. The practice did not initially include clear-cutting, but with the advent of the chain saw and mechanized heavy e q u i p m e n t clear-cutting has become widely practiced. It is opposed by conservationists but defended by the forestry industry on the grounds that the p r o c e d u r e results in the regeneration of "overmature" or partially degenerate forestsmwhich means forests of mixed age, with old trees that are degenerating from a timber production point of view. Clear-cutting results in the regeneration of even-aged stands, often with a species composition different from that of the original stand. Regenerating, even-aged stands, whether they result from clear-cutting or fire (which may follow clear-cutting), may involve loss of biodiversity, compared to old stands, because the seed stores in the soil, or transport of seeds from adjoining areas, can allow particular species well suited to take advantage of the changed conditions to become dominant in the stand. In any event, the result is a changed ecosystem (see Chapter 8). An even-aged, regenerating stand is similar, in many ways, to a plantation, and could be managed in the same way, particularly if early stage thinning was carried out to produce stem populations lower than those that might develop naturally. A major series of studies was carried out to examine the feasibility of intensively managing regrowth Eucalyptus stands in Australia (see Kerruish and Rawlins, 1991). It was concluded that regrowth can be profitably managed, mainly by appropriate thinning, to produce greater yields of commercially usable wood than unthinned stands. Regrowth can be used to substitute for, and therefore conserve,

II. PlantationForestry

21

older growth forests of high conservation value or to offset transfers of native forests to reserve status. Many countries and organizations now have forest m a n a g e m e n t plans to ensure the sustainability of natural forests: an excellent example of this is the publication A Richer Forest (1992), p r o d u c e d by the National Board of Forestry of Sweden. This book incorporates, in a clear and simple way, a great many of the principles of forest ecology that are n e e d e d for the successful m a n a g e m e n t of natural forests for multiple use. The U.S. Forest Service is moving away from the idea that the primary purpose of forest m a n a g e m e n t is wood p r o d u c t i o n to the view that the complexity of forest ecosystems must be recognized and the systems m a n a g e d so that the nonwood values of the forests are not lost. These values range from biodiversity to aesthetic values to the need to ensure that disturbance of forests in catchments does not cause serious disruption of the catchment's hydrological cycle (see Chapter 4).

II. Plantation Forestry A large p r o p o r t i o n of the timber products used in the world today, particularly pulpwood, but, increasingly, sawn timber products, come from plantations rather than from natural forests. For these purposes, plantations have many advantages over natural forests: they can be planted on p r e p a r e d land, using genetically improved and uniform seedlings at standardized spacings that allow o p t i m u m growth rates of the individual trees; and it is economically feasible to control weeds and use fertilizers to ameliorate problems of soil nutrition. Overall, the m a n a g e m e n t of plantations is m u c h more intensive than that of natural f o r e s t s m t h e objective is to achieve a specified p r o d u c t in the m i n i m u m possible time, and the procedures are essentially those used in crop production. The effects and implications of some of these procedures will be considered elsewhere in this book. With the exception of the widely grown Eucalyptus plantations, most plantations are softwoods, although the idea of introducing extensive hardwood plantations in the tropics is gaining ground. Wood from these plantations should substitute for wood from native forests and so reduce the impact of logging in the native forests. However, a great deal of research is necessary before the problems of managing tropical plantations to produce acceptable timber growth rates are solved. Eucalyptus are grown for pulp in Portugal, in Brazil--where they apparently achieve some of the highest forest growth rates in the w o r l d - - i n South Africa and other countries in Africa, and increasingly in countries such as China and India. Major softwood plantations a r o u n d the world include large areas of Sitka spruce (Picea sitchensis) in Britain, extensive plantations of

22

2. Forest Biomes of the World

loblolly and slash pine (P. taeda and P. elliottii, 1 respectively) in the southern United States, Douglas fir (Pseudotsuga menziesii) in the Pacific Northwestern United States and Canada, and P. radiata as the major softwood plantation species in Australia, New Zealand, Chile, and South Africa ( a l t h o u g h South Africa also uses several o t h e r softwood species). There are few plantations in n o r t h e r n Europe, but many stands of Scots pine (P. sylvestris) and Norway spruce (Picea abies) are intensively managed. It is interesting to note that plantation forestry is d o m i n a t e d by exo t i c s m t r e e s that are not native to the area where they are cultivated as plantations. Sitka spruce is native to the Pacific Northwest of the United States, where some of the trees grow to great size. Pinus elliottii is native to the southeastern United States, but P. radiata, now so widespread as a plantation species in the s o u t h e r n h e m i s p h e r e , is native to the Monterey Peninsula in southeastern California. Australia is the only c o n t i n e n t where Eucalyptus species are native, yet they are now grown in a b o u t 130 countries a r o u n d the world. In general, new species are i n t r o d u c e d to areas where the climate is similar to that of the region of origin, but it is a remarkable fact that many successful introduced species grow much faster in areas where they have been introduced than they do in their natural habitat. Some of the success of exotic trees as plantation species is attributed to the lack of their native pests in the countries where they have been introduced. This may contribute to the high p r o d u c t i o n rates, but it is not the only reason; factors such as better soils and cultural practices such as weed control, fertilization, and general silvicultural m a n a g e m e n t also contribute. It also does not follow that the range of climatic conditions b o u n d i n g the natural distribution of a species in its native habitat will necessarily restrict the areas where it can be grown when t r a n s p o r t e d to o t h e r regions. This has been neatly d e m o n s t r a t e d by Mitchell and Williams (1996), who f o u n d that the areas where Eucalyptus regnans grows and thrives in New Zealand do not coincide with the climatic regions to which the species is restricted in its native Australia.

III. Species Adaptations and Climatic Conditions A. Physiological Adaptations to Climate Recognition of the i m p o r t a n c e of climate in controlling the distribution of vegetation dates back almost two centuries to early plant geographers ( H u m b o l d t and Bonpland, 1805; H u m b o l d t , 1807; Schouw, 1823). Many of the early plant g e o g r a p h e r s u n d e r s t o o d that similar climates prod u c e d similar vegetation, a l t h o u g h cause and effect were not established. It was not until the late 19th century that S c h i m p e r (1898) recognized 1Pinus is, t h r o u g h o u t the b o o k , a b b r e v i a t e d to P. a n d Eucalyptus Io E. O t h e r g e n e r i c n a m e s are u s u a l l y w r i t t e n out.

III. Species Adaptations and Climatic Conditions

23

that the climatic control of the distribution of vegetation can only be explained in terms of basic physiological p r o c e s s e s - - a r g u a b l y marking the founding of the discipline of ecology now known as physiological ecology. Holdridge (1947, 1967) and Box (1981 ) demonstrated strong correlations between major physiognomic, or life form groups (i.e., desert, grasslands, deciduous or coniferous forests, etc.) and two climatic characterisitics--temperature and water availability. The distribution of the major forest biomes, and terrestrial biomes in general, is strongly influenced by climatic, geologic, ecologic, and anthropogenic factors, varying in importance across timescales ranging from historic (101_ 104 years) to geologic (> 104 years). Discussion of the physiological basis for the geographic distribution of the major forest biomes can be f o u n d in Box (1981), Walter (1979), and Woodward (1987). The importance of nutrient availability and environmental variables favorable for photosynthesis in determining the distribution of evergreen and deciduous forests is discussed in Chapters 7 and 8, respectively. T h e r e are, however, several critical m i n i m u m temperature thresholds that strongly affect key physiological processes, which in turn define environmental limits for the major forests biomes. These are discussed briefly here. Plants have evolved a n u m b e r of mechanisms to survive m i n i m u m temperatures less than + 10~ all of which are energy-demanding processes. Therefore, in many instances the gradual disappearance of a species near its altitudinal or latitudinal limit may be attributable more to the increasingly noncompetitive carbon balance than to damage directly related to low temperatures (Woodward, 1987, 1995). The first critical minimum temperature threshold ranges from - 1 to + 12~ and is related to chilling tolerance (Larcher and Bauer, 1981). Trees can survive chilling by maintaining a fluid membrane; failure to do so results in leaky membranes and, commonly, death. Raison et al. (1979) described a striking relationship between the m i n i m u m temperature at which the cell membranes change from fluid to gel state and the geographic range for a number of species, and Sakai and Weiser (1973) explained the distribution of North American tree species based on leaf and bud frost resistance. The next critical temperature is - 1 5 ~ and roughly corresponds to frost resistance. The frost resistance of leaves is m u c h lower for broadleaved than coniferous species, with a noticeable m i n i m u m threshold of -15~ (Woodward, 1987). The m i n i m u m temperature threshold for buds is similar for conifers and broad-leaved trees, but broad-leaved angiosperms are more likely to experience ice formation in the xylem during winter. This commonly results in the formation of air bubbles in the spring when the ice melts, thereby r e n d e r i n g the vessels nonfunctional (Becwar and Burke, 1982). The next critical temperature threshold is - 3 9 to -40~ which coincides with the temperature at which su-

24

2. Forest Biomes of the World

percooled, pure water nucleates spontaneously to form ice (Woodward, 1987). However, the buds of boreal tree species, such as Picea glauca, Larix laricina, and Larix sibirica, have been reported to survive temperatures b e l o w - 7 0 ~ (Sakai, 1979; 1983) and Rasmussen and Mackenzie (1972) demonstrated that by accumulating solutes in intercellular-free water, plants can lower the critical temperature of spontaneous nucleation below -40~ Boreal tree species (i.e, Picea, Abies, and Larix) at the extreme n o r t h e r n distribution often have a p a r e n c h y m a pith cavity beneath the crown of the primordial bud that prevents ice nucleation from spreading from the xylem to the bud (Sakai, 1983; Richards and Bliss, 1986). The inherent properties of the water-conducting architecture of trees may also influence their geographic distribution. The tracheid xylem of conifers is more resistant to, and has a greater potential for recovery from, ice-induced embolism than the conducting system of angiosperms (Borghetti et al., 1991, Sperry and Sullivan, 1992). Pit membranes, unique to the tracheid vessels of conifers, were once t h o u g h t to provide greater resistance to widespread embolism, but it is now believed that the substantially smaller lumen diameter of tracheids c o m p a r e d to vessel xylem is an important factor (Pallardy et al., 1995). It is interesting to note that the few deciduous genera that occur in boreal forests have either tracheids (i.e., Larix spp.) or diffuse-porous xylem (Gower and Richards, 1990; Pallardy et al., 1995). Species with diffuse porous xylem have waterconducting characteristics more comparable to tracheids than species that have ring-porous xylem. These latter species are noticeably absent from boreal forests (Woodward, 1995). Havranek and Tranquillini (1995) provide a t h o r o u g h review of the physiological processes operating in boreal and cold-temperate conifers during winter dormancy and their ecological significance.

B. Species Composition of Forest Ecosystems Ecologists have had difficulty explaining the wide range in species diversity and species distribution in forest ecosystems. Plant family diversity is positively correlated to absolute m i n i m u m temperature but even within tropical forests there is great variability, with species diversity inversely related to length of the dry season (Kira, 1983; Whitmore, 1984). It appears that there is a negative correlation between species richness and resource supply (soil fertility, water, etc.), so an explanation for the high species diversity in tropical forests is that the constancy of climate produces a constantly poor supply of mineral nutrients in the soils, suppressing the opportunities for competition among species. Connell (1978) and Doyle (1981) concluded that the highest diversity of tropical forests is achieved u n d e r moderate disturbance frequency over small areas, thereby creating a myriad of niches. Iwasa et al. (1993) reviewed all the explanations for forest species diversity and used models to examine

III. Species Adaptations and Climatic Conditions

25

various hypotheses. They concluded that narrow niche width tends to enhance diversity when niche width (the time for which conditions are suitable for species regeneration) is shorter than the time period suitable for regeneration. In tropical forests, the period suitable for regeneration can be several months and there is great seasonal variation in germination and regeneration rates, thereby providing, in effect, a storage mechanism. If the niche width is greater than the period of high regeneration opportunity, then the number of coexisting species increases with niche overlap, as first proposed by Huston (1979). Runkle (1989) speculated that seed storage is the basic mechanism that enables many similar tree species to coexist in a forest. Although the temporal patterns of gap formation and gap size are similar for tropical and temperate forests (Denslow, 1987), gaps created during the unfavorable period (i.e., winter in temperate regions or dry periods in tropical regions) remain vacant and increase in n u m b e r until the next favorable growing period. Synchronized regeneration provides a competitive advantage to species having their m a x i m u m regeneration at the start of the peak. Therefore, as the length of the unfavorable growing period increases (see Fig. 2.1), the peak rate of supply of gaps at the beginning of the favorable season becomes more important and species diversity decreases. It has been suggested that the evergreen habit provides a more favorable carbon balance in harsh climates (Mooney, 1972; Schulze et al., 1977a; see also Chapter 5), an argument supported by the dominance of evergreen trees in cold montane and boreal forests. Kikuzawa (1991), using a cost-benefit analysis, demonstrated that "evergreeness" should have a bimodal distribution with peaks at low latitudes (tropics) and high latitudes (boreal). His general biogeographical distribution pattern agrees well with observed vegetation patterns, except in two regions. First, although temperate forests occur predominantly from 30 ~ to 55 ~ latitude, broad-leaved evergreen species dominate forested landscapes in the southern hemisphere, whereas broad-leaved deciduous tree species predominate in the n o r t h e r n hemisphere. Axelrod (1966) concluded that the temperate climates, ample rainfall evenly distributed throughout the year, and rarity of frost favored the evolution of broad-leaved evergreen rather than deciduous forests in the temperate regions of the southern hemisphere (Fig. 2.1e vs 2.1b). Also, the minor amplitude of Quaternary change, the absence of large ice sheets and the lack of fullglacial environments persisting t h r o u g h the interglacial periods in the southern h e m i s p h e r e - - c o m p a r e d to the n o r t h e r n h e m i s p h e r e - - l e a d to the dominance of broad-leaved evergreen forests in the temperate regions of the southern hemisphere (Markgraf et al., 1995). The second area where Kikuzawa's prediction is incorrect relates to the widespread distribution of the deciduous conifer, L a r i x species, in the cold m o n t a n e and boreal environments, especially in Eurasia (Gower and Richards, 1990),

26

2. Forest Biomes of the World

where evergreen species would be e x p e c t e d because of the short growing season, infertile soils, and snow loading on the canopy of broad-leaved angiosperms. At the more local scale, soil fertility, disturbance frequency, and edaphic conditions affect the distribution of trees species. Temperate d e c i d u o u s forests are often restricted to the more fertile sites, whereas conifers occur on the more infertile sites because deciduous species have a greater annual nutrient requirement (Cole and Rapp, 1981; Son and Gower, 1991; see also Chapter 7). C. Climates of the Forest Regions In the second half of this chapter (Section IV) we discuss the distribution and some of the properties of the major forest biomes of the world. The environments of these forest types are illustrated by the diagrams in Fig. 2.1.

Figure 2.1 Climate diagrams for representative locations illustrating the conditions in which we can expect to find (a) boreal forests, (b) temperate deciduous forests, (c) temperate coniferous forests, (d) temperate mixed forests, (e and f) temperate broad-leaved evergreen forests, (g) tropical evergreen forests, and (h) tropical broad-leaved deciduous forests. The diagrams show long-term monthly averages of m a x i m u m (7; ..... ) and minimum (Train) air temperatures (~ precipitation (mm), radiation (MJ/day), and the water balance, calculated as the difference between precipitation and evaporation using the Thornthwaite (1948) equation. The diagrams were p r o d u c e d from data presented by M/iller (1982). Radiation data were not available for every station (they were missing for The Pas, Hobart, Manaus, a n d J a m s h e d p u r ) ; where this was the case, data from other stations at similar latitudes, which closely matched these in terms of sunshine hours, temperature, and rainfall patterns, were used.

III. Species Adaptations and Climatic Conditions

Figure 2.1

27

(Continued)

Each panel of the figure provides information about the climate of an area representative of those where the specified forest biomes occur. The water balance data (precipitation-evaporation) were derived from the monthly potential evaporation figures provided by Mfiller (1982),

98

2. Forest Biomes of the World

Figure 2.1

((;ontinued)

w h i c h were c a l c u l a t e d f r o m the e v a p o t r a n s p i r a t i o n f o r m u l a derived by T h o r n t h w a i t e (1948). This f o r m u l a is b a s e d on t e m p e r a t u r e and is unlikely to p r o v i d e a c c u r a t e values for the water use of f o r e s t s m o r i n d e e d any type of v e g e t a t i o n or even o p e n water. However, as M~ller points

III. Species Adaptations and Climatic Conditions

Figure 2.1

29

(Continued)

out, the T h o r n t h w a i t e e q u a t i o n is the only one that gave c o m p a r a b l e values for every station, a n d it does provide a reasonable indication of evapotranspiration regimes a n d h e n c e the overall water balance. M e t h o d s m o r e soundly based in physics, f r o m which m u c h b e t t e r estimates of

30

2. Forest Biomes of the World

Figure 2.1

(Continued)

forest water use rates can be obtained, are presented and discussed in C h a p t e r 3. Forest growth rates are d e t e r m i n e d primarily by average climatic conditions, but ecosystems are not generally altered by normal conditions. They are changed, often abruptly, by extreme events: Destructive storms may flatten areas of forest, causing large gaps in which regeneration takes place and which alter the microclimate and growth patterns in adjacent areas; extraordinarily high or low temperatures may kill whole cohorts of plants, making available niches in which different species establish themselves; d r o u g h t may have the same effect, and the community that develops after the breaking of a killing d r o u g h t may be different in species c o m p o s i t i o n from the original. D r o u g h t may also lead to conditions conducive to fire, which can destroy plant communities. Again, the regenerating c o m m u n i t y may not be the same as the original (see C h a p t e r 8). It is arguable that most ecosystems are in some phase of recovery from disturbance of some sort, and that ecosystem heterogeneity is largely a function of historical disturbances and edaphic conditions that have affected patches of varying size. It is therefore worth considering briefly, in relation to the climatic areas in which the major forest types occur, the likelihood of extreme events and their probable effects. The boreal forest regions, represented by climatic data from The Pas, in Manitoba, C a n a d a (Fig. 2.1a), are characterized by long cold winters.

IlL Species Adaptations and Climatic Conditions

31

The m e a n daily m i n i m u m t e m p e r a t u r e at T h e Pas is below 0~ for m o r e than 7 m o n t h s of the year, and d u r i n g the p e r i o d when t e m p e r a t u r e s remain above zero there are significant water deficits. This is clearly an extremely difficult e n v i r o n m e n t for plant growth, a fact reflected in the very slow growth of trees in the boreal forests. Above-ground net p r i m a r y p r o d u c t i o n ranges from 1 or 2 t ha -1 year -1 for poorly d r a i n e d black spruce forests to 4 - 6 t ha -1 year -1 for early successional balsam poplar, aspen, and birch stands (Van Cleve et al., 1983; Gower et al., 1997). The low t e m p e r a t u r e s of the boreal forest regions lead to slow soil dev e l o p m e n t so that the soils tend to be nutrient poor; the fact that the soils tend to be very y o u n g m d e r i v e d from p a r e n t material left by retreating ice s h e e t s - - c o n t r i b u t e s to this. Permafrost, which may not retreat m o r e than a m e t e r below the surface, causes restricted r o o t zones, a n d the p o o r drainage results in extensive fens and bogs when the soil is n o t frozen. Damaging s u m m e r storms are rare, a l t h o u g h lightening strikes may cause fires that can b u r n u n c h e c k e d over large areas. Severe winter weather is unlikely to cause d a m a g e to trees. In the t e m p e r a t e deciduous forest zone of the n o r t h e r n United States ( r e p r e s e n t e d by Chicago, IL; Fig. 2.1b), m i n i m u m t e m p e r a t u r e s are well below freezing for at least 4 m o n t h s of the year. The best p e r i o d for growth is in the spring, w h e n t e m p e r a t u r e s rise a n d water is adequate; high evaporation d u r i n g the s u m m e r m o r e than balances the precipitation, and water deficits are likely to restrict growth. The climate of t e m p e r a t e evergreen forests c a n n o t be e n c o m p a s s e d by one climate diagram; these forest ecosystems can occur in e n v i r o n m e n t s ranging from cold subalpine to n e a r subtropical. However, an area notable for such forests is the n o r t h w e s t coast of the United States, represented by Seattle (Fig. 2.1c), which has cooler s u m m e r s than the temperate d e c i d u o u s zone, w a r m e r w i n t e r s m t h e average m i n i m u m t e m p e r a t u r e for any m o n t h is never below z e r o - - a n d a different precipitation pattern. Highest precipitation is in the winter months, with very little d u r i n g the p e r i o d of highest evaporation, so significant water deficits a n d r e d u c e d tree growth d u r i n g s u m m e r are the n o r m . T h e fact that the trees in this area tend to grow to great sizes indicates that severe storms that cause physical d a m a g e to trees are relatively rare. A m o r e p r o b a b l e cause of ecosystem disturbance is fire, which may occur in exceptionally dry summers, when the n o r m a l s u m m e r d r o u g h t (Fig. 2.1c) is e x t e n d e d and exacerbated by unusually h o t weather a n d lack of precipitation. In the t e m p e r a t e mixed (evergreen, needle-leaved conifers a n d broadleaved deciduous) region, r e p r e s e n t e d by the climate at Nashville, Tennessee (Fig. 2.1d), h i g h e r rainfall is n o t e n o u g h to prevent s u m m e r water deficits, b u t early season t e m p e r a t u r e s are significantly h i g h e r than those in the d e c i d u o u s and coniferous areas. The winters are cold e n o u g h to

32

2. Forest Biomes of the World

cause leaf fall, b u t the p e r i o d when t e m p e r a t u r e s are low e n o u g h to prevent growth is relatively short. Severe weather in the t e m p e r a t e m i x e d forest regions may take the form of d r o u g h t or, in the s o u t h e r n United States, t o r n a d o e s (highly localized, high-energy rotating winds, which may flatten small areas of forest), or occasional larger-scale d a m a g e from h u r r i c a n e s that penetrate f u r t h e r n o r t h than usual. In the "old world" of E u r o p e and the Mediterr a n e a n region, most of the forests have long since been destroyed by man. The climate at H o b a r t , Tasmania, Australia (Fig. 2.1e) is not dissimilar to that of the west coast of South Island, New Zealand, and both areas are characterized by broad-leaved evergreen forests. T h e rainfall in the H o b a r t area is not high (it is high along the west coast of South Island, New Zealand) but is evenly distributed t h r o u g h the year; evaporation is low and only mild water deficits develop d u r i n g the summer. Average m i n i m u m m o n t h l y t e m p e r a t u r e s are never below zero; therefore, some tree growth can be e x p e c t e d t h r o u g h o u t the y e a r m a fact that helps explain the evergreen growth habit in broad-leaved trees. F u r t h e r north, the climate of Sydney, Australia (Fig. 2.1f) shows similar characteristics, a l t h o u g h t e m p e r a t u r e s are h i g h e r (the annual average is 17.4~ comp a r e d to 12.2~ at H o b a r t ) . H i g h e r rainfall (1200 m m ) compensates for the h i g h e r t e m p e r a t u r e s and evaporation; therefore, e n v i r o n m e n t a l conditions are g o o d for growth t h r o u g h o u t the year. The tropical evergreen forests are generally c o n s i d e r e d to be wet at all times, but Fig. 2.1g indicates that in the Amazon there are quite long periods when evaporation may exceed rainfall. However, the water deficits estimated using the T h o r n t h w a i t e equation may be particularly misleading (longer a n d m o r e intense than in fact occur) in this region. The reason is that the T h o r n t h w a i t e equation is t e m p e r a t u r e based; it does not include a h u m i d i t y term. Evaporation is strongly d e p e n d e n t on air humidity (vapor pressure deficit; see C h a p t e r 3), and the vapor pressure deficits in the tropical rainforest regions are, at all times, low (mean relative h u m i d i t y is a b o u t 80%). Wind speeds are also low. We would therefore expect evaporation to be overestimated by the T h o r n t h w a i t e equation. Nevertheless, we can accept that, for several m o n t h s of the year, the a m o u n t of water lost by evaporation exceeds the a m o u n t of rainfall in the South A m e r i c a n tropical broad-leaved e v e r g r e e n forest regions. This pattern also occurs in the African tropical rainforests, but in many of the southeast Asian areas rainfall exceeds evaporation in every m o n t h of the year. The r e m a r k a b l y stable t e m p e r a t u r e s are characteristic of the lowland tropical forests. J a m s h e d p u r , India (Fig. 2.1h), is a m o n s o o n area characterized by heavy rains for 4 - 6 m o n t h s of the year, with high water deficits developing after the m o n s o o n season. T h e d r o u g h t leads to leaf fall and the

IE Forest Biomes of the World

33

deciduous growth habit in trees. T e m p e r a t u r e s are high t h r o u g h o u t the year. A very similar climate is f o u n d in the Central a n d South A m e r i c a n tropical d e c i d u o u s areas; Managua, N i c a r a g u a has a m e a n annual temperature of 27.3~ (cf. 26.4~ a t J a m s h e d p u r ) a n d a total annual precipitation of 1142 m m , most of which falls in 5 months. Natural disturbances in tropical forests include cyclonic storms, wildfires, and volcanic eruptions. I n l a n d forests, in the A m a z o n and Congo basins, for e x a m p l e , are not subject to h u r r i c a n e s and a p p e a r to be at very little risk f r o m e n v i r o n m e n t a l hazards, b u t the hazard is significant t h r o u g h m u c h of the southeast Asian area, particularly in the I n d o n e sian archipelago, and Malesia. T h e d e s t r u c t i o n of an area of forest by h u r r i c a n e is the classic cause of large gaps. T h e weight of epiphytes has also b e e n suggested as a factor c o n t r i b u t i n g to individual tree fall in tropical forests (Strong, 1977). We have little i n f o r m a t i o n a b o u t weather hazards to the tropical d e c i d u o u s forests. Fire is an i m p o r t a n t ecological factor in these forest ecosystems ( M u r p h y a n d Lugo, 1986).

IV. Forest Biomes of the World Whatever the m e c h a n i s m s d e t e r m i n i n g the distribution of forest ecosystems, we n e e d some sort of classification s c h e m e as a f r a m e w o r k for discussing them. N u m e r o u s land cover classification schemes exist based on e n v i r o n m e n t a l factors, p h y s i o g n o m y of the vegetation, soil types, or a c o m b i n a t i o n of these variables. T h e n u m b e r of land cover types recognized in each s c h e m e largely reflects the objective for developing the classification system. For example, H o l d r i d g e ' s (1947) Life Zone classification system includes 22 vegetation cover types a n d was d e v e l o p e d to illustrate the climatic influences on vegetation distribution, whereas Running et al. (1994) p r o p o s e d a scheme i n c l u d i n g only 6 cover types; the critical features of this classification are that the structural characteristics can be identified by r e m o t e sensing a n d used as the inputs to drive global vegetation models. For the p u r p o s e s of this book, we have a d o p t e d the vegetation classification system used by Melillo et al. (1993) because it is a reasonable c o m p r o m i s e between c o m p l e x a n d simple schemes, a n d they have compiled one of the most recent sets of i n f o r m a t i o n a b o u t the global distribution of (potential) vegetation types. T h e forest types are based on major climatic zones (tropical, t e m p e r a t e , a n d boreal) a n d the physiogn o m y (broad-leaved evergreen, broad-leaved deciduous, a n d needleleaved e v e r g r e e n conifer) of the vegetation. Figure 2.2 is derived from Melillo et al. (1993). T h e m a p (Fig. 2.2) shows the regions where particular vegetation types could occur, al-

34

2. Forest Biomes of the World

t h o u g h it is unlikely that the areas c o n c e r n e d are completely covered by those vegetation types. The i m p a c t of h u m a n s has resulted in vegetation loss and c h a n g e across very large areas of the globe; to d e t e r m i n e actual areas of p a r t i c u l a r vegetation types will take a vast effort involving g r o u n d surveys a n d satellites. T h e simple land cover classification scheme of R u n n i n g et al. (1994) was designed with the objective of making this possible. Nevertheless, the data in Table 2.1 probably provide a good guide to the relative areas of the different forest biomes. The net primary p r o d u c t i o n data in the table r e p r e s e n t estimates u p d a t e d from currently available literature. We note from Table 2.1 that two c a t e g o r i e s - - b o r e a l forests and tropical evergreen f o r e s t s I d o m i n a t e all o t h e r forest types in terms of area. O f these, we know that the rates of deforestation in tropical regions are by far the highest because it is these regions that have the highest h u m a n p o p u l a t i o n densities. Because of the inhospitable nature of the climate in the regions where they occur, the boreal forests have been subject to far less d i s t u r b a n c e by h u m a n s than tropical forests. H u m a n populations are low and vast tracts are u n i n h a b i t e d . However, there are areas, for example, along the s o u t h e r n edges of the forests in Canada, and in Siberia, where there has been extensive c o m m e r c i a l logging without ecological safeguards or c o n c e r n for the sustainability of the forests (Shvidenko and Nilsson, 1994). The p r o b l e m with the boreal forests is that the rate of recovery is so low. T h e r e is no growth d u r i n g the extremely cold winters, and even in s u m m e r t e m p e r a t u r e s restrict growth rates. Soils are also generally infertile. Consequently, it may be several h u n d r e d s of years before logged or b u r n e d forests recover, in stark contrast to tropical forests where, if the forest is not deliberately completely destroyed and converted to (usually poor-quality) pasture, but is allowed to regenerate, recovery is rapid. Full canopy and fine roots of tropical forests may be restored within 1 - 8 years (Raich 1980, Berish, 1982) and there are tall trees, with multilayered canopies, within 1 0 - 2 0 years. The following sections contain descriptive outlines of the major forest biomes we have chosen to discuss. As n o t e d previously, the classification scheme we have chosen to follow is only one of a n u m b e r of possibilities; within that scheme, there will be overlaps between categories as well as gaps in i n f o r m a t i o n - - t h e a m o u n t of i n f o r m a t i o n readily available about each category varies. The information that follows is necessarily sketchy;

Figure 2.2 Global vegetation map showing the distribution of the forest types discussed in this book. The map is a modified version of the one published by Melillo et al. (1993) (with acknowledgments to D. Kicklighter and J. Melillo, who provided this version ill digital form). Reprinted from Nature, Melillo et al. 363, 234 9 1993 Macmillan Magazines I,td.

35

IE. Forest Biomes of the World

Table 2.1 Area (ha x 10-8), and Maximum, Minimum, and Mean Net Primary Production (NPP, t ha -1 year-l), of Selected Forest Biomes (' NPP Forest b i o m e

Area

Maximum

Minimum

Mean

Boreal T e m p e r a t e coniferous Temperate mixed Temperate deciduous T e m p e r a t e broad-leaved e v e r g r e e n Tropical d e c i d u o u s Tropical e v e r g r e e n

12.2 2.4 5.1 3.5 3.2 4.6 17.4

4.3 7.0 10.7 9.8 10.0 14.0 14.2

1.2 2.1 2.3 0.8 3.2 3.2 4.1

2.4 4.7 6.7 0.2 7.4 8.7 11.0

aData derived from a table by Melillo et al. (1993).

it is i n t e n d e d to provide a framework for thinking a b o u t the processes dealt with in the o t h e r chapters, and their implications for forest ecology and m a n a g e m e n t , r a t h e r than detail a b o u t the forest types considered. For such detail, reference must be m a d e to books and papers dealing exclusively with particular forest biomes. A. Boreal forests

Boreal forests cover a b o u t 12 • 10 s ha and occur exclusively in the n o r t h e r n h e m i s p h e r e . The greatest single area of boreal forests is in Eurasia, where they e x t e n d from Scandinavia to eastern Siberia. The second largest boreal forest region occurs as a 500 to 600-km wide b a n d from eastern C a n a d a and the n o r t h e a s t e r n U n i t e d Staes westward into northern British C o l u m b i a and Alaska. T h e n o r t h e r n a n d s o u t h e r n b o u n d aries of the boreal forests in N o r t h America c o r r e s p o n d , roughly, to the s u m m e r and winter position, respectively, of the arctic front. The length of growing season must be sufficient to ensure that evergreen conifers develop an adequate cuticle n e e d e d to minimize winter desiccation (Tranquillini, 1979) and mycorrhizal associations to facilitate nutrient and water uptake (Read, 1991). W o o d w a r d (1995) suggests that the n o r t h e r n limit of boreal forests may be crudely defined as the n u m b e r of m o n t h s in which the air t e m p e r a t u r e is greater t h a n 10~ The climate of the boreal forests is one of the harshest in which trees can occur. T h e r e may be less than 50 frost-free days in s u m m e r (Fig. 2.1 a) and p e r m a f r o s t is characteristic of these regions. Fire is the most important natural disturbance in boreal forests. Fires, ignited by lightning, tend to cover large areas and may b u r n as m u c h as 2 5 , 0 0 0 - 5 0 , 0 0 0 ha (Dyrness et al., 1986). Fire frequency in the boreal forests in N o r t h America ranges from 30 to 200 years, d e p e n d i n g on species c o m p o s i t i o n and topo-

36

2. ForestBiomes of the World

graphic position. Fire strongly influences species composition, nutrient availability, and forest productivity (Larsen, 1982; Dyrness et al., 1986). There are few major soil taxonomic groups 2 f o u n d in boreal forests. Histisols, or organic soils, are c o m m o n to poorly d r a i n e d forests. The upland soils are relatively young c o m p a r e d to temperate and tropical forest soils. Entisols have little or no horizon d e v e l o p m e n t and are typically associated with early successional riparian forests (Populus and Betula) and coarse-textured, excessively drained pine forests (P. banksiana in Canada and P. sylvestris in Eurasia). Some Spodosols can be found, but in general the boreal forests are too dry for these soils to develop. Tree species diversity is very p o o r in the boreal forests. There are only 9 d o m i n a n t tree species in North America (Payette, 1992) and 14 in Fennoscandia and f o r m e r Russia (Nikolov and Helmisaari, 1992). The low species diversity is attributed to the short-term geologic history and the harsh climate of this biome (Woodward, 1995). In general, boreal species arrived in this region less than 2000 years ago. Therefore, the ecology of this large forest biome is y o u n g e r than any other (Takhtajan, 1986). The distribution of species, and the species composition of stands are strongly influenced by t o p o g r a p h y and soils. I m p o r t a n t genera include Abies, Betula, Larix, Picea, Populus, and Salix. Picea and Larix commonly occur on poorly drained lowland soils. Pines c o m m o n l y occupy well-drained u p l a n d soils, whereas Populus, Abies, Salix, and certain species of Picea occur on the finer-textured upland soils. In North America and Europe, needle-leaved evergreen conifers tend to dominate the boreal landscape, especially at n o r t h e r n latitudes. However, Larix, a deciduous conifer, increases in importance in Eurasia and often dominates the boreal treeline in Siberia (Gower and Richards, 1990). Ericaceous shrubs c o m m o n l y dominate the u n d e r s t o r y of boreal forests and the soil is covered by lichens on the drier (xeric) sites or mosses and sphagnums on the mesic to wet (hydric) sites. Unique structural characteristics of boreal forests include low leaf area index (L*, See C h a p t e r 3, Section I) and spiral canopies. The spiral canopies help shed the snow and maximize light interception when the sun is low in the horizon (Oker-Blom, 1989). Above-ground net primary p r o d u c t i o n (ANPP) rates are typically low but the p r o d u c t i o n rates of u p l a n d boreal forests on southern slopes can a p p r o a c h those of cold temperate forests (Van Cleve et al., 1981; Gower et al., 1995). Typically, only a small fraction of ANPP is allocated to stem wood production. Bryophytes comprise less than 1% of the total above-ground biomass of boreal forests, yet they play n u m e r o u s i m p o r t a n t roles. Bryophytes insulate the soil, which strongly affects the thermal regime and hence overall 2 U.S~7th Approximation Soil classification system.

IE. Forest Biomes of the World

37

nutrient cycling and productivity patterns of boreal forests. Despite their low biomass relative to vascular plants, the productivity of bryophytes can comprise 18% of total ANPP (Van Cleve et al., 1983). Because of the low stemwood productivity, the inhospitable climate, and inaccessability, boreal forests are one of the least m a n a g e d forest ecosystems of the world. Nonetheless, as we noted earlier, there is great interest in them, especially in Siberia, because of the large areas (see Table 2.1) of mature forests. The political instability and p o o r infrastructure in Siberia may be the only factor stopping large-scale harvesting of this fragile ecosystem. Plantation forests are scarce in the boreal regions, although forest m a n a g e m e n t programs in Scandinavia can approach the intensity level of plantations.

B. Temperate Deciduous Forests Temperate broad-leaved deciduous forests cover 3.5 • 10 s ha (Melillo et al., 1993; Table 2.1) and occur primarily between 30 ~ to 50 ~ N latitude (Rohrig and Ulrich, 1991). The major areas of these forests occur in the eastern United States, Europe, the western parts of Turkey, the eastern b o r d e r areas of Iran, western China, and Japan (Rohrig and Uhlich, 1991). With the exception of the western coast of southern Chile, deciduous forests are noticeably absent in the southern h e m i s p h e r e (Axelrod, 1966; Schmaltz, 1991 ). Temperate forest soils are highly variable so we can provide only a cursory treatment of this topic. A more detailed treatment is provided by Pritchett (1979). Many of the riparian and swamp forests in the southeastern United States occur on Histisols and it is interesting to note that the use of these soils is a topic of intense debate in the southeastern United States today (Richardson, 1981). When drained and fertilized with nitrogen and phosphorus, they can be extremely productive and are highly valued for agriculture and forestry. However, for these soils to be productive their hydrology must be altered by drainage, which results in large freshwater intrusions into adjacent saltwater estuaries, adversely affecting the delicate food chain. Many of the mountain soils in temperate regions that are covered by deciduous and some coniferous forests are Entisols, Inceptisols, or Alfisols, with the former being young and infertile and the latter being moderately weathered but fertile. Spodosols are restricted to cool- to cold-temperate conifer forests that receive abundant rainfall. In warmer climates (e.g., southeastern United States and southern Europe), the soils have u n d e r g o n e extensive weathering and the d o m i n a n t soil order is Ultisols: These soils can be productive, especially if nitrogen and p h o s p h o r u s fertilizer are applied. Both tree and understory diversity are greater in temperate deciduous than temperate conifer and boreal forests; approximately 30 plant fami-

38

2. Forest Biomes of the World

lies and 65 g e n e r a occur in the overstory canopy of temperate deciduous forests (Rohrig and Ulrich, 1991). Species diversity is higher in the deciduous forests in North America, China, and Japan, where n u m e r o u s refugia are believed to have existed during glacial periods, than in Europe where the p r e d o m i n a n t l y east-west m o u n t a i n ranges prevented retreat to warmer climates. Species composition varies according to topography, soil fertility, and successional status. A few i m p o r t a n t temperate deciduous tree g e n e r a include Acer, Ailanthus, Albizzia, Betula, Carya, Castanopsis, Fagus, Fraxinus, Juglans, Liriodendron, Magnolia, Nothofagus (endemic to Chile and Tasmania), Populus, Quercus, Tilia, and Zelkova. Because of large-scale clearing and conversion to agriculture, pasture, and u r b a n areas, temperate deciduous forests do not usually occur in extensive tracts. Except for stands of Populus, it is also unusual to find pure stands of one species. The forests are being further altered by exchange of species, extirpation of species, and pathogen outbreaks. Collectively, all these factors have greatly altered the nature of temperate deciduous forests, especially in Europe. M a n a g e m e n t may range from periodic selective tree removal to the most intensive form of forest m a n a g e m e n t - short-rotation plantations for fiber or fuel production. Depending on the species, life history, and ecophysiology, both even-aged and uneven-aged m a n a g e m e n t practices can be successful. Even-aged m a n a g e m e n t is most prevalent for shade-intolerant and coppicing species, whereas unevenaged m a n a g e m e n t is c o m m o n l y used for shade tolerant species. Species c o m m o n l y used in short-rotation plantations include Populus, Liquidambar, Salix, and Platanus.

C. Temperate Coniferous Forests Temperate evergreen coniferous forests cover approximately 2.4 • l0 s ha (Melillo et al., 1993; Table 2.1) and are largely restricted to the northern hemisphere. Temperate evergreen conifers occur in a wide range of climates, perhaps the most diverse of all the major forest biomes, ranging from subtropical, open w o o d l a n d to ecotonal temperate-boreal forests to temperate rainforests (Fig. 2.2). Conifers dominate the montane forests in North America, Europe, and China and smaller areas of temperate conifers are located in m o n t a n e regions of Korea, Japan, Mexico, Nicaragua, and Guatemala. In addition, Pinus species have been planted extensively in the s o u t h e r n h e m i s p h e r e (see c o m m e n t in "Plantation forestry"). Temperate conifers tend to occur on xeric or infertile soils that c a n n o t supply the greater water and nutrient d e m a n d s of deciduous species (Son and Gower, 1991); p e r h a p s the best example of this is the d o m i n a n c e of evergreen conifers in the Pacific Northwestern United States, where dry summers and mild winters provide a favorable environment for evergreens (Waring and Franklin, 1979).

IV..ForestBiomes of the World

{39

C o m m o n g e n e r a in the temperate coniferous forests include Abies, Picea, Pseudotsuga, and Thuja in n o r t h e r n latitudes and m o n t a n e / s u b alpine regions, whereas g e n e r a such as Tsuga occur over a m u c h b r o a d e r range of e n v i r o n m e n t a l conditions. Pinus species, an i m p o r t a n t g e n e r a from both an e c o n o m i c and ecological perspective, occurs in a wide variety of e n v i r o n m e n t s ranging from hot, arid southwestern United States to cold regions of Scandinavia and Eurasia (Knight et al., 1994). Given the diverse environments in which t e m p e r a t e conifers occur, it is not surprising that the ecophysiology and structure of these forests are varied. For example, needle longevity can range from less than 2 years for P. taeda to greater than 40 years for P. longaeva. Above-ground biomass of m a t u r e forests can range from a low of a b o u t 100 t h a - 1 for Pinus forests in southwestern United States to 3300 t ha -1 for giant r e d w o o d (Sequoia sempervirens) forests in n o r t h e r n California (Cannell, 1982). Some of the lowest leaf area indices ($ is the longwave downward flux of radiation from the sky, and ~>1" is the longwave u p w a r d flux from the surface. Any body with a t e m p e r a t u r e above absolute zero emits radiation at a rate p r o p o r t i o n a l to the f o u r t h power of its t e m p e r a t u r e ( S t e f a n Boltzman law): i.e., q~L = eLtr T 4, where tr is the S t e f a n - B o l t z m a n constant (= 5.67 • l 0 s W m -2 K-4), T is the absolute t e m p e r a t u r e , and eL is the emissivity. For a perfect black body eL = 1. For most vegetative surfaces, eL is in the range 0.9-0.96. The flux of long-wave radiation from the earth's surface is in the wavelength range between a b o u t 3000 and 100,000 n m (long-wave radiation), with m a x i m u m energy o u t p u t at a b o u t 10,000 nm. T h e downward flux of long-wave radiation d e p e n d s on atmospheric conditions, such as cloud a m o u n t and type (e. g., cirrus, cumulus), and the a m o u n t of water vapor, and o t h e r gases in the a t m o s p h e r e . Under cloudy skies, the outward and inward fluxes of long-wave radiation will be almost in balance, whereas the net loss of long-wave radiation is highest u n d e r clear s k i e s - - h e n c e the increased likelihood of night-time frost u n d e r those conditions. As a general rule, the reflectivity of surfaces decreases with increasing roughness, a n d because forest canopies are r o u g h surfaces their reflectivity is low. Figure 3.6 is derived f r o m Stanhill (1970) a n d illustrates the changes in albedo with the h e i g h t of the elements of natural surfaces. As a general rule, shortwave albedo does n o t vary greatly a m o n g forest cover types, it decreases with canopy h e i g h t and r o u g h n e s s and is typically low for forests relative to o t h e r cover types. Representative m e a n daily values of ce are a b o u t 0.10 for coniferous forests (Jarvis et al., 1976) and 0.16 for d e c i d u o u s forests (Rauner, 1976). T h e r e have b e e n several t h o r o u g h studies of the radiation balance of coniferous forests, for example, by Moore (1976) on P. radiata in South Australia and Stewart and T h o m (1973) on P. sylvestris in England. Moore identified variations in albedo t h r o u g h the day b u t these were relatively trivial and he c o n c l u d e d that a m e a n albedo of 0.11 +_ 0.01 could be applied to the canopy for all solar elevations. Pinker et al. (1980a) f o u n d the average albedo of a tropical evergreen forest in T h a i l a n d to be a b o u t

66

3. Canopy Architecture and Microclimate

Figure 3.6 Relationship between mean albedo (or) and vegetation height (redrawn from Stanhill, 1970; note that the x axis (vegetation height) is a logarithmic scale).

0.14, with s i g n i f i c a n t d i u r n a l v a r i a t i o n , f r o m a b o u t 0.17 (early m o r n i n g / e v e n i n g ) to a b o u t 0.11 at midday. T h e r e w e r e also s e a s o n a l differe n c e s , a n d t h e a l b e d o in a grassy c l e a r i n g was g e n e r a l l y h i g h e r ( a b o u t 15%) t h a n t h a t o f the forest. S h u t t l e w o r t h et al. (1984) o b t a i n e d a value o f 0.14 for t r o p i c a l f o r e s t in t h e A m a z o n , with s o m e d e p e n d e n c e o n solar a n g l e (low angles, a n d h i g h e r a l b e d o ) . For m a n y p u r p o s e s , ~,~ can be c o n v e n i e n t l y e s t i m a t e d f r o m e m p i r i c a l relationships of the form q~,, = b,,q~ _+ a , .

