95,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Advancing River Restoration and Management
- Sprache: Englisch
Provides critiques of current practices for environmental flow assessment and shows how they can be improved, using case studies. In Environmental Flow Assessment: Methods and Applications, four leading experts critique methods used to manage flows in regulated streams and rivers to balance environmental (instream) and out-of-stream uses of water. Intended for managers as well as practitioners, the book dissects the shortcomings of commonly used approaches, and offers practical advice for selecting and implementing better ones. The authors argue that methods for environmental flow assessment (EFA) can be defensible as well as practicable only if they squarely address uncertainty, and provide guidance for doing so. Introductory chapters describe the scientific and social reasons that EFA is hard, and provide a brief history. Because management of regulated streams starts with understanding freshwater ecosystems, Environmental Flow Assessment: Methods and Applications includes chapters on flow and organisms in streams. The following chapters assess standard and emerging methods, how they should be tested, and how they should (or should not) be applied. The book concludes with practical recommendations for implementing environmental flow assessment. * Describes historical and recent trends in environmental flow assessment * Directly addresses practical difficulties with applying a scientifically informed approach in contentious circumstances * Serves as an effective introduction to the relevant literature, with many references to articles in related scientific fields * Pays close attention to statistical issues such as sampling, estimation of statistical uncertainty, and model selection * Includes recommendations for methods and approaches * Examines how methods have been tested in the past and shows how they should be tested today and in the future Environmental Flow Assessment: Methods and Applications is an excellent book for biologists and specialists in allied fields such as engineering, ecology, fluvial geomorphology, environmental planning, landscape architecture, along with river managers and decision makers.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 600
Veröffentlichungsjahr: 2019
Ähnliche
Table of Contents
Cover
About the authors
Series foreword
Preface
Acknowledgements
CHAPTER 1: An introduction to environmental flows
Summary
1.1 What are environmental flows?
1.2 Why EFA is so hard; scientific issues
1.3 Why EFA is so hard: social issues
1.4 Why EFA is so hard: problems with the literature
1.5 Why EFA is so hard: limitations of models and objective methods
1.6 Conclusions
CHAPTER 2: A brief history of environmental flow assessments
Summary
2.1 Introduction
2.2 The legal basis for environmental flows
2.3 The scope of environmental flow assessments
2.4 Methods for quantifying environmental flows
2.5 Conclusions
CHAPTER 3: A primer on flow in rivers and streams
Summary
3.1 Introduction
3.2 Precipitation and runoff
3.3 Flow regimes
3.4 Spatial patterns and variability within streams
3.5 Managing environmental flows
3.6 Conclusions
CHAPTER 4: Life in and around streams
Summary
4.1 Introduction
4.2 Structure of stream ecosystems
4.3 Adaptations of stream organisms
4.4 Adapting to extreme flows
4.5 Synthesis
4.6 Environmental flows and fish assemblages
4.7 Conclusions
CHAPTER 5: Tools for environmental flow assessment
Summary
5.1 Introduction
5.2 Descriptive tools
5.3 Literature reviews
5.4 Experiments
5.5 Long‐term monitoring
5.6 Professional opinion
5.7 Causal criteria
5.8 Statistics
5.9 Modeling
5