(3.7)

T h i s can be e s t a b l i s h e d f r o m m e a s u r e m e n t s o f q~,, over a f o r e s t a n d q~s n e e d n o t be m e a s u r e d nearby. Jarvis et al. (1976) gave values o f bn for c o n i f e r o u s forests that r a n g e f r o m 0.71 to 0.91; a value o f 0.8 will s e l d o m b e m u c h in e r r o r a l t h o u g h M o o r e ' s (1976) study is a g a i n w o r t h conside r a t i o n . H e gave q~,, = 0.67q~ - 45 ( + 10) W m -2 for the w i n t e r m o n t h s a n d q~n = 0.85q~ - 55 (_+18) W m -2 for the s u m m e r . F e d e r e r (1968) f o u n d b. = 0.83 for a h a r d w o o d forest, w h e r e a s R a u n e r (1976) i n d i c a t e d t h a t 85% o f q~ was a b s o r b e d by a d e n s e a s p e n s t a n d (i.e., b, ~ 0.85) a n d K a l m a a n d F u c h s (1976) gave 0.8 for a citrus o r c h a r d . P i n k e r et al. (1980b) f o u n d bn = 0.88 for t h e t r o p i c a l f o r e s t in T h a i l a n d , a n d Shuttlew o r t h et al. (1984) o b t a i n e d b, = 0.85 in the A m a z o n . T h e p a r a m e t e r a,, gives an e s t i m a t e o f the a v e r a g e value o f ~L a n d t h e r e f o r e d e p e n d s o n local c l i m a t e as well as f o r e s t s t r u c t u r e . T h e value o f a n for c o n i f e r o u s forests varies widely f r o m - 6 to - 1 2 6 W m - 2 ; the m e d i a n value is a b o u t - 6 0 W m -2. F e d e r e r (1968) gave an = - 8 9 W m -2

II. Energy Balance and Interception of Visible (Photosynthetically Active) Radiation

67

for a d e c i d u o u s forest, whereas the overall average of the m o n t h l y values given by Pinker et al. (1980b) was - 5 4 W m -2, which c o m p a r e s with - 3 5 W m -2 given by Shuttleworth et al. (1984). Taken overall, the values of the coefficients in Eq. (3.7) are sufficiently conservative to allow useful estimates of qPn to be m a d e for any closed canopy forest in any area. Working with daily values, we should note that an must be m u l t i p l i e d by time; the p e r i o d between sunrise a n d sunset would be the a p p r o p r i a t e interval (At) for daylight hours, w h e n q~s > 0. For example, ifq)s = 15 MJ m -2 day -1 a n d At = 11.5 hr, t h e n ifbn = 0.85 and a n = - 5 5 W m -2, qPn is (0.85 X 15 X 106 -- 55 X 11.5 X 3600), i.e., ~10.5 MJ m -2 day -1. Estimates of q0n are an i m p o r t a n t first step toward the calculation of rates of water loss, by evaporation a n d transpiration, f r o m forest canopies. The conservation e q u a t i o n describing the p a r t i t i o n i n g of q~n into latent heat (evaporation or t r a n s p i r a t i o n - - A E , where A is the latent heat of vaporization of water), sensible heat (H) a n d heat stored within the stand (G), is @n -Jr- G - - / ~ E

-Jr- H~

(3.8)

so that given values for q~n, solving Eq. (3.8) for AE would provide estimates of the rates of water use by forests. This is c o n s i d e r e d u n d e r Section III. T h e heat storage t e r m G, as written here, includes heat stored in the soil, in the vegetation, and in the air within the stand. It can be partitioned into these c o m p o n e n t s , and detailed m i c r o m e t e o r o l g i c a l studies have to take t h e m into account. Focussing on processes at the stand level, and intervals of days or longer, we can afford to ignore G, which tends toward zero on a daily basis. Interest in albedo a n d its influence on q~n [Eq. (3.6)] has increased in recent years as our u n d e r s t a n d i n g of the linkages between the atmosphere a n d terrestrial ecosystems increase. Recent m o d e l s that couple land surface a n d a t m o s p h e r i c processes have shown how large-scale changes in land surface cover can affect regional energy a n d climate. Bon a n et al. (1993) suggested that large-scale r e p l a c e m e n t of boreal forests by low-stature vegetation would increase the albedo of the region. Based on global circulation models, they suggested that the increased albedo would cause s u r r o u n d i n g oceans to stay frozen longer, which in t u r n may shorten the growing season of s u r r o u n d i n g terrestrial ecosystems. Degradation of vegetation in the Sahara a n d Sahelian regions of Africa has rep o r t e d l y increased the regional albedo of this zone, causing d e c r e a s e d rainfall by p u s h i n g the intertropical c o n v e r g e n c e zone f u r t h e r to the south (Charney, 1975; C h a r n e y et al., 1975). Removal of tropical forests may increase the global albedo, which would cause a cooler a n d drier climate (Potter et al., 1975; Sagan et al., 1979; Henderson-Sellers a n d Gor-

68

3. Canopy Architecture and Microclimate

nitz, 1984). Gash and Shuttleworth (1991) predicted that large-scale deforestation of tropical forests in the Amazon would cause local increase in t e m p e r a t u r e a n d 25% decrease in rainfall.

B. Interception of Visible Radiation The flux density of energy in the visible waveband, received at a point in a forest canopy, consists of beam and diffuse radiation that penetrates t h r o u g h gaps in the canopy. It is s u p p l e m e n t e d by radiation that is reflected from leaves and the soil and transmitted t h r o u g h leaves. Complete treatments of this situation are complex, and there is a large body of literature on the subject. A selection of key papers and reviews would include the early treatments by A n d e r s o n (1964, 1966) and Cowan (1968) and the work of N o r m a n (e.g., 1980, 1982), Oker-Blom (1986), and Baldocchi (see the review by Baldocchi and Collineau, 1994). These mathematical descriptions and models can describe the radiation regimes in plant canopies with considerable accuracy if the assumptions incorporated in them are met and if there is adequate information about canopy architecture such as whether foliage is c l u m p e d or r a n d o m l y distributed and leaf angle distribution. The simplest case assumes foliage is randomly distributed in the canopy space and leaf angle distribution is spherical, i.e., there is always an equal leaf area normal to the beam of radiation, whatever its direction. Detailed models of PAR interception are essential for simulation of canopy photosynthesis over short periods, such as half an hour, and for the simulation of the diurnal course of photosynthesis. Such treatments are i m p o r t a n t because, if successful, they provide us with the assurance that we u n d e r s t a n d the processes involved in the growth of plant communities sufficiently well to allow us to simplify with confidence, knowing the implications and consequences of the simplifications. The mathematical complexity of any treatment increases rapidly when we depart from simple cases. This book is, in general, c o n c e r n e d with stand-level ecophysiology, where detailed treatment of PAR interception is unlikely to be justified. We will, therefore, present only the (very widely used) Beer's law equation for calculating radiation interception by plant canopies, with some discussion on its limitations. Beer's law strictly applies to the absorption of radiation by simple systems such as uniformly turbid media (air, liquids, etc.), but it can be derived from the analysis of the path of beams t h r o u g h foliage (see Norman, 1979; Monteith and Unsworth, 1990). The equation is q~(z) = q~(O) e x p ( - k L * ( z )

),

(3.9)

which says that radiation decreases exponentially t h r o u g h the canopy, and that the average flux at any level is a function of the leaf area index

II. Energy Balance and Interception of Visible (Photosynthetically Active) Radiation

69

above that point. T h e value of k varies s o m e w h a t with species a n d season (sun angle), b u t for most forest types a value of k = - 0 . 5 will n o t lead to serious errors (see Jarvis a n d Leverenz, 1983). Strictly, the equation only holds if the foliage in the c a n o p y is r a n d o m l y d i s t r i b u t e d a n d leaf angle d i s t r i b u t i o n is spherical. If foliage is n o n r a n d o m l y distributed, or c l u m p e d , Beer's law will u n d e r e s t i m a t e r a d i a t i o n p e n e t r a t i o n (Ross, 1981; Lang et al., 1985). C l u m p e d foliage, c o m m o n in conifers, allows m o r e light to p e n e t r a t e the c a n o p y t h a n if the foliage was d i s t r i b u t e d r a n d o m l y (Nilson, 1977; O k e r - B l o m a n d Kellom/iki, 1983). In a d d i t i o n , the p e n u m b r a effect provides a m o r e h o m o g e n e o u s distribution, which is relatively m u c h larger in relation to needles t h a n b r o a d leaves, of radiation in the lower c a n o p y ( N o r m a n a n d J a r v i s , 1975). N o n h o m o g e n e o u s foliage is m o s t often o b s e r v e d in conifers a n d can o c c u r at several scales. An e x t r e m e e x a m p l e of c l u m p i n g is the c a n o p y of b o r e a l black spruce stands, w h e r e c l u m p i n g occurs at the shoot, b r a n c h , whorl, a n d c a n o p y scales ( C h e n et al., 1996). C h e n et al. provide t h e o r y a n d m e t h o d o l o g y that accounts for c l u m p i n g w h e n L* is e s t i m a t e d using optical instruments that m e a s u r e c a n o p y gap fractions f r o m r a d i t i o n t r a n s m i t t e d t h r o u g h a canopy. Failure to a c c o u n t for n o n h o m o g e n o u s d i s t r i b u t i o n of foliage in canopies is likely to cause i n a c c u r a t e estimates of c a n o p y photosynthesis, particularly w h e n calculations are m a d e over s h o r t periods ( N o r m a n andJarvis, 1975; K u c h a r i k et al., 1996). Despite these reservations, if the objective is to estimate the r a d i a t i o n a b s o r b e d over p e r i o d s of days, weeks, or seasons, Eq. (3.9) gives very useful results. I n t e g r a t i o n over time, a n d averaging direct a n d diffuse radiation, t e n d to eliminate the errors that m i g h t arise if the e q u a t i o n was used to estimate absorption over s h o r t p e r i o d s for canopies that do n o t m e e t the r i g o r o u s theoretical r e q u i r e m e n t s . It is w o r t h e x p l o r i n g some of the implications of Eq. (3.9) in relation to canopy photosynthesis. We will use an e m p i r i c a l e q u a t i o n for leaf photosynthesis [Eq. (5.12); i.e., Eq. (4.8) f r o m L a n d s b e r g , 1986, p. 80] 3 to evaluate it. T h e calculations are m a d e in terms of p h o t o n flux density (q~p). Assume we are c o n c e r n e d with a c a n o p y with L* = 5 a n d that abovecanopy q)p = 2 0 0 0 /xmol m -2 sec -1. For z below the b o t t o m of the live crown q~p(Z) = 1 6 4 / x m o l m -2 sec -1. Because Eq. (3.9) is strongly non-

3The equation for photsynthesis is Ane t - - ( a p q ~ p A g ) / ( a p q 0 p -k- Ag) - Rd, where a p is the quantum yield efficiency, Ag is maximum (gross, light-saturated) photosynthetic rate, and Rd is dark respiration. We have used Up -- 0.03 mol tool -1, Ag -- 10 /xmol m -~ sec-1, and Rd = 0.2 /xmol m -2 sec -1. For most physiological studies, this equation has been superseded by biochemically based descriptions of photosynthesis (see Chapter 5), but it remains useful for many applications.

70

3. Canopy Architecture and Microclimate

linear, averaging the within-canopy flux and inserting the result into the e q u a t i o n for leaf photosynthesis, to calculate canopy photosynthesis, is likely to lead to significant errors. In this case, ~ 0 p ( a v e r a g e ) = (2000 + 1 6 4 ) / 2 = 918 /xmol m -2 sec -1, which would give A n e t = 7.14 and a canopy photosynthesis rate (Ac = 5 Anet) of 35.7 /xmol m -2 sec -1. A better a p p r o x i m a t i o n is o b t a i n e d by dividing the canopy into layers, each of thickness 1 L*, calculating q~p for the center of each layer (i.e., for L* = 0.5, 1.5 . . . . 4.5), inserting those values into the photosynthesis e q u a t i o n and s u m m i n g the values. This gives q~p.i = 1558, 945,573, 347, and 210, and A n e t . i = 8.1, 7.2, 6.1, 4.9, and 3.7, i.e., Ac = 30/xmol m - 2 sec -1 - 19% lower than the first estimate. (The greater the n u m b e r of layers, the better the estimate). A value close to this can also be o b t a i n e d by calculating q~p for the middle (in terms of leaf area) of the canopy, i.e., L* = 2.5, and inserting that value into the photosynthesis equation, which gives Ane t = 6.1/xmol m -2 sec -1 h e n c e - - t a k i n g this as representative of the canopym~Anet = 30.5/xmol m -2 sec -1. The Beer's law equation as presented here assumes that the horizontal distribution of radiation flux density at any level in the canopy is the same. The e q u a t i o n provides no i n f o r m a t i o n a b o u t the relative p r o p o r tions of sunlit and shaded leaves, a l t h o u g h simple observation of any sunlit canopy reveals that there are very large differences, at any given level, between the intensities of leaf illumination. Figure 3.7 illustrates the different patterns of radiation a b s o r p t i o n and p e n e t r a t i o n that may occur in forest canopies. Bright sunflecks can penetrate to the floor of forest canopies that have L* values of 8 or 9, whereas some leaves in clumps near the top of canopies may be completely shaded. Equation (3.9) also takes no account of the fact that solar radiation may be either direct or diffuse. Beam radiation is directional, the angle of incidence on the canopy being a function of e a r t h - s u n g e o m e t r y (latitude, longitude, time of day, and time of year). The probability of a beam being intercepted by elements of a canopy, or of p e n e t r a t i n g it, d e p e n d s on the leaf area in the path of the beam and the average orientation of the leaves. Diffuse radiation comes from all parts of the sky. The ratio of diffuse to total solar radiation ranges from a b o u t 0.1, u n d e r clear sky conditions, to 1.0 when the sky is overcast. The ratio will vary with location, season, and atmospheric pollution. Diffuse radiation penetrates canopies more effectively than direct radiation. A g o o d illustration of this is provided by Hollinger et al. (1994), who f o u n d that net CO 2 uptake per unit of absorbed radiation in an old-growth Nothofagus forest was 50% higher when radiation was p r e d o m i n a n t l y diffuse as o p p o s e d to p r e d o m i n a n t l y direct. Of the models that deal with the p r o b l e m of sunlit and shaded foliage, that of N o r m a n (1980, 1982) is one of the better known and has been described in "user friendly" form by Landsberg (1986). McMurtrie's BIO-

II. Energy Balance and Interception of Visible (Photosynthetically Active) Radiation

71

M A S S m o d e l ( s e e M c M u r t r i e et al., 1990: see a l s o C h a p t e r 9) u s e s a r e l a t i v e l y s i m p l e f o r m u l a t i o n b a s e d o n N o r m a n ( 1 9 8 0 ) in w h i c h t h e f o r e s t is r e p r e s e n t e d by a r a n d o m l y s p a c e d a r r a y o f c r o w n s a n d t h e f o l i a g e is divided into three horizontal layers of equal depth. The ratio of direct a n d d i f f u s e r a d i a t i o n , f o r a n y day, m u s t b e s p e c i f i e d . S u n l i t L * ( f o r w h i c h q~p is t h e s a m e as at t h e c a n o p y t o p ) is g i v e n by L* b -

(1 - e x p ( - k L * / c o s Z ) ) c o s Z / k ,

(3.1o)

w h e r e Z is s o l a r z e n i t h a n g l e . T h i s c a n b e n u m e r i c a l l y i n t e g r a t e d o v e r t h e day. R i g o r o u s a n d a c c u r a t e f o r m s a r e r e l a t i v e l y c o m p l e x , b u t to a r e a s o n -

Figure 3.7 PAR transmisson through forest canopies. (a) Data fromJarvis et al. (1976), showing transmission along a transect below L* = 4.2 in a Sitka spruce canopy. The 6-m transect was traversed in 3.3 sec. (b and c) Data from Torquebiau (1988) showing vertical daily average gradients of PAR in a layered tropical rainforest (b) and a nonlayered forest (c). (d) Data from Baldocchi and Vogel (1996) showing diurnal patterns of PAR interception under a boreal jack pine forest (L* ~ 0.7; biomass area index ~ 2.5) and a temperate broad-leaved forest (L* ~ 4.0).

79

3. Canopy Architecture and Microclimate

able a p p r o x i m a t i o n the flux density of diffuse radiation can be estimated from Eq. (3.9) applied to the diffuse c o m p o n e n t of q~s(0), so the radiation received by sunlit leaves is @p.sunlit = q0p.direct q- q)p.diffuse,

(3.11)

a n d the radiation received by the shaded leaves (L*s = L* - L'b, or L*s = L*(1 - L * b / L * ) ) is given by the @p.diffuse term. Wang et al. (1992) have provided a series of c o m p a r i s o n s of canopy photosynthesis between results o b t a i n e d using BIOMASS and the far m o r e c o m p l e x and complete description of canopy interception provided by the model MAESTRO (Wang and Jarvis, 1990a,b; see C h a p t e r 9). As we n o t e d earlier, it is i m p o r t a n t to have detailed and accurate models to ensure that we u n d e r s t a n d the implications and consequences of simplifications. However, Eq. (3.9), used with the best available estimates of q~s to provide the i n p u t term for a simple m o d e l such as the e model (see C h a p t e r 9), will generally suffice for practicing forest scientists who may be interested in growth and yield estimates for forests for which the only i n f o r m a t i o n about canopy architecture is height, stand density, and (perhaps) L*.

III. Heat and Mass Transport A. Within-Canopy Microclimate T h e microclimate within stands d e p e n d s on energy absorption by the foliage, branches, and soil, evaporation of water from these elements to the air in the canopy, and transfer processes from within the canopy to the reference level. Typical profiles of within-canopy conditions are shown in Fig. 3.8. Profiles such as those sketched in Fig. 3.8 will be strongly influenced by canopy architecture as well as by current e n v i r o n m e n t a l conditions. In a dense canopy with high leaf area density in the u p p e r layers, most of the incident radiant energy will be absorbed in those layers, which will also absorb most of the m o m e n t u m from the wind, causing low wind speeds and ineffective e x c h a n g e of scalars (heat, water vapor, and CO2) between leaf surfaces and the air. The result will be h i g h e r temperatures in the u p p e r than in the lower part of the canopy, and high humidity if the stomata are open and transpiration is taking place. Conditions within canopies both d e t e r m i n e a n d are d e t e r m i n e d by the leaf energy balance, i.e., transpiration and leaf to air transfer processes are intimately coupled to within-canopy microclimate. T e m p e r a t u r e and h u m i d i t y in the lower part of the c a n o p y will also d e p e n d on w h e t h e r the soil is wet or dry and the rate of soil evaporation. A detailed t r e a t m e n t of leaf energy balance

III. Heat and Mass Transport

73

Figure 3.8 Characteristic mean day time vertical profiles of temperature (T ~ C), vapor pressure (e, kPa), and wind speed (u, m sec -1) in a forest. Note the increase in temperature in the region of greatest foliage density, where most of the energy is absorbed. The increase in wind speed near the bottom of the canopy is characteristic of forests with little understory and would appear to cause countergradient fluxes (see Denmead and Bradley, 1985). Figure is based on Fig. 13 ofJarvis et al. (1976).

has b e e n p r o v i d e d by L e u n i n g (1989), a n d L e u n i n g et al. (1995) have p r e s e n t e d a c o u p l e d set o f r e l a t i o n s h i p s b e t w e e n stomatal c o n d u c t a n c e , C O 2 assimilation, a n d the l e a f e n e r g y b a l a n c e u s i n g a simplified description of r a d i a t i o n by sunlit a n d s h a d e d leaves. We will n o t be d e a l i n g in any detail with t r a n s f e r p r o c e s s e s w i t h i n canopies. We simply n o t e that, a l t h o u g h t h e r e have b e e n m a n y a t t e m p t s to d e s c r i b e t h e m by o n e - d i m e n s i o n a l f l u x - g r a d i e n t r e l a t i o n s h i p s , t h e s e do n o t w o r k in c a n o p i e s . It is now r e c o g n i z e d that gusts s w e e p i n g t h r o u g h c a n o p i e s are t h e d o m i n a n t m e a n s of scalar t r a n s f e r (see Fig. 3.9). T h e velocity of the gusts t e n d s to be a b o u t twice t h e velocity o f the m e a n w i n d (M. R a u p a c h ; p e r s o n a l c o m m u n i c a t i o n ) . R a u p a c h (1989) p r o v i d e s an o u t l i n e o f m o d e r n t h e o r y o f w i t h i n - c a n o p y transfers, w h i c h is still b e i n g developed.

B. Transpiration from Stands: The Combination Equation C a l c u l a t i o n o f the rates o f e v a p o r a t i o n (of free water) a n d t r a n s p i r a t i o n f r o m stands is b a s e d o n the p r i n c i p l e s o f e n e r g y b a l a n c e a n d mass transfer. E q u a t i o n (5.8) indicates that the r a d i a n t e n e r g y a b s o r b e d by a canopy is p a r t i t i o n e d into latent h e a t (AE), sensible h e a t ( H ) , or h e a t s t o r e d in the b i o m a s s a n d the soil (G). B e c a u s e h e a t gains d u r i n g the day t e n d to be b a l a n c e d by losses at n i g h t , we can i g n o r e G if we are c o n c e r n e d with p e r i o d s o f days or l o n g e r . T r a n s p o r t f r o m the c a n o p y to t h e overlying air can be t r e a t e d as a oned i m e n s i o n a l process, a n a l o g o u s to Ficks law o f diffusion, w h i c h states

74

3. Canopy Architecture and Microclimate

Figure 3.9 Vertical profiles of temperature (solid lines) and humidity (dotted lines) in a forest during and after the passage of a gust. The dashed arrows are the contours of constant temperature and vapor pressure. The arrow depicts the penetration of the gust (redrawn from Denmead and Bradley, 1985).

that the flux o f an entity is given by the p r o d u c t o f its c o n c e n t r a t i o n grad i e n t a n d a diffusion coefficient. T h e sensible a n d latent h e a t flux densities (W m - Z ) can, t h e r e f o r e , be e x p r e s s e d as the p r o d u c t of the g r a d i e n t s o f t e m p e r a t u r e (7) a n d specific h u m i d i t y (q) a n d a diffusion coefficient. T h e g r a d i e n t s are b e t w e e n s o m e level in the c a n o p y m d e n o t e d by the s u b s c r i p t " o " - - a n d a r e f e r e n c e level at h e i g h t z in the b o u n d a r y layer a b o v e the canopy. Specific h u m i d i t y has the d i m e n s i o n s (kg v a p o r kg -1 air). It is directly r e l a t e d to v a p o r p r e s s u r e - - s e e , for e x a m p l e , M o n t e i t h a n d U n s w o r t h (1990) m a n d is a c o n v e n i e n t p a r a m e t e r to use in mass flux calculations. If the air was a b s o l u t e l y still, the rate o f scalar diffusion would be det e r m i n e d by the m o l e c u l a r diffusivities o f the s c a l a r s - - a p r o p e r t y o f the air a n d t h e i r m o l e c u l a r weights. H o w e v e r , m o s t o f the time the air over forests a n d o t h e r n a t u r a l surfaces is n o t still, b u t in t u r b u l e n t m o t i o n , so the diffusion coefficient d e p e n d s o n t u r b u l e n c e . T u r b u l e n c e arises because, w h e n w i n d blows over a surface, the m o l e c u l e s of air in c o n t a c t with any solid (soil, leaves, a n d b r a n c h e s in the case of a forest c a n o p y ) are s t a t i o n a r y ( a e r o d y n a m i c i s t s call this the "no-slip" c o n d i t i o n ) . T h e wind t h e r e f o r e e x e r t s "drag" o n the g r o u n d a n d plants, i.e., t h e r e is a flux o f m o m e n t u m to the surface ( m o m e n t u m has units of force: N m -2 or kg m -2 s e c - 1 ) . S o m e d i s t a n c e above the surface, the wind s p e e d will have s o m e relatively large value; t h e r e f o r e , b e t w e e n the surface a n d that level t h e r e m u s t be a r e g i o n o f wind s h e a r w h e r e , in any layer, air at h i g h e r levels is m o v i n g faster t h a n the air in the layer j u s t below [see Eq. ( 3 . 1 7 ) - (3.20)]. At all b u t the lowest s p e e d s this situation is u n s t a b l e a n d g e n e r a t e s t u r b u l e n c e m e d d i e s a n d gusts o f air at all s c a l e s m t h a t

III. H e a t a n d M a s s T r a n s p o r t

75

efficiently mixes the a t m o s p h e r e . T h e strength of the m o m e n t u m flux is related to the intensity of the t u r b u l e n c e p r o d u c e d by the wind shear. Because heat, water vapor, a n d CO 2 are e n t r a i n e d in the air stream, the diffusion coefficients that d e t e r m i n e the rate at which they are transferred along c o n c e n t r a t i o n gradients d e p e n d on t u r b u l e n c e , which dep e n d s on the wind speed a n d the r o u g h n e s s of the surface. We express the diffusion coefficients for heat and water v a p o r as an a e r o d y n a m i c c o n d u c t a n c e ga, which encapsulates the processes of molecular and t u r b u l e n t diffusion of heat a n d vapor t h r o u g h the air f r o m the leaves and f r o m the soil surface to the reference h e i g h t z. T h e conductances for heat, water vapor, a n d m o m e n t u m are not strictly the same, b u t the a s s u m p t i o n of similarity generally introduces only m i n o r error. T h e equation for sensible heat flux is H = pcp(To

-

T(z))ga,

(3.12)

where p = air density ~- 1.2 kg m -3 at 20~ and Cp is the specific heat of air at constant pressure ~ 1.01 X 103 j k g - 1 K - 1), and that for latent heat flux is -- ~.P(qo

q(2:))ga,

-

(3.]3)

where A is the latent heat of vaporization of water ( ~ 2 . 4 4 X 106 j k g - 1). T h e rate of water loss from a dry c a n o p y is d e t e r m i n e d by the transpiration rates of the leaves in the canopy. Water vapor moves f r o m the substomatal cavities, where the air is saturated at leaf t e m p e r a t u r e T1 , to the leaf surface t h r o u g h the stomatal apertures. This process (transpiration) can be described by an equation, similar to Eq. (3.12), for the flux of vapor: E = p(qsat(T1)

-

qo.leaf)gs,

(3.14)

where gs is the stomatal c o n d u c t a n c e a n d qo.leaf is the specific h u m i d i t y at the leaf surface at leaf t e m p e r a t u r e T1. Stomatal c o n d u c t a n c e is a physiological p a r a m e t e r , u n d e r the "control" of the plant, r e s p o n d i n g to light, a t m o s p h e r i c CO 2 concentrations, a t m o s p h e r i c humidity, a n d soil water conditions. (We discuss gs a n d the factors affecting it in C h a p t e r s 4 a n d 5, in which the influence of e n v i r o n m e n t a l factors a n d soil conditions are c o n s i d e r e d in some detail.) If we now aggregate all the leaves in the canopy and add the v a p o r flux f r o m o t h e r plant surfaces a n d the soil, we can write a n o t h e r e q u a t i o n analogous to Eq. (3.12) for the whole canopy, with the h u m i d i t y g r a d i e n t given by the difference between saturated q at To, the r e f e r e n c e c a n o p y t e m p e r a t u r e , a n d specific h u m i d i t y (qo) in the canopy: E-

p(qsat(To)

-

qo)gc"

(3.15)

76

3. Canopy Architecture and Microclimate

T h e c a n o p y c o n d u c t a n c e gc is n o l o n g e r a p u r e l y physiological quantity like g~ b u t it includes fluxes f r o m the soil a n d is also m o d i f i e d by turbulent mixing a n d variation of gs within the canopy. With some assumptions, such as that leaf a n d air t e m p e r a t u r e s are the same, a n d some a p p r o x i m a t i o n s relating to the linearity of h u m i d i t y gradients, Eqs. (3.8), (3.12), a n d (3.15) can be c o m b i n e d , using a set of now s t a n d a r d p r o c e d u r e s (see M o n t e i t h and Unsworth, 1990), to yield the P e n m a n - M o n t e i t h ( P - M ) , or c o m b i n a t i o n equation: ,~E =

eq~n +

apDqga

e + 1 + ga / 'gc

(3.16)

w h e r e Dq = q ( z ) - qsat(T(z)), i.e., the specific saturation deficit at the refe r e n c e level z, a n d e = (A/Cp)(dqsat/dT), i.e., the dimensionless rate of c h a n g e of saturated specific h u m i d i t y with t e m p e r a t u r e (e = 2.2 at 20~ T h e P - M e q u a t i o n , as we shall h e n c e f o r t h refer to it, neatly encapsulates the two e n v i r o n m e n t a l drivers of e v a p o r a t i o n m t h e net radiant energy "supply" q~,, a n d the supply of dry air D q m w i t h the two essential controls: ga, a m e a s u r e of the mixing power of the a t m o s p h e r e , and go, the (primarily) physiological control e x e r t e d by plants. An i m p o r t a n t p o i n t to n o t e is that if canopies are wet g,. ~ oc a n d the e q u a t i o n provides the rate of water loss from a wet canopy. It is possible to obtain estimates of the a e r o d y n a m i c c o n d u c t a n c e ga by e m p l o y i n g m o m e n t u m as a tracer. This involves m e a s u r i n g the m e a n h o r i z o n t a l wind speed (u) above a forest, which is usually m u c h simpler t h a n m e a s u r i n g the fluxes of heat a n d water vapor. T h e m e a s u r e m e n t s m u s t be m a d e high e n o u g h to avoid the high-frequency t u r b u l e n c e generated by e l e m e n t s of the canopy. For extensive a n d horizontally h o m o g e n e o u s canopies, the m e a n wind speed above a h e i g h t of a b o u t 2 hc is well d e s c r i b e d by the l o g a r i t h m i c wind profile: u(z) = --~ In

z0

,

(3.17)

w h e r e d is k n o w n as the zero plane displacement. It is equal to the m e a n level of m o m e n t u m a b s o r p t i o n in the canopy a n d forms a new, elevated origin for the wind profile. T h e a e r o d y n a m i c r o u g h n e s s length, Zo, is a m e a s u r e of the m o m e n t u m - a b s o r b i n g capacity of the canopy, and k is a constant with a value of 0.4, called von Karman's constant. The key quantity o b t a i n e d f r o m Eq. (3.17) is u., the friction velocity, which provides a m e a s u r e of the flux of m o m e n t u m (r) to the canopy: pu '2= r,

(3.18)

w h e r e r is c o n s t a n t for a c o n s i d e r a b l e h e i g h t above horizontally u n i f o r m canopies. We can now write, by analogy with Eqs. (3.12) a n d (3.13),

IlL Heat and Mass Transport ~" = pu 2 = p(u(z) -- u0)g a = pU(Z)gaM,

77

(3.19)

where Uo = u(d+zo) = 0. C o m b i n i n g Eqs. (3.17) and (3.19) leads to a simple expression for gaM, the a e r o d y n a m i c c o n d u c t a n c e for m o m e n t u m :

g~M = ku.

Iln ( z - d )

J -1 .

(3.20)

z0

Equation (3.17) and the c o n s e q u e n t expression for g~M are altered when there are strong t e m p e r a t u r e gradients in the a t m o s p h e r e . If the g r o u n d or canopy is h o t (heated by the sun), the air in contact with the surface is warmed, expands, and rises to be replaced by cooler air from above, generating t u r b u l e n t mixing in the process. If the canopy or g r o u n d is cooler than the air (e.g., if it has b e e n cooled by radiation at night), then the air at lower levels is d e n s e r and extra t u r b u l e n c e m u s t be g e n e r a t e d by wind shear to stir it up. Hence, bouyancy can e n h a n c e or decrease t u r b u l e n t mixing, d e p e n d i n g on w h e t h e r the conditions are unstable (daytime) or stable (nighttime). T h e r e are well-understood equations for adjusting the diffusivities to take account of bouyancy effects (see Kaimal and Finnigan, 1994); however, the a d j u s t m e n t factors are different for heat, water vapor, and m o m e n t u m exchange so that the ass u m p t i o n of similarity of c o n d u c t a n c e s no longer holds if bouyancy is important. This is unlikely to be of c o n c e r n except to those u n d e r t a k i n g detailed short-term m i c r o m e t e o r o l o g i c a l measurements. Values of d have generally been f o u n d by iterative adjustments of the value used to linearize Eq. (3.17) and the use of regression to minimize least-squares deviations. This m e t h o d is fraught with difficulty a n d uncertainty, and there are better p r o c e d u r e s (Thorn, 1975; Jackson, 1981). In most cases, a value of d ~ 0.75 h~ will give consistent results. This accords with the values reviewed by L a n d s b e r g (1986, p. 57), who also indicated a value of z0 ~ 0.1 hc as a useful general a p p r o x i m a t i o n . Jarvis et al. (1976) reviewed values of Zo and d r e p o r t e d from studies on coniferous forests: T h e r e was considerable variation but the ratios zo/hc a n d d/h~ were 0.08 _+ 0.05 and 0.8 +_ 0.09, respectively. Shuttleworth (1989) r e p o r t e d values of zo/hc and d/h~ of 0.06 and 0.86 for an A m a z o n i a n tropical forest. In general, we can expect the u p w a r d displacement of the wind profile to be m o r e p r o n o u n c e d w h e n canopies are dense than w h e n they are sparse: In the limit of an extremely dense canopy (unrealistic in relation to radiation i n t e r c e p t i o n and canopy photosynthesis), d/h~ values would t e n d toward hc, whereas Zo would be e x p e c t e d to b e c o m e progresively smaller, as it does, for example, for s m o o t h land surface covered by short grass. Kelliher et al. (1995), evaluating the m a x i m u m c o n d u c t a n c e s for global vegetation types, m a d e a distinction between the bulk surface con-

78

3. Canopy Architecture and Microclimate

d u c t a n c e (which they called Gs), w h i c h is essentially an a g g r e g a t e d measure o f stomatal c o n t r o l , a n d the b u l k c a n o p y c o n d u c t a n c e (Gc), w h i c h reflects e v a p o r a t i o n f r o m the soil as well as f r o m the canopy. T h e y s h o w e d that m a x i m u m values of Gs (Gsmax) significantly e x c e e d Gcmax w h e n L* is less t h a n a b o u t 3. For L* > 3, Gsmax ~ Gcmax, a n d Gsmax is linearly r e l a t e d to m a x i m u m leaf stomatal c o n d u c t a n c e , gsmax, as o b t a i n e d f r o m m e a s u r e m e n t s o n leaves. T h e r e l a t i o n s h i p b e t w e e n Gsmax a n d gsmax was r e m a r k a b l y consistent, with a slope o f 3, i.e., Gsmax/gsmax -- 3; therefore, in o u r n o t a t i o n , gcmax -- 3 gsmax. K 6 r n e r (1994) p u b l i s h e d a valuable distillation of data in the f o r m of average values o f gsmax for over 20 vegetation types a n d 200 species. Estimates o f gsmax can be o b t a i n e d f r o m that p a p e r . K e l l i h e r et al. (1995) f o u n d the average value o f gcmax [our notation; Eq. (3.16) ] to be a b o u t 18 m m sec -1 (0.018 m sec -1) for w o o d y v e g e tation. ( T h e data they e x a m i n e d i n c l u d e d c o n i f e r o u s forests, t e m p e r a t e d e c i d u o u s forests, a n d tropical rainforests.) T h e study by K e l l i h e r et al. (1995) provides the basis for e s t i m a t i n g m a x i m u m t r a n s p i r a t i o n rates for any forest n o t s h o r t of water u n d e r h i g h r a d i a t i o n c o n d i t i o n s . I n s e r t i n g the p r e v i o u s values of ga a n d gcmax into Eq. (3.16), with r e p r e s e n t a t i v e values o f the o t h e r variables (q~n = 500 W m - z , Dq = 6.10 -3 kg k g - 1 ) , gives E = 1.3 • 10 -4 kg m -2 sec -1 0.5 m m h r -1. If we a s s u m e sinusoidal variation in E t h r o u g h a 14-hr day, with 0.5 m m h r - 1 as the m a x i m u m rate, total daily t r a n s p i r a t i o n is 4.5 m m . Obviously, t h e r e will be s o m e variation for d i f f e r e n t forests a n d environmental conditions. Values o f g,. are c o m m o n l y e s t i m a t e d f r o m stomatal c o n d u c t a n c e a n d L* as gc = ~g.~L*, w h i c h c o r r e s p o n d s exactly with g,.,,,.~x ~ 3 g.~maxat L* = 3. H o w e v e r , R a u p a c h a n d F i n n i g a n (1988) d e m o n s t r a t e d that the approxim a t i o n b a s e d o n L* is n o t an a c c u r a t e m e a s u r e o f gc b e c a u s e m a s we p o i n t e d o u t e a r l i e r - - g , , i n c l u d e s an a e r o d y n a m i c c o m p o n e n t a n d is t h e r e f o r e n o t a p u r e l y physiological p a r a m e t e r . T h e physiological cond u c t a n c e also d o e s n o t reflect c o n t r i b u t i o n s to c a n o p y e v a p o r a t i o n f r o m s o u r c e s such as the soil a n d free ( i n t e r c e p t e d ) water. Values of gc for stands can be o b t a i n e d f r o m e x p e r i m e n t a l l y d e t e r m i n e d values of E a n d the s o l u t i o n o f Eq. (3.26) for go, i.e., 1 _- 1 eq~n + / l p D g,. AE \ ga

.

(3.21)

g'~

For e x a m p l e , a s s u m e that m e a s u r e m e n t s over a p e r i o d of weeks indicated that, for a p a r t i c u l a r stand, E = 4 m m day-1. We will take ga = 0.2 m s e c - 1, average q~,~ = 18 MJ m -z day -1, i.e., 18 • 1 0 6 j m -z a n d average air t e m p e r a t u r e = 28~ with relative h u m i d i t y 65%, giving D = 8.23 • 10 -3 kg kg -1. Because 4 m m day -1 is e q u i v a l e n t to 4 kg m -z, M'2 = 4 • 2.44 • 1 0 6 j m -2 a n d gc = 0.028 m sec -1 in this e x a m p l e .

III. Heat and Mass Transport

79

It is clear from the foregoing discussion that g o o d estimates of the m a x i m u m transpiration rates for virtually all forest types, w h e n L* >-- 3, can be o b t a i n e d from the P - M e q u a t i o n with values of ga of a b o u t 0 . 1 - 0 . 2 m sec -1 and gc ~ 0.020 m sec -]. The potential t r a n s p i r a t i o n rate, i.e, the rate driven by a t m o s p h e r i c factors interacting with canopy surfaces but w i t h o u t the constraint of stomata, can be estimated by assuming that surfaces in the canopy are wet so that stomata exert no influence; i.e., gc ---)00. This reduces the P - M e q u a t i o n to AE =

eq~ + n

pADqga

e+l

(3.22)

which is a useful form for evaluating water balances on a long-term or wide-scale basis. Where gc < gcmax, for example, because of water shortage in the r o o t zone or because the potential transpiration rate "demands" water faster than the s o i l - r o o t - t r e e c o n d u c t i n g system can supply it, then actual stand transpiration rates will be less than potential rates. These situations may occur on a daily (see Fig. 3.9) or l o n g e r - t e r m basis. They are discussed in some detail in C h a p t e r 4, Section I,D.

C. Transpiration from Stands: Bowen Ratios Equations (3.12) and (3.13) provide us with the means of estimating the fluxes of sensible and latent heat, and Eq. (3.16) combines the energy and mass-transfer processes that drive evaporation. T h e ratio of these fluxes, widely used to estimate the rate of evaporation from surfaces for which q~n is known, is called the Bowen ratio (/3). I g n o r i n g G, the flux of heat in and o u t of storage, H 13 - AE'

(3.23)

from which, substituting in Eq. (3.8), ~n

AE= 1 +/3'

(3.24)

i.e., given values for 13 and (~n we can solve for ,~. In principle, values for 13 are relatively easily o b t a i n e d experimentally by using Eq. (3.23) in finite difference form (13 = T A T / A e , where y is the p s y c h r o m e t r i c constant ~ 0.066 kPa ~ and m e a s u r i n g t e m p e r a t u r e and v a p o r pressure gradients across some h e i g h t difference (A). In practice, even w h e n A is quite large, the gradients are often very small and difficult to m e a s u r e with sufficient accuracy. Restrictions to this m e t h o d are i m p o s e d by the r e q u i r e m e n t that b o t h sets of m e a s u r e m e n t s should be m a d e high e n o u g h above the canopy for the assumptions a b o u t t u r b u l e n t e x c h a n g e

80

3. Canopy Architecture and Microclimate

to hold. It is also necessary that b o u y a n c y does n o t cause significant diff e r e n c e s in the e d d y diffusivities, a n d that the m e a s u r e m e n t s s h o u l d be m a d e within the layer of air above the stand that reflects the p r o p e r t i e s of the stand, i.e., within the b o u n d a r y layer of the stand. Air flowing across the stand f r o m elsewhere will carry the p r o p e r t i e s of the surfaces across which it has passed; t h e r e f o r e , m e a s u r e m e n t s m a d e in that airs t r e a m will n o t provide i n f o r m a t i o n a b o u t the processes taking place in the stand of interest. T h e s e r e q u i r e m e n t s may be difficult to m e e t in forest r e s e a r c h , e x c e p t in the case of large plantations in fiat areas, b u t / 3 is a valuable a n d i n f o r m a t i v e p a r a m e t e r , which has b e e n widely used a n d m e a s u r e d in r e s e a r c h on forest m i c r o m e t e o r o l g y a n d water use. Jarvis et al. (1976) review m a n y of the early ( 1 9 6 0 s - e a r l y 1970s) Bowen ratio m e a s u r e m e n t s over forests. T h e Bowen ratio can also be d e r i v e d f r o m Eq. (3.16), for which we use the p r o c e d u r e d e s c r i b e d by T h o m (1975) (see also Kaimal a n d Finnigan, 1994). Rewriting the e q u a t i o n with resistances (r = 1 / c o n d u c t a n c e ) , for algebraic c o n v e n i e n c e , gives AE =

sop ,~ + a p D / r a

.

(3.25)

I n t r o d u c i n g a "quasi r e s i s t a n c e " - - s o m e t i m e s resistancem

called a climatological

e + 1 + rc/ra

ApD

ri = ~, q~n

(3.26)

a n d n o t i n g (as o b s e r v e d earlier) that the resistances to heat and water vap o r transfer can be taken as a p p r o x i m a t e l y equal, t h e n f r o m Eqs. (3.16) a n d (3.23), AE

q~,~

=

1

1 + j8

=

13 + r i / r.~

e + 1 + rc/r~

,

(3.27)

hence, /3 =

e + 1 + rc/r.,~ e + ri/r a

- 1.

(3.28)

This provides a valuable diagnostic tool f r o m which, given re,/3 can be est i m a t e d or the implications of values of/3, in terms of re, can be evaluated. Alternatively, the effects of c h a n g e s in rc o n / 3 can be assessed. T h e r e have b e e n e n o u g h studies of the e n e r g y balance, and e n e r g y p a r t i t i o n i n g over forests, to provide a g o o d i n d i c a t i o n of the values of/3

III. H e a t a n d M a s s Transport

Table 3.1

81

Observed Values of the Bowen Ratio (,8 = H/aE) for a Range of Forest Ecosystems

Forest ecosystem and location

/3

Notes

Tropical

Broad-leaved evergreen, Thailand (1)a Broad-leaved evergreen, Thailand (1) Broad-leaved evergreen, Amazon (2)

6.38 0.45 0.43

Dry season Wet season Integrated daily values (6)

Loblolly pine, Southern United States (3)

0.2-2

Scots pine, England (4)

1.38-3.06

Deciduous mixed, Tennessee, United States (5) Deciduous mixed, Tennessee, United States (6)

0.6

Daily values over 4 days, spring Midday values, fine days Representative value, summer Representative value, summer

Temperate

0.47

Boreal

Jack pine, Canada (6)

6

Representative value, summer

aSources: 1, Pinker, et al. (1980a); 2, Shuttleworth et al. (1984); 3, Murphy et al. (1981); 4, Stewart and Thom (1973); 5, Verma et al. (1986); 6, Baldocchi and Vogel (1996).

t h a t c a n b e e x p e c t e d u n d e r v a r i o u s c o n d i t i o n s . A s e l e c t i o n is p r o v i d e d i n T a b l e 3.1, a n d Fig. 3 . 1 0 i n d i c a t e s t h e w a y r c v a r i e s . T h e v a l u e s o f / 3 i n T a b l e 3.1 i l l u s t r a t e d i u r n a l v a r i a t i o n as w e l l as v a r i a tion caused by conditions

and species. The data analyzed by Stewart and

Figure 3.10 Typical diurnal patterns of forest canopy resistance (rc) based on data of (interalia) Stewart and Thorn (1973), Lindroth (1985), and Verma et el. (1986).

82

3. Canopy Architecture and Microclimate

T h o m (1973) showed considerable diurnal variation. T h e patterns were not very consistent but the general trend was for/3 to increase t h r o u g h the day and t h e n fall toward evening; on one occasion, it r e a c h e d a value of 10, indicating that m o r e than 90% of available energy was going to sensible heat. T h e values from T h a i l a n d can p r e s u m a b l y be taken as representative of tropical m o n s o o n forests in the dry and wet season. Pinker et al. (1980b) did not say w h e t h e r canopies were ever wet when they m a d e their m e a s u r e m e n t s , but it is probably safe to assume they were not. T h e i r wet season value is consistent with that of Shuttleworth et al. (1984) for the Amazon forest. The values given by Verma et al. (1986) showed a t e n d e n c y opposite to that of the Scots pine data (Stewart and T h o m , 1973)"/3 t e n d e d to fall from relatively high values early in the day to low values in the a f t e r n o o n and evening. However, their "high" values were only a b o u t 0 . 8 - 1 . 0 . Perhaps the most interesting contrast is provided by the data of Baldocchi and Vogel (1996). They found that only a b o u t 23% of available energy (q~n) was c o n v e r t e d to latent heat (transpiration) by the boreal jack pine forest, c o m p a r e d to 66% by the broad-leaved deciduous forest in Tennessee. Some of the reason for this difference lies in the fact that the jack pine had a leaf area index of only a b o u t 0.7, c o m p a r e d to about 5 for the broad-leaved d e c i d u o u s forest. Increasing vapor pressure deficits caused increasing transpiration rates in the mixed broad-leaved forest, where stomata are apparently not sensitive to humidity, but caused decreasing transpiration rates in the jack pine, where h i g h e r D presumably caused stomatal closure. These results are consistent with the theory put forward by Jarvis and M c N a u g h t o n (1986), who e x p l o r e d the coupling between vegetation and the e n v i r o n m e n t using the P - M equation as a basis. They d e v e l o p e d a coupling factor (1~), which gives a measure of the extent to which transpiration is c o u p l e d to (driven by) available energy or d e c o u p l e d from it. T h e t e m p e r a t e broad-leaved deciduous forest is quite strongly c o u p l e d to q),, whereas the jack pine is m o r e sensitive to variations in a m b i e n t humidity; hence, it is less strongly coupled to q~nThe implications of canopy resistance (r,.) values can be explored using the previous i n f o r m a t i o n a b o u t / 3 and Eq. (3.28). However, it is useful to consider some data so diurnal results presented by Stewart and T h o m (1973), L i n d r o t h (1985, a Scots pine forest in Sweden), Shuttleworth et al. (1984), and Verma et al. (1986) are s u m m a r i z e d in Fig. 3.10. T h e r e is r e m a r k a b l e consistency between the values o b t a i n e d for different forest t y p e s m t h e values used to construct Fig. 3.10 were not greatly different from one a n o t h e r and were not n o r m a l i z e d - - a n d in the tendency for rc to reach a m i n i m u m in the late m o r n i n g and then rise steadily t h r o u g h the day. T h e data in Fig. 3.10 are consistent with the findings of Kelliher et al. (1993), who f o u n d m a x i m u m surface conductance (gc) values for forests to be generally in the range 20-30 m m sec -1,

III. Heat and Mass Transport

83

equivalent to the m i n i m u m resistances of a b o u t 5 0 - 8 0 m sec-1 ( 2 0 - 1 2 . 5 m m sec-1) in Fig. 3.10. The a f t e r n o o n rise in rc is, presumably, generally attributable to increasing vapor pressure deficits (see the discussions in Chapters 4 and 5).

D. CO2 Transfer This section is c o n c e r n e d mainly with heat and water vapor transfer, but we should note that the same processes are responsible for the transfer of CO 2 to and from forest canopies. Equations similar to Eqs. (3.12) and (3.13) can be written to describe the m o v e m e n t of CO 2 from the air to the canopy during the day, in response to its uptake by leaves for photosynthesis, and from the canopy to the air at night, when there is no photosynthesis but respiration continues (see C h a p t e r 5) so that CO 2 concentrations in canopies rise. Daytime exchange is given by Ac = ( C a . o -

ci)gs.c =

(Ca(Z) -- C a . 0 ) g a ,

(3.29)

where Ac denotes canopy assimilation rate, Ca.o is the concentration at the reference level in the canopy, and Ca(Z) is the concentration at height z above the canopy. Stomatal c o n d u c t a n c e is written here as gsc to indicate that it is not numerically the same for CO 2 and water vapor [see Chapter 5, Eq. (5.7)]. Nevertheless, stomatal c o n d u c t a n c e controls the flux of CO 2 into leaves, and water vapor out of them, providing the link between photosynthesis and transpiration. The assumption of similarity holds for t u r b u l e n t transfer in the a t m o s p h e r e so that the value of ga would be (approximately) the same for heat, water vapor, and m o m e n tum. During the night, the right-hand side of the equation holds, but the gradients will be reversed: CO 2 is transferred away from the canopy, although wind speeds are c o m m o n l y low at night, and turbulence heavily damped; therefore, CO 2 concentrations in canopies may rise steeply (see Grace et al., 1995). Equation (3.29) underlies CO 2 flux measurements, but in itself it is of limited value for explaining physiological processes. Photosynthesis rates may be constrained by the "supply function" (see C h a p t e r 5, Section I), but the process is essentially driven by visible radiation, not by the transfer of CO 2 t h r o u g h the air. Respiration (both a u t o t r o p h i c and heterotrophic) is also i n d e p e n d e n t of transfer processes and radiation. The linkage between photosynthesis and radiation ( p h o t o n flux) is described in detail in C h a p t e r 5.

IV. Effects of Topography on Microclimate Underlying most of the discussion so far has b e e n the assumption that the land areas we are considering are, at least approximately, level and

84

3. Canopy Architecture and Microclimate

uniform. In practice, many forests and forested areas are in uneven terrain and are n o t u n i f o r m in their characteristics. In these situations, rigorous theoretical treatments may not be very useful, a l t h o u g h they always provide a valuable guide. Considering radiation, it is always possible to calculate the radiant flux on a surface of known slope and azimuth from knowledge of q)s, the sun's p o s i t i o n - - w h i c h d e p e n d s on latitude, on time of day, and d a t e - - a n d the ratio of direct to diffuse radiation. The equations can be easily prog r a m m e d into a c o m p u t e r and the diurnal course of incident energy calculated. However, for forestry applications such as estimation of yield (see C h a p t e r 9) using a simple radiation i n t e r c e p t i o n model (Eq. 3.9), or for the estimation of net r a d i a t i o n m i n both cases over periods that may range from days to a s e a s o n m t h e r e may be no n e e d to run detailed equations on a day by day basis. It will usually suffice to calculate correction factors based on the best available i n f o r m a t i o n a b o u t the slope(s) of interest. Unless the slopes are very steep, there is no need to correct for diffuse radiation, which tends to come preferentially from high in the sky (near the sun's zenith angle; Monteith and Unsworth, 1990). For direct radiation, a c o r r e c t i o n for any given m o n t h (for example) would be based on the ratio (m~) of radiation received by the slope to that received on a horizontal surface, with the calculations being run for a single day in the middle of the period. The radiation received by the slope is then q~s.s = ~s.di,-ectm~ + q~diff~,se.

(3.30)

Relations between q~ and q~,, (Eq. 3.7) can be assumed to hold. In the s o u t h e r n hemisphere, north-lacing slopes will receive more radiation than horizontal surfaces or south-facing slopes" obviously, the converse applies in the n o r t h e r n h e m i s p h e r e . In the case of complex land units (hills and valleys, catchments, etc.), the calculations could be done for all slope segments if a digital terrain map was available. This would involve decisions a b o u t criteria for h o m o g e n e o u s slopes ("what constitutes a u n i f o r m slope?") and is a n o t h e r example of a scaling problem. The effects of slope on radiation balance is very i m p o r t a n t for forest ecology. Tajchman (1984) carried out an exercise of the type outlined previously for a valley in the Appalachian region in the United States. He f o u n d net radiation varied between 1.66 and 3.21 GJ m -') year -1, the differences being almost entirely attributable to slope azimuth. This caused changes in a water balance (aridity) index, which ranged from 0.47 to 0.88. This a p p r o a c h merits f u r t h e r investigation" T h e r e are c o m p u t e r packages available that, given (digitized) c o n t o u r maps or digital terrain maps, plus the information necessary to calculate sun angles, can produce maps of relative slope irradiance. T h e r e is therefore little excuse for assuming that all slopes receive the same radiation load.

w. Effects of 7bpography on Microclimate

85

T h e wind profile e q u a t i o n s p r e s e n t e d e a r l i e r p r o v i d e us with u n d e r s t a n d i n g a b o u t t r a n s f e r p r o c e s s e s f r o m n a t u r a l surfaces, b u t they c a n n o t be u s e d to calculate e x c h a n g e coefficients a n d scalar fluxes f r o m u n e v e n t e r r a i n . T h e r e are two sets of " c o n d i t i o n s of u n e v e n e s s " that we n e e d to c o n s i d e r . O n e is flow over c h a n g i n g r o u g h n e s s , e.g., f r o m a f o r e s t to a grassland, or vice versa, a n d flow in gaps; the o t h e r is flow over hills. We will n o t deal with these q u e s t i o n s in d e t a i l - - K a i m a l a n d F i n n i g a n (1994) p r o v i d e an e x c e l l e n t , up-to-date t h e o r e t i c a l t r e a t m e n t - - b u t will o u t l i n e a few of the m a i n points r e l a t i n g to t h e m . C o n s i d e r i n g the s i m p l e s t case o f c h a n g e in surface r o u g h n e s s , w h e n air flows across o n e surface type a n d on to a n o t h e r t h e r e will be an a b r u p t c h a n g e in the s h e a r stress as the w i n d profile adjusts to the new u n d e r l y i n g surface. T h e b o u n d a r y layer of the new s u r f a c e m t h e layer in w h i c h the p r o f i l e r e p r e s e n t s the p r o p e r t i e s o f that s u r f a c e - - a d j u s t s over s o m e d i s t a n c e that d e p e n d s on the r o u g h n e s s l e n g t h c h a r a c t e r i s t i c of t h e new surface. T h e p r o b l e m s o f b o u n d a r y layer a d j u s t m e n t are o f conc e r n in d e a l i n g with p r o b l e m s o f a d v e c t i o n and, of c o u r s e , with wind profile m e a s u r e m e n t . O f m o r e c o n c e r n to t h o s e c o n c e r n e d with f o r e s t r y is the q u e s t i o n of flow over hills. Clearly, in any t o p o g r a p h i c a l l y c o m p l i c a t e d s i t u a t i o n wind flow will be e x t r e m e l y c h a o t i c a n d c o m p l e t e l y u n p r e d i c t a b l e . All we k n o w with any c e r t a i n t y is e n c a p s u l a t e d in Fig. 3.11, w h i c h shows the following essential features: 9 t h e flow d e c e l e r a t e s slightly n e a r the base of the hill b e f o r e accelerating to the hilltop. 9 the wind r e a c h e s its m a x i m u m velocity at t h e h i l l t o p t h e n d e c e l e r a t e s b e h i n d the hill. If the hill is s t e e p e n o u g h d o w n w i n d , a s e p a r a t i o n b u b b l e f o r m s a n d a wake r e g i o n d e v e l o p s b e h i n d the hill with a m a r k e d

Figure 3.11 Wind flow over a hill. Note that the streamlines are compressed across the top of the hill, indicating acceleration, and there is separation of streamlines and reversed flow in the lee of the hill. The extent to which this occurs depends on hill shape, surface cover, and wind direction, but it indicates why there may be regions of high turbulence in the lee of barriers (diagram redrawn from Kaimal and Finnigan, 1994, Fig. 5.3).

86

3. Canopy Architecture and Microclimate

velocity deficit e x t e n d i n g for m a n y hill heights downwind (Kaimal and Finnigan, 1994). The principles can be applied, in a very general sense, to get some idea of how prevailing winds will affect forests on hillsides. Air flow in gaps or clearings may be of c o n c e r n in forestry or forest ecology. The p r o b l e m can be defined in terms of the size of the gap and the h e i g h t and density of the forest s u r r o u n d i n g it. Gusts, or eddies, penetrating the stand upwind of a gap will be slowed by m o m e n t u m absorption by the vegetation, whereas air flowing over the top of the canopy will be forced down into the gap or clearing, decelerating slightly downwind of the leading edge. The result will be a region of slow, probably confused flow i m m e d i a t e l y downwind of the edge of the stand. Flow above the floor of the clearing will be accelerated by the downward transfer of m o m e n t u m from faster-moving h i g h e r air layers; when the flow strikes the upwind edge of the gap, it is forced up and t h r o u g h the canopy elements, as well as being lifted over the edge. T h e r e will be some back circulation near the g r o u n d , a n d a pool or eddy of slow moving air will tend to form there. Above the canopy, on the downwind side, there will be a very t u r b u l e n t zone where the slower air flow coming up t h r o u g h the canopy mingles with the faster flow being accelerated over the edge (see Fig. 3.12). T h e previous description will only be (approximately) correct if the gap or clearing is sigificantly larger than the canopy height; otherwise, the gust size that can p e n e t r a t e will not reach g r o u n d level. The last m a t t e r we will note in relation to t o p o g r a p h y and microclimate is cold air drainage. This occurs at night in cool or cold regions, where heat loss by radiation from hill or m o u n t a i n tops results in cold, viscous air that drains into the valleys below and collects t h e r e ~ a s d e m o n s t r a t e d by mist and fog on winter mornings. D e p e n d i n g on the

z=h

9

L

~'

Figure 3.12 Air tlow in a gap. T h e patlerns ~)f tl~)w i~l any partictllar sittlalir will d e p e n d on the size of gap, density ()f the stlrr()undillg fc)rest, and wind speeds. Tilt" region of highest t u r b u l e n c e will t e n d l() he the s h a d e d area ()ll the d()wnwilld side ()f the gal), particularly if the forest is dense a n d the linear d i m e n s i o n (l) of lhe gap is significanlly larger than the forest h e i g h t (h).

V. Concluding Remarks

87

topography of the area, cold air collects in pools and may persist for some time, particularly in valleys sheltered from the sun until late in the day. This may result in temperature conditions in such valleys being less favorable for the growth of some plants than at higher altitudes. U n d e r extreme conditions, cold air drainage can cause a reversal in forest vegetation zones, with more cold-tolerant species occuring at lower elevations, typically at the bottom of slopes where cold air accumulates. Cold air drainage can be disrupted by large fans that mix the a i r - - a management technique used in some areas for high-value crops, such as fruit orchards, but not in forestry.

V, Concluding Remarks Microclimatology provides the basic discipline and theoretical underpinning for the calculation of evapotranspiration and radiation interception by canopies. Virtually all the process-based models of forest productivity (Chapter 9) are based on radiation interception by canopies and the conversion of radiant energy into carbohydrates. Because forest growth is as much d e p e n d e n t on the availability of water as on temperature and soil properties, a clear understanding of the factors affecting stand, forest, and regional water balances is essential. In more immediate terms, the consequences of clear-cutting forests, in terms of the changed conditions for seedling r e g e n e r a t i o n - - a s o p p o s e d to conditions within c a n o p i e s m c a n be evaluated in terms of microclimatological processes, particularly the surface energy and water balances. The principles outlined in this chapter also provide the basis for evaluating the consequences of practices, such as thinning or clear-felling, or the destruction of an area of forest by fire in terms of energy balance and transfer processes. Clearly, the energy loads on, and energy balance of, bare or sparsely vegetated soils are vastly different than those on soils u n d e r canopies, which has massive implications for water balance and for seedling regeneration. The implications are ecological as well as managerial. On a global scale, we are c o n c e r n e d with l a n d - a t m o s p h e r e interactions and the role of forests in the global carbon balance. The probable trajectory and consequences of global climate change are being explored using global circulation models, but one of the problems with such models is the p o o r coupling with land surfaces. Forests comprise one of the major land surface types, and any knowledge that we can acquire about f o r e s t - a t m o s p h e r e interactions will be of value in improving this coupling and hence, the performance of those models. For example, the unexpectedly large heat fluxes observed from the boreal jack pine forest

88

3. Canopy Architecture and Microclimate

(Baldocchi and Vogel, 1996) appear to cause significant differences in the development of the planetary boundary layer over those forests relative to the temperate broad-leaved forest. Baldocchi and Vogel comment that the effects of temperature, humidity, and surface wetness may need to be considered when scaling gas exchange from stands to regions.

Recommended Reading Jarvis, P. G.,James, G. B., and Landsberg,J.J. (1976). Coniferous forest. In "Vegetation and the Atmosphere" (J. I,. Monteith, Ed.), Vol. 2, pp. 171-264. Academic Press, New York. Jarvis, P. G., Monteith, J. I,., Shuttleworth, W. J., and Unsworth, M. H. (Eds.) (1989). "Forests, Weather and Climate." The Royal Society, London. Jones, H. G. (1992). "Plants and Microclimate," 2nd. ed. Cambridge Univ. Press, Cambridge, UK. Kelliher, F. M., Leuning, R., and Schulze, E.-D. (1993). Evaporation and canopy characteristics of coniferous forests and grasslands. Oecologia 95, 153-163. Landsberg, J.j. (1986). "Physiological Ecology of Forest Production." Academic Press, Londoll. Monteith, J. I~., and Unsworth, M. H. (1990). "Principles of Environmental Physics," 2nd ed. Edward Arnold, London. Oker-Blom, P., and Kellom/iki, S. (1983). Effect of grouping of foliage on the within-stand and within-crown light regime: comparison of random and grouping canopy models. Agric. Meteorol. 28, 143-155. Waring, R. H. (1983). Estimating forest growth and efficiency in relation to canopy leaf area. Adv. Ecol. Res. 13, 325-354.

4 Forest Hydrology and Tree-Water Relations

Forest hydrology and t r e e - w a t e r relations are intimately a n d inextricably linked. Forest hydrology is c o n c e r n e d with the m o v e m e n t of water t h r o u g h forested landscapes. T h e water balance of stands d e p e n d s on precipitation, interception, runoff, a n d evaporation; with the e x c e p t i o n of precipitation all these processes are strongly i n f l u e n c e d by tree populations, stand structure, and canopy architecture. Over a p e r i o d of time, stand water balance influences the growth of trees a n d h e n c e stand structure and architecture. The principle u n d e r l y i n g all hydrological processes is mass balance. T h e objective of hydrological studies or calculations m u s t be to a c c o u n t for all the water c o m i n g into a system, w h e t h e r it comes as rainfall, snow, overland flow in rivers, r u n o f f from outside the system, or as belowg r o u n d flow. Water will leave the system as evaporation from o p e n water, soil, or from o t h e r wet surfaces after rain as transpiration by plants, as overland flow, or as drainage out of the system. In the case of a forest stand, drainage out of the system m e a n s d r a i n a g e out of the root zone of the trees. This includes lateral subsurface flow t h r o u g h the soil into streams. Figure 4.1 provides a schematic r e p r e s e n t a t i o n of the m a i n facets of stand water balance. The question of scale is of p r i m a r y i m p o r t a n c e in hydrology. We may consider any scale from the water status of plant tissue to the global water balance. At each scale there is variability. Forests are u n d o u b t e d l y imp o r t a n t in the global hydrological cycle, b u t their significance at that scale is difficult to estimate because of the overriding effects of the oceans. T h e i r significance at regional levels has b e e n illustrated recently by studies using global circulation m o d e l s (GCMs) such as those carried out by Dickinson a n d Henderson-Sellers (1988) a n d Shukla et al. (1990). Both these groups simulated the c o n s e q u e n c e s of converting the Amazon basin f r o m tropical forest to d e g r a d e d grassland. Dickinson a n d 89

90

4. Forest Hydrology and Tree-Water Relations

Figure 4.1 Diagrammatic representalion of tile main components of tile hydrologic balance [see Eq. (4.1) ]. The problem in torest hydrology is to quantify the various terms for different forest ecosystems and soil types. The size of the various terms, in relation to precipitation, will vary with stand density and canopy architecture. For a catchment, the runoff and drainage terms determine the catchment water yield. H e n d e r s o n - S e l l e r s u s e d t h e c h a n g e s t h a t w o u l d o c c u r in a l b e d o a n d surface r o u g h n e s s , c a n o p y i n t e r c e p t i o n , a n d s t o r a g e (see S e c t i o n I,B) in a s i m u l a t i o n o f a 1 3 - m o n t h p e r i o d . T h e y p r e d i c t e d h i g h e r air a n d soil temp e r a t u r e s , r e d u c e d e v a p o r a t i o n ( u p to 5 0 % ) a n d p r e c i p i t a t i o n , as well as l e n g t h e n i n g t h e d r y s e a s o n a f t e r c o n v e r s i o n . S h u k l a et al. c o u p l e d a G C M with a m o d e l o f b i o s p h e r e f u n c t i o n a n d r e s p o n s e a n d c a r r i e d o u t t h e s a m e e x p e r i m e n t . T h e y o b t a i n e d similar results. N u m e r i c a l e x p e r i m e n t s o f this sort, u s i n g m o d e l s , a r e t h e o n l y way to f o r e c a s t t h e sign i f i c a n c e o f s u c h c h a n g e s in l a n d cover. A l t h o u g h t h e y may still involve m a n y i n a c c u r a c i e s a n d a r g u a b l e a s s u m p t i o n s in t h e way t h e y are f o r m u lated, t h e s e m o d e l s r e p r e s e n t a c o n s i d e r a b l e step f o r w a r d in o u r c a p a c i t y to e v a l u a t e t h e c o n s e q u e n c e s o f d e f o r e s t a t i o n , p a r t i c u l a r l y in t e r m s o f t h e f e e d b a c k s to t h e a t m o s p h e r e .

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At smaller scales, the effects of the forest on the a t m o s p h e r e are less important, a l t h o u g h we have to recognize that there are always feedbacks 1 because the heat and water vapor fluxes f r o m vegetation influence the local p l a n e t a r y b o u n d a r y layer (see M c N a u g h t o n andJarvis, 1983, and the c o m m e n t in C h a p t e r 3 a b o u t the influence of the boreal jack pine forests, with their high heat flux) a n d h e n c e the coupling between vegetation a n d the a t m o s p h e r e . However, for most purposes, forest ecologists and m a n a g e r s will (reasonably) assume that local climates are i m p o s e d on, a n d i n d e p e n d e n t of, the forests in an area, a n d that the influences of c o n c e r n are those of the weather acting on the vegetation system. From the point of view of the forest m a n a g e r , the most i m p o r t a n t issues associated with forest hydrology are the questions of t h i n n i n g a n d clear-cutting and the effects of the water balance of forests on the growth of trees. Forested catchments are usually associated with high water quality and stable water outflows and c a t c h m e n t deforestation is widely reg a r d e d as an environmentally d a m a g i n g practice, likely to lead to soil erosion and lower water quality. These p r o b l e m s may i n d e e d occur, b u t they are not axiomatic (Binkley and Brown, 1993). Tree removal will, almost invariably, lead to increased water yield; w h e t h e r there are problems associated with that depends, to a great extent, on the t o p o g r a p h y of the catchment, the rainfall of the area, the type of forest removed, the way it was removed, a n d the s u b s e q u e n t m a n a g e m e n t of the catchment. Bruijnzeel (1990) gives a g o o d overview a n d discussion of all these points in relation to tropical forests. Here, we review briefly some of the m o r e i m p o r t a n t findings in the literature and, to provide a basis for the u n d e r standing a n d i n t e r p r e t a t i o n of empirical results, deal with the principles and processes u n d e r l y i n g the hydrology of forested catchments. U n d e r Section III of this chapter, we discuss t r e e - w a t e r relations a n d their effects on growth. It is i n t e n d e d to provide a cursory coverage of a vast field of research. T h e p r o b l e m is that n o t m u c h of that research has p r o d u c e d results of direct value to the forest scientist who needs to und e r s t a n d the influence of water on growth and productivity or ecosystem function. T h e t r e a t m e n t p r e s e n t e d h e r e provides an outline of the basic biophysical processes u n d e r l y i n g p l a n t - w a t e r relations, a n d of the rigorous a p p r o a c h possible w h e n dealing with p r o b l e m s in the field. T h e r e is

1 Feedback occurs when the effects of an i n p u t to a system (the receiving system, in this case, is the forest) result in alterations to the level of the input. Feedback may be positive, when the level of the i n p u t is e n h a n c e d , or negative, when the level is reduced; therefore, the system is d a m p e d . For example, evaporation into the p l a n e t a r y b o u n d a r y layer, driven by the vapor pressure deficit of the air mass, tends to cause negative feedback: The water vapor content of the air mass is increased so that evaporation rate tends to be reduced.

92

4. Forest Hydrology and Tree-Water Relations

also e m p h a s i s on the relationships b e t w e e n m e a s u r e s of plant water status a n d tree growth; m o d e l s that describe these relationships and that can be used to calculate the effects of given events or sets of conditions are p r e s e n t e d . T h e n e x t section presents the basic mass balance e q u a t i o n that describes all fluxes in forest water cycles. T h e various terms of the e q u a t i o n are first discussed in a general sense, t h e n dealt with in m o r e detail in s u b s e q u e n t sections of the chapter. S o i l - w a t e r relations are i m p o r t a n t as b a c k g r o u n d to all aspects of forest hydrology, a l t h o u g h there is an e m e r g i n g r e c o g n i t i o n a m o n g hydrologists that detailed, theoretically rigorous, physically based models, using e q u a t i o n s known to work in welldefined, spatially h o m o g e n e o u s situations, are often of limited value in c a t c h m e n t h y d r o l o g y (Beven, 1989). T h e r e f o r e , a l t h o u g h it is essential to u n d e r s t a n d the basic physical processes involved in soil-water movement, a n d to be able to describe t h e m in rigorous, quantitative terms, the m a i n benefit of this u n d e r s t a n d i n g may be to serve as a guide to the d e v e l o p m e n t of relatively simple, but physically soundly based, models of c a t c h m e n t hydrology.

I. Hydrologic Balance T h e h y d r o l o g i c balance is given by the e q u a t i o n P-

I-

qR-- q [ ) - A 0 -

E = 0,

(4.1)

where P is p r e c i p i t a t i o n (rain, snow, or fog), I is i n t e r c e p t e d water that is e v a p o r a t e d from the canopy, qR is surface runoff, (ID is drainage out of the r o o t zone, A0 is the change (A) in soil moisture c o n t e n t (0), and E is evaporation. T h e e v a p o r a t i o n term includes b o t h transpiration and evaporation from the soil. These are not c o n s i d e r e d separately here. E q u a t i o n (4.1) holds for a single precipitation e v e n t , or series of events, a l t h o u g h the time scales across which the different processes operate vary considerably. T h e shortest time scale usually applies to water interc e p t e d by a tk)rest canopy, which may evaporate from that canopy over periods of minutes to hours, d e p e n d i n g on c o n d i t i o n s a n d the structure of the canopy. Surface r u n o f f may c o n t i n u e for h o u r s or days. The change in soil m o i s t u r e c o n t e n t d e p e n d s on the intial moisture content a n d the rate of infiltration relative to the rate of d r a i n a g e out of the r o o t zone, which in turn d e p e n d s on soil hydraulic p r o p e r t i e s , the d e p t h of the soil, a n d r o o t p e n e t r a t i o n . Clearly, when there is no precipitation a n d the c a n o p y is dry so that I = 0, if qR a n d qo are zero, then E = A0. Differences in the water balances of different vegetation types, e.g., grassland a n d forest, can be analyzed and u n d e r s t o o d in terms ot Eq. (4.1),

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with appropriate f o r m u l a t i o n of, and p a r a m e t e r values for, the various terms. We deal with the terms in Eq. (4.1) in some detail in the following sections.

A. Precipitation Precipitation in any region is usually described in terms of the m e a n annual a m o u n t r e c e i v e d - - m i l l i m e t e r s of rain or meters of snow. However, precipitation is e n o r m o u s l y variable, b o t h spatially and temporally. The amounts received in any given m o n t h in a particular area will vary considerably, even in regions considered to have relatively stable climates, whereas in areas where the precipitation patterns are e r r a t i c - - p a r t i c u larly low rainfall a r e a s - - t h e precipitation received in any particular m o n t h may vary by orders of magnitude. The seasonal distribution of precipitation can influence the composition, structure, and function of terrestrial ecosystems. For example, the lack of precipitation during the growing season in the Pacific Northwest in the United States favors evergreen conifers c o m p a r e d to deciduous trees (Waring and Franklin, 1979). The p r o n o u n c e d m o n s o o n a l climate in tropical regions (see Fig. 2.1h) favors the deciduous leaf habit that enables trees to avoid desiccation, and the equitable distribution of precipitation and generally moderate temperatures, typical of most temperate regions in the s o u t h e r n hemisphere, favor the evergreen habit (Axelrod, 1966). Spatially, amounts of precipitation in particular areas within a region are affected by the patterns of s t o r m s m w h i c h may be frontal or convect i v e - a n d weather systems and by t o p o g r a p h y interacting with these factors. Mountains exert d o m i n a n t effects on regional rainfall by interacting with cyclonic or convective processes, forcing air upwards and causing o r o g r a p h i c rain, and by causing rain shadows. The great n o r t h - s o u t h m o u n t a i n ranges of the world (Andes, Rockies, Urals, S o u t h e r n Alps) provide dramatic examples. It is not only the a m o u n t of precipitation that affects hydrologic relationships, it is also the intensity with which the precipitation occurs. In the case of snow, there may be accumulation over winter, when the only term in Eq. (4.1) that is not effectively zero is/, because small amounts of snow are lost by evaporation (sublimation) from canopies during the winter. Large amounts may accumulate on the g r o u n d - - w i t h some held in the c a n o p y m u n t i l temperatures rise in spring and the snow melts, resulting in a large input of water to the system over a relatively short time. During snowmelt, the rate of input (P) to the system is the rate at which the snow is converted to water. Consequently, the soil is likely to be saturated, and r u n o f f and drainage will be high until the snow has dissipated. In some dry forests, snow provides a significant p r o p o r t i o n of the water available to the trees d u r i n g the growing season and annual productivity

94

4. Forest Hydrology and Tree-Water Relations

in these forests is positively c o r r e l a t e d to winter snowfall ( H u n t et al., 1991; Gower et al., 1992). In the case of rainfall, t h e r e is a c o n s i d e r a b l e difference, in terms of effects on forests, b e t w e e n a high-intensity storm, which delivers a large a m o u n t of water quickly, a n d the same a m o u n t of water delivered by light rain over a m u c h l o n g e r p e r i o d of time. If p r e c i p i t a t i o n occurs as a series of i n t e r m i t t e n t light showers, with time b e t w e e n t h e m to evaporate the water i n t e r c e p t e d by a n d held on the foliage, i n t e r c e p t i o n losses are likely to be high relative to those f r o m the same a m o u n t of p r e c i p i t a t i o n o c c u r r i n g as a single heavy shower (Calder, 1990). T h e r e will also be diff e r e n c e s in the p a t t e r n s of infiltration into the soil of the rainfall that r e a c h e s the g r o u n d . T h e effectiveness of rainfall in relation to the water status of forests also d e p e n d s on the a m o u n t of water h e l d in the r o o t zone at the beginn i n g of e a c h p r e c i p i t a t i o n event. If the soil is saturated, t h e n all rainfall r e a c h i n g the soil will be lost f r o m the system e i t h e r as r u n o f f or drainage. Rain after a dry p e r i o d is m o r e likely to be stored in the soil a n d will, t h e r e f o r e , be m o r e useful to the trees t h a n the same a m o u n t of rainfall received w h e n the soil is wet. T h e r e f o r e , the p r o b a b i l i t y of p a r t i c u l a r p a t t e r n s of p r e c i p i t a t i o n is a factor that m u s t be taken into a c c o u n t w h e n analyzing the hydrology of an area. T h e rainfall at any site may be analyzed in terms of the variance a b o u t m o n t h l y m e a n values a n d the p r o b a b i l i t y of the a m o u n t likely to be received in any m o n t h deviating significantly f r o m those values. We can also assess the probability, at any season, of storms of specified size (precipitation a m o u n t ) a n d intensity a n d calculate the cumulative probabilities of p e r i o d s of wet or dry weather. T h e s e statistical characteristics s h o u l d be r e c o g n i z e d a n d may n e e d to be taken into a c c o u n t in both evaluating r e s e a r c h results a n d the potential p r o d u c t i v i t y of forests in any p a r t i c u l a r place. We s h o u l d also n o t e that e x t r e m e e v e n t s m f l o o d s and, particularly in the case of forests (or any o t h e r ecosystems), d r o u g h t s - - m a y have m u c h g r e a t e r i m p a c t on the l o n g - t e r m potential a n d p e r f o r m a n c e of an ecosystem t h a n a n n u a l variations that do not deviate too far f r o m the m e a n .

B. Interception Rain falling on a forest c a n o p y may pass t h r o u g h gaps in the canopy or be i n t e r c e p t e d by, a n d drip from, the foliage (both called t h r o u g h f a l l ) . It may also be i n t e r c e p t e d by b r a n c h e s a n d foliage, to be r e t a i n e d until lost by e v a p o r a t i o n , or it may be c h a n n e l e d down the b r a n c h e s and trunks as stemflow (Fig. 4.1). Rainfall i n t e r c e p t e d by a n d e v a p o r a t e d from the canopy c o n t r i b u t e s n o t h i n g to the soil moisture. Litter layers also i n t e r c e p t rainfall; the a m o u n t of water that can be lost to the soil in this way will ob-

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viously d e p e n d on the mass and physical characteristics of that layer. T h e principles of calculation are the same as those for the canopy. T h e r e are a great m a n y reports of i n t e r c e p t i o n loss studies based on direct m e a s u r e m e n t s of rainfall, throughfall, a n d stemflow. These, not surprisingly, give a wide range of values for i n t e r c e p t i o n losses. For example, Feller (1981) gives i n t e r c e p t i o n loss values of between 10 and 20% of total rainfall for several Eucalyptus stands, and 2 1 - 3 0 % for Picea radiata; Ford a n d Deans (1978) give 30% for a Sitka spruce stand, D u n i n et al. (1985) f o u n d that i n t e r c e p t i o n by E. maculata was 13% of rainfall, a n d Waterloo (1994) o b t a i n e d values between 13 and 19% for P. caribaea. T h e variation is not surprising because i n t e r c e p t i o n d e p e n d s on stand structure, canopy architecture, a n d L ' r e m o t e dense stands will intercept and store larger a m o u n t s of water, b u t very high foliage densities will reduce the effectiveness of t u r b u l e n t e x c h a n g e b e t w e e n the wet foliage and the air. T h e influence of stand density was quantified by Myers a n d Talsma (1992), who f o u n d that net i n t e r c e p t i o n by P. radiata was strongly related to canopy mass. It r a n g e d f r o m 16% of total annual rainfall in sparse canopies to 24% in dense canopies. They also f o u n d that average i n t e r c e p t i o n d e c r e a s e d f r o m 55% of P d u r i n g small rain events (less than 5 m m ) to less t h a n 10% d u r i n g events > 4 0 m m . To explain the observed variation and provide a basis for prediction, i n t e r c e p t i o n losses f r o m forest canopies over short periods were modeled by Rutter (1975). Gash (1979) derived an analytical solution to Rutter's m o d e l that e n a b l e d it to be used over longer time periods. This version [Eq. (4.2)] has b e e n widely a d o p t e d a n d applied in m a n y studies. T h e e q u a t i o n is Ein t

=

[Pg'

(1

-

Pt-

Ps) - S] + [ E / R ( P g

-

Pg')] nt- S,

(4.2)

where Pg' is the a m o u n t of rain n e e d e d to saturate the c a n o p y a n d Pg the total a m o u n t d u r i n g the event, Pt and Ps are the fractions of rain reaching the forest floor as t h r o u g h f a l l and stemflow, R is the m e a n rainfall rate during an event, a n d E is the rate of evaporation d u r i n g rainfall events. The evaporation rate f r o m a wet canopy, either d u r i n g or after rain, can be estimated using the P e n m a n - M o n t e i t h e q u a t i o n [ (Eq. 3.16) with gc set to infinity (see, for example, Stewart, 1977). T h e two terms in square brackets relate to the wetting up a n d saturation periods, a n d the last t e r m (S) is the loss of water stored on the canopy after a rain event. T h e logic of the m o d e l is straightforward: W h e n rain c o m m e n c e s the canopy begins to "wet up," a l t h o u g h some drops will pass t h r o u g h unintercepted. Stemflow a n d drip increase as the canopy nears saturation, a n d there is continual evaporation of the free water on the foliage, a l t h o u g h obviously evaporation rates are n o t high d u r i n g rain. T h e r e f o r e , to calculate accurately the i n t e r c e p t i o n losses f r o m a forest requires knowl-

96

4. Forest Hydrology and Tree-Water Relations

edge of the canopy storage capacity (S, m m ) and estimates of the m e a n rainfall rate (R, m m h r - 1 ) , of the evaporation rate (E, m m h r - 1 ) , and of throughfall and stemflow. Given these p a r a m e t e r values from measurements above a n d below the canopy, the i n t e r c e p t i o n loss (Eint) from any rain event is the sum of the losses during the p e r i o d of wetting up, plus losses d u r i n g the period w h e n the canopy is s a t u r a t e d m i f it reaches this s t a g e h a n d the p e r i o d of drying. As a general value, it appears that canopies h o l d a b o u t 1 m m or less of water (i.e., S ~ 1 m m ) . Shuttleworth (1988) tabulated values of S from a n u m b e r of studies; they range from 0.3 m m for oak in winter to 2.5 for Sitka spruce. Some specific values are the following: Amazonian rainforest, S = 0.74 m m (Shuttleworth, 1988); P. caribaea, S = 0.8 m m (Waterloo, 1994); and E. maculata, S = 0.3 m m (Dunin et al., 1985). Calder (1986) p r o p o s e d a different f o r m u l a t i o n for the interception m o d e l based on the stochastic m a n n e r in which the individual elements of the surface of a tree are struck a n d wetted by individual raindrops. Calder et al. (1986) applied this m o d e l to a tropical rainforest and used statistical t e c h n i q u e s to optimize the p a r a m e t e r values. They arrived at a canopy storage capacity of 4.5 m m m f a r h i g h e r than the values r e p o r t e d for o t h e r stands using simpler models. Rates of water loss from canopies are strongly d e p e n d e n t on the effectiveness of t u r b u l e n t exchange. This was studied by T e k l e h a i m a n o t et al. (1991), who m e a s u r e d i n t e r c e p t i o n in Sitka spruce stands with trees spaced at intervals of 2, 4, 6, and 8 m. Average interception losses over a 17-week period were 29, 23, 13.8, and 8.9% for those spacings, respectively. Figure 4.2 shows the relationship between b o u n d a r y layer conductance and n u m b e r of trees per hectare. T e k l e h a i m o n o t el al. noted that, as expected, there was m o r e throughfall at the wider spacings, but they also f o u n d that the b o u n d a r y layer c o n d u c t a n c e increased linearly with spacing so that interception losses per tree were greater at the wider spacings because of the more effective t u r b u l e n t e x c h a n g e between the widespaced trees and the air. The empirical relationships established by Tekl e h a i m o n o t et al. for Sitka spruce will not hold for o t h e r species and configurations, but the principles almost certainly will. The principles underlying canopy interception apply equally to g r o u n d vegetation and litter, but these layers can be e n o r m o u s l y variable even within a single forest type and there are relatively few data available on interception losses from them. Doley (1981) cites two studies on tropical forest understories that indicated that litter could intercept a b o u t 1 - 1 . 5 m m water per kilogram dry litter. Waterloo (1994) m e a s u r e d interception losses u n d e r P caribaea in Fiji to be between 9.5 and 11.5% of total rainfall and Kelliher et al. (1992) r e p o r t e d that 11% of precipitation was i n t e r c e p t e d by litter c o m p a r e d to 19% by the canopy of a broadleaved evergreen forest.

L Hydrologic Balance 0.20

o~

.5

o.15

o

0.10

i

I

97

I

, 0.05

m

0

0

1000

2000

3000

4000

Number of trees (ha q) F i g u r e 4.2 The relationship between boundary layer conductance per tree (ga) and number of trees per hectare, established by Teklehaimanot et al. (1984). The rate of evaporation of intercepted water is strongly dependent on the value of ga" Reprinted fromJ. Hydrol., 123, Teklehaimanot et al., Rainfall interception and b o u n d a r y layer conductance in relation to tree spacing. 261-278 (1984) with kind permission of Elevier Science-NL Sara Burgerhartstraat 25, 1055 KV Amsterdam, The Netherlands.

C. Soil Hydraulic Characteristics Soil is a h e t e r o g e n e o u s , p o r o u s m e d i u m that holds water at a r a n g e of water contents. T h e e n e r g y with w h i c h the soil holds water is m e a s u r e d in terms of water p o t e n t i a l (~b, MPa), w h i c h has units of p r e s s u r e , or e n e r g y p e r u n i t v o l u m e , e q u i v a l e n t to a force p e r u n i t area. It is s o u n d l y b a s e d in t h e r m o d y n a m i c t h e o r y [see Slatyer, (1967) a n d Passioura, (1982) for a discussion of an a r e a w h e r e c o n f u s i o n has arisen, a n d Tyree a n d Jarvis (1982) for a d e t a i l e d discussion in r e l a t i o n to plants]. Because water potential g r a d i e n t s indicate different e n e r g y states, the c o n c e p t is fundam e n t a l to analysis of water m o v e m e n t t h r o u g h the soil p l a n t system. T h e subscript 's' is u s e d h e r e to distinguish soil water p o t e n t i a l (~bs) f r o m foliage water p o t e n t i a l (~bf). Soil water p o t e n t i a l (~bs) is the sum of gravitational, p r e s s u r e , or matric a n d o s m o t i c potentials. T h e units are negative: T h e y r e p r e s e n t the a m o u n t of w o r k that m u s t be d o n e to move a u n i t q u a n t i t y of p u r e , free water at the same t e m p e r a t u r e a n d elevation to the p o i n t in the soil in question. In the case of soils, the m a t r i c p o t e n t i a l is a very i m p o r t a n t c o m p o n e n t , b e i n g a f u n c t i o n of particle size a n d a r r a n g e m e n t . As soil dries, the e n e r g y r e q u i r e d to e x t r a c t water f r o m it increases rapidly, a n d water m o v e m e n t b e c o m e s slow a n d restricted. Soil wetness is usually exp r e s s e d in mass or v o l u m e terms: Water c o n t e n t (mass water p e r u n i t mass of soil) is r e l a t e d to v o l u m e t r i c water c o n t e n t (0, v o l u m e water p e r

98

4. Forest Hydrology and Tree-Water Relations

unit volume soil) by the soil bulk density (Ps) and the density of water (Pw). 0 can be o b t a i n e d from gravimetric m e a s u r e m e n t s (mass water, mw, per unit mass soil, ms) d e t e r m i n e d by weighing and oven-drying samples" 0 =

(mw/ms)Ps PW

.

(4.3)

Note that 0, multiplied by the appropriate soil d e p t h m f o r example, the d e p t h of the effective rooting zone of trees, zrmgives the depth equivalent of the water held in the soil. Soil porosity is the ratio of the volume of pore space per unit volume of soil. Porosity values range from about 0.3 in sands to 0.5 in clay soils. Saturated soil water content tends to be (roughly) p r o p o r t i o n a l to soil porosity; however, available water content tends to reach m a x i m u m values on soils of intermediate porosity or texture (see below). Soils are spatially highly variable in terms of the fractions of coarse and fine sand, clay, silt, and organic matter. T h e i r properties generally change with d e p t h and the depths at which significant changes occur will almost always vary from point to point in the landscape (see Fig.4.4). The equations given in this section, describing soil moisture characteristics, a l t h o u g h firmly based in high-quality research and measurement, will always provide only approximate values for the way water is held by and moves t h r o u g h soil in the field. The equations are generally derived from studies on sieved, h o m o g e n e o u s soil samples. In the field, matters are complicated by cracks, channels, holes, plant roots, and peds (blocks of soil). Nevertheless, it is important that the physical relationships be described and u n d e r s t o o d . Soil water content and potential are related by the soil moisture retention curve (see Fig. 4.3), which can be described over m u c h of the range by the empirical relation (Gardner et al., 1970): g's = as 0-".

(4.4)

Equation (4.4) has been t h o r o u g h l y tested by Clapp and H o r n b e r g e r (1978) and by Williams et al. (1983). Figure 4.3, drawn from Williams et al., illustrates the range of water-holding properties typical of soils. The soil described by the curve in Fig. 4.3a (Soil 1) contained 21% clay, 21% silt, 16% coarse sand, and 42% fine sand. This would be a free-draining soil; because of the large a m o u n t of coarse sand the u p p e r (saturated) limit of 0 (0sat) would be about 0 . 3 5 - 0 . 4 m 3 m -:~, with most of the water held at relatively high potentials. By the time the water content has fallen to 0.2 m 3 m -'~, g's is falling rapidly. The lower limit of water-holding capacity for that soil would be about 0 - 0 . 1 0 - 0 . 1 2 m :~ m -'~. Soil 2 (Fig. 4.3b) contained 28% clay, 38% silt, 4% coarse sand, and 30% fine

L Hydrologic Balance -30

I

I

I

99

I

a

-2~I -10,

I

0 13_

- 2000

I

I

I

I

I

b

- 1500

v

r

0 Q. "c"o

- 1000

- 500 0 - 15 000

I

I

I

C - 10000

- 5000

0

-

0.1

0.2

0.3

0.4

Soil w a t e r c o n t e n t ( 0 s )

Figure 4 . 3 . Soil water characteristic curves: matric potential as a f u n c t i o n of volumetric water content. T h e curves were calculated from equations given by Williams et al. (1983); they are soils 1, 5, a n d 8 in their Table 5. T h e p r o p o r t i o n s of clay, coarse sand, a n d fine sand were: (1) 21, 16, a n d 42%; (5) 28, 4, a n d 30%; (8)55, 4, a n d 17%. sand. T h e @s values are lower for e q u i v a l e n t water c o n t e n t s at t h e "wet e n d " o f the r a n g e ; e.g., g's ~ - 4 0 0 kPa at 0 = 0.25 m 3 m -3, w h e r e a s in the s a n d i e r soil @s = 25 kPa at 0 = 0.25 m 3 m -3. T h e lower limit o f waterh o l d i n g capacity for that soil w o u l d b e a b o u t 0 = 0.20 m 3 m -3. Soil 3, with 55% clay, 24% silt, 4% coarse sand, a n d 17% fine sand, is c o m p l e t e l y different. 0sa t for that soil w o u l d be a b o u t 0 = 0 . 4 - 0 . 4 5 m3 m - 3 , b u t any r e d u c t i o n in water c o n t e n t causes r a p i d d e c r e a s e in p o t e n t i a l . In g e n e r a l , plants can e x t r a c t water m o r e easily at h i g h p o t e n t i a l s t h a n at low potentials. T h e old c o n c e p t o f the " p e r m a n e n t wilting p o i n t " was b a s e d o n w o r k that i n d i c a t e d that plants wilted w h e n @s fell to a b o u t

1 O0

4. Forest Hydrology and Tree- Water Relations

- 1 5 0 0 kPa. This is not necessarily true in the field, but it does provide a guide to the lower limits of plant-available water, whereas the curves in Fig. 4.3 show clearly why it is necessary to have information about the soil moisture r e t e n t i o n curves. In this respect, the work of Williams et al. (1983) is very valuable because they provide detailed descriptions of the (relatively easily m e a s u r e d ) soil physical properties, which enable estimates to be m a d e of the water r e t e n t i o n characteristics. However, we should note that empirical relationships between soil texture and soil water content will vary, d e p e n d i n g on organic matter content. Clapp and H o r n b e r g e r (1978) provide similar data for a range of soils, with less detailed description of the soil physical properties. Water m o v e m e n t t h r o u g h soil d e p e n d s on the difference in Os between any two points in the soil and on the hydraulic conductivity, Ks. It can be described by Darcy's law: Js = K~ ( dO~/ dx),

(4.5)

where J.~ is the volume flux of water t h r o u g h unit cross-sectional area per unit time (flux density) in the direction of the lower potential, and x is distance. Darcy's law was originally f o r m u l a t e d to describe flow t h r o u g h saturated media. Hydraulic conductivity falls rapidly as the soil wetness falls below saturation so that if Eq. (4.5) is to be used to describe flow in u n s a t u r a t e d soil, K~ must be m a d e a function of the water potential, i.e., Ks =

K,(O,).

Over the range of interest in plant studies ( ~ ~ 3 - 5 to 1 5 0 0 2000 kPa), K~ may be r e d u c e d by several orders of m a g n i t u d e . Campbell (1974) provided the very useful equation K~ = (K~(~)/K~.,,t) = (0/0~,t) 2b+:~,

(4.6)

where b is an empirical coefficient. Clapp and H o r n b e r g e r (1978) note that Eq. (4.6) has proved to be reasonably accurate over a range of values of b between 0.17 and 13.6. They d e t e r m i n e d values for soils ranging from sand (clay fraction = 0.03, b ~ 4) to loam (clay fraction = 0.19, b ~ 5.4) and clay (clay fraction = 0.63, b ~ 11.4). From the point of view of h y d r o l o g y m a s o p p o s e d to studies on plant p h y s i o l o g y m t h e p a r a m e t e r of most interest and i m p o r t a n c e is the saturated hydraulic conductivity K~t because, as Eq. (4.5) shows, when 0 falls below 0.~t, K~(O~) falls very rapidly. Talsma and Hallam (1980) m e a s u r e d the hydraulic conductivity in four catchments, chosen for (apparent) uniformity, in the Australian Capital Territory. T h e i r data show that Ksat values are very nearly log-normally distributed (Fig. 4.4). I.arge variability was f o u n d within each catchment, but most of the variability was present in very small areas.

I. Hydrologic Balance I

I

%

I

I

10 Ss S

s S

101

I ss

~

X

"7

O~

s ~ s SSI

E

v

~S S

> .m ,4=-, 0

s t"

SS

C

o 0

// /

0.1

I

0.01

I 1

I 10

//

/

////

I 50

I 90

98

Cumulative probability

Figure 4.4 Distributions of hydraulic conductivity at different depths across a u n i f o r m catchment. The u p p e r (dotted) line is for 0 . 1 5 - 0 . 2 5 m, the middle line is for 0 . 3 - 0 . 6 m, and the lower (dashed) line is for 0 . 7 - 1 . 0 m (drawn f r o m a d i a g r a m by Talsma and Hallam, 1980). 1. Runoff, Infiltration, and Drainage Runoff f r o m n o n s a t u r a t e d soils tends to be u n i m p o r t a n t in forests because of the obstructions to movem e n t that exist on the forest floor a n d also because, with their high organic m a t t e r content, high activity of microfauna, a n d the large root masses they contain (see Chapters 6 and 7), forest soils are likely to have high porosity and infiltration rates a n d can accept high p r e c i p i t a t i o n rates. E n o r m o u s changes may be b r o u g h t a b o u t w h e n forests are clearcut (see Section II), b u t these will d e p e n d on the s u b s e q u e n t t r e a t m e n t of the site: T h e r e will be great differences between a site left with logging residues a n d a site at which these are b u r n t a n d the vegetation is completely c h a n g e d (Fredriksen et al., 1975; van Lear et al., 1985). Managem e n t should take a c c o u n t of the fact that, in general, organic m a t t e r will increase infiltration rates and possibly the water storage capacity of soils. T h e influence of cracks, surface litter, inequalities in the surface, and the modification of water i n p u t rates by drip f r o m foliage a n d stemflow r e n d e r any rigorous and detailed t r e a t m e n t of infiltration somewhat superfluous in a text c o n c e r n e d generally with processes as they operate at stand and forest level. We n e e d only note here the r a t h e r obvious facts that infiltration will be faster into sand t h a n clay s o i l s - - i n the absence of soil cover the rates will be a r o u n d 20 m m hr -1 for sands a n d a b o u t 1 - 5 m m hr -1 in clayey soils (Hillel, 1980), a l t h o u g h these figures will vary

"102

4. Forest Hydrology and Tree-Water Relations

e n o r m o u s l y d e p e n d i n g on the soil surface condition. Infiltration rates also d e p e n d on the a m o u n t of water already present in the soil: Much of the elegant (but n o t very practically useful) theory d e v e l o p e d in relation to this process deals with wettting fronts and changes in Ks at those

B o x 4.1.

Soil M o i s t u r e M e a s u r e m e n t

I m p r o v e m e n t s in soil moisture m e a s u r e m e n t techniques over the past 2 5 - 3 0 years have c o n t r i b u t e d greatly to u n d e r s t a n d i n g the effects of soil moisture on plant growth as well as to the understanding and quantification of hydrological processes. The n e u t r o n moisture m e t e r provides the capacity to measure soil moisture content, to a high level of accuracy, at a range of depths at any point where a suitable tube ( ~ 4 cm in diameter) can be installed in the soil. The disadvantages of this i n s t r u m e n t are that the measurements are quite slow to t a k e - - t h e instruments are generally too expensive to leave in situ, so they must be moved from tube to tube m and, even if m e a s u r e d daily (almost universally impractical), do not provide i n f o r m a t i o n about the short-term dynamics of soil moisture, a l t h o u g h the vertical fluxes of water t h r o u g h the soil can be d e d u c e d from sequential measurements. The n e u t r o n sources may also be unsafe if used improperly. N e u t r o n moisture meters will reflect the point to point variability of soil moisture; if this is very high it may make the results difficult to interpret. A technique called Time D o m a i n Reflectometry (TDR), which allows virtually continuous observations, derived from simple probes inserted in the soil (Topp el al., 1980), has been developed in recent years (Hook et al., 1992). TDR is m o r e suited to p r o b l e m s such as evaluation of the dynamics of soil moisture in the root zones of plants than neutron moisture meters and can be expected to supersede n e u t r o n moisture meters for most soil moisture m e a s u r e m e n t applications in the near future. The systems are readily automated; switching and data logging techniques are improving rapidly. TDR systems are now commercially available. O t h e r techniques, such as (electrical) capacitance systems, are also b e c o m i n g available. All have advantages and disadvantages. None, as yet, provide the large-scale integrated measure (over meters or tens of meters) of soil moisture that is n e e d e d to deal with the p r o b l e m s of soil h e t e r o g e n e i t y and spatial variability, a l t h o u g h falling costs and advances in TDR multiplexing technology can be expected to alleviate this problem.

L Hydrologic Balance

~ 03

fronts, the r e d i s t r i b u t i o n of infiltrated water, a n d so on (Hillel, 1980). Because of the spatial variability that will be e n c o u n t e r e d , it is n o t easy to measure infiltration rates in forests, but the collection of as m u c h data as possible should be e n c o u r a g e d . By drainage we m e a n drainage of water out of the root zone vertically, by downward flow, or horizontally across i m p e r m e a b l e , or less p e r m e able, layers. Because hydraulic conductivity falls very rapidly as soil moisture content falls below 0sa t [Eqs. (4.4) a n d (4.5)], drainage in unsaturated soils is slow. U n d e r saturated conditions, d r a i n a g e t h r o u g h holes and cracks can be an i m p o r t a n t pathway for water and solute t r a n s p o r t (Wang et al., 1996). Drainage is difficult to m e a s u r e , a l t h o u g h estimates of water m o v e m e n t in the vertical can be m a d e f r o m sequential measurements with i n s t r u m e n t s such as n e u t r o n m o i s t u r e m e t e r s or time d o m a i n r e f l e c t o m e t r y (TDR) (see Box 1). In c a t c h m e n t hydrology e x p e r i m e n t s , it is i m p o r t a n t to work in "sealed" catchments, where there is no vertical drainage out of the catchment. Horizontal d r a i n a g e is m e a s u r e d using flow-gauging structures or wiers at the c a t c h m e n t outlet. In most water balance models, drainage is calculated by allowing the soil profile to fill with water a n d t h e n "discarding," as r u n o f f or drainage, p r e c i p i t a t i o n that occurs w h e n the profile is full.

2. Rooting Depths and Distribution The effectiveness of water u p t a k e by trees d e p e n d s on the effectiveness with which the soil is e x p l o i t e d by roots (defined as root length per unit v o l u m e of soil, Lv), contact between roots a n d soil, and the hydraulic potential gradients between roots an d soil. Tree root a r c h i t e c t u r e varies between species (Lyr and H o f f m a n n , 1967; Karizumi, 1974, 1976; Phillips and Watson, 1994) a n d is affected by soil type and growing conditions. Root systems are dynamic: Trees do not simply p r o d u c e a root system of a particular type a n d c o n f o r m a t i o n a n d sustain it u n d e r all c o n d i t i o n s m t h e r e is considerable evidence that the p r o p o r t i o n of carbohydrates p r o d u c e d by trees that is allocated to root growth is h i g h e r in p o o r growing conditions t h a n where water a n d nutrients are less limiting (Landsberg, 1986; C o m e a u a n d Kimmins, 1989; Gower et al., 1992; Haynes a n d Gower, 1995; see also C h a p t e r 5). From a water collection a n d t r a n s p o r t point of view, the o p t i m u m system will be a considerable root mass, with high Lv, in the surface layers of the soil to harvest as m u c h water as possible, with larger, m o r e widely spaced roots d e e p e r in the soil. These will absorb water m o r e slowly, but the d e e p e r soil layers do not dry as fast as the surface layers; therefore, d e e p roots are likely to play an i m p o r t a n t role in m a i n t a i n i n g water uptake a n d transpiration d u r i n g dry periods. Fowkes a n d L a n d s b e r g (1981)

104

4. Forest Hydrology and Tree-Water Relations

p r o v i d e d a t h e o r e t i c a l analysis that s u p p o r t s these arguments. Soil textural characteristics and h a r d pans can greatly alter the d e v e l o p m e n t and vertical d i s t r i b u t i o n of large roots. A high density of fine roots ensures that the p a t h l e n g t h from the soil to the n e a r e s t r o o t is always s h o r t so that even w h e n the roots have extracted m u c h of the water in their i m m e d i a t e vicinity, a n d K(~s) falls, water can still move to the roots along the p o t e n t i a l g r a d i e n t between the r o o t surface a n d the soil fast e n o u g h to provide a useful c o n t r i b u t i o n to the t r a n s p i r a t i o n stream. Calculations with the m o d e l p r e s e n t e d by Passioura (1982) indicate that r o o t length density is unlikely to be a major limiting factor until Lv falls below a b o u t 2 or 3 • 1 0 4 m m -3, a n d even t h e n it is n o t likely to be i m p o r t a n t unless the soil is dry (low Ks) (see N e w m a n , 1969). However, fine roots have high resistance to water flow a n d have to be relatively short; otherwise they c a n n o t carry e n o u g h water to the m a i n roots and the stem. Dry soil c o n d i t i o n s will cause fine roots to stop growing or to die back (Teskey a n d Hinckley, 1981; Eissenstat and Yanai, 1996) a n d they are expensive to m a i n t a i n in terms of respiration (Marshall a n d Waring, 1986; see C h a p t e r 5). Maintaining a large system of fine roots a n d m y c o r r h i z a e increases respiration costs; consequently, seasonal changes in fine r o o t mass are c o m m o n , with m a x i m u m values occ u r r i n g at times of high water a n d n u t r i e n t availability, with dieback w h e n c o n d i t i o n s b e c o m e adverse (Deans, 1979; Persson, 1980a; Grief et al., 1981). Gerwitz a n d Page (1974) collated data on r o o t length distribution under crops a n d f o u n d that, in virtually every case, L,, declined e x p o n e n tially with d e p t h (z) so that the r o o t length density at any d e p t h can be described by Lv(z) = Lv(0) e x p ( - k i z),

(4.7)

w h e r e Lv(0) is the density in a layer just below the surface. T h e data used by Gerwitz a n d Page (1974) all came from vegetables a n d agricultural crops, but Gale a n d Grigal (1987) collected p u b l i s h e d data on the r o o t distributions of N o r t h A m e r i c a n tree species a n d f o u n d that they could all be fitted by the e q u a t i o n EFR = 1 -- B',

(4.8)

w h e r e ~FR is the cumulative r o o t mass fraction from the surface to d e p t h z (cm) a n d B is an estimated p a r a m e t e r . Values of B were in the range 0 . 9 2 - 0 . 9 5 , which indicates that most ( > 9 5 % ) roots of those species are in the top half m e t e r of the soil. Eq. (4.8) is consistent with Eq. (4.7): If we assign a l e n g t h / m a s s conversion factor mi~ to Lv, we can show that ~FR(Z ) -- 1 - e x p ( - k i z), which implies that B = e x p ( - k l , ) . This gives

L Hydrologic Balance

105

values of kL of a b o u t 0.07 when z is in centimeters. Using the usual SI measure of meters, B b e c o m e s ~ 0.001, giving kL ~ 7. The exact values of these p a r a m e t e r s are not very i m p o r t a n t . The conversion factor mL will vary with r o o t d i a m e t e r and p r o b a b l y with species. It is unlikely that the values that describe r o o t distribution in the N o r t h American trees studied, which were mainly conifers, will be a p p r o p r i a t e for d e e p - r o o t e d species such as many of the Australian eucalypts. For example, C a r b o n et al. (1980) e x a m i n e d the distribution of roots u n d e r j a r r a h (E. marginata) in Western Australia, in soils with a sandy layer 1-m deep overlying a b o u t 2 - 2 . 5 m sandy loam and up to 20 m clay. They presented two representative profiles derived from sampling at 25 sites and showed an e x p o n e n t i a l r e d u c t i o n in Lv(z) t h r o u g h the sand and sandy loam, but large vertical roots p e n e t r a t e d to the 20-m limit of their sampling. C a r b o n et al. c o m m e n t e d on the "almost total d o m i n a n c e of r o o t length by fine to very fine roots." Studies in tropical forests have shown fine roots at a 20-m d e p t h (Nepstad et al., 1994). T h e r e are few values of Lv for forests in the literature. Vogt (1991) collated a great many data on r o o t mass u n d e r forests, and some of these data could be used to make estimates of r o o t mass distribution using Eqs. (4.7) and (4.8). The data of C a r b o n et al. (1980) show Lv > 1 • 104 m m -~ for the top 2 m of the j a r r a h forests, and it is safe to assume that this will be e x c e e d e d by a considerable a m o u n t in forests where most of the roots are in the top 1 m. T h e r e f o r e , it will generally be safe, w h e n analyzing the effects of soil moisture on tree growth, to assume that soil water content (and hence potential) in the top half m e t e r will provide a g o o d measure of the moisture content relevant to the plants (see Tan et al., 1978). In calculating the water balance of stands, the best estimates of r o o t i n g zone will be based on soil type and d e p t h and the best available i n f o r m a t i o n a b o u t species r o o t i n g characteristics. W h e r e v e r possible, such estimates should be s u p p o r t e d by m e a s u r e m e n t s a n d observations.

D. Transpiration Transpiration rates vary with weather conditions, leaf area and canopy architecture, and soil moisture. The process has been dealt with in some detail in C h a p t e r 3, Section III, where the d e p e n d e n c e of forest transpiration rates on canopy c o n d u c t a n c e s is clear from Eq. (3.16). The main p u r p o s e of this section is to consider the effects of soil and a t m o s p h e r i c conditions that cause gc to be less than gcmax so that actual t r a n s p i r a t i o n rates are less than the possible m a x i m a (It is i m p o r t a n t to avoid confusion between m a x i m u m and p o t e n t i a l m a s defined by Eq. ( 3 . 2 2 ) - - t r a n spiration rates.) Because canopy c o n d u c t a n c e is d o m i n a t e d by stomatal c o n d u c t a n c e (we n o t e d in C h a p t e r 3 that gc is c o m m o n l y calculated as

"106

4. Forest Hydrology and Tree-Water Relations

B o x 4.2

Stomatal Behavior

A l t h o u g h research on stomatal behavior has, for the past 20 years, b e e n focused on the effects of light, a t m o s p h e r i c factors [vap o r pressure deficit (strictly leaf to air)], CO 2 concentration), and leaf water status, consideration of flow pathways and resistances [see Eq. (4.9)] indicates that we should be c o n c e n t r a t i n g more on these areas. If the potential rate of atmospherically driven transpiration, with some m a x i m u m value of gs, is greater than the rate at which water can move from the soil to the roots, t h r o u g h the plants to the leaves, then unless stomata close, the plant will dessicate. T h e r e f o r e , vapor pressure deficit (D) in particular can be r e g a r d e d as a surrogate for evaporation rate: Stomata will close when the plant c a n n o t sustain the rate of water loss driven by vapor pressure deficit. This suggests that all the empirical relationships established between gs a n d D must have b e e n influenced by the capacity of the s o i l - p l a n t hydraulic c o n d u c t i n g system. Recognition of this does not solve the p r o b l e m of the actual m e c h a n i s m of closure, and it also means that prediction of closure is m o r e difficult: We c a n n o t calculate leaf transpiration rate unless we know g~, but g~ d e p e n d s on the resistances in the flow pathways, including those between the leaves and water stored in plant tissues. This also indicates that there is unlikely to be any u n i q u e relationship between leaf water potential (Of) and g ~ - - a n d i n d e e d n o n e has b e e n f o u n d m b e c a u s e Of is itself strongly influenced by flow rates and resistances to flow t h r o u g h the plant. We will, unavoidably, continue to use established relationships to predict g~, but the point discussed here highlights the need to take into account the soil moisture situation and the n e e d for studies on stomatal behavior in relation to m e a s u r e m e n t s of whole plant transpiration rates.

gc = Eg~-L*), fluctuations in g~ will generally cause c o m m e n s u r a t e l y large fluctuations in AE. Because of its relevance to photosynthesis, stomatal c o n d u c t a n c e and the way it responds to e n v i r o n m e n t a l factors is discussed in C h a p t e r 5; it is c o n s i d e r e d here with particular emphasis on its i m p o r t a n c e in relation to transpiration and the interactions between gs and p l a n t - - o r s o i l n w a t e r status (Box 4.2). T h e three m a j o r e n v i r o n m e n t a l influences on stomatal c o n d u c t a n c e at the canopy level are light (q~p), vapor pressure deficit (D), and the water status of the leaves (~r)- T h e equations given by L e u n i n g [1995; see

L Hydrologic Balance

107

C h a p t e r 5, eqs. (5.9) and (5.10)] describe the responses to q~p a n d D; they d e m a n d a u n i q u e value of stomatal c o n d u c t a n c e to satisfy the rate of photosynthesis r e q u i r e d by the c u r r e n t value of q~p a n d internal leaf CO 2 concentration, ci. This provides the critical link between gs, transpiration, and photosynthesis. I n t e r p r e t a t i o n and p r e d i c t i o n of stomatal responses to D are complicated by the fact that the response a p p e a r s to be to transpiration rate (E) rather than to D (Monteith, 1995). M o n t e i t h has shown that virtually every p u b l i s h e d set of m e a s u r e m e n t s p u r p o r t i n g to show a n o n l i n e a r relation between g~ and D can be i n t e r p r e t e d as a linear relation between gs and transpiration rate, with the general f o r m gs = a -

bE,

(4.9)

or, in n o n d i m e n s i o n a l form g s / g m a x = 1 -- E / E m a x ,

(4.10)

where gmax a n d E m a x a r e e x t r a p o l a t e d m a x i m u m values of gs at E = 0 a n d 1/b, respectively. However, this relationship is not particularly helpful to those faced with a p r a g m a t i c n e e d to p r e d i c t transpiration on the scale of forest stands or relatively small land units, a n d it seems likely that we will continue to rely on empirical relationships between gs a n d D f o r some time to come (see C h a p t e r 5). In considering the influence of foliage or soil water status on stomatal c o n d u c t a n c e , a n d h e n c e transpiration, we have to note that there is strong evidence that the effects of leaf water status can be overridden, and certainly modified, by chemical signals from the roots or by changes in the c o n d u c t a n c e of the hydraulic pathways. T h e "traditional" explanation for the effects of leaf water potential on stomatal c o n d u c t a n c e is that there is a negative feedback between g~ a n d leaf water potential (see Ludlow, 1980), b u t this does not explain decreases in stomatal c o n d u c t a n c e while leaf water potential remains constant. O t h e r scientists have proposed a feedforward control of leaf water potential (i.e., stomata r e s p o n d directly to variables that influence leaf water potential r a t h e r t h a n responding to leaf water potential p e r se as p r o p o s e d by Ludlow). It has b e e n suggested that a feedforward response to soil drying involves the transport of a chemical message (abscisic acid) f r o m roots to foliage, causing r e d u c e d stomatal c o n d u c t a n c e i n d e p e n d e n t of leaf water potential (Wartinger et al., 1990; Zhang a n d Davies, 1991; T a r d i e u et al., 1992). Rapid r e d u c t i o n s in stomatal c o n d u c t a n c e , i n d e p e n d e n t of changes in leaf water potential, can be i n d u c e d by d e c r e a s e d hydraulic conductance (Teskey et al., 1983; Meinzer a n d Grantz, 1990; Sperry et al., 1993) caused by severing part of the stem or r o o t or cooling the roots. This

108

4. Forest Hydrology and Tree- Water Relations

speculation is s u p p o r t e d by results from an e x p e r i m e n t a l shading treatm e n t of well-watered P. r a d i a t a trees. W h i t e h e a d et al. (1996) f o u n d that shading the lower canopy decreased tree canopy c o n d u c t a n c e immediately. W h e n the cover was removed, c o n d u c t a n c e in the u p p e r canopy decreased with a c o n c o m m i t a n t increase in the lower canopy. Bulk leaf water potential c h a n g e d little while these changes occurred. These resuits suggest that hydraulic c o n d u c t a n c e s in the xylem were altered, which may have stimulated the p r o d u c t i o n of chemical messengers to regulate stomatal c o n d u c t a n c e . Additional evidence both s u p p o r t i n g and refuting the control of stomatal c o n d u c t a n c e by hydraulic c o n d u c t a n c e as o p p o s e d to chemical messages has b e e n o b t a i n e d from e x p e r i m e n t s using a device to apply pressure to root systems of plants in sealed c h a m b e r s (e.g., Passioura and Munns, 1984). Increasing the atmospheric pressure inside the c h a m b e r increases the hydraulic and p n e u m a t i c pressure of the soils and root system equally, but outside the c h a m b e r the shoot experiences an increase in hydraulic pressure. Pressurizing the soil has been shown to have no significant effect on stomatal c o n d u c t a n c e of h e r b a c e o u s plants in dry soils (Gollan et al., 1986; Schurr el al., 1992) but has been shown to rapidly reverse decreasing stomatal c o n d u c t a n c e of woody plants grown in drying soil (Fuchs and Livingston, 1994; Saliendra et al., 1995). The a p p a r e n t discrepancy may, in part, be i n h e r e n t to the type of plant (herbaceous or woody) studied. Tardieu and Davies (1993) have proposed that an integrative chemical and hydraulic signal control stomatal conductance and leaf water potential. Saliendra et al. (1995) speculated that chemical root signals may be less i m p o r t a n t in woody than herbaceous plants because the long transport time in tall woody plants would make this m e c h a n i s m ineffective for short-term stomata regulation. In summary, stomatal control by a single factor seems unlikely and is inconsistent with the multiple-control systems that operate in trees for almost all processes regulating carbon, water, and nutrient flow. Even without this information, it is clear from our earlier discussion a b o u t root distribution that the water status of foliage must be strongly influenced by the average soil water potential in the root zone and by the resistances in the flow pathways from soil to roots t h r o u g h the plant to the a t m o s p h e r e . Flow t h r o u g h plants is driven by transpiration, and if the rate at which water can move from the soil to the roots, and t h r o u g h the plant to the evaporating sites in the leaves, is equal to or greater than the atmospherically driven (potential) transpiration rate, then gs can be at or near its m a x i m u m value, and the plants will not b e c o m e dessicated. However, if the supply rate c a n n o t m e e t the d e m a n d , then stomata must close to r e d u c e losses from the plant to the point where they can be m e t

L Hydrolouc Balance

109

by supply f r o m the soil. If s t o m a t a do n o t close e n o u g h , the plants will dessicate. T h e m e c h a n i s m s we have c o n s i d e r e d can be r e g a r d e d as having evolved as r e p o n s e s to this basic c o n s t r a i n t . E x p e r i m e n t a l results illustrating the effects o f soil water p o t e n t i a l , or c o n t e n t , in t h e r o o t z o n e on t r a n s p i r a t i o n a n d s t o m a t a l c o n d u c t a n c e have b e e n p r e s e n t e d by Tan et al. (1978), w h o m e a s u r e d s t o m a t a l resist a n c e (rs = 1 / g s ) t h r o u g h the c a n o p i e s of D o u g l a s fir trees in a t h i n n e d s t a n d over a p e r i o d of a b o u t a m o n t h , in s u m m e r , w h e n t h e r e was n o rain. T h e y s h o w e d that the effects o f D were e n h a n c e d by dry soil. W i t h i n the t h r e e soil wetness r a n g e s ( m o d e r a t e l y wet, 0 > ~s > - 3 5 0 kPa, to dry, - 9 5 0 > ~0s > - 1 2 5 0 kPa) at a v a p o r p r e s s u r e deficit of 1.5 kPa, r~ = 0.78, 1.57, a n d 5.31 m m sec -1, i.e., gs = 1.27, 0.44 a n d 0.18 m m sec -1 so that ( f r o m gc - s gc for a c a n o p y with L* = 4 w o u l d be ( a p p r o x i mately) 5.1, 2.5, a n d 0.75 m m sec -1. T h e s e values are very m u c h lower t h a n the m a x i m u m values i d e n t i f i e d by K e l l i h e r et al. (1995), s u g g e s t i n g t h a t even the " m o d e r a t e l y wet" soil was e x e r t i n g a c o n s i d e r a b l e effect o n s t o m a t a a n d h e n c e on t r a n s p i r a t i o n rates. In contast, C a l d e r (1978) o b t a i n e d results that i n d i c a t e d n o d e p e n d e n c e of gs o n soil m o i s t u r e . T h e y u s e d a d r a i n a g e l y s i m e t e r to m e a s u r e the water b a l a n c e , a n d h e n c e t r a n s p i r a t i o n rates, of Sitka s p r u c e in

Ep(mmd -1) 8 "13

E E v (D

6

t-

.o_ 4 L,(,.-

~ 2 0

~:

0

0

40

80

120

160

200

Soil water deficit (mm)

Figure 4.5 Effects of soil water deficit on transpiration rates of Eucalyptus maculata. The diagram shows that, at any potential transpiration rate Ep (calculated f r o m the P - M equation with a standard value of go), the actual transpiration rate fell slightly with increasing soil water deficit until a critical value was reached, after which actual transpiration rates fell rapidly. The critical soil water deficit values increased as Ep decreased, indicating that, if Ep was low, the trees were able to m e e t atmospheric d e m a n d even when the soil was dry (diag r a m redrawn f r o m Dunin et al., 1985).

110

4. I~brest Hydrology and Tree-Water Relations

Wales. An o p t i m i z a t i o n p r o c e d u r e was used to establish the values of gs that in the P e n m a n - M o n t i e t h ( P - M ) e q u a t i o n , gave the closest agreem e n t with m e a s u r e d values of transpiration. T h e results indicated that gs was a f u n c t i o n of a t m o s p h e r i c v a p o r p r e s s u r e deficit D, b u t no d e p e n d e n c e on soil m o i s t u r e deficits was f o u n d , down to deficits of 200 m m , e q u i v a l e n t to a b o u t - 6 0 0 kPa in the r o o t zone. O n e of the reasons for this lack of effect was, a l m o s t certainly, the very low t r a n s p i r a t i o n rates d u r i n g the e x p e r i m e n t a l p e r i o d - - d o w n to 0 . 0 2 - 0 . 0 3 m m day -1. In these c i r c u m s t a n c e s , water can, p r e s u m a b l y , always move t h r o u g h the soil fast e n o u g h to supply t r a n s p i r a t i o n d e m a n d . D u n i n et al. (1985) used a large weighing lysimeter, on the south coast of New South Wales, Australia, to evaluate t r a n s p i r a t i o n rates f r o m a segm e n t of E u c a l y p t u s forest d o m i n a t e d by E. m a c u l a t a . L * on the lysimeter, a n d in the s u r r o u n d i n g forest, f l u c t u a t e d over the p e r i o d of m e a s u r e m e n t s ( m o r e t h a n 12 m o n t h s ) b u t was g e n e r a l l y a b o u t 3. For 102 days w i t h o u t rain, w h e n L* > 3, D u n i n et al. p l o t t e d m e a s u r e d daily (actual) t r a n s p i r a t i o n rates against soil m o i s t u r e deficit in the lysimeter, which c o n t a i n e d a b o u t 210 m m ( d e p t h equivalent) of water in the 1.8-m d e e p r o o t zone. T h e y also calculated t r a n s p i r a t i o n using the P - M equation, with rc = 60 m s e c - l (go ~ 17 m m sec-1), calling this the potential transpiration rate. It is interesting to note that the value of g,. used by D u n i n et al. to calculate "potential" t r a n s p i r a t i o n rates was almost exactly the same as the average value of g,.,,,.,• for woody plant c o m m u n i t i e s that Kelliher et al (1995) o b t a i n e d 10 years later. T h e results f r o m D u n i n et al. are r e p r o d u c e d in Fig. 4.5. Figure 4.5 indicates that, w h e n the soil m o i s t u r e deficit was less t h a n a b o u t 140 r a m - - o r a b o u t 6 0 - 6 5 % of available water in this s o i l m t h e rate of water loss by the trees a p p r o x i m a t e d the m a x i m u m rate: O b s e r v e d rates fell a b o u t the m a x i m u m rate lines until the soil m o i s t u r e deficit exc e e d e d this "critical" a m o u n t . T h e rates of t r a n s p i r a t i o n t h e n fell rapidly. T h e range of soil water across which t r a n s p i r a t i o n was a p p a r e n t l y unaff e c t e d by soil m o i s t u r e c o n t e n t was c o n s i d e r a b l e . It would have b e e n possible to derive i n f o r m a t i o n a b o u t c a n o p y resistance values in relation to soil m o i s t u r e in the r o o t zone, similar to that of Tan et al. ( 1 9 7 8 ) , by solving Eq. (3.27), but after the critical point the effects of low soil water contents on t r a n s p i r a t i o n rates were clear e n o u g h . Obviously, actual t r a n s p i r a t i o n rates can vary f r o m zero to the maxim u m rates achievable with p a r t i c u l a r values of L* a n d g~ (see C h a p t e r 3, Section Ill,B). For e x a m p l e , Myers a n d Talsma (1992), in Australia, meas u r e d daily values r a n g i n g f r o m 1 or 2 m m d a y - l in a P. radiala p l a n t a t i o n in winter to 2 or 3 m m day-1 in spring, with values of 6 - 8 m m day -1 in an irrigated a n d fertilized p l a n t a t i o n in s u m m e r . S h u t t l e w o r t h (1989) m e a s u r e d rates of 3 or 4 m m day -l, with little day-to-day variation, f r o m

II.

Catchment Hydrology

111

Amazon rainforest, and McNaughton and Black (1973) measured values ranging from 1 to 4.5 mm day -1, with considerable day-to-day variation, from a Douglas fir forest in British Columbia, Canada.

II. Catchment Hydrology Because forested catchments are important sources of water for many human population centers and aquatic ecosystems, it is essential that we have some appreciation of catchment hydrology and the implications of various forest m a n a g e m e n t practices on the groundwater. The presence or absence of forest cover on part or the whole of a catchment, and cover type, will have a considerable effect on the catchment water yield (Fahey and Rowe, 1992) and may also affect water quality, particularly immediately after disturbance (Binkley and Brown, 1993). There have b e e n many studies of the effects of forest clearance on streamflow, nutrient fluxes in stream water, and sediment transport. The main findings in terms of streamflow can be summarized as follows (see Fig. 4.6):

r

g i..

-t0

(D

O (D

.->

,,i-, (D

"-

cat}

,_

+

b

0 _

(D ..v-,

r (D

E r

+ 0

r

O

0

Time (years)

,,v, 50

F i g u r e 4.6 Time course of water yield patterns after thinning or clearing forested catchments. The initial increase will d e p e n d on the proportion of the tree cover removed in the treatment. Subsequent patterns of water yield d e p e n d on the rate of recovery of the vegetation and its treatment. (a) Gradual recovery of vegetation to its original state; (b) results of heavy regrowth, leading to higher leaf area than in the original stand, hence greater water demand and lower water yield (self thinning over a long period tends toward the original conditions); (c) a stand cleared and maintained as, for example, pastoral land.

112

4. Forest Hydrology and Tree-Water Relations

9 Clearing a f o r e s t e d c a t c h m e n t g e n e r a l l y results in an initial increase in streamflow, with a g r a d u a l r e t u r n to original levels as the vegetative cover reestablishes itself. T h e size of the increase d e p e n d s on the prop o r t i o n of the v e g e t a t i o n r e m o v e d a n d on the p o t e n t i a l e v a p o r a t i o n rates, taking a c c o u n t of slope a z i m u t h a n d angle a n d solar radiation on the slopes. 9 In some cases, t h e r e is an initial increase in streamflow after clearance, followed by a long p e r i o d w h e n the streamflow is below the original baseline flow. 9 T h e r e is a s u s t a i n e d increase in streamflow after clearance, particularly likely to be the case w h e r e l a n d use is c h a n g e d to pasture or agricultural crops. We provide s o m e b a c k g r o u n d to these results a n d review some of the literature that describes t h e m . As an i n t r o d u c t i o n , we restate the point m a d e in relation to Eq. (4.1); that is, that the differences in c a t c h m e n t water yield u n d e r different v e g e t a t i o n types can be u n d e r s t o o d a n d exp l a i n e d in t e r m s of differences in the various t e r m s of that e q u a t i o n a n d the way they are affected by the structural a n d f u n c t i o n a l characteristics of the vegetation. Each c o m p a r i s o n or analysis m u s t take into a c c o u n t possible d i f f e r e n c e s in the a m o u n t of water i n t e r c e p t e d a n d e v a p o r a t e d f r o m canopies, the p a t t e r n a n d d u r a t i o n of L* [a f u n c t i o n of stand density a n d leaf h a b i t (e.g., d e c i d u o u s , e v e r g r e e n ) ] , a n d the d e p t h of the r o o t i n g zone, which d e t e r m i n e s the capacity of the vegetation to access d e e p g r o u n d w a t e r . T h e rates of t r a n s p i r a t i o n f r o m the vegetation, at any p a r t i c u l a r season, will be d e t e r m i n e d by the factors we have discussed at some l e n g t h - - t h e e n e r g y balance, a e r o d y n a m i c , a n d canopy conductances. R u n o f f a n d d r a i n a g e are strongly d e p e n d e n t on the c o n d i t i o n of the soil surface a n d the water c o n t e n t of the r o o t zone as well as precipitation patterns. Classical c o n c e p t s divide water yield f r o m c a t c h m e n t s into quickflow, the p e a k outflow that occurs d u r i n g a n d shortly after storms, a n d baseflow, the m o r e or less continuous, long-term outflow f r o m a catchment. T h e reasons for these flows, a n d the pathways followed by the water, have b e e n the subjects of study a n d d e b a t e a m o n g hydrologists for a long time. W a r d (1984) has p r o v i d e d an excellent t r e a t m e n t of the situation, of w h i c h the following is a summary. C a t c h m e n t s are highly variable, with differences in slope, t o p o g r a p h i c shape, a n d soil d e p t h s dictating the r u n o f f response. A hypothesis that h e l d sway for m a n y years was that of H o r t o n (1933). This said that if rain falls on a c a t c h m e n t at a rate g r e a t e r t h a n the rate at which it can be absorbed, the excess water will flow over the g r o u n d as overland flow ( r u n o f f ) . However, it is now r e c o g n i z e d that in h u m i d regions, overland

II. Catchment Hydrology

] ]3

flow very rarely occurs until there are areas of saturated soil in the catchment. These c a n n o t accept m o r e water a n d act as sources for quickflow. These saturated areas link with one a n o t h e r t h r o u g h above- a n d belowg r o u n d channels. It appears that all rainfall infiltrates and is t h e n transmitted t h r o u g h the soil profile, with upslope rainfall r e c h a r g i n g the soil moisture store, providing for subsquent baseflow, and downslope rainfall and c h a n n e l precipitation providing most of the quickflow. T h r o u g h f l o w ( m o v e m e n t of water t h r o u g h the soil profile) occurs t h r o u g h c h a n n e l s and c o n n e c t i o n s between saturated areas at m u c h h i g h e r rates than would be p r e d i c t e d on the basis of assumptions of soil matrix flow. It also appears that, in some cases, w h e n c a t c h m e n t s are saturated each new increm e n t of rainfall displaces all p r e c e d i n g increments, causing the oldest water to exit from the b o t t o m e n d of the system. C a t c h m e n t e x p e r i m e n t s are usually of two types: they are e i t h e r based on a p e r i o d of p r e c a l i b r a t i o n before t r e a t m e n t or based on p a i r e d catchments. Pereira (1973) provided a useful general t r e a t m e n t of forest hydrology experiments, which includes a n u m b e r of illustrations of the effects, in terms of erosion, of forest clearance. A famous study by Borm a n n a n d Likens (1979) shows the d r a m a t i c effects of clear-felling the forest on the hydrology of a watershed ( H u b b a r d Brook). S u m m e r streamflow increased by a factor of a b o u t 4, r e a c h i n g a peak d u r i n g the second year after cutting. Bruijnzeel (1990) has provided a c o m p e n d i u m of the results of e x p e r i m e n t s on moist tropical forests and a collation of extant i n f o r m a t i o n a b o u t the effects of land use change in those areas. He s u m m a r i z e d his f i n d i n g s - - w h i c h i n c l u d e d i n f o r m a t i o n on c a t c h m e n t water yields and flow patterns, n u t r i e n t fluxes, erosion, a n d s e d i m e n t t r a n s p o r t m i n 37 points, to which he did n o t allocate priority or relative i m p o r t a n c e . However, point 35 is worth emphasizing: "The i n f o r m a t i o n s u m m a r i s e d in this r e p o r t leads to the observation that the adverse envir o n m e n t a l conditions so often observed following "deforestation" in the h u m i d tropics are n o t so m u c h the result of "deforestation" per se b u t rather of p o o r land use practices after clearing the forest." In relation to water yield alone, Bosch a n d Hewlett (1982) p r o d u c e d a review of 94 c a t c h m e n t e x p e r i m e n t s f r o m all over the world. All these, with the e x c e p t i o n of one, showed that c a t c h m e n t water yield increased following r e d u c t i o n s in vegetation cover. O n average, for each 1% of cover of conifer, m i x e d h a r d w o o d , a n d scrub removed, annual streamflow increased 4.2, 2.0, a n d 1.2 m m , respectively. (Note: t h e r e is h u g e variation associated with these statistical averages, which s h o u l d n o t be used as general quantitative estimates.) T h e e x c e p t i o n to the g e n e r a l pattern was a series of long-term observations r e p o r t e d by L a n g f o r d (1976) who showed that, following r e g e n e r a t i o n after m a j o r fires, the water yield from E. regnans forest catchments in Victoria, Australia, first increased

1 14

4. Forest Hydrology and Tree-Water Relations

but t h e n d e c r e a s e d significantly 3 - 5 years after the fire. The decreases were of o r d e r 200 m m per year in areas with annual rainfalls ranging from 900 to 2000 mm. T h e indications were that it will take up to 100 years for the c a t c h m e n t water yields to r e t u r n to their original levels. Work by Cornish (1993), on logged Eucalyptus catchments, is showing the same t r e n d s - - e a r l y increase in c a t c h m e n t water yield, then a steady decrease. T h e initial increases in runoff after logging were p r o p o r t i o n a l to the percentage of the c a t c h m e n t that was logged. T h e r e are now data from Hubb a r d Brook (HB) that show similar patterns, r e p o r t e d by H o r n b e c k et al. (1993), who p r e s e n t e d a d i a g r a m showing thatwater yield from HB Catchm e n t 2 increased 350% after clear-cutting and t r e a t m e n t with herbicides for several years. It then declined steadily until, a b o u t 12 years after treatment, water yield fell below p r e t r e a t m e n t levels. Twenty-five years after treatment, water yield was still below p r e t r e a t m e n t yields. Langford (1976) was not able to offer any satisfactory explanation for his observations, but the e x p l a n a t i o n now accepted is that the decreases in water yield observed a few years after logging or b u r n i n g are caused by the large increases in leaf area associated with Eucalyptus regrowth. L* in regrowth may b e c o m e significantly h i g h e r than in old growth forest (see C h a p t e r 8). This is s u p p o r t e d by the work ofJayasuriya et al. (1993) who used a heat pulse t e c h n i q u e to m e a s u r e sap flow velocities in E. regnans on the catchments studies by Langford. They showed that differences in the transpiration rates of the trees could be entirely accounted for by differences in sapwood area [and h e n c e leaf area; see C h a p t e r 3, Eq. (3.2)]

Figure 4.7 The eftcots of thinning on groundwater levels ill two catchments in Western Australia. The data have been normalized. Reprinted fi'om J. Hydrol. 150, (;. I~. Stoneman, Hydrological response to thinning a small j a r r a b (Eucalyptus mar~,~inata) 393-407 (1993) with kind permision from Elsevier Science-NI,, Sara Burgerhartstraat 25, 1055 KV Amsterdam, The Netherlands.

II. Catchment Hydrology

1 15

and that streamflow differences after t h i n n i n g could be a c c o u n t e d for by the differences in stand transpiration rates. H o r n b e c k et al. (1993) did not explain the H u b b a r d Brook results in terms of leaf area, but acc e p t e d that they are consistent with such an explanation. Illustrations of the hydrological responses of forests to t h i n n i n g and the effects of land use change (conversion to pasture) c o m e f r o m Western Australia. In a p a i r e d c a t c h m e n t e x p e r i m e n t , R u p r e c h t et al. (1991) t h i n n e d a small forested c a t c h m e n t from 700 to 110 trees p e r h e c t a r e in an area with a b o u t 1200 m m rainfall per year. G r o u n d w a t e r levels in the t h i n n e d c a t c h m e n t began to rise within the first year after thinning. Deep g r o u n d w a t e r attained a new e q u i l i b r i u m after a b o u t 2 years, rising by a p p r o x i m a t e l y 2 m in a downslope area a n d a b o u t 5 m upslope. Streamflow increased from a p p r o x i m a t e l y 6% of annual rainfall before t h i n n i n g to a b o u t 20% after thinning. R u p r e c h t et al. did not i n t e r p r e t these results in terms of the m e c h a n i s m s discussed previously, b u t it is likely that the r e d u c t i o n in crown (canopy) from 60 to 14% would have resulted in substantial r e d u c t i o n s in i n t e r c e p t i o n and transpiration losses, leading to the observed rise in average g r o u n d w a t e r levels. These would have resulted in m u c h quicker filling of g r o u n d w a t e r storage at the start of a rainstorm, h e n c e more, and m o r e rapid, streamflow. Similar results have b e e n r e p o r t e d by S h a r m a et al. (1987). An e x p e r i m e n t by S t o n e m a n (1993), in which one of two p r e c a l i b r a t e d c a t c h m e n t s was t h i n n e d to a b o u t 20% of the original canopy cover, provides a d r a m a t i c illustration of the effects of t h i n n i n g on g r o u n d w a t e r levels. His results are shown in Fig. 4.7 Conversely, p a i r e d c a t c h m e n t studies in New Zealand have shown that the conversion of native tussock grassland to P. r a d i a t a plantations resuited in an initial increase in water yield, followed by a g r a d u a l decline as the canopy of the pine plantation developed. O t h e r changes in vegetation cover, such as native forest to pine forest, which is occuring worldwide ( C h a p t e r 2), have m o r e subtle effects on water yield. Obviously, water yield will increase dramatically i m m e d i a t e l y after the conversion because of the r e d u c t i o n of L* (see Fig. 4.6a). Removal of native p o d o c a r p forest in New Zealand initially increased water yield by as m u c h as 535 m m year -1, or a p p r o x i m a t e l y 30% of annual precipitation (Rowe, 1983). T h e increased water yield c o m m o n l y o c c u r r e d in the form of increased quickflow in areas of high precipitation a n d in the form of delayed flow in areas of lower p r e c i p i t a t i o n (Fahey a n d Rowe, 1992). However, water balance calculations have shown that within 8 - 1 0 years of conversion of native e v e r g r e e n p o d o c a r p forest to pine plantation the water yields for the two forest types were similar (Davoren, 1986; Dons, 1986 in Fahey and Rowe, 1992). In the s o u t h e a s t e r n U n i t e d States, conversion of broad-leaved d e c i d u o u s forest to pine plantation increased

1 16

4. Forest Hydrology and Tree-Water Relations

water yield, p r e s u m a b l y due to the larger L* of the conifer forests during the growing season and additional transpiration losses by the conifer forests while the d e c i d u o u s forest was leafless (Swank and Douglas, 1974). The complexity of the processes involved in water m o v e m e n t into and t h r o u g h forested catchments makes it clear why, as n o t e d previously, theoretically rigorous, physically based models, using equations known to work in well-defined, spatially h o m o g e n e o u s situations, are often of limited value in predicting water yield. The p r o b l e m lies in the difficulty of specifying a p p r o p r i a t e p a r a m e t e r values for highly variable systems. T h e r e are some d i s t r i b u t e d - p a r a m e t e r models, utilizing terrain analysis, which are providing good predictions and, hence, confidence that we are developing the capability to predict the consequences of mana g e m e n t actions or inadvertant modification on the basis of the mechanisms involved (e.g., Vertessy et al., 1993), but for most m a n a g e m e n t and decision-making purposes we have to rely on empirical relationships and use o u r u n d e r s t a n d i n g of the u n d e r l y i n g mechanisms to interpret the results. As a general "rule of t h u m b , " we can say that the water yield of forested catchments will be increased by clearing or thinning by an a m o u n t p r o p o r t i o n a l to the percentage of the canopy removed by the operation. The actual a m o u n t of the increase will d e p e n d on the type of plant cover that fills the gaps, on soil d e p t h and storage characteristics, and, of course, on the precipitation patterns. The persistence of any increase will d e p e n d on subsequent land use and the treatment of the vegetation.

III. T r e e - W a t e r Relations and Their Effects on Growth A. T r e e - W a t e r Relations

In the context of catchment hydrology, trees can be treated as i m p o r t a n t factors ill the water balance in terms of their influence on interception, transpiration, and, hence, r u n o i I a n d drainage. From the point of view of the forest m a n a g e r c o n c e r n e d with wood p r o d u c t i o n or ecological balance and change, the i m p o r t a n t considerations are the influence of the water balance on tree growth and indeed survival. It would be useful to be able to predict the effects of water shortage on tree growth, either in the short term, t h r o u g h effects on stomatal conductance, or in the longer term, t h r o u g h effects on the p r o d u c t i o n and maintenance of foliage and biomass p r o d u c t i o n . It is also i m p o r t a n t to be able to predict the effects of stand thinning on water relations and growth of trees, as well as the soil water balance of sites. T h e r e has been an e n o r m o u s a m o u n t of research on these issues. Slatyer's (1967) book was a l a n d m a r k in the field of plant-water rela-

III. Tree-Water Relations and Their Effects on Growth

] 17

tions, a n d there have b e e n m a n y books a n d m a j o r reviews since, some devoted exclusively to the water relations of trees (see, for example, Vol. VI (1981) in the series edited by Kozlowski). T h e c h a p t e r by W h i t e h e a d and Jarvis (1981) in that volume provides a c o m p r e h e n s i v e t r e a t m e n t of water m o v e m e n t t h r o u g h conifers a n d L a n d s b e r g (1986) provided a general analytical t r e a m e n t of water m o v e m e n t t h r o u g h trees. U n d e r Section I,D, we c o n s i d e r e d the effects of soil moisture content on transpiration rates and a r g u e d that the u n d e r l y i n g factor controlling E is the rate at which water moves f r o m the soil to the roots, a n d then t h r o u g h the plant to the leaves. It is useful to consider this process in a formal way. Water moves from soil to roots a n d t h r o u g h plants along potential gradients, caused primarily by changes in leaf water potential as a result of transpiration. T h e generally a c c e p t e d m o d e l of water m o v e m e n t t h r o u g h plants is based on an analog of O h m ' s law, which follows from this gradient-driven flow. Using this analog, the flow of water ( J , m 3 sec -1) t h r o u g h the t r e e - s o i l system can be r e p r e s e n t e d by flow t h r o u g h a series of hydraulic resistances and described by the equation J--

(~s-

~bf)/R s + Rr + Rx + Rf,

(4.11)

Figure 4.8 Diurnal patterns of leaf water potential showing characteristic hysteresis between m o r n i n g and afternooon. The diagram shows that, at the same transpiration rate, ~f tends to be lower in the afternoon than in the morning, with the difference being (relatively) greater as the soil dries out. In dry soils, transpiration rates cannot be as high as in wet soil because the plants cannot absorb water fast e n o u g h to meet the atmospheric dem a n d (diagram redrawn from Hinckley et al., 1978).

118

4. Forest Hydrology and Tree-Water Relations

where Rs d e n o t e s resistance to water flow t h r o u g h the soil to the root, Rr resistance t h r o u g h the root to the xylem, Rx resistance to flow t h r o u g h the xylem, and Rf from the xylem to the evaporating surfaces in the leaves. If Eq. (4.11) holds, there must be flow continuity a n d mass conservation t h r o u g h the system so that a given volume of water (Jdit) lost from the leaves in time interval At will result in the extraction of the same volu m e of water from the soil over that interval. The relationship between J and A4, b e c o m e s n o n l i n e a r if the resistances are not constant, but there has b e e n no evidence of this from m e a s u r e m e n t s m a d e on trees in the field, a l t h o u g h it may a p p e a r to be the case if capacitance (water stored in tissue such as stems) is a factor (see L a n d s b e r g et al., 1976; W h i t e h e a d andJarvis, 1981; Landsberg, 1986). If resistances are constant and Eq. (4.11) holds, it can easily be solved by rewriting it as q'r = g ' s - J Y - , Ri,

(4.12)

This is essentially an equation for a tree with a single leaf (at potential ~/,f) to which all the water flows. More rigorously, it would be necessary to subtract the s u m m e d p r o d u c t s of the partial flows a n d resistances, which change in a b r a n c h e d system. This was discussed by Richter (1973). However, Eq. (4.12) may provide a good first a p p r o x i m a t i o n to the average value of g'r. If it does, plotting A4, [= (~f - 4'.~)] against J should yield a straight line with a slope s giving the sum of the resistances in the flow pathway. If the soil is wet, ~/,~ can be taken as approximately zero; therefore, the plot reduces to Of a g a i n s t . / ( o r against transpiration rate, which may be taken as an estimate of.]'). Equation (4.12) has, on some occasions, been a d e q u a t e for this type of analysis (Landsberg et al., 1975), but in most cases, particularly if the diurnal course of transpiration is plotted against (for example) hourly average values of 0f, such plots yield a hysteresis loop (Fig. 4.8). This appears to be a consequence of the fact that tissues such a sapwood may store significant quantities of water, i.e., exhibit capacitance (Holbrook, 1995). Figure 4.8 shows relationships between transpiration and leaf water potential at different values of g,~. It can be i n t e r p r e t e d as follows: As the transpiration rate increases during the first part of the day, water is withdrawn from the soil and from tissue storage. In n o n s a t u r a t e d soils, water will have moved, d u r i n g the night, to rewet the regions a r o u n d the roots from which it had been withdrawn the previous day. The extent of the drying out, the distance the water has to move, and the wetness of the soil and its hydraulic p r o p e r t i e s [see Eq. ( 4 . 4 ) - ( 4 . 6 ) ] all influence the extent to which the soil s u r r o u n d i n g the roots is recharged. We would expect that, before transpiration c o m m e n c e s ( J = 0), leaf, xylem, and soil water potential would all be a p p r o x i m a t e l y equal. This is the assump-

III. Tree- Water Relations and Their Effects on Growth

1 19

tion underlying the c o m m o n l y made m e a s u r e m e n t of predawn water potential (4'pd). If the soil is relatively dry, the intercept on the ~ axis of the downward (morning) arm of the g's/J plot can be taken to give an indication of average ~/'s in the root zone. It should c o r r e s p o n d closely to 4'p8. In general, the diurnal course of 0f follows the same path, falling from ~//pd to m i n i m u m values (~//min) t h r o u g h the d a y m t h e pattern of reduction being d e t e r m i n e d via Eq. (4.11) by transpiration r a t e m a n d rising again in the evening (see Fig. 4.8). It would a p p e a r that, in the absence of comprehensive data on flow resistances t h r o u g h trees, it will not usually be possible to apply Eq. (4.11), but we note that ~//min is a conservative quantity: in P. radiata trees in Australia, in treatments with widely different soil moisture, ~min varied from a b o u t - 1 . 4 MPa in the irrigated treatment to - 1 . 9 MPa in the dry (Myers and Talsma, 1992); in P. sitchensis trees in Scotland, u n d e r totally different conditions, ~tmi n r e a c h e d - 1 . 6 MPa (Hellkvist et al., 1974); and in Quercus alba in Missouri, ~tmi n r e a c h e d a b o u t - 2 . 3 MPa (Hinckley et al., 1978). It therefore seems likely that values of Ri [Eq. (4.12) ] will also be relatively conservative, a l t h o u g h they will vary with stand density and possibly with various e n v i r o n m e n t a l conditions (see Mencuccini and Grace, 1995). A m o n g the various problems e n c o u n t e r e d in analyses of p l a n t - w a t e r relationships is the fact that most m e a s u r e m e n t s of plant water status are i n s t a n t a n e o u s ~ t h e y provide a value for the variable at the m o m e n t of m e a s u r e m e n t , which reflects the interactions of dynamic processes discussed previously. The water status of the plant at any time may influence the rates of plant growth processes taking place at that time, but it may not provide m u c h i n f o r m a t i o n a b o u t the way those processes have acted in the past; therefore, analysis of plant growth in terms of m e a s u r e m e n t s of plant water status made at points in time may not be very enlightening. Because the state of the plant at any time, in terms of its mass and the distribution of that mass, is the end result (integral) of rate processes over a period (strictly, from g e r m i n a t i o n ) , what is r e q u i r e d is a measure of water status integrated over the p e r i o d of interest. Myers (1988) made a useful c o n t r i b u t i o n to the solution of this problem by introducing the water stress integral So

= ~(@pd-

c)n,

(4.13)

where @pd is the m e a n value of predawn water potential over any interval, c is a datum value (the m a x i m u m value of ~r m e a s u r e d (or calculated) during the period), and n is the n u m b e r of days. Myers (1988) showed, for a stand of P. radiata, that needle lengths and basal area increments of trees subjected to five water and fertilizer regimes were closely related (r 2 = 0.90 and 0.91, respectively) to S o over the growing season. This app r o a c h deserves more attention and development.

"190

4. Forest Hydrology and Tree-Water Relations

B. E f f e c t s o f W a t e r S t r e s s o n G r o w t h

We are c o n c e r n e d here with stand-level effects. To describe these in quantitative terms, we n e e d relatively simple relationships that can be used in a predictive way. These will inevitably be empirical but, if they are soundly based on the detailed mechanistic knowledge that we have of the dynamics of p l a n t - w a t e r relationships, they should be reproducible and therefore of value in a predictive sense. To illustrate, we outline below the type of relationships that we believe could be established and should be developed. We also indicate how such relationships could be translated into information that would be of value in practical m a n a g e m e n t and policy decisions. The underlying assumption is that growth is directly related to absorbed radiant energy (see C h a p t e r 9), and that the efficiency of energy utilization is affected by plant water status. In effect, we propose that growth (W), which may be estimated as dry mass i n c r e m e n t per unit area per unit time or as a surrogate such as stem volume, can be expressed as dW dt

= f ( q ~ , N ) , g(~9),

(4.14)

from which W(t) = ./(q~,N) fg-(t/,) 9dr.

(4.15)

Here, N denotes nutrients in g e n e r a l m n o t only nitrogen. The relationships are general and of course Eq. (4.15) is not strictly true because f(~,N) is not invariant with time, but they illustrate the point. We assume, following Myers (1988), that the integral of predawn tissue water potential over time provides a p a r a m e t e r (Sj,) that will influence growth patterns in easily predictable ways. The p r o b l e m therefore becomes one of providing a m e t h o d for calculating g'pd. The starting point will be the assumption that ~pd will be related to soil moisture potential in the root zone. A study by Fahey and Young (1984) indicates that this assumption is correct; their results also illustrate the n e e d for information about soil moisture characteristicsmthe nonlinearity of the ~.~/0~ relationship [Eq. (4.4) ] makes it very unlikely that relationships consistent across soil types will be obtained between 0s and ~p~. More such studies are required. Given the values of the coefficients of Eq. (4.4), the average value ofg,~ in the root zone of a stand can be estimated from values of 0~ derived from water balance calculations [using Eq. (4.1) with a daily time step]. We would expect ~//pd tO be linearly related to ~s; ~//pd will be lower because of the resistances in the flow path-

III. Tree- Water Relations and Their Effects on Growth

121

ways [Eq. (4.11)], a l t h o u g h how these act w h e n J = 0 is difficult to say. Again, the results of Fahey and Young s u p p o r t this assumption. Given that we can estimate Opd on the basis of water balance calculations, it follows that we can obtain values of Sr for any p e r i o d across which growth m e a s u r e m e n t s have b e e n m a d e a n d use t h e m to explain variations in growth. It can be a r g u e d that the most i m p o r t a n t mechanism by which water status influences plant growth (in terms of dry matter p r o d u c t i o n ) is r e d u c t i o n in gs, which leads to r e d u c t i o n in CO 2 assimilation and, hence, dry matter p r o d u c t i o n . In the short t e r m (hours a n d days), this seems likely to be correct; therefore, there is a case to be m a d e for expressing the effects of ~r in terms of effects on stomatal conductance; Sala a n d T e n h u n e n (1994) have p u b l i s h e d the results of a study in which such relationships were investigated. However, on longer time scales, water stress affects growth t h r o u g h its effects on leaf area b o t h t h r o u g h effects on leaf e x p a n s i o n a n d r e t e n t i o n a n d t h r o u g h effects on c a r b o n allocation (see C h a p t e r 5). T h e evidence for this is n o t strong, b u t it is s u p p o r t e d by data from irrigation studies that showed that increased water availability increases c a r b o n allocation to foliage a n d decreases carbon allocation to fine roots (Gower et al., 1992). It t h e r e f o r e seems likely that a c o m p l e t e m o d e l of the effects of water status on growth would have at least two c o m p o n e n t s : relationships expressing the (short-term) effects of ~pd on gs and, hence, on c a r b o n assimilation, a n d relationships expressing the (longer-term) effects of 4'pd on growth in terms of leaf e x p a n s i o n a n d stem d i a m e t e r growth. O n e of the i m p o r t a n t effects of t h i n n i n g is to change the water relations of stands. R e d u c t i o n in the n u m b e r of trees is likely to improve the water balance of the stand by r e d u c i n g i n t e r c e p t i o n - - s o that m o r e rain reaches the g r o u n d - - a n d r e d u c i n g transpiration losses from the stand as a whole. W h e t h e r stand transpiration rates r e m a i n lower than those of a similar u n t h i n n e d stand will d e p e n d on the relative leaf areas of the stands as well as the transpiration rate per unit leaf area. T h e t h i n n i n g e x p e r i m e n t d o n e by S t o n e m a n (1993), cited earlier (see Fig. 4.7), provided an interesting d e m o n s t r a t i o n of the effects of t h i n n i n g on g r o u n d water levels a n d streamflow a n d the results ofJayasuriya et al. (1993) are also relevant. A very t h o r o u g h and a useful study of the effects of thining on a stand of oak (Quercus petraea) was carried out by Br6da et al. (1995). They f o u n d that, across two seasons following thinning, @pd was generally h i g h e r in the t h i n n e d stand, a l t h o u g h midday values of Of were a b o u t the same. Sap flux in trees in the t h i n n e d stand was considerably h i g h e r t h a n in the controls, which implied that the hydraulic path c o n d u c t a n c e s were higher. Calculations indicated that these c o n d u c t a n c e s were h i g h e r (statistically significantly) in the t h i n n e d trees in the second season, b u t

192

4. Forest Hydrology and Tree- Water Relations

Br6da et al. attributed this to decreasing c o n d u c t a n c e s in the control trees. The cause of the differences may have b e e n the i m p r o v e d soil water conditions in the t h i n n e d stand. Stand transpiration was lower in the t h i n n e d stand in the first year after thinning, but it was the same in the t h i n n e d and control stands in the second year, a l t h o u g h there was no significant change in leaf area. A t h o r o u g h theoretical t r e a t m e n t of the processes involved in these relationships has b e e n given by W h i t e h e a d and Jarvis (1981). E x p a n d i n g from the stand level to regional estimates of the effects of water on forest growth, the only feasible a p p r o a c h is t h r o u g h water balance calculations. The most i m p o r t a n t i n f o r m a t i o n n e e d e d is soil type, rainfall, and e n o u g h weather data to p e r m i t the calculation of transpiration. The p r o c e d u r e would then be to solve the hydrologic equation [Eq. (4.1)] as accurately as possible for each stand or forest block and calculate the periods for which soil moisture content can be expected to be limiting to growth. T h e r e d u c t i o n in dry mass p r o d u c t i o n as a result of periods of d r o u g h t would have to be established empirically by measurements and records m a i n t a i n e d over long periods; ideally, these would be consistent with m o r e detailed information and models of the type described in the previous p a r a g r a p h . Applying such calculations to longterm weather records provides the basis for estimates of the probability of d r o u g h t periods of specified duration and intensity.

IV. Concluding Remarks We n o t e d in the i n t r o d u c t i o n to this c h a p t e r that, from the point of view of the forest manager, there are two major issues associated with forest hydrology and tree water relations. These are the effects of forest manipulation (logging, burning, etc.) on c a t c h m e n t water yield and quality (see C h a p t e r 7) and the effects of stand water balance and soil water content on forest growth. T h e analysis of both issues requires the ability to calculate stand water balance. Much of the research d o n e in hydrology has been empirical, unacc o m p a n i e d by the m e a s u r e m e n t s n e e d e d to identify and quantify the processes c o n t r i b u t i n g to the results obtained. Classical c a t c h m e n t hydrology and streamflow m e a s u r e m e n t s are of this type, which perhaps explains why Langford's (1976) E. r e g n a n s results were initially r e g a r d e d as s o m e t h i n g of an anomaly. T h e reason for the results seems, in retrospect, relatively obvious, but it is only in recent years that the importance of leaf area index and the reasons for the three categories of catchment response to clearing (see Fig. 4.5) have b e e n clearly recognized and appreciated. T h e increase in m e a s u r e m e n t s of water flow up trees (e.g., Jayasuriya et al., 1993) and in u n d e r s t a n d i n g of the role of stomatal con-

IV. Concluding Remarks

123

ductance a n d a e r o d y n a m i c e x c h a n g e m e c h a n i s m s has led to increasing recognition of the n e e d to m e a s u r e these variables as well as soil water content and streamflow. We note Beven's (1989) a r g u m e n t , m e n t i o n e d earlier, that rigorous, physically based m o d e l s are often of limited value in c a t c h m e n t hydrology; however, we also note that, a l t h o u g h Beven's arg u m e n t was general, he a p p e a r e d to focus primarily on the s o i l m t r a n spiration rates a n d vegetation were not m e n t i o n e d . We would argue, from the evidence that we have reviewed a n d the i n f o r m a t i o n p r e s e n t e d elsewhere in this book, that it should be possible to estimate quite accurately the effects of tree clearance on the water yield of catchments using relatively simple models. Managers should also be able to m a k e qualitative estimates of the effects of clearing from the i n f o r m a t i o n p r e s e n t e d here. Vertessy et al. ( 1 9 9 3 ) have d e m o n s t r a t e d that physically based models can provide accurate simulations of the r u n o f f a n d water yields of complex catchments. Theirs is esentially a " b o t t o m up" a p p r o a c h : T h e system is described in detail using equations that r e q u i r e m a n y p a r a m e t e r values and a t t e m p t i n g to a c c o u n t for all the c o m p o n e n t s of the system for each small subunit of the catchment. This is excellent f r o m a research point of view, but such a m o d e l is n o t useful to m a n a g e r s . However, it can serve the very valuable p u r p o s e of testing and evaluating m u c h simpler models to d e t e r m i n e how m u c h c o m p l e x m o d e l (s) can be simplified w i t h o u t losing the capacity to p r o d u c e useful results. At the time of writing, Vertessy et al. h a d n o t m a d e m u c h progress on evaluating the extent to which their m o d e l ( T O P O G ) could be simplified a n d used in this way, but it is one of their research objectives. T h e alternative a p p r o a c h is to develop a simple m o d e l f r o m first p r i n c i p l e s m p e r h a p s based on the areas in a c a t c h m e n t where L* is within particular limits, some simple, general descriptions of soils, a n d simple topography. This would be tested against models such as T O P O G . In any e n v i r o n m e n t where the potential (atmospherically driven) evaporation [Eq. (3.22)] exceeds precipitation, trees will tend to use all the water available to t h e m , which will be total p r e c i p i t a t i o n less losses caused by i n t e r c e p t i o n , r u n o f f d u r i n g high-intensity rain events, a n d evaporation f r o m the soil surface or f r o m litter layers. If rainfall is highly seasonal, such as in some tropical or subtropical areas that may s u p p o r t deciduous forests (see C h a p t e r 2), t h e n the losses are likely to be greater because soil storage may be filled d u r i n g the rainy season, a n d losses by drainage a n d r u n o f f may be significant. In areas where the precipitation exceeds potential evaporation, annual water use by trees will be equal to cumulative transpiration, which will d e p e n d on a t m o s p h e r i c conditions and their interaction with leaf area, stomatal c o n d u c t a n c e , a n d canopy architecture. In the first instance, (water-limiting) tree growth will dep e n d on water availability: W h e n soil water in the r o o t zone is limiting to the extent that water c a n n o t move to the roots fast e n o u g h to m e e t the

124

4. Forest Hydrology and Tree-Water Relations

a t m o s p h e r i c d e m a n d , m o d i f i e d by s t o m a t a l r e s p o n s e s to light, s t o m a t a will c l o s e so t h a t t r a n s p i r a t i o n r a t e s d o n o t e x c e e d s u p p l y r a t e s . T h i s m u s t r e s u l t i n r e s t r i c t i o n o f g r o w t h r a t e s . I n t h e l i m i t , g r o w t h will c e a s e . F r o m t h e p o i n t o f view o f p r o d u c t i o n forestry, t h e o b j e c t i v e m u s t be to b e a b l e t o i d e n t i f y t h e p e r i o d s w h e n t h i s will h a p p e n .

Recommended Reading Borman, F. H., and Ifikens, G. F. (1979). "Patterns and Progress in a Forested Ecosystem." Springer-Verlag, New York. Hornbeck, J. W., Adams, M. B., Corbett, E. S., Verry, E. S., and I,ynch, J. A. (1993). Longterm impacts of torest treatments on water yield: A summary for north-eastern USA. Hydrol., l~i0, 323-344. McNaughton, K. G., and Jarvis, p. G. (1986). Predicting effects of vegetation changes on transpiration and evaporation. In "Water Deficits and Plant Growth" (T. T. Kozlowski, Ed.), pp. 1-47. Academic Press, New York. Pallardy, S. (,., Cermack, J., Ewers, F. W., Kaufman, M. R., Parker, W. C., and Sperry, J. s. (1994). Water transport dynamics ill trees and stands. In "Resource Physiology of Conifers: Acquisition, Allocation and Utilization." (W. R. Smith and T. M. Hickley, eds), pp. 301-389. Academic Press, San Diego. Pereira, H. C. (1973). "I,and Use and Water Resources in Temperate and Tropical Climates." (;ambridge Univ. Press, Cambridge, UK. Tardieu, F., and Davies, W.J. (1993). Integration of hydraulic and chemical signaling in the control of stomatal conductance and water status of droughted plants. Plant CellEnviron. 16, 341-349. Verstessy, R. A., Hatton, T.J., ()'Shaughnessy, p.J., andJayasuriya, M.I).A. (1993). Predicting water yield from a m > rs (see C h a p t e r 3), t h e n C O 2 c o n c e n t r a t i o n at the leaf surface (Cs) can be e q u a t e d to Ca, a n d Eqs. (5.6) a n d (5.7) simplify to A = gs (Ca -- Ci),

(5.8)

T h e r e have b e e n m a n y m o d e l s o f stomatal c o n d u c t a n c e a n d the factors affecting it; s o m e c o m m e n t is p r o v i d e d in the following section. T h e c u r r e n t m o s t c o m p l e t e , a n d a p p a r e n t l y accurate, m o d e l is the m o d i f i e d version, d e v e l o p e d by L e u n i n g (1990; 1995), o f the Ball et al. (1987) e q u a t i o n . E l i m i n a t i n g a constant, a n d a c o r r e c t i o n for F, which a c c o u n t s for b e h a v i o r at low C O 2, it can be written

g,~ = a l A / (1 + D s / D o ) c~,

(5.9)

w h e r e Dl is the v a p o r p r e s s u r e deficit at the leaf surface a n d al a n d Do are e m p i r i c a l p a r a m e t e r s for w h i c h L e u n i n g gives a r a n g e of values for E. grandis f r o m 20 to 43 for al w h e n D,, = 350 Pa. S u b s t i t u t i n g for gs f r o m Eq. (5.8) leads to

ci/cs = 1 - (1 + D s / D o ) /

al,

(5.~o)

w h i c h indicates that the ratio o f i n t e r n a l to a m b i e n t (leaf surface) C O 2 c o n c e n t r a t i o n s varies with D~. T h e conservative n a t u r e o f this ratio is consistent with the idea that s t o m a t a r e s p o n d to the e n v i r o n m e n t in such a way that ci is m a i n t a i n e d m o r e or less c o n s t a n t ( W o n g et al., 1979). By the same a r g u m e n t u s e d to e q u a t e c~ a n d c.,, we can take D~ ~ D, w h e r e D is the a t m o s p h e r i c v a p o r p r e s s u r e deficit in the r e g i o n o f the leaf. It follows that, given values for D,, a n d a l m a n d we n o t e L e u n i n g ' s w a r n i n g that t h e r e is c o n s i d e r a b l e variation in t h e s e m w e can e s t i m a t e ci/c~ (~- ci/Ca), a n d h e n c e ci, for i n s e r t i o n into the p h o t o s y n t h e s i s e q u a t i o n s . We s h o u l d also n o t e h e r e that this m o d e l m a k e s n o allowance for the effects o f leaf water status a n d soil water c o n t e n t o n stomatal c o n d u c t a n c e . T h e s e are discussed in C h a p t e r 4.

A. S t o m a t a l C o n d u c t a n c e

A l t h o u g h the m o d e l o u t l i n e d previously [Eqs. (5.9) a n d (5.10)] provides an ( a p p a r e n t l y ) s o u n d m e c h a n i s t i c d e s c r i p t i o n o f variations in stomatal c o n d u c t a n c e in r e l a t i o n to p h o t o n flux density, v a p o r p r e s s u r e deficit, a n d a m b i e n t C O 2 c o n c e n t r a t i o n s , it has the d i s a d v a n t a g e that an estim a t e o f A is r e q u i r e d b e f o r e estimates o f g~ can be calculated. It is t h e r e fore w o r t h briefly reviewing s o m e m o r e e m p i r i c a l m o d e l s of stomatal conductance. Jarvis (1976) p r e s e n t e d a m o d e l to d e s c r i b e the r e s p o n s e s of s t o m a t a to e n v i r o n m e n t a l variables a n d a p p l i e d it to t e m p e r a t e conifers. H e u s e d results f r o m c o n t r o l l e d e n v i r o n m e n t studies to c h o o s e the e m p i r i c a l func-

II. Canopy Photosynthesis

133

tion that best described the response of stomata to each variable a n d comb i n e d them in a multiplicative m o d e l of the form

gs -- f l (O) 9f2 (~p) "f3 (~f) "f4 ( g ) "

(5.1 ])

The general forms of the functions used by Jarvis (1976) have b e e n f o u n d to be suitable for use with a n u m b e r of plants o t h e r than temperate conifers (see, for example, W h i t e h e a d et al., 1981), a l t h o u g h the values of the coefficients may vary. The m o d e l has b e e n widely used. A similar m o d e l was developed by T h o r p e et al. (1980). This omits the foliage water potential term (~f) m b e c a u s e water stress does n o t b e c o m e a factor until 4r falls quite low (Landsberg et al., 1976; Beadle et al., 1 9 7 8 ) m a n d the effects of varying CO 2 concentrations, which n e e d n o t be i n c l u d e d in empirical models for plants well c o u p l e d to the e n v i r o n m e n t . T h o r p e et al. expressed their m o d e l as a single equation:

gs = gref( 1 - a D ) / ( 1

+ b/(.pp),

(5.12)

where a and b are empirical "constants" and gref is a reference conductance. The p a r a m e t e r values may be d e t e r m i n e d from m e a s u r e m e n t s of stomatal response to q~p at low values of D (e.g., from 0.5 to 1.0 kPa) and responses to D when q~p is n o t a limiting variable. In b o t h cases, the analysis uses values of gs n o r m a l i z e d to the highest observed value, i.e., that value is taken as unity. This greatly reduces the scatter in data. The reference value (gref) is then the value of gs e x p e c t e d w h e n b o t h D and q~p are nonlimiting, i.e., it is the m a x i m u m value of gs. K 6 r n e r et al. (1979) list m a x i m u m leaf c o n d u c t a n c e values for 294 species. For woody species, the values range from 1 to 5 m m sec -1. If we take a = 0.3 kPa -1 and b = 70 lamol m -2 sec -1, t h e n gs = 0 when D = 3 kPa a n d gs = 0.5 gref w h e n q~p - 70 /amol m -2 sec -1. Schulze et al. (1994) s u m m a r i z e d stomatal c o n d u c t a n c e values for major vegetation biomes and d e m o n s t r a t e d several useful scaling algorithms, and their theoretical b a c k g r o u n d , for stomatal c o n d u c t a n c e and water v a p o r and c a r b o n dioxide fluxes at the canopy level. They r e p o r t e d a strong positive linear correlation between stomatal c o n d u c t a n c e and leaf n i t r o g e n c o n c e n t r a t i o n for 15 m a j o r vegetation cover types in the world, a l t h o u g h they c o n c l u d e d that the relationship within a vegetation type was relatively conservative. Reich et al. (1992) r e p o r t e d a negative e x p o n e n t i a l relationship between gs and leaf longevity.

II. Canopy Photosynthesis Equations ( 5 . 1 ) - ( 5 . 1 0 ) provide a c o u p l e d m o d e l of assimilation and stomatal c o n d u c t a n c e . To apply t h e m to the calculation of canopy pho-

134

5. Carbon Balance of Forests

tosynthesis requires calculation of p h o t o n flux density (q~p) at any level in the canopy a n d values of the vapor pressure deficit (D) for the air in the canopy. T h e p r o c e d u r e for calculating ~p in the canopy has been described in C h a p t e r 3. More accurate results will be o b t a i n e d for canopy photosynthesis if the canopy is divided into layers r a t h e r than being treated as a single layer with an average value of q~p (see C h a p t e r 3). P h o t o n flux density in the m i d d l e of any layer in a canopy is applied to the leaf area in that layer a n d Eq. (5.4) solved forJ. For calculations over relatively short periods, such as days, it is i m p o r t a n t that the radiation absorbed by the canopy be separated into diffuse and direct c o m p o n e n t s [see Eqs. (3.10) and (3.11)], because shaded leaves receive diffuse radiation only, whereas sunlit leaves receive diffuse plus direct radiation. W i t h o u t this separation, canopy assimilation is likely to be overestimated because it will be assumed that all the leaves, in any layer of the canopy, receive the average q~p for that layer. For calculations over longer periods, errors resulting from treating radiation simply as a total flux are likely to balance out and be negligible. (See the c o m m e n t s in C h a p t e r 3 relating to radiation a b s o r p t i o n by canopies and the relative effectiveness of direct and diffuse radiation.) Equation (5.10) is used, with the average value of D ( ~ D I ) for the period of interest, to calculate ci/c~ from which, assuming c~ ~ ca, we obtain ci. Inserting this in Eqs. (5.3) and (5.5) with a p p r o p r i a t e values of J and the o t h e r p a r a m e t e r s gives values for Wj and W,.; the smaller of these is the rate-limiting step and is used in Eq. (5.1) to calculate the leaf photosynthesis rate. If required, this can be used in Eq. (5.8), with c~ and ci, to solve for g.~. Canopy photosynthesis is the sum of the rates in each layer multiplied by the leaf area in those layers. Alternatively, other models, such as Eqs. (5.11 ) or (5.12), can be used to estimate values for gs, in which case the value is inserted in Eq. (5.8) and used to calculate ci. The p r o c e d u r e is then as outlined. The a p p r o a c h described here has the advantage that it can be scaled up in space a n d time. For large-scale calculations, canopies would be treated as single layered. If calculations are to be m a d e over long periods, such as m o n t h s or seasons, greater accuracy will be achieved if daily average values of shortwave i n c o m i n g radiation (q~s) are used as the basis for estimating q~p, with average daily values of vapor pressure deficit (D). Because we are dealing with n o n l i n e a r processes, long-period averages are m o r e likely to lead to significant error than s u m m a t i o n of results derived from a series of short periods. L e u n i n g et al. (1996) have used a somewhat m o r e c o m p l e x version of the p r o c e d u r e s outlined here to calculate canopy photosynthesis. Leaf photosynthesis is related to leaf nitrogen (N) c o n c e n t r a t i o n (e.g., Reich et al., 1992; Gower et al., 1993a) and leaf N has b e e n shown to be distrib-

II. Canopy Photosynthesis

135

uted vertically t h r o u g h canopies in a m a n n e r that suggests a positive correlation to average q~p at any level (Hollinger, 1989). Leuning et al. calculated the distribution of N t h r o u g h a canopy and adjusted the values of the p a r a m e t e r describing Rubisco activity (gc.max) t h r o u g h the canopy (see Fig. 5.4). They also inserted the values of gs, obtained as described here, into the P e n m a n - M o n t e i t h equation, which leads to a solution for leaf temperature and, hence, better values for the temperature corrections to the photosynthesis parameters. Their model analysis allowed them to examine the relative importance of the various parameters of the model to obtain insight into the behavior of real plant canopies. Leuning et al. c o m p a r e d their results with observations in a wheat crop. Analyses of that complexity are, perhaps, not yet justified in forestry, but may be valuable as a basis for evaluating the p a r a m e t e r values appropriate for use in simple models that will be applied over large areas and relatively long periods and for evaluating the impact and implications of the various ecophysiological factors that affect photosynthesis (see Section II,C). For many purposes, it is convenient to use empirical equations to model canopy photosynthesis rather than the mechanistic equations of the Farquhar and von C a e m m e r e r (1982) model. The two most c o m m o n l y used are the rectangular and nonrectangular hyperbolae. Thornley and Johnston (1990) have provided a detailed mathematical treatment of the properties of the nonrectangular hyperbola and its use in modeling photosynthesis. For gross photosynthesis, it is Eq. (5.4), with CO 2 assimilation (A) substituted for electron transferJ. The shape factor | can be expressed in terms of the diffusion (rs) and carboxylation (rx) resistances: |

= r s / ( r x + rs),

(5.13)

and the rectangular hyperbola, with Rd included to give net assimilation rate, is A = a p ~ p A m a x / ( t y p ~ p A- Amax) - R d.

(5.14)

~ p is given by the initial slope of the a s s i m i l a t i o n / p h o t o n flux response

curve and the photon-saturated assimilation rate is given by a m a x = (s

- F ) / / r x.

(5.15)

Landsberg (1986) tabulated values for t~p and Ama x for various tree species (see also H o l b r o o k and Lund, 1995). They are moderately variable, d e p e n d i n g on the m e t h o d of m e a s u r e m e n t as well as on whether the leaves were acclimated to sun or shade conditions. Typical values of typ a r e 0.03-0.05 mol mo1-1 (~ 1.4-2.1 g mo1-1) and for Ama x 1015 pmol m - 2 s e c -1 ( ~ 4.5-6.8 X 10 - 2 mg m - 2 s e c - a ) .

136

5. Carbon Balance of Forests

A. Relations between Canopy Photosynthesis and Productivity Several of the models discussed in Chapter 9 consist essentially of procedures for calculating canopy photosynthesis and respiration, and hence stand productivity, utilizing knowledge about the processes discussed in this chapter. Figure 5.3 is a plot of gross primary productivity--calculated using the model BIOMASS (McMurtrie et al., 1990), which is essentially a canopy photosynthesis m o d e l - - a g a i n s t absorbed photosynthetically active radiation (~P~b~) after the q~absvalues had been corrected to account for environmental conditions that reduce photosynthesis and hence the radiation utilization efficiency. The figure is presented here to illustrate the very strong positive relationship between canopy photosynthesis and the radiant energy absorbed by the canopy. Despite the relationship illustrated in Fig. 5.3, it is unclear whether there are simple relationships between canopy photosynthesis and productivity; as we noted in the introductory section of this chapter, canopy photosynthesis is only one factor in the equation. To calculate forest productivity (as NPP), we need estimates of autotrophic respiration losses as well as total net photosynthesis over the period of interest. We deal with

2000

13. 13_ (.9 cr "O

1000

E o3

+

0

0

1000

Absorbed PAR,

I 2000 ~abs

3000

(MJ m-2yr 1)

Figure 5.3 (:anopy gross primary p r o d u c t i o n as a tuncti~)ll of al)s{~rbcd photosynthetically active radiation (q~,b,) c o r r e c t e d for t c m p c r a t l n c and wlpor pressure efti;cls. The figure (redrawn from McMurtrie el al., 9 1994, OIK()S) illustrates the cl~)se relationship between canopy photosynthesis and a b s o r b e d radiation. T h e symbols relate" to pine stands in different locations (Australia, New Zealand, USA, and Sweden) and ditt'erent water and nutritional treatments.

II. Canopy Photosynthesis

137

autotrophic respiration u n d e r Section III and with NPP and carbohydrate allocation u n d e r Section IV. B. Ecophysiological Aspects of Leaf Photosynthesis

The factors affecting leaf photosynthesis are leaf age, water r e l a t i o n s m primarily through their effect on stomatal c o n d u c t a n c e m a c c l i m a t i o n to sun or shade conditions, and nutritional status. Areax almost universally declines with leaf age within a tree (Troeng and Linder, 1982; Teskey et al., 1984). This decline is related to increased shading by new leaf cohorts (Schoettle and Smith, 1991; Schoettle and Fahey, 1994) and retranslocation of nutrients from aging needles (Son and Gower, 1991). Figure 5.4 illustrates the commonly observed light response of leaf photosynthesis for various nitrogen concentrations. The curves are typical hyperbolic p h o t o n f l u x / C O 2 assimilation response curves and simple mathematical theory links leaf photosynthesis, nitrogen concentration, and radiation regime to predict vertical distribution of nitrogen in the canopy needed to maximize canopy photosynthesis (Sellers et al., 1992; Leuning et al., 1995). Many stand- to global-level models use the relationship between leaf photosynthesis and nitrogen concentration to scale carbon assimilation from the leaf to canopy level (e.g., Running and Gower, 1991; Aber and Federer, 1992; Schulze et al., 1994). Despite the relationships shown in Fig. 5.4, it appears that, in the case of conifers, the contribution of increased leaf photosynthesis (on a leaf area or weight basis) to the growth responses to fertilization is small; the reasons for the responses may lie in other processes. Fertilization has "7o~ 15

I

I

I N1

E -6 12 E ._~

N2

9

N3

tD

6 ~0

o o

3

7

0

c-

0

t 500

I 1000

I 1500

2000

Photon flux density (/~mol m 2 s 1) Figure 5.4 H y p o t h e t i c a l p h o t o s y n t h e s i s - p h o t o n flux density (qOp) r e s p o n s e curves for leaves of broad-leaved species with t h r e e different n i t r o g e n concentrations: N1 > N2 > N3. T h e values of N 1 - N 3 would be e x p e c t e d to be in the range 1 - 3 g N m -2 (leaf area) [The diagram is consistent with data of H o l l i n g e r (1989), Abrams a n d Mosteller (1995), a n d Field (1983)].

138

5. Carbon Balance of Forests

b e e n r e p o r t e d to have little or no stimulatory effect on photosynthesis of P. radiata (Sheriff et al., 1986), Pseudotsuga menziesii (Van den Driessche, 1973) a n d P. contorta (Reid et al., 1983); however, o t h e r scientists have f o u n d that fertilization increased A for P. sylvestris ( S m o l a n d e r and OkerBlom, 1990), Pseudotsuga menziesii (Brix and Ebell, 1969; Brix, 1981), and P. radiata ( T h o m p s o n and Wheeler, 1992). In a study involving four conifer species, there were no consistent differences in A of cut branches, m e a s u r e d u n d e r optimal e n v i r o n m e n t a l conditions, between control and fertilized trees, a l t h o u g h fertilization increased needle N concentration by 4 0 - 6 0 % (Gower et al., 1996a). Similarly, Teskey et al. (199.5) found no differences in A, m e a s u r e d u n d e r controlled environmental conditions in shoots, of a P. elliottii stand where fertilization had increased foliage mass and above-ground NPP and resulted in a relative decrease in carbon allocation to fine roots. It seems likely that, in these experiments, fertilization resulted in luxury c o n s u m p t i o n of N, which was stored in the foliage in the form of amino acids (Yoder et al., 1994) instead of Rubisco. N u m e r o u s studies have d e m o n s t r a t e d that leaf n i t r o g e n concentration, specific leaf area, and leaf longevity are linked (see Lambers and Poorter, 1992; Reich et al., 1995, and papers cited therein), and a general m o d e l c o n n e c t i n g leaf longevity, structure, and carbon balance is b e g i n n i n g to emerge. Long-lived leaves have lower specific leaf area than short-lived leaves (see review by Reich et al., 1995; Fig. 5.5) because of

I

I

1

I

I O

40 '7

E

tO

C

30

o* []

20

_

10

--

ID _.1

~ 0

m

O cO O

z

A

o

AA

O

. m#A

9

[]

j~,A

_

0

I 5

I 10

I 15

I

I

20 25 Specific leaf area (o'f, m2kg -1)

30

Figure 5.5 I,eaf nitrogen concentration as a function of specitic leaf area in a range of ecosystems. The symbols denote temperate needle-leaved deciduous (A), temperate needleleaved evergreen (A), temperate broad-leaved deciduous ([-1), temperate broad-leaved evergreen (11), tropical deciduous (O), tropical evergreen ( 0 ) ; The asterisks denote five tree species grown in adjacent plantations (Gower el al., 1993b).

IlL Autotrophic Respiration

139

the greater concentration of structural carbon-based constituents and thicker cuticle. This results in a positive relationship between leaf nitrogen concentration and specific leaf area. Survey studies have indicated that leaf N concentration is inversely related to leaf lifespan across a b r o a d range of plant groups in contrasting (Field and Mooney, 1986; Reich et al., 1992) and in similar e n v i r o n m e n t a l conditions (Gower et al., 1993a). Greater photosynthetic rates m e a s u r e d in d e c i d u o u s conifers (Larix spp.) than in co-occurring evergreen conifers are consistent with these results (Kloeppel et al., 1995). This is useful for u n d e r s t a n d i n g the relationship between leaf photosynthesis (on a weight basis) and the lifespan of leaves on plants in the field, whereas the causal relationship between leaf N concentration, photosynthesis, and leaf longevity suggests that it may be possible to derive general empirical relationships to estimate m a x i m u m net photosynthetic rates based on leaf N and leaf lifespan. In general, plants growing in harsh climates or on nutrient-poor soils tend to have longer-lived leaves. The notable exception is the circ u m p o l a r i m p o r t a n c e of Larix, a d e c i d u o u s conifer, in the n o r t h e r n h e m i s p h e r e (Gower and Richards, 1990).

III. Autotrophic Respiration A u t o t r o p h i c respiration (Ra) involves the oxidation of organic substances to CO 2 and water, with the p r o d u c t i o n of ATP and r e d u c i n g power (NADPH) : 0 2 -+- CH20---> C O 2 + H 2 0 .

(5.16)

Total autotrophic respiration is c o m m o n l y divided into three components: maintenance (Ra.m) , growth ( R a . g ) a n d ion uptake ( R a . u ) . Ra. m is the cost of p r o t e i n synthesis and replacement, m e m b r a n e repair, and maintenance of ion gradients (Penning de Vries, 1975); it is the compon e n t of R a that is most sensitive to environmental change (Ryan, 1991). T h e most i m p o r t a n t environmental factor affecting m a i n t e n a n c e respiration is t e m p e r a t u r e because p r o t e i n synthesis rates increase e x p o n e n tially with increasing temperature. T h e t e m p e r a t u r e d e p e n d e n c e of R a is c o m m o n l y expressed in terms of the Q10, or the change in rate with a 10~ rise in temperature: 9 ,.1"-}[ ( T - T r e f ) / l O ]

R a = R a rerx Ca(OH)z]. Cation-exchange capacity is measured as milliequivalents (meq) per 100 grams of dry soil (or cmol + / k g of dry soil if SI units are followed). The percentage of the total cation-exchange sites occupied by the base cations potassium, sodium, calcium, and magnesium is referred to as base saturation. The source of the net negative charge arises from either isomorphic substitution or the dissociation of H from a hydroxide radical (OH). Isomorphic substitution results from the "substitution" of a cation of very similar size but with a smaller valence charge (e.g., Mg 2+ substitutes for A13+ ) inside the lattice of the secondary mineral during formation. This charge is pH i n d e p e n d e n t because it is the result of the structure of the clay and cannot be reversed by covalent bonding. The dissociation of H + from exposed hydroxide groups (R-OH) along the edges of clay particles and soil organic matter produces a net negative charge (R-O-) that attracts and holds cations. Unlike isomorphic substitution, this form of cation-exchange capacity is highly pH dependent, increasing as soil pH increases (less acidic), and is especially important for 1:1 secondary clay minerals such as kaolinite (see Table 6.1). Often, the pH-dependent charge of soil organic matter is far more important for sesquioxides and exceeds that of clay minerals. Cation-exchange capacity and percentage base saturation both increase from young (Entisols) to moderately weathered soils (Inceptisols and Alfisols) but decline in heavily weathered soils (Ultisols and Oxisols) (Bockheim, 1980). In the most heavily weathered tropical soils, composed solely of aluminum oxides, the small cation exchange capacity is almost entirely associated with the organic matter.

II. Nutrient Distribution

191

Unlike most temperate forest soils, many tropical soils are composed of iron and aluminum oxides and hydroxides; these soil colloids have a variable net charge depending on soil pH (Uehara and Gillman, 1981), especially those soils that are derived from volcanic ash (Kinjo and Pratt, 1971). At low soil pH, the surface of iron and aluminum oxides and hydroxides adsorb H+, resulting in a net positive charge, referred to as anion-exchange capacity, whereas the opposite occurs at high soil pH (i.e., a net negative charge o c c u r s ) - - h e n c e the name "variable charge." It is important to note that the 2:1 secondary minerals c o m m o n to temperate forest soils can also have a net positive charge (anion exchange capacity) but it only occurs at pH less than 2.0, making it absent in all natural forest soils (Sposito, 1984). However, heavily weathered temperate forests soils that contain aluminum and iron oxides and hydroxides may have positive anion-exchange capacity ( J o h n s o n et al., 1986).

II. Nutrient Distribution A. E l e m e n t a l Ratios

It has been known for some time that functional relationships exist between microbial chemistry and soil nutrient cycling processes (Waksman and Starkey, 1931; Redfield, 1958), although we are still learning about the full implications of the relationships between the nutrient requirements of microorganisms and nutrient cycling in terrestrial ecosystems. Redfield (1958) first recognized that plankton have relatively constant carbon, nitrogen, and p h o s p h o r u s ratios, which could be used to estimate the abundance of these and other elements in the oceans. Although it is now known that the cellular stoichiometry of plankton is simple and does not hold for terrestrial vegetation (Bolin et al., 1983; Reiners, 1986), Redfield's analysis has become the basis for explaining the biogeochemical cycles of terrestrial ecosystems. For example, the stoichiometry of C / N , C/P, and C / S is substantially smaller for decomposers than for vegetation (Table 7.1) because of the high p r o p o r t i o n (by weight) of cellulose in plants (see Chapter 6). This is especially the case in trees, which have a large fraction of their total biomass in structural support tissue (i.e., woody tissue). Moreover, plants produce a wide variety of C-based c o m p o u n d s that are difficult to decompose (e.g., lignin, tannins, and suberin). The high C / e l e m e n t ratios for forest vegetation relative to decomposers is further widened by the removal of nutrients prior to tissue senesence (see below). This large disparity in the element stoichiometry of decomposers and vegetation leads to an imbalance between C and essential nutrients for decomposers and causes nutrients to be immobilized by the decomposing organisms, resulting in nu-

192

7. Nutrient Distribution and Cycling Table 7.1

Carbon/Element Ratios of Microorganisms and Angiosperms a

C: element ratio Organism group

C/N

C/P

C/S

C/Ca

6-7 8

18-46 9

>46 --

46-62 145

10 21

230 1103

>230 221

58 184

Microorganisms

Bacteria Fungi Angiosperms

Herbs Woody

"Data from Bowen.1979;Anderson and Domsch, 1980.

trient limitation for the synthesis of plant tissue. Reiners (1986) summarized the interrelationships between elemental stochiometry of organisms and biogeochemical cycles of terrestrial ecosystems in the form of axioms and theorems. The stoichiometry of the various parts of trees, especially foliage, can vary. Vitousek et al. (1988) reviewed tissue e l e m e n t concentrations in material from major forest biomes and c o n c l u d e d that biomass allometry differences between biomes had little effect on element stochiometry. They did, however, note several systematic C / e l e m e n t ratio patterns: in general, C / N , C / P , and C / S ratios were greater in conifers than in angiosperms; foliage of tropical trees that contain large concentrations of nitrogen-based defense c o m p o u n d s (e.g., alkaloids) have lower C / N and h i g h e r N / P ratios than most o t h e r forest types, and nitrogen-fixing trees, such as alder, locust, and acacia, also have lower C / N and h i g h e r N / P ratios. Conversely, trees with carbon-based defense c o m p o u n d s (e.g., phenolics and terpenes) in the foliage have high C / e l e m e n t ratios (McKey et al., 1978; Bryant et al., 1983). High C / N or C / P ratios are c o m m o n in forests growing on nitrogen- or phosphorus-deficient soils. The C / N ratios of leaves are h i g h e r in colder climates; this has been observed both within (Van Cleve el al., 1983) and a m o n g biomes (Vitousek et al., 1988) and may be explained by the adverse effects of low temperatures on d e c o m p o s i t i o n and nutrient mineralization (Chapter 6). B. N u t r i e n t D i s t r i b u t i o n in Forests

Table 7.2 provides information on nutrient content and distribution for select forests from many of the major forest biomes. The purpose of the table is not to provide an exhaustive review of the literature, but merely to illustrate several general patterns, a l t h o u g h because nutrient content varies greatly a m o n g forests within a similar b i o m e it is difficult to make

7. Nutrient Distribution and Cycling

1

26

75

21

Tennessee Wisconsin

7,650 95 4,400 91

4 7

1 2

960 107

98 31

2 56

Tennessee Sollings, W. Germany

3,500 6,332

95 94

4 5

1 1

2840 3150

98 98

1 200 years, respectively (adapted from Gries, 1995).

c o m p o n e n t of forest ecosystems. It has a very large c a r b o n / n i t r o g e n ( C / N ) ratio and small surface a r e a / v o l u m e ratio and is therefore a significant factor in nutrient immobilization and storage in forest ecosystems (see Chapter 6). The spatial distribution of coarse woody debris in forests is highly variable, making it difficult to detect patterns associated with stand age. H a r m o n et al. (1986) suggested that coarse woody debris biomass should exhibit a bimodal pattern during stand development, with peaks occurring immediately after d i s t u r b a n c e m o b s e r v e d by Gore and Patterson (1986) and by McCarthy and Bailey ( 1 9 9 4 ) m a n d late in stand development, when there is increased mortality of large trees (Tyrrell and Crow, 1994; Spies et al., 1988; Dahir, 1994). Gries (1995) observed a U-shaped pattern for the n o r t h e r n hardwood age sequence with a m a x i m u m occurring in the old-growth stand (Fig. 8.3). The accumulation of coarse woody debris in mature forests and the increasing fraction of aboveground detritus composed of this material will lead to a gradual decline in nutrient availability during stand development (see below).

IV. Forest and Ecosystem Productivity The first hypotheses describing changes in functional characteristics such as carbon and nutrient flow during succession were published almost 30 years ago (see Odum, 1969)but, until recently, there have been few tests of these hypotheses. T h e r e has also been virtually no research

I~. Forest and Ecosystem Productivity

237

into the structural and physiological changes u n d e r l y i n g the functional changes. In keeping with the major focus of this book, we will largely restrict the discussion to carbon and nutrient cycling, and their interrelationships, during succession. Gross primary p r o d u c t i o n ( G P P ) m o t the sum of NPP plus a u t o t r o p h i c respiration (Ra) (see C h a p t e r 5 ) m i s difficult to measure in large-stature forests; therefore few empirical data are available, especially for a complete forest c h r o n o s e q u e n c e . However, forest ecosystem process models have been used to examine relationships between GPP and stand develo p m e n t and, a l t h o u g h the results will reflect limitations or shortcomings in the models, they provide the best information we have. In general, GPP is very low immediately after disturbance and increases in proportion to leaf area index during the early years of stand development. GPP is predicted to reach a m a x i m u m near canopy closure and then decline, partly as a result of the decline in leaf area. An interesting point is that the decline in GPP does not a p p e a r to be as great as the decline in ANPP. T h e r e is increasing evidence from b o t h the ecological and forestry literature that a b o v e g r o u n d NPP decreases as stands age, irrespective of forest type (Table 8.1). Kira and Shidei (1967) first speculated that the age-related decrease in ANPP was caused by increased a u t o t r o p h i c respiration. Foliage has the highest m a i n t e n a n c e respiration rates of tree tissues (Ryan et al., 1994), but it is unlikely that foliage respiration can explain the decline in ANPP because leaf area remains stable or decreases with stand age. P h l o e m has high m a i n t e n a n c e respiration costs, but the surface area of branches increases while stem surface area increases only slightly (Whittaker and Woodwell, 1967; Sprugel, 1984). Sapwood is the tissue most c o m m o n l y considered responsible for the decline in ANPP with stand age (Yoda et al., 1965; Whittaker and Woodwell, 1967; Kira and Shidei, 1967), but the assertion that increasing sapwood respiration is the cause of this decline has been u n d e r m i n e d by recent observations that it represents only a small percentage ( 5 - 1 0 % ) of the annual stand carbon b u d g e t (Ryan et al., 1995). Few attempts have b e e n m a d e to estimate R a for forest stands of different age; however, the results from all the studies made so far suggest that Ra is constant, or increases slightly, but c a n n o t account for the large decreases in ANPP (Moller et al., 1954; Ryan and Waring, 1992; Murty et al., 1996). Ryan and Waring estimated that maintenance respiration for woody tissue increased by 18 g C m -2 year -1 between ages 40 and 245 years for a lodgepole pine ( P i n u s contorta) age sequence, whereas wood p r o d u c t i o n and associated construction respiration decreased by 164 g C m -2 year -a. A second explanation for the decline in ANPP with stand age is decreased nutrient availability. The availability of n i t r o g e n is strongly controlled by litter d e c o m p o s i t i o n that, in turn, is controlled by environ-

6-7 8

C/N 18-46 9

C/P

>230 221

>46 --

C/S

58 184

46-62 145

C/Ca

C: element ratio

and Angiosperms a

Carbon/Element Ratios of Microorganisms

7. Nutrient Distribution and Cycling Table 7.1

Organism group Microorganisms

Bacteria Fungi Angiosperms

230 1103

'From Lower ct al., 1996; "minus sign denotcs a decrease in ANPP; ' Gries, 1995; "continuous measurements

10 21

Afaka, Nigeria Mrghalaya, India Amazonia

Herbs Woody

New York Michigan Colorado Mt. Mino, Japan Wsconsin Michigan Washington

Florida Puruki, New Zealand Tahi Rr~a Toru

"Data from Bowen.1979;Anderson and Domsch, 1980.

Yakutsk, Sibera Russia

Source

% Change6 Mature Maximum

192

ANPP

trient limitation for the synthesis of plant tissue. Reiners (1986) summarized the interrelationships between elemental stochiometry of organisms and biogeochemical cycles of terrestrial ecosystems in the form of axioms and theorems. The stoichiometry of the various parts of trees, especially foliage, can vary. Vitousek et al. (1988) reviewed tissue e l e m e n t concentrations in material from major forest biomes and c o n c l u d e d that biomass allometry differences between biomes had little effect on element stochiometry. They did, however, note several systematic C / e l e m e n t ratio patterns: in general, C / N , C / P , and C / S ratios were greater in conifers than in angiosperms; foliage of tropical trees that contain large concentrations of nitrogen-based defense c o m p o u n d s (e.g., alkaloids) have lower C / N and h i g h e r N / P ratios than most o t h e r forest types, and nitrogen-fixing trees, such as alder, locust, and acacia, also have lower C / N and h i g h e r N / P ratios. Conversely, trees with carbon-based defense c o m p o u n d s (e.g., phenolics and terpenes) in the foliage have high C / e l e m e n t ratios (McKey et al., 1978; Bryant et al., 1983). High C / N or C / P ratios are c o m m o n in forests growing on nitrogen- or phosphorus-deficient soils. The C / N ratios of leaves are h i g h e r in colder climates; this has been observed both within (Van Cleve el al., 1983) and a m o n g biomes (Vitousek et al., 1988) and may be explained by the adverse effects of low temperatures on d e c o m p o s i t i o n and nutrient mineralization (Chapter 6).

Table 8.1 Above-ground Net Primary Production (ANPP, t h a 1 year-') for Forest Age Chronosequences"

Tropical l'inu, rnrihnrcc I'inu, kr.\iyn Trofiiral rain Jorrct

B. N u t r i e n t D i s t r i b u t i o n in Forests

Boreal Lnrix gmrlinn Picpa nhirc Cold temperate Ahwr hnltremrcl Arrr .srcrmrhun1' I'znu., ronlorlic Pinu.c drn ci/lorrr Pi~puluslrrm~~loidrt I'opulus ~endidrntrctcc I'rrudotsugc~ ~nrnzirrii Warm temperate Pinus rlliol11i Pinus mdenlcc

Table 7.2 provides information on nutrient content and distribution for select forests from many of the major forest biomes. The purpose of the table is not to provide an exhaustive review of the literature, but merely to illustrate several general patterns, a l t h o u g h because nutrient content varies greatly a m o n g forests within a similar b i o m e it is difficult to make

Age range (year)

I~. Forest and Ecosystem Productivity

239

mental conditions and the chemical and physical characteristics of litter (Chapter 6). Several scientists have noted that the fraction w o o d / t o t a l (wood and foliage) litter increases during stand development for boreal (Van Cleve and Noonan, 1975), temperate evergreen (Turner and Long, 1975), and temperate deciduous forests (Cries, 1995). Cries (1995) reported that this ratio increased from near zero for a 10-year-old to 0.73 for an old-growth n o r t h e r n hardwood stand (Fig. 8.3). Also, the major components of the woody litter change from twigs and branches to large stems during succession, which is important because the larger woody material has a significantly lower surface a r e a / v o l u m e ratio, thereby adversely affecting the rate of microbial attack on the wood and, hence, the decomposition rate (see Chapter 6). The slow decomposition rates and very high C / N ratio of large logs cause coarse woody debris to be a maj o r nutrient immobilization source for decades (Grief, 1978; Foster and Lang ,1982; Lambert et al., 1980). If there is a positive feedback between the vegetation and soil, this could further exacerbate the decline in nitrogen availability during stand development (Gosz, 1981). Retranslocation of nitrogen from senescing foliage increases the C / N ratio of leaf detritus, lowering litter quality and further increasing N limitation. Lower litter quality during stand develo p m e n t increases N immobilization during litter decomposition, which in turn decreases net N mineralization as stands age (Davidson et al., 1992; Hart et al., 1994). Nutrient limitation, especially nitrogen, adversely affects leaf photosynthesis and L*. Reduction in L* tends to reduce light interception and GPP (see Chapter 3; McMurtrie et al., 1994). Nitrogen limitation also shifts biomass allocation from above-ground c o m p o n e n t s to fine roots and mycorrhizae (Keyes and Grier, 1981; Gower et al., 1992; Haynes and Gower, 1995), but no forest age sequence studies have, to our knowledge, included fine root and mycorrhizal NPP so that such shifts in biomass allocation during succession have not been detected. A third explanation for the decline in ANPP during stand development is stomatal constraint. Z i m m e r m a n (1983) p i o n e e r e d the study of the hydraulic architecture of trees and the ascent of water to the canopy. Much of his research focused on the ecological compromises resulting from the need to maximize water transport to the canopy while minimizing the probability of p e r m a n e n t damage to the xylem caused by cavitation during periods of d r o u g h t or very rapid transpiration (dynamic water stress). Z i m m e r m a n suggested that trees avoid p e r m a n e n t xylem dysfunction by decreasing leaf-specific conductivity (i.e., flow rate of water in the xylem per unit pressure gradient causing the flow) toward the apices, thereby increasing the nodal resistance and confining cavitation and embolism to branches that play a less important role in carbon as-

240

8. Changes in Ecosystem Structure and Function during Stand Development

similation. This hydraulic architecture would imply that resistance to water transport should be greater for large branches of older trees than for small branches of younger trees. The importance of this adaptation appears to be greater than originally thought: Findings by Tyree and Sperry (1988) suggest that all trees operate near the point of catastrophic xylem dysfunction caused by dynamic water stress. Because water vapor loss and CO 2 uptake both occur through the stomates, conservative hydraulic architecture affects carbon assimilation. Ryan and Waring (1992) noted that a 15% reduction in canopy photosynthetic rate would account for the large discrepancy between modeled and measured ANPP. Mattson-Djos (1981) noted reduced stomatal conductance in older trees, as did Yoder et al. (1994), who also found that photosynthesis rates in the foliage of older lodgepole pine and ponderosa pine trees were 1 4 - 3 0 % lower than in the foliage of younger trees. These results support the point made by Yoder et al. ( 1 9 9 4 ) and are supported by carbon isotope ratios measured by Ryan and Waring in young and old p o n d e r o s a and lodgepole pine. [Differences in the ratios of the stable carbon isotopes 13C/12C in foliage and stem tissues reflect differences in average stomatal conductance because the carboxylating enzymes involved in photosynthesis discriminate against the heavier carbon isotope (13C), with discrimination decreasing as the leaf mesophyll CO 2 concentration decreases (Farquhar et al., 1982). Therefore, the 13C/12C ratio will fall when photosynthesis is limited more by stomatal conductance than by carboxylating enzyme activity.] On a diurnal basis, delayed opening and earlier closing of stomata have been related to reduced hydraulic conductance (Borghetti et al., 1989; Sperry et al., 1993).

A. Net Ecosystem Productivity NEP, or the net change in carbon storage in the ecosystem, includes fluxes from vegetation, detritus, and mineral soil and is an important descriptor of the overall functioning of ecosystems. NEP quantifies carbon accumulation or loss and, because carbon and nutrient cycles are strongly coupled, allows inferences to be drawn about the loss of essential nutrients from forests. Few estimates of NEP exist for forests (see Chapter 5), with fewer still for similar forest types at different stages of succession; therefore, the points made in this section are more speculative than those in the sections on changes in ecosystem structure and function. However, despite the paucity of data, a reasonable picture can be pieced together based on information about changes in each of the major carbon fluxes during succession. O d u m (1969) first suggested that NEP should approach zero for steady-state or old-growth forests, and the argument we develop here supports this hypothesis, although it has never been validated empirically.

IV.. Forest and Ecosystem Productivity

241

Catastrophic disturbances, such as wildfire and clear-felling, reduce leaf area to near zero, causing NPP to a p p r o a c h zero and setting the scene for secondary succession. The removal of the forest canopy results in elevated soil t e m p e r a t u r e s (in summer) and changes in soil moisture status, both of which tend to lead to increases in h e t e r o t r o p h i c respiration (Rh) immediately after stand reinitiation. We can therefore expect NEP to be negative at the start of a secondary succession cycle or d u r i n g the stand reorganization phase (Fig. 8.4, top). The length of time that NEP remains negative is likely to vary a m o n g ecosystems. Forests in which L* recovers rapidly after disturbance (e.g., tropical forests) probably experience very short periods of negative NEP, whereas forests in which recovery in L* is slow (e.g., boreal forests) are likely to experience negative NEP for longer periods. Disturbance intensity may also affect the m a g n i t u d e and duration of negative N E P - - t h e m o r e severe the disturbance, the longer NEP is likely to remain negative. As leaf area recovers, net carbon accumulation by a u t o t r o p h s increases rapidly and exceeds Rh, resulting in positive NEP; NEP for aggrading forests d u r i n g early succession ranges from 0.9 to 5.0 ha -1 year -1 (see C h a p t e r 5).

I

(+) I1. LLI Z

O t(11 L

(-)

(+) O

= (-)

z

I I I

Stand reorganization Succession

Figure

._ Steady " state

8.4 Relationship between net ecosystem p r o d u c t i o n (NEP), nutrient loss, and their interaction during forest succession. Immediately following disturbance, NEP goes negative (i.e., the ecosystem is losing carbon to the atmosphere) because h e t e r o t r o p h i c respiration exceeds net primary production. During this period, nutrient d e m a n d by the vegetation is low and microbes b e c o m e a source instead of a sink for nutrients as the d o m i n a n t process changes from immobilization to mobilization. Modified from Vitousek and Reiners (1975) to include a negative NEP pulse immediately after disturbance.

242

8. Changes in Ecosystem Structure and Function during Stand Development

Gross primary production follows L*, which reaches a m a x i m u m during the early aggradation stage and then oscillates a r o u n d a "quasi steadystate."

It is important to note that small-scale disturbances can result in small patches of early successional forests e m b e d d e d in a steady-state forest landscape (Frelich and Lorimer 1991b). At any one time, some patches may be accumulating carbon while others are losing carbon, but the net result is a near-zero carbon accumulation for old-growth forests.

V. Nutrient Cycling It is not surprising that soil biogeochemical cycles change during succession because nutrients, especially nitrogen, are so tightly coupled to carbon cycling (Chapter 6 and 7). It is therefore possible to explain decreased nitrification rates during stand development in terms of changes in environmental and litter quality conditions. Nutrient mineralization rates will be affected by the type of disturbance: They may be relatively high if a substantial portion of debris with a high C / N ratio is removed, but if woody debris is left on a site it helps to immobilize nutrients and reduce nutrient loss immediately following disturbance (Vitousek and Matson,1985). Rice (1974) has argued strongly that late successional species produce allelochemicals that suppress nitrification, thereby increasing nutrient retention. If organic matter decomposition rates are reduced, this will also result in slower mineralization of essential nutrients, especially those that are tightly coupled to organic matter (Davidson et al., 1992; Hart et al., 1994). The low C / N ratios of forbs and understory species that first colonize disturbed sites contribute to high mineralization rates during stand reinitiation: Mineralization rates are often highest during this stage and decrease during the remainder of stand develo p m e n t (Robertson and Vitousek, 1981; Vitousek et al., 1989). The varying availability of nutrients during stand succession influences nutrient use and recycling by the vegetation. Nutrient requirements and uptake reach a peak about the time of canopy closure (Turner, 1975; Gholz et al., 1985a; Fig. 8.5), which coincides with the period of maxim u m tissue production, especially nutrient-rich tissue such as foliage. From this point on in succession, retranslocation of nutrients from foliage to woody tissues increases and a smaller proportion of the annual nutrient r e q u i r e m e n t is met by uptake. As a result, litter quality decreases, decreasing nutrient availability. During stand development, nutrient use efficiency appears to increase as soil nutrient availability decreases (Fig. 8.6), but the positive feedback mechanism between litter quality and nutrient availability in later stages

V. Nutrient Cycling

243

Figure 8.5 Relationship between (a) above-ground net primary production (solid dots connected by line), nitrogen requirement (bars), and (b) percentage of nitrogen requirement supplied by internal retranslocation for a slash pine (Pinus elliottii) age chronosequence in north-central Florida (adapted from Gholz et al., 1985a).

led O d u m (1969) to speculate that forest biogeochemical cycles get tighter, or more closed, as forests age. Vitousek and Reiners (1975) suggested that Odum's (1969) hypothesis was too simplistic: They proposed, on the basis of the strong microbial controls on decomposition and nutrient mineralization, that the overall "tightness" of forest nutrient budgets is closely coupled to NEP (Fig. 8.4, bottom). Following disturbance, when NEP is < 0, nutrient loss occurs

Figure 8.6 Nitrogen (open columns) and phosphorus (shaded columns) use efficiency for a slash pine (Pinus eUiottii) age chronosequence (adapted from Gholz et al., 1985a). Note logarithmic scale on y axis.

244

8. Changes in Ecosystem Structure and Function during Stand Development

Figure

8.7 Comparison of average growing season (June 1-September 30, 1973 and 1974) stream water concentration of essential (N, K, Mg, and Ca) and nonessential (C1 and Na) nutrients from successional (open columns) and mature (shaded columns) spruce-fir (Picea rubens-Abies balsamea) forests in the White Mountains of New Hampshire (adapted from Vitousek and Reiners, 1975).

unless nutrients are i m m o b i l i z e d by m i c r o o r g a n i s m s ( C h a p t e r 6). T h e loss of each e l e m e n t is p r o p o r t i o n a l to the d e m a n d by the regrowing vegetation; t h e r e f o r e , as L* recovers, n u t r i e n t u p t a k e s h o u l d parallel NEP. However, as NPP a n d NEP decline in o l d e r stands ( C h a p t e r 5), n u t r i e n t storage also decreases. This hypothesis is s u p p o r t e d by the average growing season streamflow n u t r i e n t c o n c e n t r a t i o n data f r o m a g g r a d i n g a n d old-growth s p r u c e - f i r forests in the White M o u n t a i n s in New H a m p s h i r e , w h e r e the outflow of nutrients essential for plant growth was consistently h i g h e r f r o m m a t u r e t h a n f r o m successional stands (Fig. 8.7). However, t h e r e a p p e a r s to be little d i f f e r e n c e ill streamflow c o n c e n t r a t i o n s of n o n e s s e n t i a l nutrients such as Na b e t w e e n successional and m a t u r e stands. T h e hypothesis leads to the p r e d i c t i o n that storage of essential nutrients s h o u l d a p p r o a c h zero as NEP r e a c h e s zero in old-growth or clim a x forest. This has never b e e n tested.

VI. Concluding Remarks Major points that e m e r g e f r o m this c h a p t e r are that species c o m p o s i t i o n a n d structural a n d f u n c t i o n a l characteristics are inextricably linked a n d c h a n g e d u r i n g s u c c e s s i o n . T h e r e f o r e , any forest m a n a g e m e n t practice that alters the natural successional trajectory is likely to affect o t h e r ecosystem characteristics. It is i m p o r t a n t that forest m a n a g e r s recognize that c h a n g e s will o c c u r after d i s t u r b a n c e a n d are able to assess their likely c o n s e q u e n c e s in t e r m s of p r o d u c t i v i t y a n d sustainability. Given that i n f o r m a t i o n a b o u t those changes, a n d k n o w l e d g e of the soil a n d

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weather, we can use s o m e of the ideas discussed h e r e to m a k e p r o j e c t i o n s a b o u t the likely c o u r s e of c h a n g e s in f o r e s t s t r u c t u r e a n d f u n c t i o n . We can m a k e r e a s o n a b l e p r e d i c t i o n s a b o u t c h a n g e s in s t e m p o p u l a t i o n s (see C h a p t e r 3) a n d the t i m e c o u r s e of leaf a r e a d e v e l o p m e n t , a b o u t n u t r i e n t m i n e r a l i z a t i o n rates, and, on the basis of all that, a b o u t the fluxes o f carb o n a n d water. With i n c r e a s i n g c o n c e r n a b o u t biodiversity a n d its m a i n t e n a n c e , it is b e c o m i n g essential that the objectives o f f o r e s t m a n a g e r s , in this r e s p e c t , s h o u l d be clear. T h e q u e s t i o n of scale is p e r t i n e n t in r e l a t i o n to m a n a g e m e n t for biodiversity. Biodiversity will t e n d to be low if forests are red u c e d to a series of e v e n - a g e d stands that are n o t allowed to p r o g r e s s to the p o i n t at w h i c h they c o n t a i n old trees a n d the gaps associated with old-growth stands: T h e n u m b e r of niches, with a r a n g e of c o n d i t i o n s that w o u l d be c o n d u c i v e to species with d i f f e r e n t a d a p t i v e m e c h a n i s m s , is red u c e d in such e v e n - a g e d stands. However, if the stands are small, inters p e r s e d b e t w e e n areas o f very d i f f e r e n t c h a r a c t e r i s t i c s a n d g r o w t h patterns, the r a n g e o f habitats will be i n c r e a s e d a n d we can e x p e c t species diversity for t h e r e g i o n as a w h o l e to be high. O n a global scale, we n e e d to seek p r e d i c t a b l e c h a n g e s in e c o s y s t e m f u n c t i o n d u r i n g d e v e l o p m e n t a n d the various stages of succession; t h e s e will allow us to i m p r o v e o u r c a p a c i t y to p r e d i c t c h a n g e in g l o b a l c a r b o n b a l a n c e . We n o t e , in passing, that a l t h o u g h we stated in the i n t r o d u c t o r y s e c t i o n that we are n o t c o n c e r n e d with l o n g - t e r m succession, the c u r r e n t rapidly c h a n g i n g g l o b a l a t m o s p h e r i c CO 2 c o n c e n t r a t i o n s will a l m o s t certainly affect f o r e s t succession t h r o u g h effects o n g r o w t h a n d b i o m a s s acc u m u l a t i o n rates, p a r t i c u l a r l y in the early stages ( r e o r g a n i z a t i o n a n d a g g r a d a t i o n ) a n d , h e n c e , on t h e p a t t e r n s of f o r e s t d e v e l o p m e n t . It is clear t h a t the i m p r o v e m e n t of t h e o r i e s o f f o r e s t succession, in the g e n e r a l sense u s e d h e r e , w h i c h e n c o m p a s s e s c h a n g e s in s t r u c t u r e a n d f u n c t i o n as well as c h a n g e s in species, is an a r e a t h a t w a r r a n t s a g r e a t deal m o r e r e s e a r c h a t t e n t i o n in the f u t u r e .

Recommended Reading Bormann, F. H., and Likens, G. E. (1979). "Pattern and Process in a Forested Ecosystem." Springer-Verlag, New York. Frelich, L. E., and Lorimer, C. G. (1991). A simulation of landscape-level stand dynamics in the northern hardwood region. J. Ecol. 79, 223-233. Gorham, E., Vitousek, P. M., and Reiners, W. A. (1979). The regulation of chemical budgets over the course of terrestrial ecosystem succession. Annu. Rev. Ecol. Syst. 10, 53-84. Gower, S. T., McMurtrie, R. E., and Murty, D. (1996). Aboveground net primary production decline with stand age: potential causes. Trends Ecol. Evol. 11,378-382. Odum, E. P. (1969). The strategy of ecosystem development. Science 164, 262-270.

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Sprugel, D. G. (1984). Density, biomass, productivity and nutrient-cycling changes during stand development in wave-regenerated balsam fir forests. Ecol. Monogr. 54, 165-186. Vitousek, P. M., Van Cleve, K., and Matson, P. A. (1989). Nitrogen availability and nitrification, primary, secondary, and old-field seres. Plant Soil 115, 229-239. Yoder, B.J., Ryan, M. G., Waring, R. H., Schoettle, A. W., and Kaufmann, M. R. (1994). Evidence of reduced photosynthetic rates in old trees. For Sci. 40, 513-526.

9 Ecosystem Process Models

Scientists have different ideas a b o u t what is m e a n t by a "model," in the context of biology and ecology. L a n d s b e r g (1986, p. 2) defined a m o d e l as a "formal and precise statement, or set of statements, e m b o d y i n g our c u r r e n t knowledge or hypotheses a b o u t the workings of a p a r t i c u l a r system and its responses to stimuli." Models t h e r e f o r e provide an o r g a n i z e d way of stating hypotheses a n d of focusing on the questions that m u s t be asked at the b e g i n n i n g of a research p r o g r a m . They may also be practical tools to be used by forest m a n a g e r s to estimate stand growth rates (productivity) and to p r e d i c t the effects of m a n a g e m e n t actions or of insect or p a t h o g e n attack. Ecologists may develop a n d use m o d e l s to calculate carbon, nutrient, and water flow t h r o u g h ecosystems or to investigate species dynamics in ecosystems. On a wider scale, c o n c e r n a b o u t increasing a t m o s p h e r i c CO 2 c o n c e n t r a t i o n s and the p r o b a b l e resulting changes in global climate, a n d a b o u t factors such as a t m o s p h e r i c pollution, make it essential that we have m o d e l s capable of p r e d i c t i n g the p r o b a b l e effects of these p h e n o m e n a ( L a n d s b e r g et al., 1991). This c h a p t e r is c o n c e r n e d with process-based models, i.e., m o d e l s that are based on the processes, or m e c h a n i s m s , u n d e r l y i n g the responses to change (stimuli) of the system u n d e r study. T h e objective of processbased forest growth, or ecosystem, models is to simulate the growth of stands in terms of the u n d e r l y i n g physiological processes that d e t e r m i n e growth and the way the stands are affected by the physical c o n d i t i o n s to which the trees are subject a n d with which they interact. This means, for example, that forest growth may be described in terms of radiation interception, photosynthesis, a n d c a r b o n allocation r a t h e r t h a n by empirical equations developed f r o m statistical analysis of m e a s u r e m e n t s m a d e on trees a n d stands (see Section I). Process-based models have the potential to be far m o r e flexible t h a n empirical relationships a n d can be used in a heuristic sense to evaluate the c o n s e q u e n c e s of change a n d the likely effects of stimuli. However, they are not w i t h o u t their p r o b l e m s , the most 247

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i m p o r t a n t of which is that they t e n d to require many p a r a m e t e r values that are often difficult to obtain. F u r t h e r m o r e , the fact that a m o d e l is c o m p o s e d of (mathematical) descriptions of various physiological processes does not m e a n it contains no empirical relationships; at some point, all the relationships used are e m p i r i c a l m t h e justification for processbased models rests on the fact that "lower-level" empiricism translates into m o r e flexible and realistic responses to change than relationships that subsume the processes responsible for the responses. T h e r e are, clearly, various levels at which process-based models can be written. In general, it is not practical to attempt to m o d e l a system across m o r e than two levels of organization; that is, to a t t e m p t to explain an observation in terms of processes m o r e than one level down. Landsberg (1986, pp. 1 3 - 2 0 ) has discussed this in some detail. Empirical models are based on relationships between outputs and inp u t s m f o r example, site index curves giving stand yield estimates based on stem d i a m e t e r and tree h e i g h t measurements. Such models are very widely used in forestry, but they are site specific, strongly influenced by the conditions pertaining during the period when the measurements on which they are based were made, and they c a n n o t be used to examine the consequences of any significant d e p a r t u r e from the conditions pertaining over that period. In particular, empirical models cannot simulate the consequences of changes in such factors as atmospheric CO 2 concentrations, t e m p e r a t u r e , or water regimes (Landsberg el al., 1991). However, from the point of view of the forest manager, simple empirical models may be of m o r e immediate practical use than process-based models, most of which have not yet been b r o u g h t to the point at which they can be said to be useful practical tools. The position was stated unequivocally by Leech (1985), who made it clear that forest managers are c o n c e r n e d with robust, pragamatic models, that they are likely to be more c o n c e r n e d with local politics, economics, and wood flow than precise predictions of the p r o b a b l e response to conditions in a particular stand, and that they are unlikely to use complex physiological models. W h e n they are aware of them at all, managers tend to regard such models as esoteric and abstract, requiring values for parameters they have no knowledge of and no way of measuring. The question therefore arises: Are we (scientists) justified in our pursuit of knowledge a b o u t systems, without c o n c e r n for short-term utility but with the u n d e r l y i n g assumption that this knowledge will s o m e h o w filter t h r o u g h to those who might actually use it, or should we consciously orient our science, and the u n d e r s t a n d i n g it gives rise to, toward practical objectives? W i t h o u t b e c o m i n g involved in this (interesting) philosophical argument, we state here our view that the future must lie with processbased models, but we accept that these must be p r o d u c e d in forms rele-

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vant to forest m a n a g e r s and decision- and policymakers at o t h e r levels, and that the o u t p u t s must be practically useful. We note that forest ecosystem models are already at the stage at which they can c o n t r i b u t e to debates a b o u t ecosystem sustainability, global c a r b o n balance, and biomass productivity, but in the area of m a n a g e m e n t for wood p r o d u c t i o n some advances and changes will be n e e d e d to c o m p e t e with the conventional forest growth models. A useful discussion of the considerations involved in this question of m o d e l i n g for p r e d i c t i o n or analysis of the behavior of ecological systems is given by Sands (1988). We discuss models developed as research tools, which can be used to collate our knowledge of systems and to explore the c o n s e q u e n c e s of o u r hypotheses, and we consider the possibility of using those models, or the principles e m b o d i e d in them, as the basis for m a n a g e m e n t tools. These tools must be relatively simple, reasonably reliable, and p r o d u c e answers in the form the managers need: CO 2 uptake or biomass estimates would n o t generally be c o n s i d e r e d useful. T h e r e f o r e , i n c l u d e d a m o n g the advances n e e d e d there must be some p r o c e d u r e for converting biomass calculations into wood p r o d u c t i o n because, a l t h o u g h the emphasis in natural forest m a n a g e m e n t is shifting away from the historical focus on w o o d p r o d u c t i o n toward sustainability of the ecosystems as a whole, the same types of m o d e l can be used to simulate and analyze the growth of plantations and the r e q u i r e d e n d result from t h e m is certainly wood. This c h a p t e r provides brief reviews and assessments of some of the m o r e i m p o r t a n t and well established of the c u r r e n t g e n e r a t i o n of processbased forest ecosystem models and, in the last section, indicates how these models can and should be used to assist in the decisions m a d e in the course of practical forest m a n a g e m e n t . The models c o n s i d e r e d in this c h a p t e r are at several levels, which will b e c o m e a p p a r e n t in the course of the discussions. First, however, we provide an outline of some conventional forest models and the a p p r o a c h used in their development.

I. Forestry Models Conventional forestry models, as we n o t e d earlier, are essentially statistically derived stand growth curves based on m e a s u r e m e n t s that provide i n f o r m a t i o n a b o u t the progressive changes in stem v o l u m e of trees on particular sites or in particular regions. These m e a s u r e m e n t s c o m m o n l y include tree n u m b e r (stand density), stem diameters, average or m e a n d o m i n a n t height, and, in some cases, stem taper functions. The models derived from t h e m do n o t generally invoke any physiological processes or have any mechanistic basis for simulating response to changing conditions. The growth curves may be related to site indices, where the site in-

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dex is an arbitrary m e a s u r e of the productivity of a site, usually expressed in terms of average d o m i n a n t tree h e i g h t at 20 or 50 years. Site indices reflect the influence of soil fertility, climate a n d microclimate, tree species, and genetics. The logic is arguably completely circular: We c a n n o t say anything a b o u t the productivity of a site until we have grown trees there and m e a s u r e d them, at which point obviously, we know how well they have grown. F u r t h e r m o r e , the site index for a particular location could vary from one p e r i o d to a n o t h e r d e p e n d i n g on w h e t h e r there were m o r e years with g o o d growing conditions than bad or vice versa, w h e t h e r there were pest attacks that may n o t have been identified and were almost certainly n o t quantified, and so on. Nevertheless, curves representing the growth to be expected from particular species growing on sites with specified indices are standard tools for forest yield estimation and will r e m a i n so until something better is available. Clearly, these curves c a n n o t be used with any confidence for areas and forest types o t h e r than those for which they were derived. Being statistical descriptions, forestry models are based on measurements made on sample plots. Ideally, tree populations in these plots should be m o n i t o r e d and the dimensional m e a s u r e m e n t s repeated at regular intervals. The location of m e a s u r e m e n t plots that can be said to represent a forest is a matter of considerable importance. A book by Vanclay (1994) provides a good overview and treatment of the techniques and considerations involved in modeling forest growth and yield using the conventional, or traditional, approaches. Forest growth models tend to be either stand models, which project the growth and d e v e l o p m e n t of entire stands, or individual tree-based models, which have the advantage that age structure, spacing, and individual tree attributes can be included. The m o d e l STANDSIM (Opie, 1972; Campbell et al., 1979), developed in Australia using data from Eucalyptus regnans forests, mainly 15 years of age or older, is a relatively simple stand m o d e l requiring as i n p u t the site index of a stand and its stocking density. The m o d e l contains functions to predict from this information the basal area of the stand at 15 years of age and the frequency distribution of the diameters at breast height of the trees in the stand. It also calculates annual basal area i n c r e m e n t as a function of stand age, basal area, and site index. O t h e r functions distribute this basal area increment a m o n g the individual trees of the stand and predict mortality. A good example of a c o m p l e x model of forest dynamics is the model of Ek and M o n s e r u d (1974; see also Ek and Monserud, 1979), which is an individual tree-based simulator that consists of separately estimated c o m p o n e n t models for overstory tree height growth, d i a m e t e r growth, and survival. It also has separately estimated c o m p o n e n t s for reproduction and u n d e r s t o r y tree h e i g h t growth and survival. A c o m p e t i t i o n index, calculated at the start of every growth period, is c o m p u t e d for every

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tree by considering all other trees to be potential competitors in terms of crown overlap. E n o r m o u s amounts of data are required to develop such models. Ek and Monserud (1979) evaluated the performance of this model for Lake States (United States) n o r t h e r n hardwood stands for a range of ages and harvest conditions. The model p e r f o r m e d well (see also Hasse and Ek, 1981). Dale et al. (1985) reviewed tree growth models and divided them into forest growth and c o m m u n i t y dynamics models. Forest growth models tend to be site specific, whereas c o m m u n i t y dynamics models are species specific. Competition is a major c o m p o n e n t of both types and may be described in terms of tree size, n u m b e r (stand density), or water relations; in the c o m m u n i t y dynamics m o d e l s m o f t e n called gap m o d e l s m the approach to simulating tree growth is based on extremely simplistic representations of the physiological processes that determine tree growth. Nevertheless, these c o m m u n i t y dynamics models are more transportable than the forest growth models (see Shugart, 1984; see also Section II,H). A stand growth model, based on descriptions of individual tree growth, which uses physiological processes as the basis for quantifying competition, was p r o d u c e d by Mfikelfi and Hari (1986). It is more soundly based, mechanistically, than any conventional forestry model or extant community dynamics model. Instead of defining a competition index, the concept of photosynthetic light ratio is applied. This is the ratio between actual photosynthesis p r o d u c e d u n d e r shade from other trees and the potential, or unshaded, photosynthesis. Stand structure determines the degree of shade cast on any particular tree and the individual tree model is based on the carbon metabolism of the trees, with the assumption that water and nutrient availability do not change over time. (The model was parameterized using data from trees in southern Finland. For many other areas, the assumption about water and nutrient supplies would not necessarily hold, but restrictions caused by these factors could be written into the model.) The model calculates stem biomass growth; tree death is determined by shortfalls in the rate of carbon supply. The Mfikelfi and Hari model contains many high-level empirical components, including tree form equations and the carbon allocation routine, which determines the growth patterns. It has not (to our knowlege) been generalized or widely used but contains ideas that should be followed up, developed, and generalized.

II. Current P r o c e s s - B a s e d Models We present, in the following sections, relatively detailed discussion of a n u m b e r of the best known and most widely used of the current "family" of forest ecosystem models. Some of these, as well as other models not

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discussed here, have b e e n t h o r o u g h l y reviewed in a useful paper by Agren et al. (1991). Many models that may have justifiable claims to be i m p o r t a n t , or that contain novel and useful routines, are n o t considered, b u t we believe that those discussed cover the spectrum of approaches to the p r o b l e m of forest productivity modeling. Most of the models do n o t deal with the range of ecosystem processes from nutrient cycling to plant c o m p e t i t i o n and species relationships and thus c a n n o t strictly be called ecosystem models. They are generally carbon flow models dealing with different levels of detail and different time scales. The descriptions and discussion given here are based on the latest papers in the literature at the time of writing. They will soon be out of date, either because the models fall into disuse and disappear or because the models are developed, changed, and improved. Nevertheless, we are confident that the principles involved in these models are now well established, and such change and d e v e l o p m e n t is unlikely to involve any radical new approaches. The treatment moves from detailed, short time-step models to simpler, longer time-step models designed for stand, forest, and regional simulation. To set them in context, the most i m p o r t a n t properties of the models discussed are outlined below; they are then discussed in some detail. Figure 9.1 provides a schematic illustration of the way these models re-

Figure 9.1 S c h e m a t i c i l l u s t r a t i o n o t the c o m p l e x i t y a n d o p e r a t i o n a l t i m e scales of the m o d e l s discussed. C r o s s - h a t c h i n g indicates c o m m o n e l e m e n t s , e.g., the soil c a r b o n d e c o m p o s i t i o n r o u t i n e s in F O R E S T - B G C are similar to t h o s e in CENTURY; the water b a l a n c e a n d s o m e p h y s i o l o g i c a l p r o c e s s r o u t i n e s are similar in B I O M A S S a n d F()REST-BGC.

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late to one another. The diagram shows that, to deal with processes such as canopy photosynthesis in detail over short periods tends to r e q u i r e highly p a r a m e t e r i z e d models; as the time scales e x p a n d , and the level of detail decreases, the n u m b e r of p a r a m e t e r s r e q u i r e d also decreases. It can be taken as axiomatic that simulations c o n c e r n e d with large diverse areas, for which calculations may have to be d o n e for a n u m b e r of subunits (see C h a p t e r 10) or long periods, require relatively simple models with few parameters. These should, however, be firmly based on physical and physiological principles, rather than on empirical i n p u t - o u t p u t relationships, and should be consistent with the m o r e detailed models. One of the uses of detailed, process-based models is to provide the basis for simpler models. We r e t u r n to this point later. The first m o d e l c o n s i d e r e d - - M A E S T R O m i s a research tool, with short time steps, very detailed descriptions of canopy structure, and detailed p r o c e d u r e s for calculating photosynthesis and transpiration rates. The next three models can be said to belong to the same group. They are all essentially mechanistic c a r b o n balance models based on radiation interception and photosynthesis routines. BIOMASS, a i m e d at m u c h longer-term simulations than MAESTRO, has far less detailed radiation interception and photosynthesis r o u t i n e s but includes respiration, the allocation of c a r b o n to tree c o m p o n e n t s , and a water balance m o d u l e , n o n e of which are in MAESTRO. [At the time of writing, the c a r b o n allocation routines in BIOMASS were based on observed time series of allometric ratios (see C h a p t e r 5, Eq. (5.22)), and n o t on physiological mechanisms, and are t h e r e f o r e empirical.] FOREST-BGC (and its derivative BIOME-BGC) has many of the same structural characteristics as BIOMASS b u t was designed for larger-scale (spatial) simulations. It includes a m o r e mechanistic a t t e m p t to p a r t i t i o n carbon, based on m o r e detailed t r e a t m e n t of soil water and n i t r o g e n availability. BEX, a m o d e l developed specifically to simulate fluxes in boreal forests, has m a n y of the same characteristics as BIOMASS and FOREST-BGC. The next level "down" (in terms of r e d u c e d complexity) is r e p r e s e n t e d by PnET, an empirical, l u m p e d - p a r a m e t e r m o d e l of the c a r b o n and water balances of t e m p e r a t e and boreal forests that represents a c o m p r o mise between physiological process-based and empirical models. PnET is very different in its design from BIOMASS and FOREST-BGC. The empiricism is at the level of canopy processes, c a r b o n allocation, a n d stand water balances, and the m o d e l works on a m o n t h l y time step. Its develo p m e n t was driven by the realization that if models are to be applied extensively (over wide areas) they must r e q u i r e only a few widely available p a r a m e t e r s as inputs. Of the models we consider, LINKAGES is nearest to a g e n u i n e ecosystem model. It is a m o n g the most d e v e l o p e d m i n a mechanistic s e n s e - - o f

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the family of "gap" models (Botkin et al., 1972; Shugart, 1984), a l t h o u g h it uses simple, entirely empirical relationships to describe the interactions between the biological processes of r e p r o d u c t i o n , growth, and death and the effects of physical factors such as water, temperature, and n i t r o g e n on growth. CENTURY is a soil-based model, with a relatively crude net p r i m a r y p r o d u c t i o n m o d u l e , based on the assumption that growth must be prop o r t i o n a l to available n i t r o g e n (N). CENTURY is primarily c o n c e r n e d with s o i l - o r g a n i c matter d e c o m p o s i t i o n and turnover. T h e "end point" in the process of simplification is the E m o d e l - - a simple quasimechanistic (e can be justified in terms of physiological processes) equation suitable for the wide-scale estimation of net p r i m a r y p r o d u c t i o n (NPP) (see C h a p t e r 5). It requires very few parameters, but the values of E used are of p a r a m o u n t i m p o r t a n c e and d e t e r m i n e the o u t p u t of the model. This m o d e l uses a m o n t h l y to seasonal time step and is a m e n a b l e for use with m e a s u r e m e n t s from satellites. There is currently no tested version that includes carbon allocation, although this will be relatively simple to build into the model (see Section III). We provide somewhat m o r e extensive discussion of the E m o d e l than of the others c o n s i d e r e d because we believe that it offers i m p o r t a n t opportunities for utilizing ecophysiological principles in forest m a n a g e m e n t and decision making. A. M A E S T R O

MAESTRO, d e v e l o p e d by Ying Ping Wang and Paul Jarvis (1990 a,b), is a m o d e l of an array of trees in a stand. It deals with each individual tree in considerable detail and calculates net photosynthesis and transpiration rates in canopies. MAESTRO does not include stand respiration and is essentially a research tool that allows exploration of the effects of various conditions (e.g., climate, canopy architecture) on canopy photosynthesis. The positions of all trees are specified by their x, y, and z coordinates. Each is described by the radii of its crown in the x and y directions, crown length, m e a s u r e d as the distance from the top of the tree to the lowest live whorl (MAESTRO was developed for conifers), height from the g r o u n d to the crown base, a n d total area of leaves within the crown. T h e positions of leaves in the vertical and radial directions are defined by functions describing the leaf area density (LAD) distribution. The slope of the g r o u n d in the x and y directions, and the orientation of the x-axis, are also specified to take account of possible horizontal limitations caused by topography. T h e spatial scale for MAESTRO for radiation absorption is a point; for photosynthesis a n d transpiration it is individual leaves. The point represents a canopy subvolume, within which conditions are assumed to be h o m o g e n e o u s . T h e physical a n d physiological p r o p e r t i e s of the leaves

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within each subvolume have to be specified. T h e m o d e l partitions inc o m i n g radiation into direct b e a m a n d diffuse photosynthetically active radiation (PAR), n e a r infrared, a n d t h e r m a l radiation. MAESTRO works on an h o u r l y time step. T h e leaves within the crowns are classified into three different age classes; if i n f o r m a t i o n on the spatial distributions of leaves within tree crowns is n o t available, the leaf area density is treated as u n i f o r m or rand o m within crowns a n d only the p r o p o r t i o n of leaves in each category would be n e e d e d as inputs. MAESTRO can be r u n with a range of leaf angle distributions (see Wang et al., 1992). Photosynthesis is calculated using the F a r q u h a r a n d von C a e m m e r e r (1982) model. This model, a n d the calculation of transpiration, requires stomatal c o n d u c t a n c e values (see C h a p t e r s 3 - 5 ) , which are calculated using the s u b m o d e l described byJarvis (1976). [Recent versions may i n c o r p o r a t e the m o d e l p r o p o s e d by Ball et al. (1987) a n d modified by L e u n i n g (1990, 1995) ]. Leaf dark respiration rates are assumed to be functions of leaf t e m p e r a t u r e ; MAESTRO does not include a solution of the leaf energy balance for leaf t e m p e r a t u r e , which is assumed u n i f o r m t h r o u g h the canopy, as is ambient CO 2 concentration. Transpiration is calculated using the P e n m a n Monteith e q u a t i o n (see C h a p t e r 4), which requires n e t radiation flux density at leaf surfaces, the vapor pressure deficit of the air a n d b o u n d ary layer, as well as stomatal c o n d u c t a n c e s . MAESTRO requires a great deal of i n p u t data, including soil surface t e m p e r a t u r e s , crown structure, leaf t r a n s m i t t a n c e a n d reflectance values for PAR, leaf age c l a s s / d e n s i t y distributions, and a range of physiological p a r a m e t e r s for each age class. T h e inputs to the radiation p a r t i t i o n i n g m o d e l include the zenith angle of the sun a n d the incident radiation flux densities above the canopy. Radiation e x t i n c t i o n coefficients are derived internally f r o m leaf angle distribution data. Validation of a m o d e l such as MAESTRO is difficult. T h e photosynthetic characteristics of the foliage are o b t a i n e d f r o m laboratory measurements, b u t there are few m e a s u r e m e n t s of CO 2 u p t a k e by whole trees, or canopies, that provide suitable test data. M i c r o m e t e o r o l o g i c a l (flux) m e a s u r e m e n t s provide i n f o r m a t i o n on net ecosystem productivity (NEP), not canopy photosynthesis. In their original paper, Wang a n d Jarvis (1990a) tested the m o d e l in terms of radiation i n t e r c e p t i o n a n d t r a n s m i t t a n c e d e m o n s t r a t i n g , for two Sitka spruce sites in Scotland, that p r e d i c t e d m e a n hourly transmittances c o r r e s p o n d e d closely to those m e a s u r e d by arrays of sensors b e n e a t h the canopies. McMurtrie a n d Wang (1993; see also Wang et al., 1992) c o m p a r e d canopy photosynthesis rates calculated by the m o d e l BIOMASS (see Section II,B) and MAESTRO and f o u n d that they c o r r e s p o n d e d r e m a r k a b l y well (see Fig. 9.2). MAESTRO is m o r e suited to r u n n i n g simulations for short periods, such as a single day, or at most a season. BIOMASS, with its simpler canopy

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s t r u c t u r e d e s c r i p t i o n s a n d radiation i n t e r c e p t i o n routines, can readily be used to simulate p h o t o s y n t h e s i s for p e r i o d s r a n g i n g f r o m days to decades. T h e r e f o r e , a l t h o u g h the c o m p a r i s o n s c a r r i e d o u t by Wang et al. (1992) c a n n o t be called r i g o r o u s tests, the c o r r e s p o n d e n c e of the outputs f r o m the two m o d e l s r e i n f o r c e s c o n f i d e n c e in both. T h e g r e a t e r physiological detail in M A E S T R O is likely to provide m o r e a c c u r a t e simulation of reality in terms of rates of c a n o p y p h o t o s y n t h e s i s over s h o r t periods u n d e r well-specified c o n d i t i o n s . T h e obvious d i s a d v a n t a g e of M A E S T R O is the very large n u m b e r of par a m e t e r values, a n d a m o u n t of i n f o r m a t i o n , r e q u i r e d . It is not likely to be useful, or even interesting, to a n y o n e c o n c e r n e d with the growth of trees a n d stands a n d the m a n a g e m e n t of forests. It also has limitations in that it contains no s u b m o d e l dealing with water s t r e s s m a l t h o u g h this could be i n c o r p o r a t e d t h r o u g h the stomatal c o n d u c t a n c e s u b m o d e l m a n d it is n o n d y n a m i c in terms of the canopy: T h e trees do not grow. However, M A E S T R O is a valuable r e s e a r c h tool that can be used to evaluate the p r o b a b l e effects of nutrition on c a n o p y p h o t o s y n t h e s i s (given a relat i o n s h i p b e t w e e n CO 2 assimilation rates a n d leaf nutritional status; see C h a p t e r 5) to study the influence of b e a m a n d diffuse radiation on PAR a b s o r p t i o n , photosynthesis, a n d t r a n s p i r a t i o n (Wang a n d Jarvis, 1990 b) or the effects of c a n o p y s t r u c t u r e on p h o t o s y n t h e s i s (Wang et al., 1992). T h e c o m p l e t e c a n o p y d e s c r i p t i o n c o n t a i n e d in M A E S T R O could be exp l o i t e d to evaluate the c o n s e q u e n c e s of m a n a g e m e n t o p t i o n s such as various spacings in p l a n t a t i o n s

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Although it may not be possible to test MAESTRO against canopy CO 2 flux measurements, the model can be used to disaggregate such data by providing reliable values for the photosynthesis term, hence allowing estimation of the total respiratory flux from a forest (NEP = photosynthesis - respiration), particularly if the understory is a minor factor. It also provides (one of) the "baseline" models that can be used to test and evaluate simpler models. MAESTRO is completely general, it is not calibrated for any particular stand, species, or region, and it can be used anywhere in the world provided the necessary input information is available. B. B I O M A S S

BIOMASS, developed by Ross McMurtrie, is a process-based, or mechanistic, model consisting of a series of submodels that describe the operation of various physiological processes involved in the growth of trees (McMurtrie et al., 1990; McMurtrie and Landsberg, 1992). The basic processes m o d e l e d are radiation interception by the foliage, carbon fixation by photosynthesis and losses by respiration, the allocation of dry matter to the c o m p o n e n t parts of the trees and, hence, the growth patterns of the trees (see earlier comment), and the effects of water availability on the growth of stands. BIOMASS now incorporates p h e n o l o g y and development (McMurtrie et al., 1994). BIOMASS is essentially a stand-level model consisting of a n u m b e r of submodels that calculate the carbon balance of the canopy from a radiation interception model that uses information about the canopy structure and foliage photosynthetic characteristics. Tree crown shape is represented by geometrical constructions (ellipsoids, cones, etc.) and the plant community by a randomly spaced array of trees. The foliage is divided into three horizontal layers and different photosynthetic parameters can be specified for the foliage of each layer. The radiation interception model that provides the basis for the calculation of canopy net photosynthesis estimates the interception of direct and diffuse radiation by foliage (see Chapter 3). BIOMASS is less d e m a n d i n g computationally than MAESTRO, with more detailed descriptions of the distribution of radiation within canopies, but tests against such models indicate that the resulting errors are minor (Wang et al., 1992). In the original version of BIOMASS (McMurtrie et al., 1990), leaf photosynthetic properties were described in terms of an empirical equation (the Blackman function), but in later versions (McMurtrie et al., 1992a, b) they were formulated in terms of Farquhar and von Caemmerer's (1982) biochemically based model of photosynthesis in C3 plants and the stomatal model of Ball et al. (1987) (see Chapter 5). Photosynthesis is calculated for both sunlit and shaded foliage.

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T h e c a r b o n balance of the canopy is u p d a t e d daily after the calculation of m a i n t e n a n c e respiration of above-ground tissues, which is dependent on t e m p e r a t u r e and assumed to be p r o p o r t i o n a l to tissue nitrogen c o n c e n t r a t i o n (Ryan, 1991). In BIOMASS, empirical partitioning coefficients, which vary over the annual cycle and are linear functions of foliar n i t r o g e n concentration, are used to allocate carbohydrates to leaves, stems, branches, and roots. Litterfall is subtracted from each c o m p o n e n t with rates allowed to vary over an annual cycle (McMurtrie and Landsberg, 1992) (see Fig. 9.3). Stand water use and, hence, the water balance, is calculated from the P e n m a n - M o n t e i t h equation (see C h a p t e r 3); biological control of the process is e x e r t e d t h r o u g h the stomatal response model. Stomatal response is a s s u m e d to decrease in reponse to soil water deficits or overn i g h t frost as well as (atmospheric) vapor pressure deficits. BIOMASS requires many p a r a m e t e r s that must be supplied by the user such as canopy architecture, the values for the p a r a m e t e r s of the foliage photosynthetic response relationships, the stomatal c o n d u c t a n c e model, rooting d e p t h a n d soil moisture retention characteristics, rainfall interception m o d e l parameters, as well as a n u m b e r of physical p a r a m e t e r s (leaf reflectance, air density, psychrometric constant, etc.) The m i n i m u m m e t e o r o l o g i c a l data set n e e d e d is daily precipitation and m a x i m u m and m i n i m u m air temperatures. O t h e r information n e e d e d , such as daily total shortwave energy income, from which PAR can be estimated, can be read from files or g e n e r a t e d from formulae provided within the simulation package. T h e model operates with a daily time step, but because of the n o n l i n e a r d e p e n d e n c e of photosynthesis and transpiration on meteorological conditions the diurnal course of several variables is r e q u i r e d to calculate daily totals. Much of the early d e v e l o p m e n t of BIOMASS was in conjunction with a field e x p e r i m e n t on P. radiata that i n c l u d e d a range of irrigation and fertilization treatments (see Benson el al., 1992). The p a r a m e t e r s of the m o d e l are therefore well established for this species, and its perform a n c e in simulating the growth of P. radiata is encouraging. McMurtrie et al. (1994) have also shown that the m o d e l performs well for a range of pine species (P.. resinosa, P. sylvestris, and P. elliiottii) growing in Wisconsin, Sweden, a n d Florida, as well as P. radiata growing in Australia and New Zealand (Fig. 9.3a). BIOMASS has also been successfully tested with E. globulus in Western Australia (Hingston et al., 1995), although to get a good fit with observed growth data it was necessary to adjust the carbohydrate allocation coefficients. The values used to allocate carbon to stem growth r a n g e d from 0.55 to 0.75 of carbon fixed, which emphasizes the n e e d for better u n d e r s t a n d i n g of the factors controlling carbon allocation.

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The BIOMASS model suffers from the shortcomings that it does not include nutritional dynamics and that the carbon allocation coefficients are empirical. A model developed from BIOMASS, called G'DAY (McMurtrie et al., 1992a; Comins and McMurtrie, 1993), includes soil nitrogen dynamics based on litter decay processes and nitrogen mineralization rates calculated in a m a n n e r similar to those for CENTURY (Parton et al., 1987; see Section II,G). Photosynthetic productivity and the dynamic behavior of G'DAY are strongly d e p e n d e n t on C / N pools and ratios, but the model is primarily aimed at evaluation of long-term processes and the attainment of equilibria. The physiological processes are greatly simplified (see McMurtrie et al., 1992; Comins and McMurtrie, 1993). C. F O R E S T - B G C

FOREST-BGC (BioGeoChemical), developed mainly by Steve Running (Running and Coughlan, 1988), is an ecosystem model designed primarily to provide estimates of carbon, nitrogen, and water cycling across forested landscapes. The key p r o p e r t y of the model is its d e p e n d e n c e on L* to define canopies; the model requires no information about canopy properties such as stand density or canopy architecture. Requiring only L*, which can be estimated by remote sensing, allows FOREST-BGC to be driven by remote sensing. Key climatic variables required to execute the model include daily m a x i m u m and m i n i m u m temperature, average relative humidity, daily total precipitation, and shortwave radiation. FORESTBGC is associated with a group of models [Regional Ecosystem Simulation System (RESSys) ] (Running et al., 1989) that perform climatological simulations, on the basis of t o p o g r a p h y and prevailing weather, to provide the input data to FOREST-BGC. FOREST-BGC simulates the carbon cycle by calculating photosynthesis, respiration, above- and below-ground net primary production, litterfall, decomposition, and leaf area index. The model uses "green sponge" canopy architecture, i.e., the canopy is treated as a h o m o g e n e o u s mass of thickness L*. Because L* is the key structural attribute of the m o d e l e d canopies, and because the model is intended for use over long time periods, the carbon available after respiration must be partitioned to leaf, root, and stem growth. Running and Gower (1991) describe the version of the model in which this partitioning is introduced: leaf, stem, and fine root growth are controlled by leaf water status and nitrogen availability. The soil carbon and nitrogen cycling subroutine in FOREST-BGC is patterned after CENTURY (see below). Litterfall and fine root turnover are controlled by functions that link the soil carbon and nitrogen cycles. The model calculates complete stand water balances (canopy interception, evaporation, transpiration, and drainage). It has a dual time step;

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canopy CO 2 and water exchange are simulated on a daily basis and s u m m e d for the year and C allocation, d e c o m p o s i t i o n , and N circulation are simulated on an annual time step. FOREST-BGC has 20 state variables and requires 41 parameter values. The state variables in the model, which describe the C and N distribution in the vegetation and soil, and the ecophysiological attributes, such as specific leaf area, m a x i m u m , stomatal conductance, leaf turnover, leaf lignin concentration, and respiration coefficients, can be modified to match the information available about particular species. Complete lists of state variables and parameters used in the static and dynamic carbon allocation versions of FOREST-BGC are provided in Running and Coughlan (1988) and Running and Gower (1991), respectively. Comparisons between measured and calculated aboveground net primary production for FOREST-BGC are shown in Fig. 9.3b. 30 '7

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D. BEX

The primary aim of BEX, developed by G o r d o n Bonan (Bonan, 1990a, 1991b), was to simulate energy, water, and carbon fluxes in boreal forests. The model has several attributes that are necessary to simulate processes in boreal forests accurately; namely, incorporation of permafrost and the explicit modeling of energy, water, and C budgets of the moss layer (see Chapter 2). No other forest ecosystem model attempts to model these two critical components of boreal forests. The forest energy budget (see Bonan, 1991a) is c o m p u t e d for a multilayered forest canopy using a daily time step. Daily estimates of g r o u n d surface temperature, evapotranspiration, and snowmelt are used to determine soil moisture and soil temperature. The energy balance of n canopy layers is c o m p u t e d as a function of n unknown surface temperatures and the equations are solved for these temperatures using a multiple nonlinear N e w t o n - R a p h s o n iteration (Press et al., 1986). Environmental constraints on tree photosynthesis are mediated t h r o u g h mesophyll resistance, thereby including rate limitations imposed by CO 2 diffusion within the plant cells and biochemical processes of CO 2 fixation. The actual algorithms used to simulate the irradiance and leaf N concentration effects are identical to those used in FOREST-BGC. Carbon allocation is empirically derived and assumed constant. Decomposition is m o d e l e d as a function of soil moisture and temperature using empirical constants that are scaled according to forest floor N concentration. Climate data required to execute BEX include daily averages of air temperature, relative humidity, air pressure, wind speed, and cloudiness. Twenty-seven physiological parameters that define photosynthesis and respiration are needed. Results from 200 Monte Carlo simulations for which all parameters were selected r a n d o m l y suggested that 12 parameters define environmental influences on stomata and mesophyll resistance and 15 parameters define tree photosynthesis. BEX has been rigorously tested for evergreen and deciduous boreal forests in interior Alaska (Bonan, 1991b) and used in sensitivity analyses of boreal forest ecosystems to climate change (Bonan et al., 1991). Measured and simulated ANPP did not differ for each of five forests types in Alaska (Bonan, 1991b), although BEX tended to overestimate ANPP for birch and balsam poplar forests (Fig. 9.3c). E. P n E T

PnET, developed by J o h n Aber and Anthony Federer (1992), represents an interesting compromise between empirical models such as gap models (see LINKAGES) and process-based or physiological models. It is a simple, lumped parameter, dual time step model that simulates C and water circulation. PnET attempts to avoid the ecophysiological complexities inevitably e n c o u n t e r e d when dealing with numerous species, such as

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typically occur in the forests of the eastern United States; Aber and Federer (p. 464) state that "an effort has been made to retain only as much structural complexity as is required to capture the major dynamics in the systems." PnET consists of four modules or computational components: climate, canopy photosynthesis, water balance, and carbon allocation. The model has five c o m p a r t m e n t s (foliage, wood, and fine roots, soil water, and snow), three annual C fluxes (net canopy photosynthesis, and fine root and wood p r o d u c t i o n ) , and eight water fluxes (precipitation, interception, snowmelt, s n o w - r a i n division, uptake, evapotranspiration, drainage, and fast-flow drainage). PnET operates on a monthly time step. I n p u t data requirements are substantially smaller than those for the models discussed previously, and there are fewer parameters, largely because of the simplistic approach to photosynthesis and transpiration calculations (see below). Only three soil parameters are n e e d e d and only one (soil water-holding capacity) is varied from stand to stand. Latitude, month, average m a x i m u m and mini m u m air temperature, and insolation are used to calculate vapor pressure deficit, day length, average day- and nighttime temperatures, and daily radiation. Twenty-two vegetation structure and ecophysiological variables are required, a l t h o u g h many are generic relationships for forest ecosystems (i.e., fraction of C in tissue mass, fraction of C allocated to growth or m a i n t e n a n c e respiration, and t e m p e r a t u r e parameters for photosynthesis). Species-specific variables include characteristics such as leaf %N, specific leaf weight, leaf longevity, vapor pressure deficit effects on photosynthesis, and transpiration. Leaf COz and water fluxes are based on two first-order ecophysiological relationships: (i) m a x i m u m net photosynthesis (A,.m~• calculated as a linear function of foliar N concentration (percentage by weight); and (ii) dry mass p r o d u c e d per unit water transpired, known as the water use efficiency (WUE, d e n o t e d WE), which provides a link between carbon gain and transpiration. Based on observed relationships between rates of net photosynthesis and stomatal c o n d u c t a n c e for C3 plants, and the assumption that atmospheric CO 2 concentration can be taken as constant, WUE can be calculated from the simple relationship: W~: = c~:/D, where CE is a constant and D is vapor pressure deficit. PnET, like FOREST-BGC, uses "green sponge" canopy architecture and Beer's law to calculate light absorption by the canopy. Assuming base-rate respiration of foliage to be 10% of the m a x i m u m net photosynthesis rate, gross leaf photosynthesis (A, i.e., carbon uptake) is calculated as (A = 1.1 An.max)- Day- and nighttime respiration are calculated as 0.1 An.ma x and modified by a Q10 of 2, using average monthly day- and nighttime temperatures. M a x i m u m annual canopy photosynthesis is calcu-

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lated by s u m m i n g m o n t h l y canopy photosynthesis and respiration a n d then adjusted downward based on t e m p e r a t u r e , water availability, a n d vapor pressure deficit. A m o n t h l y available water balance is calculated by assuming that a constant fraction of p r e c i p i t a t i o n (0.1) is i n t e r c e p t e d a n d / o r e v a p o r a t e d and a constant fraction (0.1) drains rapidly t h r o u g h the soil and is unavailable for plant uptake. Potential transpiration is calculated as (potential gross p h o t o s y n t h e s i s / W E ) and adjusted downward using a water-deficit scalar. C a r b o n is allocated to leaves, fine roots, a n d wood using the following algorithms: leaf C - Cf = L * / o f (where o-f is specfic leaf area; see C h a p t e r 3); fine r o o t C = Cfr = 130 + 1.92 Cf (cf. Raich a n d Nadelhoffer, 1989); a n d wood C is the difference between total C a c c u m u l a t e d a n d Cf + Cfr. Net p r i m a r y p r o d u c t i o n and water balance calculations using P n E T have b e e n validated in a diverse range of boreal and t e m p e r a t e evergreen and d e c i d u o u s forests. A strong n e a r 1 : 1 relationship was observed between m e a s u r e d and simulated net p r i m a r y p r o d u c t i o n (Fig. 9.3d) and reasonable a g r e e m e n t was o b t a i n e d between simulated a n d measured stream discharge from two t e m p e r a t e d e c i d u o u s forests (Abet a n d Federer, 1992). PnET is u n i q u e a m o n g the models discussed in that it represents the first a t t e m p t to c o n d e n s e the n u m e r o u s ecophysiological attributes of tree species to some simple "physiological principles." We believe the inc o r p o r a t i o n of such principles (e.g., relationships between leaf N a n d leaf longevity, a n d o-f a n d leaf longevity; see C h a p t e r 7) for different functional g r o u p s with a simple, b u t m e c h a n i s t i c t r e a t m e n t of CO 2 a n d water flux at the leaf level may be a useful d i r e c t i o n for ecological modeling to follow. T h e empirical a p p r o a c h to the estimation of c a r b o n allocation to fine roots is p r o b a b l y satisfactory for global estimates; however, it is unsuitable for stands that are n o t in steady state and is likely to provide unreliable estimates for individual stands (see Gower et al., 1996a). Despite the g r e a t e r t e m p o r a l time step of P n E T relative to o t h e r m o d e l s (months c o m p a r e d to hours or days), a g g r e g a t i o n of climate data at the m o n t h l y time step did n o t a p p e a r to have an u n d e s i r a b l e effect on NPP estimates, a l t h o u g h stream discharge estimates may suffer f r o m the longer time step. P n E T has no specified spatial d i m e n s i o n a n d has b e e n a p p l i e d at the stand to small watershed scale. E LINKAGES

LINKAGES, d e v e l o p e d by J o h n Pastor a n d Wilfred Post (Pastor a n d Post (1986) is o n e of n u m e r o u s forest stand growth simulator models of the FORET family (Shugart a n d West, 1977) that evolved f r o m JABOWA (Botkin et al., 1972). F O R E T models, c o m m o n l y r e f e r r e d to as gap models, are different from all o t h e r models discussed in this c h a p t e r because

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9. Ecosystem Process Models

they simulate the birth, growth, and death of individual tree species based on autecological characteristics of tree species. A l t h o u g h now somewhat outdated, Shugart's (1984) book provides a s u m m a r y of various FORET models. LINKAGES is u n i q u e a m o n g these because it links carbon and n i t r o g e n cycles with the p o p u l a t i o n dynamics of overstory tree species. LINKAGES uses subroutines called BIRTH, GROW, and KILL to simulate the birth, growth, and death, respectively, of trees in stands that contain a n u m b e r of species. All the subroutines are stochastic functions. BIRTH d e t e r m i n e s the n u m b e r of individuals of each species that become established in a plot based on the autoecology of each species and four variables: availability of m i n e r a l soil nutrients, the presence of leaf litter, relative t e m p e r a t u r e , and susceptibility to m a m m a l i a n herbivory. GROW is an empirical tree growth simulator, and KILL simulates tree mortality based on the m a x i m u m age r e p o r t e d in the literature of each tree species and average growth for the past 2 years. T h e equation used to calculate tree volume growth in optimal conditions is an empirical, asymptotic relationship between leaf area per tree and d i a m e t e r at breast height, with the asymptote d e t e r m i n e d by speciesspecific m a x i m u m values for tree h e i g h t and diameter. The model incorporates the interaction of trees with one a n o t h e r by shading: Because tree heights are c o m p u t e d explicitly, the radiation that reaches a given tree can be calculated in terms of the incident radiation and the radiation absorbed by the leaf areas of taller trees. Growth multipliers are calculated in s u b r o u t i n e GMULT based on d e g r e e days, soil moisture, and soil n i t r o g e n availability. The d e g r e e day m u l t i p l i e r is a symmetric parabolic function between m i n i m u m and m a x i m u m degree days r e c o r d e d for the entire range of each species. The soil moisture growth multiplier is calculated from the fraction of growing season days in which soil moisture is less than the "wilting point" and the site water b u d g e t is calculated on a m o n t h l y time step using the water b u d g e t m e t h o d of Thornthwaite and Mather (1947). The nitrogen growth m u l t i p l i e r is taken from Abet et al. (1979a). A d e c o m p o s i t i o n s u b r o u t i n e (DECOMP) calculates weight loss, N immobilization, lignin decay, and CO 2 loss from d e c o m p o s i n g litter cohorts and N mineralization, weight loss, a n d CO x loss from humus. Weight loss is calculated as a fi~nction of l i g n i n / N ratio and actual evapotranspiration (E~). Nitrogen mineralization is calculated as a function of litter C / N ratio a n d is multiplied by a d e c o m p o s i t i o n scalar and an evaporation rate scalar based on degree days, soil moisture, and soil nitrogen availability. LINKAGES contains p a r a m e t e r s for 72 u p l a n d tree species of the eastern United States. O t h e r data r e q u i r e d to run the model include latitude, start and e n d day of the growing season, average and standard deviation of m o n t h l y t e m p e r a t u r e and precipitation, soil moisture field

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capacity and wilting point, and initial soil organic matter and n i t r o g e n contents. G. C E N T U R Y

CENTURY, developed by Bill Parton and colleagues, (1987, 1988), is a generic plant-soil ecosystem model that simulates the water, carbon, and selected nutrient (N, P, and S) budgets of grasslands. Later versions include plant p r o d u c t i o n models for forests, crops, and savannas (Patton et al., 1993). CENTURY uses different plant p r o d u c t i o n submodels for each of the major vegetation types and a c o m m o n soil organic matter (SOM) submodel. We refer readers to Sanford et al. (1991) for a m o r e detailed description of the forest version of CENTURY. The SOM submodel divides the soil carbon into three pools: active, slow, and passive. These pools c o r r e s p o n d roughly to microbes and microbial products, resistant plant material such as lignin and ligninlike components, and physically and chemically stabilized SOM, respectively. Carbon flow between soil pools is controlled by an i n h e r e n t maxim u m turnover coefficient and scaled downward based on water- and temperature-controlled functions. The t e m p e r a t u r e scaling function is based on monthly soil t e m p e r a t u r e at the surface and the water availability scalar is calculated as the ratio of stored water (0- to 30-cm depth) plus current monthly precipitation to potential evapotranspiration. Microbial respiration occurs for each soil carbon transformation (active to slow to passive) and with the partitioning between CO 2 and SOM determ i n e d by soil texture. Turnover coefficients for live tissue are based on empirical monthly constants. Leaf and fine root detritus are transferred into surface and fine root residue pools and allocated to structural and metabolic residue based on the l i g n i n / n i t r o g e n ratio of residue material. The woody tissue c o m p o n e n t s have specific decay rates and the lignin and nonlignin c o m p o n e n t s are transferred to the slow and active SOM pools, respectively. The water budget, which is used to modify SOM dynamics and plant p r o d u c t i o n , is a simplified m o n t h l y time step s u b m o d e l that calculates evaporation and transpiration, soil water content, snow water content, and flow of water between soil layers. Soil water-holding capacity for each soil layer is calculated as a function of bulk density, soil texture, and SOM content using the equation developed by Gupta and Larson (1979). Water loss by transpiration is derived empirically as a function of live leaf mass, rainfall, and potential evapotranspiration. The forest p r o d u c t i o n m o d e l divides trees into five components: leaves, fine branches, large wood, and fine and coarse roots. C a r b o n allocation is constant and empirically controlled. Like the SOM submodel, the forest p r o d u c t i o n s u b m o d e l runs on a m o n t h l y time step. M a x i m u m m o n t h l y

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p r o d u c t i o n is calculated as a f u n c t i o n of available water and temperature. Monthly m a i n t e n a n c e respiration for each tree c o m p o n e n t is calculated using modified (daily to m o n t h l y time step) equations in FORESTBGC ( R u n n i n g and Coughlan, 1988). A Q10 of 2.B is used in the model. T h e SOM s u b m o d e l of CENTURY has been widely used, with little or no modification, in m a n y of the ecosystem process models that simulate soil organic matter. This s u b m o d e l has been tested for a wide variety of terrestrial ecosystems and land m a n a g e m e n t practices. It is particularly well suited for exploring the effects of vegetation change and land use effects on soil b i o g e o c h e m i s t r y dynamics. The grassland version of CENTURY has been c o u p l e d to geographical i n f o r m a t i o n systems (GIS) and used to simulate regional grassland dynamics (Burke et al., 1989), alt h o u g h we are not familar with the application of the forest version to large spatial scales. T h e long time step (monthly), empirical treatment of the water budget, and forest p r o d u c t i o n submodels are less desirable than the m o r e mechanistic treatments of forest water and p r o d u c t i o n b u d g e t s f o u n d in many o t h e r models.

H. The Radiation Utilization Efficiency (e) Model To estimate forest growth over large areas and long time intervals, we n e e d a simple model with a small n u m b e r of parameters, preferably with robust, conservative values. The detailed, process-based and generally highly p a r a m e t e r i z e d models that we have discussed so far are generally not suitable for widescale use. If they are applied to large areas, it is inevitable that considerable simplifications will have to be made and that p a r a m e t e r values will often be a p p r o x i m a t e d by very general m e a n values. All this tends to r e d u c e the value of having a detailed model while probably doing little to reduce the c o m p u t a t i o n a l r e q u i r e m e n t s and the time that must be spent setting the m o d e l up. Nevertheless, the simple m o d e l n e e d e d for practical applications and estimates of CO 2 sequestration by forests over large areas must be soundly based on physical and physiological processes. Such a model exists. It is essentially a linear relationship between the photosynthetically active solar radiation absorbed by forest canopies and the p r o d u c t i o n of dry mass by forests, i.e., NPP = e ECp.a,

(9.1)

where NPP denotes net p r i m a r y dry mass p r o d u c t i o n (see C h a p t e r 5) and ~p.a denotes a b s o r b e d photosynthetically active radiation over some interval that, in the case of forests, may be a year, a l t h o u g h there is a g o o d case for working with seasons. If ~p.~, is expressed in MJ m -z, and NPP is dry mass, E will have units of mass MJ -1. It is usually expressed in g MJ -1. T h e r e is a range of values in the literature (see Russell et al.,

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267

1989; Prince, 1991), some of which are expressed in different units; one of the more c o m m o n sources of confusion is the use of incident or absorbed solar radiation rather than absorbed photosynthetically active radiation. The problem is discussed in some detail by Landsberg et al.

(1996).

The 9 model is soundly based on physical and physiological processes. The rate of carbon fixation by a plant canopy depends on the radiation absorbed by the canopy and the photosynthetic characteristics of the leaves. The change in the rate of photosynthesis by a single leaf with increasing p h o t o n flux density (q~p) is nonlinear; however, if a canopy is closed so that the foliage intercepts all or most of the incident radiation, and much of the foliage is not p h o t o n saturated, photosynthesis by the canopy as a whole is likely to be p h o t o n flux limited and the relationship between intercepted energy and net photosynthesis by the canopy tends toward linearity. This tendency is s t r e n g t h e n e d if a high p r o p o r t i o n of incoming radiation is diffuse. Jarvis and Leverenz (1983) d e m o n s t r a t e d that the rate of net photosynthesis by a spruce canopy t e n d e d toward linearity with q~p, largely as a consequence of shoot and canopy structure, and an elegant demonstration of the tendency toward linearity, as the period across which integration is carried out increases, was provided by McMurtrie et al. (1992a) using BIOMASS. They showed that, on a daily basis, there was considerable scatter between simulated daily photosynthesis and ~p.a" However, the variability collapsed to a single, approximately linear, relationship between canopy photosynthesis and q~p.a when expressed in annual terms, providing a clear demonstration that the simplified relationship encapsulates the complexities of the photosynthetic process and the interactions of directly illuminated and shaded foliage. (It is commonly found that integrating over time or space scales reduces nonlinearities.) As we have n o t e d in Chapter 3, radiation absorption by plant canopies has been exhaustively analyzed and m o d e l e d in great detail (see, for example, Wang and Jarvis, 1990a; Wang et al., 1992, and references cited therein), but for most purposes, particularly when integrating over time periods longer than a few days, simple models provide adequate descriptions of p h o t o n flux interception. The q~p.a values n e e d e d for the 9 model can therefore be obtained from the simple Beer's law equation [Eq. (3.8)], converting solar radiation into PAR. (The conversion is generally between 0.45 and 0.5 • q~s.) It is clearly essential, as a basis for using the 9 model, that we have good estimates of L* for any stand for which we wish to make productivity estimates (see Chapter 3). For individual stands, L* can be estimated by various means (see Chapter 3), including nondestructive methods based on light interception and inversion of the equations describing light interception by canopies,

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9. Ecosystem Process Models

and from relationships between waveband reflectance ratios and L* (see Chapter 10, Section II and Fig. 10.2). However, if it were possible to estimate q)p-a directly there would be no need for L* values (at least for the purposes of this model). Satellite measurements of the reflectance properties of surfaces also provide an o p p o r t u n i t y to estimate (pp. a and Sellers et al. (1992) have moved in this direction, showing that ~p.a is proportional to a simple waveband reflectance ratio (SR = near i n f r a r e d / infrared). Hall et al. (1990) p r o p o s e d the use of the second derivative of the reflectance/wavelength function obtained from n a r r o w b a n d spectra to estimate qPp.a" At the time of writing this book, these relationships were being tested in the field, and we can expect them to become important, particularly in the area of large-scale (regional) and global carbon modeling. The central p r o b l e m of the 9 model is, of course, the value of the coefficient. The model was first p r o p o s e d in the form given here by Monteith (1977) and has been used and tested with a n u m b e r of plant communities. Values of 9 obtained from experimental work vary widely: The u p p e r limit, for intensively m a n a g e d field crops, appears to be about 2.8 g MJ -1 (Russell el al., 1989); young, well-watered tree canopies, with good nutrition, give values of a b o u t 1.6 g MJ -1 (Cannell et al., 1987, 1988), whereas values obtained for above-ground productivity by plantations and forest canopies are in the range 0.7-1.5 g MJ -1 (Linder, 1985; Runyon et al., 1994). The lowest r e p o r t e d 9 values ( ~ 0.2 g Mj-I) appear to be those derived by Saldarriaga and L u x m o o r e (1991), who investigated the productivity of tropical rain forest stands at 23 sites in Colombia and Venezuela. It is unlikely that environmental variables can account for all the variation in 9 values; there appears to be a consistent reduction in 9 with age, illustrated by Landsberg et al. (1996) from values in the literature. This has not yet been satisfactorily explained (but see C h a p t e r 8 for a possible explanation). A d e v e l o p m e n t that holds promise for this model is the use of modifiers so that Eq. (9.1) is written NPP = e ~ q~p,,fofl)frr,

(9.2)

(see Landsberg, 1986). The modifying factors (jq) describe, respectively, reductions in the effectiveness of unit q~p.~ resulting from soil water deficit (O), the vapor pressure deficit of the air (D), and temperature (T), all of which affects stomatal conductance. The q)p.~ value resulting after modification may be r e g a r d e d as utilizable radiation. A good example o f t h e use of this p r o c e d u r e is provided by McMurtrie etal. ( 1 9 9 4 ) , who simulated gross primary p r o d u c t i o n (GPP) of pine trees at sites in Australia, New Zealand, the United States (two locations), and Sweden and plotted

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269

the results against utilizable q~p.a (see Fig. 5.3). They o b t a i n e d a very strong positive relationship from which they derived 9 = 1.77 g C MJ -1. T h e r e is a g o o d a r g u m e n t for using values of q~p.a c o r r e c t e d in this way to derive values of 9 a l t h o u g h there is a clear n e e d for rigorous research on the calculation of modifier values that are a p p r o p r i a t e to the time scales used. The use of u n m o d i f i e d values of ~p. a to derive 9will continue to result in wide variations because the effects of factors that alter (reduce) the capacity of trees to convert radiation into dry mass are i n c o r p o r a t e d in the estimates. It is also i m p o r t a n t to r e m e m b e r that values of 9 derived from above-ground data will be subject to the u n k n o w n variation caused by differences in the a m o u n t of carbohydrate allocated below-ground. However, there is now sufficient i n f o r m a t i o n a b o u t r o o t mass and turnover, and the effects of soil and growing c o n d i t i o n s on the allocation of carbon to the roots, to allow useful working estimates to be made. The e-model implicitly assumes that respiration is n o t a major cause of variation in the estimates of dry mass p r o d u c t i o n . Prince and Goward (1995) and Waring et al. (1995) used the q u a n t u m efficiency of leaf photosynthesis, with modified q)p.~, as the basis for estimating GPP. This solves the p r o b l e m of 9 values, but requires that the a s s u m p t i o n a b o u t respiration holds, which it may well do over relatively long periods.

III. Practical Applications O n the global scale, Ruimy et al. (1994) used a b i o m e map of the world and obtained average empirical values of e f r o m the literature, as far as possible, for each b i o m e type, and the E m o d e l has b e e n used by Potter et al. (1993) to calculate seasonal patterns of net ecosystem productivity across the globe. At the level of local forest m a n a g e m e n t , we c o n t e n d that a m o d e l of this type, based on s o u n d physiological processes (energy absorption a n d photosynthesis), provides a means of i n c o r p o r a t i n g nutrition and water status into productivity models and is potentially far m o r e useful t h a n statistically based models and growth curves. To realize this potential requires that forest growth rates and yield are either described in terms of conventional volume growth, which can be c o n v e r t e d into i n f o r m a t i o n a b o u t e c o n o m i c timber yields on the basis of stand density and tree height, or that dry matter can be p a r t i t i o n e d between stems, branches, and leaves on the basis of allometric ratios. To describe growth in volume terms simply requires that E be in those units for which values could, presumably, be d e t e r m i n e d empirically by plotting volume growth increments against gYp.a" E q u a t i o n (9.2) would be applied, based on calculated water balances, soil nutrient status, and

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t e m p e r a t u r e , with the p r o c e d u r e s used to calculate the modifiers being g u i d e d by s o u n d ecophysiological principles as discussed in earlier chapters. This has advantages from the practical point of view, but has the maj o r disadvantage that the results c a n n o t be c o m p a r e d to other dry matter p r o d u c t i o n research; the m o d e l is r e d u c e d to a level of pragmatic empiricism that greatly reduces its value, a l t h o u g h useful information can still be obtained. From the ecological point of view, estimates of biomass p r o d u c t i o n may be the r e q u i r e d end point in a m o d e l i n g exercise, but p r o d u c t i o n forestry requires results in terms of stem volumes.These can be o b t a i n e d from estimates of growth expressed as dry mass per unit land area by using allometric ratios [Eq. (3.2)] a n d the p r o c e d u r e described in C h a p t e r 5, Section II,B). Given values for ci and ni, the equation can be used to partition total above-ground dry mass W(t) into its c o m p o n e n t parts:

W(t) = wv + ZObr-Jr- ZOst,

(9.3)

where wv, Wb,-, a n d wst d e n o t e the mass of foliage, branches, and stems, respectively. If stem n u m b e r per unit area in the stand is known, then average stem mass is known a n d average stem volume is o b t a i n e d from a value of wood density. More detailed analysis could include the use of standard forestry stem size distribution equations (e.g., the Weibull distribution) and taper equations, which would allow detailed estimates of the value of the product. The analysis suggested previously clearly d e p e n d s on values of the coefficients in the allometric equation, but these are readily o b t a i n e d from analysis of destructive harvest data (see C h a p t e r 5). If the procedure o u t l i n e d is developed, it will provide the m e t h o d we need as a substitute for the site index models (see C h a p t e r 10). The calculation of absorbed PAR can be made as simple or as detailed as is felt necessary; research must focus on the characterization of the modifiers f0, fD, and fT [Eq. (9.2)] as well as a nutritional modifier fN. The form of these modifying functions will be guided by physiological considerations; fN may be based on calculations of nutrient uptake rates, particularly in the case of nitrogen, in which a model such as CENTURY can be used. Otherwise, it may be necessary to develop empirical relationships with some m e a s u r e of fertility such as litterfall n i t r o g e n or n i t r o g e n mineralization, fo would be based on soil water content, derived from water balance modeling and (possibly) relationships between integrated predawn water potential (Myers, 1988) and growth; J } ) a n d f r would be based on physiological considerations (see R u n y o n et al., 1994; McMurtrie et al., 1994). All the modifiers would be n o r m a l i z e d relative to some value at which the factor

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271

c o n c e r n e d is considered not to limit growth. From the experimental point of view, it will always be of value to have data from a site or sites at which, as far as possible, all constraints to growth imposed by water and nutrition are removed by fertilization and irrigation (see Landsberg, 1986; Benson et al., 1992). Testing of the model will take the form of seeking linear relationships between utilizable absorbed PAR and the appropriate measure of yield (see McMurtrie et al., 1994). The model should be tested against growth and yield data from a wide range of sites with different growing conditions. W h e t h e r any particular forest m a n a g e m e n t system is sustainable in the long term, or will lead to forest degradation, will always be very difficult to answer experimentally. Models provide the only feasible a p p r o a c h to the problem. The approach used by Dewar and McMurtrie (1996a,b), alt h o u g h conceptual rather than (as yet) practical, illustrates the information that can be obtained from models that deal with soil organic matter and nutrient mineralization (see also Rastetter, 1991), the uptake of nitrogen, and the influence of nitrogen supply rate on carbohydrate partitioning and tree growth patterns. Dewar and McMurtrie analyzed the consequences of fire t h r o u g h its effects on soil nitrogen reserves and, hence, nitrogen supply rates. Developing such approaches so that the models can be parameterized for particular stands, and the consequences of particular m a n a g e m e n t practices (e.g., fertilization, rotation length, residue treatment, etc.) assessed, will provide a tool for evaluating sustainability. However, we must emphasize the (well-recognized) danger of accepting results from models that are elegant and convincing without adequate testing of those models. T h e r e is m u c h to be done in this respect in forest p r o d u c t i o n modeling. Another area in which process-based models can be of particular value for stand-level m a n a g e m e n t is the analysis of the decrease in NPP with stand age. This is a major difference between the empirical yield models and the process models: The yield models are based on age-sequence data, whereas the process models have generally (and necessarily) been developed and parameterized in relation to intensive, short-term field experiments. The o p t i m u m point to harvest a wood-producing stand is at the time of m a x i m u m mean annual i n c r e m e n t (a conventional measure of forest growth). The time when this occurs is strongly d e p e n d e n t on the growth curve of the stand, the shape of which is, in turn, d e p e n d e n t on current growth rates. Few models currently provide m u c h insight into the decline of growth rates with age, but as the appropriate mechanisms are built in, and information on physiology and c o m m u n i t y dynamics is collected, our ability in this area will improve and the value of the models will increase. Progress in this area has been made by Murty et al. (1996) and Gower et al. (1996).

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IV. Concluding Remarks A n u m b e r of points emerge from this brief review and evaluation of some of the c u r r e n t g e n e r a t i o n of forest ecosystem models. The first question that m i g h t be posed is: Do we need so many models? Would it not be possible to amalgamate some of those that have b e e n developed into fewer, soundly based and widely used versions? T h e r e are arguments for and against this. In favor of such amalgamation is the fact that some p r o c e d u r e s ( p h o t o n flux interception and photosynthesis calculation, water balance calculations, and, increasingly, SOM turnover and N availability calculations) are very similar in all m o d e l s m the principles are established, only the details vary. Therefore, if the algorithms and structure of the models dealing with these processes were available researchers could concentrate on work to improve understanding of the processes and examine the factors causing them to vary. If they find that the algorithms are inadequate to describe the processes, this in itself constitutes progress. It is possible to obtain the code from the authors of most models, parameterize the models for the situations of interest, and run them. Problems arise from the fact that the comp u t e r codes, having been written by research scientists, tend to be somewhat idiosyncratic; they are not commercial software and, although the scientists are generally willing to make the code available, they c a n n o t provide s u p p o r t for "debugging" or spend a great deal of time sorting out p r o b l e m s that may arise in parameterizing the models and adapting code to different machines. However, these are technical problems and can be overcome. They will be overcome more rapidly if complex models are developed, as far as possible, in m o d u l a r form. Landsberg et al. (1991, p. 13) c o m m e n t e d that "it is time for rational reconstruction of m o d e l development, which must include technology for easy visualization and flexible graphics. The challenge is for information technologists, but scientists c o n c e r n e d with biophysics will have to work with them, at least initially." The main decision to be made in relation to models relates to the purpose of the m o d e l and the level of detail needed. Is it a research tool in itself, or a framework for e x a m i n i n g hypotheses a b o u t particular parts of the system? In the research area, it is likely that the proliferation of models will continue for some time because each scientist or g r o u p has particular r e q u i r e m e n t s and tends to have a particular view of problems. It is i m p o r t a n t that the use of models in a practical sense is not stopped by the fact that there is uncertainty a b o u t various aspects of tree and forest growth. T h e r e will always be uncertainty; we n e e d to move forward using the knowledge we have while recognizing that it is, and will remain, in-

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complete and, in some senses, inadequate. Meanwhile, empirical allometric relationships may prove to be surprisingly stable, or it may prove possible to explain variations in terms of clearly identifiable factors or conditions. Nevertheless, we sugggest that the family of c a r b o n balance models, ranging from MAESTRO t h r o u g h BIOMASS and FOREST-BGC to G'DAY, if made widely available would obviate the n e e d for d e v e l o p m e n t of m u c h new software in the radiation i n t e r c e p t i o n , photosynthesis, respiration, and water balance areas. It seems unlikely that we n e e d a great deal m o r e detailed research on stomatal f u n c t i o n i n g and the development of stomatal response models for tree and stand models; there are m u c h larger uncertainties a b o u t o t h e r parts of the system. C o n t i n u e d research will be n e e d e d in relation to c a r b o n allocation (to foliage, branches, stems, and r o o t s m i n c l u d i n g fine r o o t and mycorrhizal turnover), respiration, and even factors such as the influence of changes in foliage p h o t o s y n t h e t i c capacity with tree aging. Description of tree growth in terms of c a r b o n allocation is crucial if models are to be used in a dynamic sense over long periods of time. If we assume that systems are in steady state, then we can make the c a r b o n balance calculations on the assumption that, over periods of the o r d e r of seasons, foliage mass does not change. However, if we are dealing with y o u n g plantations, or regenerating forests, c a r b o n allocation patterns d e t e r m i n e the behavior of the system. Models such as PnET warrant increased attention. In this respect, we note that the p r o c e d u r e of using nested models of different levels of complexity should be widely a d o p t e d . L a n d s b e r g et al. (1991, p. 9) p o i n t e d out that "detailed process descriptions . . . may have considerable value as a means of testing simpler, stand-level models." The procedure has b e e n used by McMurtrie et al. (1992a), who used a strategy of developing a series of nested models of widely differing levels of resolution. They said "models high in the h i e r a r c h y (more detailed) aid in the u n d e r s t a n d i n g of biological processes u n d e r l y i n g ecosystem f u n c t i o n i n g , a n d also aid the d e v e l o p m e n t of simpler m o d e l s which are m o r e likely to be useful in ecosystem m a n a g e m e n t . . . . Data a n d m o d e l s at each level of the h i e r a r c h y are e m p l o y e d in p a r a m e t e r i s a t i o n and validation at the n e x t level down."

McMurtrie et al. (1992a) and McMurtrie a n d Wang (1993) also comp a r e d MAESTRO and BIOMASS a n d f o u n d the o u t p u t s over periods of a day to be very similar. Given that the m o r e detailed and c o m p l e x models are well f o u n d e d in terms of the processes simulated and, as far as possible, well tested, this provides a basis for confidence in stand a n d regional scale models. Re-

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gional scale m o d e l s , such as P n E T a n d the E m o d e l , can be tested in relation to d e t a i l e d m o d e l s by the "mosaic" m e t h o d . Forests can be described in spatially o r g a n i z e d data bases (GIS; see C h a p t e r 10) that can h o l d the state variable a n d m o d e l p a r a m e t e r values for each land u n i t d e e m e d to be h o m o g e n o u s . R e m o t e sensing provides a m e a n s of examining large areas a n d describing t h e i r p r o p e r t i e s in terms of variables that can be u s e d in m o d e l s (see Sellers et al., 1992). If a landscape consists of a series of units ( e l e m e n t s ) , e a c h of which can be d e s c r i b e d in e n o u g h detail to provide the i n f o r m a t i o n n e e d e d to apply one or m o r e d e t a i l e d models, t h e n the p r o c e d u r e would be to calculate the o u t p u t of interest (e.g., n e t CO 2 assimilation over a p e r i o d ) for each e l e m e n t for the time interval of interest. A r e a - w e i g h t e d s u m m a t i o n (the o u t p u t f r o m e a c h e l e m e n t is w e i g h t e d by its area as a f r a c t i o n of the total landscape area) over the l a n d s c a p e gives the total o u t p u t . T h e same o u t p u t is calculated for the whole l a n d s c a p e using the l u m p e d - p a r a m e t e r , simple m o d e l . If the l u m p e d p a r a m e t e r s of the simpler m o d e l ( s ) are biophysically realistic averages of the c o r r e s p o n d i n g p a r a m e t e r s in the detailed m o d e l ( s ) , the results o u g h t to match. In the case of PnET, it would be necessary to use several d e t a i l e d m o d e l s to simulate the c a r b o n a n d water b a l a n c e results. This test exercise could be c a r r i e d out with simulated l a n d s c a p e s a n d s t a n d s m i t would n o t be essential to use real, m e a s u r e d values. T h e "gap," or c o m m u n i t y dynamics models, s h o u l d receive c o n t i n u e d attention; the obvious move in this a r e a is to c o m b i n e this type of m o d e l with the physiologically based c a r b o n b a l a n c e models. A start on this has b e e n m a d e by F r i e n d et al. (1993) a n d the a p p r o a c h used in LINKAGES is certainly w o r t h developing. A m o d i f i c a t i o n that m i g h t be suggested w o u l d be to calculate the (net) c a r b o n balance of a stand, using a c a r b o n b a l a n c e m o d e l , a n d allocate the c a r b o n on an a n n u a l or seasonal basis to the c o m p o n e n t p o p u l a t i o n classes. Growth would t h e n be calculated by c o n v e r t i n g c a r b o n to wood volume. In o t h e r words, LINKAGES would b e c o m e m o r e process based. W h e n we t u r n to m a n a g e m e n t questions, the n e e d for simple, easily p a r a m e t e r i z e d m o d e l s b e c o m e s clearer. FOREST-BGC has b e e n deliberately d e s i g n e d m a t least in its later v e r s i o n s ~ t o be driven t h r o u g h L* and, as n o t e d earlier, the values of the state a n d ecophysiological variables can be readily adjusted to those applicable to p a r t i c u l a r forest ecosystems. It a p p e a r s , t h e r e f o r e , to have c o n s i d e r a b l e potential as a mana g e m e n t tool. This also applies to the E model: We are of the view that t h e r e s h o u l d be strong focus on r e s e a r c h into variations in e, particularly into E values d e r i v e d using estimates of utilizable r a d i a t i o n (see McMurtrie et al., 1994). This provides a clearly d e f i n e d f r a m e w o r k for investi-

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gating the effects of environmental factors on dry mass production. The importance of improved knowledge about carbon allocation, to provide the basis for estimating tree growth, as opposed to stand carbon balance has already been discussed. Up to this point, we have focused largely on the use of process-based models to simulate forest growth and carbon allocation. However, many of the models reviewed previously have been used for other purposes, including the examination of the effects of different vegetation cover types on the water and nutrient budgets of forest catchments, determining the effects of different forest harvest intensities and rotation lengths on soil organic matter turnover and nutrient budgets, and exploring the effects of different disturbance regimes on species diversity. Such uses provide another reason why process-based models are likely to replace empirical growth models, a trend likely to be strengthened by the increasing emphasis now being placed on managing forests on a sustainable basis (Lubchenko et al., 1991). We provide two examples of the use of processbased models to examine the effects of different land use practices on the long-term sustainability of forest ecosystems. As discussed in Chapter 7, there is great concern about the effects of different forest harvesting intensities (e.g., stem only versus whole tree) and rotation lengths on long-term productivity of sites (Hook et al., 1982). Empirical soil fertility and growth data for forests that have been subjected to different tree harvesting intensities and frequencies for several rotations simply do not exist, and even if the data existed they would be site and nutrient specific (see Chapter 7, Table 7.7). Aber et al. (1979a, b) developed one of the first process-based models to examine the effects of two different forest rotation lengths on soil organic matter and nitrogen budgets for n o r t h e r n hardwood forests and f o u n d that decreasing the rotation length from 90 to 45 years resulted in a dramatic reduction in soil nitrogen availability. Dewar and McMurtrie (1996a) used the G'DAY model and a graphical analysis a p p r o a c h to determine whether different forest utilization intensities were sustainable based on whether soil organic matter and nitrogen poools were in equilibrium. The use of graphical analysis is particularly appealing and has been shown to be useful in identifying ecophysiological constraints on forest growth in reponse to climate change (McMurtrie and Comins, 1996) and forest aging (Murty et al., 1996). In some regions of the world, forest catchments are m a n a g e d for water yield, i.e., the primary objective is to maximize water yield, not timber. Process-based models that incorporate canopy interception and evaporation subroutines, such as those discussed in Chapter 4, have been shown to simulate water yields accurately (Harding et al., 1992; Vertessy et al.,

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1993). Other important issues related to forest hydrology concern the effects of land use practices (Bruijnzeel, 1990; Fahey and Rowe, 1992) and climate change on water yield. Our understanding of the hydrologic cycle is good enough to allow the use of process-based models to examine the effects of management practices without actually implementing those practices and waiting to study their reponse (Bormann and Likens, 1979).

Recommended Reading Aber, J. D., and Federer, C. A. (1992). A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92, 463-474. Dale, V. H., Doyle, T. W., and Shugart, H. H. (1985). A comparison of tree growth models. Ecol. Model. 29, 145-169. Landsberg, J. J., Kaufinann, M. R., Binkley, D., Isebrands, J., andJarvis, P. (;. (1991). Evaluating progress toward closed foresl models based on fluxes of carbon, water and nutrients. Tree Physiol. 9, 1 - 15. McMurtrie, R. E., Rook, D. A., and Kelliher, F. M. (1990). Modelling the yield of Pinus radiata on a site limited by water and nitrogen. For. Ecol. Manage. 30, 381-413. Pastor, J., and Post, W. M. (1986). Intluence of climate, soil moisture, and succession on forest carbon and nitrogen cycles. BioKeochem. 2, 3-27. Running, S. W., and (;oughlan, J. (;. (1988). A general model of foresl ecosystem processes for regional applicalions. I Hydrologic balance, canopy gas exchmlge and primary production processes. E a Z

0.6 0.4

_O

0.2 0

10

12

L*

Figure 10.1 The relationship between (a) the simple ratio (SR) and leaf area index (L*) and (b) normalized difl'erence vegetation index (NDVI) and L*. Data for evergreen needle-leaved conifer and broad-leaved deciduous hardwood stands in northern Wisconsin are represented by open triangles and circles, respectively. Closed circles represent data for evergreen needle-leaved conifer stands in Oregon (see Spanner el al., 1994) (from Fassnacht, 1996).

sensing a p p r o a c h e s to resolve the structural and spectral variations within vegetated landscapes. However, they also r e c o m m e n d that "before either a p p r o a c h is seriously considered, an aggressive effort should be u n d e r t a k e n to d e m o n s t r a t e a need for such a d d e d complexity." O n e of the great values of r e m o t e sensing, as n o t e d earlier, is the opp o r t u n i t y to repeat m e a s u r e m e n t s or observations. M a n a g e m e n t requires not only predictions but also constant feedback. Were the decisions m a d e correct? What is the new state of the system (forest)? From the point of view of managers, model predictions do not have to be correct to within a few p e r c e n t a g e points; they n e e d to be directionally (increase or decrease; system improving or degrading, etc.) and relatively correct (section a has a h i g h e r growth potential than section b) and sufficiently accurate quantitatively to avoid disastrous mistakes. Obviously, the more

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a c c u r a t e p r e d i c t i o n s a n d estimates are, the better. However, the capacity to m o n i t o r c h a n g e a n d m a k e decisions a b o u t r e s o u r c e m a n a g e m e n t p r o m p t l y is often as i m p o r t a n t as accuracy. R e m o t e sensing, p a r t i c u l a r l y satellite r e m o t e sensing, offers this facility: T h e same m e a s u r e m e n t s can be m a d e , of the same system, time after time. This allows forests to be m o n i t o r e d over seasons or years if the i n f o r m a t i o n is c o n s i d e r e d of sufficient value.

III. The Use of GIS, Remote Sensing, and Models as Management Tools In this section, we w o r k t h r o u g h a h y p o t h e t i c a l e x a m p l e in w h i c h we ass u m e that r e m o t e sensing has b e e n u s e d to d e t e r m i n e s o m e of the attributes of a f o r e s t e d area. T h e i n f o r m a t i o n , t o g e t h e r with o t h e r data pertaining to the area, is h e l d in a GIS that has associated with it a d y n a m i c ecophysiological m o d e l . T h e m o d e l we will use as illustration is the e m o d e l , a l t h o u g h in fact any a p p r o p r i a t e m o d e l c o u l d be u s e d m t h e same p r i n c i p l e s apply. It w o u l d also be possible to specify d i f f e r e n t p a r a m e t e r s a n d variables for e a c h c e l l m a n u m b e r of possible s c h e m e s c o u l d be designed, b u t as a m e a n s of illustrating the points m a d e in the first p a r t of the c h a p t e r we w o r k t h r o u g h the o n e o u t l i n e d h e r e , c o m m e n t i n g on e a c h data layer. Note that different time scales may be u s e d for the variables that c h a n g e with time: W e a t h e r data may be a v e r a g e d over a m o n t h so that the d e r i v e d variables c a l c u l a t e d f r o m t h e m will be c a l c u l a t e d for the same interval, b u t n e t p r i m a r y p r o d u c t i o n (NPP) may be s u m m e d over a year. We i n c l u d e c o m m e n t s on the likely origins a n d availability of the data a n d the work that may be n e e d e d to o b t a i n t h e m . T h e c o m m e n t s are c r o s s - r e f e r e n c e d to earlier c h a p t e r s w h e r e v e r p e r t i n e n t . We m a k e no comm e n t on the c o m p u t e r t e c h n o l o g y that may be involved in linking GIS files c o n t a i n i n g i n f o r m a t i o n of varying types (site factors, forest state, m e t e r o l o g i c a l data, etc.). T h e r e s h o u l d be no difficulty in this a r e a for appropriately trained personnel. Figure 10.2 is central to this section. It depicts a u n i t of land, w h i c h may be of any size, consisting of a n u m b e r of s u b u n i t s e a c h of w h i c h may be r e a s o n a b l y h o m o g e n e o u s in t e r m s of, for e x a m p l e , t o p o g r a p h y a n d forest type, b u t within w h i c h we may e x p e c t to find s o m e v a r i a t i o n in forest a t t r i b u t e s - - i n o t h e r words, a typical forest m a n a g e m e n t unit.We ass u m e that the r e m o t e l y sensed i m a g e s are available for the w h o l e landscape. T h e section shown with cells (pixels) is a p o l y g o n i d e n t i f i e d as h o m o g e n e o u s in terms of its attributes so that the signals f r o m e a c h pixel within the p o l y g o n will n o t be significantly different. If the a r e a is large,

Figure 10.2 S c h e m a t i c o u t l i n e of the use of (;IS, with r e m o t e sensing a n d o t h e r observations a n d m e a s u r e m e n t s , as a m o d e l i n g a n d m o n i t o r i n g tool. T h e m o d e l o u t l i n e d h e r e is the e m o d e l . It is a s s u m e d that a value of e is available (see text). Notes on the data layers: 1, u s e d with 9 to calculate i n c i d e n t r a d i a t i o n on the surface; 2, may be c o n v e n t i o n a l measure of soil n u t r i e n t status a n d / o r may be i n p u t f r o m CENTURY-type m o d e l of N supply rate; 3, r e q u i r e s an estimate of r o o t i n g d e p t h ; 4 - 6 , f r o m g r o u n d observations. Initial esti-

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so e a c h p o l y g o n is m a n y s q u a r e k i l o m e t e r s , t h e c r i t e r i a for s i g n i f i c a n t d i f f e r e n c e s will be d i f f e r e n t f r o m t h o s e t h a t w o u l d be r e l e v a n t to f o r e s t s t a n d s w i t h i n a f o r e s t estate. We a s s u m e , as shown, t h a t t h e r e a r e 22 d a t a layers a s s o c i a t e d with e a c h p o l y g o n . T h i s n u m b e r may vary widely, dep e n d i n g o n t h e i n f o r m a t i o n r e q u i r e d by, a n d available to, t h e l a n d m a n a g e m e n t agency. T h e p r o c e d u r e s o u t l i n e d b e l o w m o r s o m e v e r s i o n o f t h e m m w o u l d b e a p p l i e d to e v e r y p o l y g o n o f t h e l a n d unit. For t h e type o f analysis s u g g e s t e d , the c o m p u t i n g r e q u i r e m e n t s c o u l d b e c o m e p r o h i b i t i v e if we c o n s i d e r e d t h e system pixel by pixel; in any case, t h e inform a t i o n r e q u i r e d w o u l d n o t be available at pixel scales.

A. The Data Layers T h e following c o m m e n t a r y r e l a t e s to t h e d a t a layers d e p i c t e d in Fig. 10.2 1. Site Characteristics a. Layer 1: Topographic Characteristics (Slope and Aspect) T h e s e will b e a v e r a g e values for e a c h p o l y g o n u s e d to c a l c u l a t e i n c i d e n t r a d i a t i o n (see C h a p t e r 3 a n d S e c t i o n III,A,3,a). If a digital e l e v a t i o n m a p is n o t available, t h e y can b e d i g i t i z e d f r o m s t a n d a r d o r d i n a n c e survey m a p s . b. Layer 2: Soil Nutrient Status T h e s e d a t a may be c o n v e n t i o n a l m e a s u r e s o f soil c h e m i c a l p r o p e r t i e s (e.g., m a c r o - a n d m i c r o n u t r i e n t s ) obt a i n e d f r o m d i g i t i z e d soil survey m a p s , o r t h e i n p u t to this layer may c o m e f r o m a s u b m o d e l o f t h e C E N T U R Y type, in w h i c h case t h e b a s e l i n e i n f o r m a t i o n w o u l d c o n s i s t o f soil o r g a n i c m a t t e r , a n d t h e s u b m o d e l w o u l d call o n i n f o r m a t i o n h e l d in o t h e r layers to c a l c u l a t e n u t r i e n t m i n e r a l i z a t i o n rates. c. Layer 3: Soil Water-Holding Capacity This will b e a single n u m b e r app l y i n g to t h e w h o l e r o o t i n g z o n e o r c a l c u l a t e d f o r e a c h soil h o r i z o n f r o m t h e soil physical p r o p e r t i e s a n d w a t e r - h o l d i n g c h a r a c t e r i s t i c s a n d r o o t d e p t h . It may b e e s t i m a t e d f r o m k n o w n r e l a t i o n s h i p s b e t w e e n soil type, as d e s c r i b e d in c o n v e n t i o n a l m a p s , a n d soil physical c h a r a c t e r i s t i c s (see C h a p t e r 4) or t h o s e e s t i m a t e s may b e i m p r o v e d by s a m p l i n g across t h e a r e a s o f interest. (Note: T h e d a t a in b o t h layers 3 a n d 4 will c o m e f r o m

mates of 7 on the basis of 4-6; 8, from NDVI and ground observations; 9-11, from regional meteorological station. Interpolate local values on the basis of topography. Local values of precipitation (12) are important; 13, derived from 1,8, and 9; calculate 14 from 9-11 (see Chapters 3 and 4); calculate 15 from 3, 12, and 14; 16, based on average monthly water balance (see Fig. 10.3); 17, based on average monthly temperature (see Fig. 10.4) and frost occurrence; 18, based on 2; 19, as indicated; 20, partitioning coefficient based on allometric ratios (see Chapter 5, Section IV,A); 21, as indicated; 22, wood volume (vs) = ws/~rw, where o-wis wood density.

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i n d e p e n d e n t survey work; for any particular polygon, the quality of the data will d e p e n d on the quality of the surveys in terms of sampling densities, the techniques used to interpolate values, and the variation across each polygon.) 2. Stand Characteristics

a. Layer 4: Forest Type Forests may be defined as general types, e.g., as one of the categories used in this book, such as temperate broad-leaved deciduous, temperate needle-leaved evergreen, and so on. This may be relevant in the case of a large area, where h o m o g e n e i t y of forest type may be an adequate criterion for polygon homogeneity. If the polygons are stands, or parts of stands, such criteria are likely to be too general and species-level (composition) information may be required. b. Layer 5: Stand Density This is m e a s u r e d by stems per unit area. The conventional m e t h o d of obtaining stand density data is g r o u n d inventory, but it would be possible to obtain an indication of stand density from aerial p h o t o g r a p h s or from direct observation from light aircraft. Calibration against g r o u n d observations would be necessary to ensure the accuracy of such estimates. c. Layer 6: Average Tree Height Average tree height is a conventional forestry variable estimated from g r o u n d survey. d. Layer 7: Standing Biomass [W(t)] An estimate of this should be available from standard forestry yield tables, if they exist for the area, or can be made on the basis of layers 4, 5 and 6, and knowledge of the forest type in question. Every o p p o r t u n i t y should be taken to increase the a m o u n t of these data available, including d e t e r m i n a t i o n s made on harvested trees. It is ironic that the e n d result of commercial forestry is harvested trees but it is not c o m m o n practice to record the exact location of a few harvested trees, t o g e t h e r with their mass, and the c o m p o n e n t parts of that mass, from which allometric equations could be established. The acc u m u l a t i o n of such data over a period of years would provide an invaluable resource that could be used to check and refine m o d e l calculations. e. Layer 8: Leaf Area Index L* is an i m p o r t a n t p a r a m e t e r for modeling and the calculation of transpiration rates and hence water balance. It is derived from NDVI or o t h e r remotely sensed indexes and calibrated for the forest types u n d e r consideration (see Section II,A). 3. Weather Data

a. Layer 9: Solar (q~s) and Net Radiation (go,z) Despite its central importance as the variable that drives all plant growth, solar radiation is seldom routinely m e a s u r e d by meteorological networks and virtually never mea-

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sured by organizations c o n c e r n e d with forest m a n a g e m e n t . The reason for this is, presumably, that there has b e e n little a p p r e c i a t i o n of the value of the data and, lacking operational ecophysiological models, no use for it. Regional measures of solar radiation would, in most cases, suffice. Daily variation is always high, and spatial variation, over short periods, is also high. However, because solar radiation at the outside of the atmosp h e r e is a f u n c t i o n of latitude and season only, for any areas with similar weather patterns a measure of daily solar radiation made within kilometers will provide good m o n t h l y average data. It is also possible to estimate solar radiation using the Total Ozone M a p p i n g S p e c t r o m e t e r (TOMS) using a p r o c e d u r e developed by Eck a n d Dye (1991) and tested by Goward et al. (1994), who o b t a i n e d excellent c o r r e s p o n d e n c e between photosynthetically active radiation (PAR) m e a s u r e d at the surface and m o n t h l y estimates of PAR o b t a i n e d from TOMS. Net radiation can be calculated as described in Ch.3 (see also Moore et al. (1993) for a procedure a p p r o p r i a t e to hilly terrain). b. Layers 1 0 a n d 11: Temperature a n d A i r H u m i d i t y Regional temperature and air h u m i d i t y data can usually be o b t a i n e d from s t a n d a r d meteorological stations. Extrapolation to areas that may have different exposure and t o p o g r a p h y can be done using climatological principles; a g o o d example of the application of such principles is the work of R u n n i n g et al. (1987), who p r o d u c e d a c o m p u t e r m o d e l called MT-CLIM, which is used to simulate climatological data for areas in the m o u n t a i n o u s regions of the western United States. This m o d e l can be r u n on a s t a n d a r d personal c o m p u t e r . The i m p o r t a n c e of such interpolative techniques will vary d e p e n d i n g on topography, distance f r o m the location of the reference measurents, and the averaging time to be used. It is n o t safe to use MT-CLIM outside the range for which it was developed, a l t h o u g h the general principles and ideas will be useful. Simulations should be comp a r e d to actual weather station data w h e r e v e r this is feasible. c. Layer 12: Precipitation Precipitation, as n o t e d in C h a p t e r 3, tends to be extremely variable spatially as well as temporally. E x t r a p o l a t i o n to areas any distance away from reference m e a s u r e m e n t sites will always carry the danger of large errors. Because the water balance of a forest d e p e n d s on the t e m p o r a l distribution of precipitation as well as on the a m o u n t , a dense network of precipitation gauges is always desirable. Fully automated precipitation gauges, as well as c o m p l e t e weather stations, are now available and can be set up so that the data are d o w n l o a d e d r e m o t e l y t h r o u g h a m o d e m . Analysis of the rainfall patterns in any region, in terms of storm size and intensity distributions, will provide useful information and improve the confidence with which a m o u n t s of rain can be extrapolated to areas away from the point of m e a s u r e m e n t .

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10. Applications of Modern Technology and Ecophysiology to Forest Management

4. Derived Data These data r e q u i r e calculations that, in most cases, use data stored in layers 1 - 1 2 . T h e calculations w o u l d be d o n e using subroutines. a. Layer 13: Absorbed Photosynthetically Active R a d i a t i o n (q)p.a) This has b e e n extensively discussed in C h a p t e r s 3 a n d 9. For h o m o g e n e o u s canopies for which L* is reasonably well known, g o o d estimates of q~p can be o b t a i n e d using Beers law [Eq. (3.8)]. The use of r e m o t e sensing to provide estimates of q~p.a over polygons j u d g e d to be u n i f o r m in terms of c a n o p y characteristics will always be an advantage, particularly if the estimates are c h e c k e d against o t h e r m e t h o d s such as calculations based on Beers' law. Clearly, estimates of q~p.a are only as g o o d as the values of solar r a d i a t i o n (q~s) from which they are n o r m a l l y d e r i v e d m t h e e r r o r in q~p.a calculations c a n n o t be less t h a n the errors in q~s, a l t h o u g h it does n o t follow that the errors are additive. b. Layer 14: Water Use Water use calculations will normally be m a d e using the P e n m a n - M o n t e i t h e q u a t i o n (see C h a p t e r 3), assuming there are sufficient m e t e o r o l o g i c a l data, with whatever a p p r o x i m a t i o n s are necessary, e.g., because of the relatively small effects of the b o u n d a r y layer c o n d u c t a n c e term it can be taken as c o n s t a n t in most cases, or a general r e l a t i o n s h i p with average wind speed, based on long-term data for a region, a n d canopy structure will provide useful values. C u r r e n t knowledge of c a n o p y c o n d u c t a n c e s was reviewed in C h a p t e r 3 and the effects of soil water c o n t e n t on stomatal c o n d u c t a n c e in C h a p t e r 4 (see also layer 16). Water use rate may also be estimated from q~,~and Bowen ratio values, w h e r e / 3 is assumed to vary with soil m o i s t u r e status (see C h a p t e r 3, Section III,c). c. Layer 15: Water Balance detail in C h a p t e r 4.

Water balance calculations are dealt with in

d. Layer 16: Water Constraint on Growth (f~,) This is the water balance m o d i f i e r in Eq. (9.6), which c o n t r i b u t e s to the calculation of utilizable radiation. We will split this modifier into two p a r t s m a c o m p o n e n t attributable to a m b i e n t vapor pressure deficit [fw(D)] a n d a c o m p o n e n t attributable to soil water content [fw(0~)]. This a p p r o a c h has b e e n followed by R u n y o n et al. (1994), who calculated Jw on the basis of v a p o r pressure deficits (D) and p r e d a w n water potential (~0pd). They r e d u c e d the value of APAR by half (i.e., utilizable r a d i a t i o n = 0.5 APAR) w h e n D was between 1.5 and 2.5 kPa a n d by 100% w h e n D was greater than 2.5 kPa. They m a d e similar adjustments for ~r Considerably m o r e research is n e e d e d to d e t e r m i n e the best f u n c t i o n a l relationships to use for the calculation of jw; a l t h o u g h the form of the relationship may be similar for different species, it is likely that the coefficients will differ. McMurtrie et al. (1994) used a linear relationship with D and daily soil

III. The Use of GIS, Remote Sensing, and Models as Management Tools

295

water content. We s u p p o r t this a p p r o a c h in relation to D; in fact, inspection of the results reviewed by L e u n i n g (1995) suggests that the e q u a t i o n given by T h o r p e et al. (1980) [Eq. (5.12) in this book] will serve well as a general relationship. Omitting the radiation term in that equation, it becomes gs

= gref( 1 -

1.2).

a'(D-

(10.2)

If D < 1.2 kPa, gs = gref, the values of which can be o b t a i n e d from Kelliher et al. (1993) if an estimate of gs is required, g~/gref is to be set to unity to obtain fw(D). If D > 1.2, the equation applies, with a' = 0.55. The accuracy of this equation applied to, for example, m o n t h l y m e a n values of D, has not been established, but it provides a g o o d starting point. We suggest that, because ~/'pa is a function of soil water potential in the root zone, and in view of Myers' (1988) result showing that growth is inversely p r o p o r t i o n a l to the integral o f ~tpd in terms of time (see Chapter 4), fw(0,) should be derived as the ratio of actual (average) soil moisture content to that value of 0~ (from layer 15) above which it is considered to have no effect (see Fig. 10.3). This "no effect" point may vary with soil type and rooting d e p t h but as a first estimate might be set to 75% of "available water" (see C h a p t e r 4) The value of fw will be the lower of fw (D) and fw (0). e. Layer 17: Temperature Constraint on Growth (Jr) McMurtrie et al. (1994) used a formula for fT that i n c l u d e d frost, spring, and a u t u m n temperatures. Frost on day i reduces utilizable radiation on that day to zero; fspring d e p e n d s on day degree sums and fautumn is related to day length. McMurtrie et al. do not provide details. We suggest that the effects of frost be i n c o r p o r a t e d as suggested, but that other t e m p e r a t u r e s

1.0

fw (@s) 0.5

0

Osm

y

0 - Ocrit =0.75 Osat- Ocrit

Omin

Figure 10.3 R e l a t i o n s h i p o f the type t h a t may be u s e d to c a l c u l a t e t h e w a t e r b a l a n c e m o d i f i e r for u t i l i z a b l e APAR. 0 d e n o t e s v o l u m e t r i c w a t e r c o n t e n t . T h e g r a p h i n d i c a t e s t h a t t h e r a t i o (0 - 0 c r i t ) / ( 0 s a t -- 0 c r i t ) , w h i c h a p p l i e s to t h e w h o l e r o o t z o n e , is a s s u m e d to be n o n l i m i t i n g u n t i l it r e a c h e s a v a l u e o f 0.75.

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10. Applications of Modern Technology and Ecophysiology to Forest Management

(layer 10) be related to an o p t i m u m growing t e m p e r a t u r e for the species in question, e.g., 30~ (see Fig. 10.4). In the case of deciduous forests, in climates where foliage is shed in the a u t u m n (fall) it will be necessary to introduce a declining efficiency factor that reduces from one to zero, starting from the time when foliage begins to change color and ending at leaf fall.

f Layer 18: Nutritional Constraint on Growth (fN) Nutritional modifiers have not been tested in research studies. We suggest that fN be calculated as the ratio of the soil nutritional s t a t u s - - a m o u n t of "available" nitrogen, phosporus, potassium, and major trace elements, as indicated by conventional chemical analysismusing the best soils in the region to define f N = l . The ratios would, presumably, be based on the total nutrients available in the root zone (layer 3). Alternatively, n i t r o g e n availability would be calculated from a m o d e l such as CENTURY, which would provide an index of the rate of nitrogen mineralization. The temptation to scale nutrition on the basis of tree growth should be resisted; this would be analogous to the p r o c e d u r e s used in deriving site indices, which we have criticized as being circular. Because there are clear indications (see C h a p t e r 5) that the p r o p o r t i o n of the carbon available for growth that is allocated to roots is h i g h e r in infertile than in fertile soils, it is likely that the allometric ratios of trees on sites of different fertility will vary. This, in c o m m o n with all o t h e r aspects of nutritional modifiers, is an area that requires considerable research. g. Layer 19: Biomass Increment (A 149 This will be calculated over the selected interval (month, season, etc.) using an a p p r o p r i a t e value of e with APAR (layer 13) and fw, f r , and fN. Based on the discussion in Chapter 9,

1.0

fT

0.5

0

10

1

2O

30

40

T (~

Fig. 10.4 Functional relationship between average air t e m p e r a t u r e and the temperature modifier for utilizable APAR. Various curves could also be used; the value of more realist i c - a n d c o m p l e x - - f o r m s must be weighed against the accuracy of information about temperature and biological responses.

IV.. Concluding Remarks

297

a starting value of 1.5 g MJ -1 for above-ground NPP would be reasonable. Alternatively, A W could be calculated using a m o r e c o m p l e x model, such as BIOMASS or FOREST-BGC, which could be e x p e c t e d to provide better estimates. However, use of one of these models implies m o r e complex calculations, because an e q u a t i o n for photosynthesis is required, and respiration must be calculated. The new value of standing biomass (layer 7 ) i s W(t) + A W.

h. Layer 20: Partitioning Coefficients [rli, Eq. (5.22)] We n o t e d in Chapter 5 that empirical partitioning coefficients can be o b t a i n e d from biomass and allometric data by differentiating Eq. (3.2) to give d w i / d b = ci n i db n-1. It is for this reason that the a c c u m u l a t i o n of biomass and allometric data, from as wide a range of situations and species as possible, is important. At a m o r e mechanistic level, the a p p r o a c h used by R u n n i n g and Gower (1991) could be applied. They placed the emphasis on partitioning to foliage growth, which is based on leaf water status and nitrogen availability. L e a f / r o o t p a r t i t i o n i n g ratios are d e t e r m i n e d by soil water and n i t r o g e n status and allocation to stems is a residual. This is also, essentially, an empirical scheme, and should be tested and refined by field data wherever possible. i. Layer 21: Mass of Tree Components These are o b t a i n e d by applying the partitioning coefficients to standing biomass. j. Layer 22: Stem Volume If stem mass per unit g r o u n d area is Ws, stem density is m ha -a, then individual stem mass is w s / m , and wood density is o"w kg m -3, a n d average stem volume is w s / m o w. Refinements in terms of stem size distributions can be i n t r o d u c e d (see C h a p t e r 3). C o m b i n i n g this i n f o r m a t i o n with standard equations for stem taper leads to estimates of tree height, which can be checked against observations r e c o r d e d in layer 6.

IV. Concluding Remarks The outline previously discussed could apply to any scale, and the procedures suggested, and data said to be r e q u i r e d , will vary with particular applications. Such a scheme offers m a n a g e r s c o n c e r n e d with p r o d u c t i o n forestry the o p p o r t u n i t y to update their estimates of standing biomass at any time and use those estimates as the basis for productivity and w o o d flow calculations. The a p p r o a c h o u t l i n e d here, a n d many of the tools described, could be used to m a n a g e a watershed for water yield or for wildlife p r o d u c t i o n or to assess the impact of m a n a g e m e n t practices such as clear-cutting or b u r n i n g (deliberate or inadvertent) on particular areas. It is also possible to link a succession m o d e l to a simple biomass prod u c t i o n m o d e l so that the impact of forest m a n a g e m e n t in each land

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10. Applications of Modern Technology and Ecophysiology to Forest Management

u n i t can be evaluated for b o t h forest p r o d u c t i v i t y a n d biodiversity. W h e n a s u b u n i t is logged, or lost by fire, it is "re-set" in terms of its stand characteristics. R u n over m a n y cycles, the p r o c e d u r e s will allow assessment of the sustainability of the system, which may be r e f l e c t e d in terms of c h a n g e s in soil o r g a n i c m a t t e r (Fig. 10.2, layer 2), or possibly changes in forest type a n d stand characteristics. T h e s e may be suspected, as a result of p a r t i c u l a r t r e a t m e n t s or events, or observed: Evaluation of the conseq u e n c e s w o u l d be speculative, b u t s o m e such p r o c e d u r e would provide the only m e a n s of assessing the c o n s e q u e n c e s of the scenario. T h e p o i n t has b e e n m a d e several times that t h e r e s h o u l d be continuous o b s e r v a t i o n a n d m e a s u r e m e n t to calibrate a n d c h e c k m o d e l predictions. W h e r e t h e r e are m a j o r discrepancies, analysis to identify the reason(s) will lead to i m p r o v e m e n t s in f u t u r e calculations. Periodic satellite m e a s u r e m e n t s provide the o p p o r t u n i t y to u p d a t e a n d u p g r a d e information a b o u t forest structural p r o p e r t i e s , L*, a n d ~p. G r o u n d observations m a d e at any time s h o u l d be e n t e r e d in the GIS to provide a d d i t i o n a l inf o r m a t i o n that can be used in i n t e r p r e t a t i o n a n d e x p l a n a t i o n or analysis. T h e p h i l o s o p h y u n d e r l y i n g the a p p r o a c h o u t l i n e d is that m a n a g e m e n t a n d scientific r e s e a r c h s h o u l d n o t be separate activities, with scientists providing m a n a g e m e n t with i n f o r m a t i o n c o n s i d e r e d relevant to the problems faced by m a n a g e r s a n d m a n a g e r s often i m p a t i e n t of the impracticality of the i n f o r m a t i o n provided. T h e system, as we see it, s h o u l d work as d e p i c t e d in Fig. 10.5: P r o b l e m s

MANAGEMENT

RESEARCH Problem

~l

Decision/Action

HypothesisI

_ ~ ~ Pj~ Im Monitoring ~ _ / ~1~ ~k x 1~ / ~, \~ Improvedpractical / ~

so,ution /

) J

"~ ~

Experiment/model

Recognition

/

Fig. 10.5 Diagram depicting a good interactive relationship between research and management. A "problem" may be simply a situation ill which there are alternative possible decisions and actions based on currently available knowledge. Monitoring the consequences of the action(s) taken will lead to improved knowledge. Research treats alternatives as hypotheses and tests them by experiment or using models. Research may reveal previously unrecognized problems.

V. Peroration

299

with a scientific or technological basis may be identified by m a n a g e r s or scientists; these may be major, multifaceted problems, such as p r e d i c t i o n of growth, yield, and sustainability, or single-issue p r o b l e m s such as how to control a pest. (Note: T h e r e is no g u a r a n t e e that single-issue ecological p r o b l e m s will be simple; for example, research aimed at control of a pest will usually involve a great deal of investigation relating to the life cycle of the pest and the circumstances in which it is likely to b e c o m e serious.) Managers have to make decisions and take action on the basis of the best currently available knowledge; they will be m o r e successful if they have a s o u n d u n d e r s t a n d i n g of the processes at work in the system, h e n c e our a r g u m e n t that managers n e e d to u n d e r s t a n d ecophysiological processes. Scientists u n d e r t a k e research on the p r o b l e m , in the course of which they increase the knowledge available a b o u t it and are able to offer i m p r o v e d advice a b o u t solutions. It is also likely that the research will reveal areas in which knowledge is inadequate, indicating the n e e d for f u r t h e r research at basic levels. The i m p r o v e d advice leads to a b e t t e r basis for f u r t h e r action. M a n a g e m e n t m o n i t o r i n g should lead to assessment of the consequences of the actions taken or the accuracy of the predictions made; this i n f o r m a t i o n will be of value to the scientists, the managers, and p r e s u m a b l y to society. It would be easy to illustrate the previous a r g u m e n t with examples from almost any part of this b o o k b u t there seems no n e e d to labor the point. If we have to spell it out, we have p r o b a b l y failed in o u r objective of bridging, at least to some extent, the gap between those who m a n a g e forests, and make decisions a b o u t their m a n a g e m e n t , and those who study forests, and make p r o n o u n c e m e n t s a b o u t the way they function.

V, Peroration To conclude this book, we note that there is widespread failure to practice sustainable forest m a n a g e m e n t . Long-term societal priorities, and the n e e d to maintain ecological balance a n d integrity, are i g n o r e d or o v e r r i d d e n by individuals, groups, or even nations (Mooney and Sala, 1993). The reasons for this range from ecological i g n o r a n c e to so-called rational economics and the a n t h r o p o c e n t r i c view that the short-term needs of c u r r e n t h u m a n societies take p r e c e d e n c e over ecological balance and sustainability. The short-sightedness of this attitude is obvious to ecologists, and its dangers are b e c o m i n g a p p a r e n t even to some in politics and various fields of h u m a n activity u n r e l a t e d to ecology. It was n o t one of our objectives to address b r o a d societal p r o b l e m s and attitudes, b u t we have tried to d e m o n s t r a t e that basic ecophysiological science and u n d e r s t a n d i n g are essential as u n d e r p i n n i n g for the profession of forestry and the m a n a g e m e n t of forests for long-term sustainability. We are

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10. Applications of Modern 7?chnology and Ecophysiology to Forest Management

in g o o d c o m p a n y in p u t t i n g f o r w a r d this a r g u m e n t ( M o o n e y a n d Sala, 1993; H o r n b e c k a n d Swank, 1992; Pitelka a n d Pitelka, 1993). Ecologists are o f t e n h e s i t a n t in p r e s e n t i n g t h e i r findings to l a n d managers b e c a u s e they are d e a l i n g with highly variable systems that are constantly c h a n g i n g a n d evolving. I n f o r m a t i o n t h e r e f o r e t e n d s to be u n c e r tain a n d the statistical variability o f data is usually high. However, as E h r l i c h a n d Daily (1993) have p o i n t e d out, science can n e v e r p r o v i d e certainty, b u t it can h e l p to m a k e a safe bet. T h e m e t h o d s o f science, rigo r o u s l y a p p l i e d , a n d k n o w l e d g e b a s e d o n science will tilt the b a l a n c e o f p r o b a b l e o u t c o m e s f r o m decisions, actions, or p r e d i c t i o n s in the direction i n d i c a t e d by science. Scientific m e t h o d s also p r o v i d e the m e a n s o f e v a l u a t i n g objectively the c o n s e q u e n c e s of decisions a n d actions; subjective e x p e r i e n c e a n d assertions b a s e d on e m o t i o n a l values or "politically c o r r e c t " attitudes are n o s u b s t i t u t e for objective criteria a n d r i g o r o u s testing. Moral a n d e m o t i o n a l values can be a p p l i e d w h e n the f a c t s i a s far as science can establish t h e m i a r e known. T h e a p p r o a c h that we advocate in this c h a p t e r , a n d the e x a m p l e we have d e v e l o p e d ( c o u p l i n g a p r o c e s s - b a s e d m o d e l to l a n d s c a p e attributes using GIS a n d r e m o t e sensing) m a k e s it possible to use scientific m e t h o d s to look b a c k in time a n d see the results o f m a n a g e m e n t a n d e n v i r o n m e n t a l c o n d i t i o n s u p to the p r e s e n t a n d p r o j e c t f o r w a r d in time to p r e d i c t the effects o f forest mana g e m e n t p r a c t i c e s on l o n g - t e r m sustainability. T h e use o f linked ecosystem-level p r o c e s s - b a s e d m o d e l s o p e n s the possibility of e x t e n d i n g the analyses of forest blocks to a d j a c e n t watersheds, aquatic ecosystems, a n d the a t m o s p h e r e .

Recommended Reading Aronoff, S. A. (1989). "(;eographic Informalir Systems: A Managemenl Perspective," pp. 294. WDI~ Put)licaliolls, ()tlawa, (]allada. Burrough, P.A. (1989). "Principles of (,eographical Illformalion Systems for Land Resource Assessment." ()xford Ulliv. Press, New Yr Crist, E. P., and Katith, R.J. (1986). The lasselled ca t) de-mystitied, l'hotog,wammetricEngi. Remote Sensing 52, 81-86. Jensen,J. R. (1986). "Illtrodticlory Digital Image Processing," pp. 379. Prentice Hall, Englewood Cliffs, NJ. Maguire, D.J., Gor M. F., and Rhind, D. W. (Eds.) (1991). "(;eographical Information Systems. Vr 1: Principles, pp. 647; "Vol.2: Applications," pp. 447. I,ongman, Harlow, Essex, UK. Tucker, C.J., and Sellers, p.J. (1986). Satellite remote sensing of primary production. Int.J. Remote Sensing 7, 1395-1416.

Symbols and Definitions

T h e symbols u s e d in this b o o k are as consistent as possible with those that have b e c o m e e s t a b l i s h e d in the literature. Because of the r a n g e of m a t e r i a l c o v e r e d in the b o o k it has b e e n difficult to avoid some duplication, b u t for the m o s t p a r t this has b e e n o v e r c o m e by the use of a p p r o priate subscripts. We avoided, as m u c h as possible, the i n e l e g a n t use of a c r o n y m s in place of s y m b o l s w a p r a c t i c e that is i n c r e a s i n g with the wides p r e a d use of c o m p u t e r models. O n l y the m o r e i m p o r t a n t symbols are d e f i n e d here. Symbols u s e d only once, or " m i n o r " constants or coefficients, are n o t listed. All symbols are d e f i n e d in the text w h e r e they first a p p e a r . S t a n d a r d I n t e r n a t i o n a l (SI) units are u s e d t h r o u g h o u t , a l t h o u g h in the text a n d in practical usage, m a g n i t u d e s may be c h a n g e d for conv e n i e n c e or for consistency with p u b l i s h e d data.

A, Ac

assimilation rate, c a n o p y assimilation rate (mol m - 2 s - 1 ) a b s o r b e d p h o t o s y n t h e t i c a l l y active r a d i a t i o n (MJ m -2 or mo1-1 m -2) foliage density (m 2 foliage surface a r e a m -3 c a n o p y v o l u m e ) CO2 c o n c e n t r a t i o n , or partial pressure of CO 2, at a r e f e r e n c e level in the air, a n d i n t e r c e l l u l a r (mo1-1 or Pa; also c o m m o n l y e x p r e s s e d as v o l u m e f r a c t i o n in air, i.e., parts p e r million) specific h e a t of air at c o n s t a n t pressure ( = 1 . 0 1 • 103J kg -1 K -a) v a p o r p r e s s u r e deficit (kPa) zero p l a n e d i s p l a c e m e n t stem d i a m e t e r (m) at b r e a s t h e i g h t ( 1.4 m) mass flux of water v a p o r (kg m -2 s -1 b o u n d a r y layer c o n d u c t a n c e , aerod y n a m i c c o n d u c t a n c e for m o m e n tum, stomatal c o n d u c t a n c e , c a n o p y c o n d u c t a n c e (m s -1) latent h e a t flux (W m -z)

= CO 2

APAR = af ca, ci =

Cp = D = d (in the wind profile e q u a t i o n ) dB= E = ga, gaM, gs, gc =

H =

301

302

Symbolsand Definitions

hc = m e a n c a n o p y t o p h e i g h t (m) I = water i n t e r c e p t e d by c a n o p i e s ( m m ) Jmax = p o t e n t i a l e l e c t r o n t r a n s p o r t rate (/~mol m -2 s - I ) Js = v o l u m e flux o f w a t e r across p o t e n t i a l g r a d i e n t (soil water) k (in t h e w i n d p r o f i l e e q u a t i o n ) = van K a r m a n ' s c o n s t a n t = 0.4 Kc, Ko = M i c h a e l i s c o n s t a n t s for CO2 a n d O2 K~ = soil h y d r a u l i c c o n d u c t i v i t y L* = leaf a r e a i n d e x ( d i m e n s i o n l e s s ) NDVI = normalized difference vegetation index NEP = net ecosystem production NPP = net primary production P = precipitation (mm) Pi, Ps = f r a c t i o n o f rain r e a c h i n g the g r o u n d b e n e a t h a c a n o p y as t h r o u g h f a l l or stemflow, r e s p e c t i v e l y p = stem population per unit land area q, q,~at = specific h u m i d i t y , s a t u r a t e d specific h u m i d i t y (kg m -'~) qR, qD = (in t h e h y d r o l o g i c e q u a t i o n ) = runoff, d r a i n a g e Ra = a u t o t r o p h i c r e s p i r a t i o n R~.,n, Ra.g, R~.u = m a i n t e n a n c e , g r o w t h , a n d ion uptake r e s p i r a t i o n r~, re, ri - b o u n d a r y layer r e s i s t a n c e ( = 1 / g a ) , c a n o p y r e s i s t a n c e ( = 1 / g ~ ) , climatological r e s i s t a n c e (m s - 1) R~, Rr, Rx, Rf = h y d r a u l i c r e s i s t a n c e s to flow t h r o u g h soil to r o o t , r o o t to xylem, t h r o u g h t h e xylem, f r o m x y l e m to evapor a t i n g surfaces in leaves PAR = p h o t o s y n t h e t i c a l l y active r a d i a t i o n (MJ m - 2 or mo1-1 m -2 ) S = water s t o r e d o n c a n o p y a n d lost by evaporation (mm) T = t e m p e r a t u r e (~ u = w i n d s p e e d (m s - 1) u* = f r i c t i o n velocity ( m s -1) V~ - c a r b o x y l a t i o n rate W, Wmax = total a n d m a x i m u m t r e e mass

Symbols and Definitions

303

wf, Wr, ws = foliage, r o o t , a n d s t e m mass (kg o r t o n s h a - 1) z = h e i g h t (m) o r d e p t h in t h e soil z0 - z e r o p l a n e d i s p l a c e m e n t (m) a = albedo, or reflection coefficient fl = B o w e n ratio ( = H / A E ) y = psychrometric constant ( 0.066 kPa ~ - 1) e = radiation utilization coefficient (g d r y mass p r o d u c e d MJ -1 p h o t o s y n t h e t i c a l l y active r a d i a t i o n ) es = d i m e n s i o n l e s s rate o f c h a n g e o f qsat with T ( = 2 . 2 at 20~ a = latent heat of vaporization of water (J kg -1) ~s, q~n, q~p, q~p'a, q~Lq', q~$ -" short-wave r a d i a n t e n e r g y (W m - 2 ) , n e t r a d i a t i o n (W m - 2 ) , p h o t o s y n t h e t i c a l l y active r a d i a t i o n (W m -2 o r m o l m -2 s - l ) , p h o t o s y n t h e t i cally active r a d i a t i o n a b s o r b e d by a c a n o p y , long-wave u p w a r d flux (W m - 2 ) , long-wave d o w n w a r d flux (W m -2) Os, Of, Opd = soil w a t e r p o t e n t i a l , f o l i a g e w a t e r potential, p r e d a w n (foliage) w a t e r p o t e n t i a l (MPa) p = air d e n s i t y ( ~ 1.3 kg m - s at 20~ Ps = soil b u l k d e n s i t y (kg m -s) o-f = specific l e a f a r e a ( m Z k g -1) r = s h e a r stress o r m o m e n t u m flux (N m -2) 0 = v o l u m e t r i c soil m o i s t u r e c o n t e n t

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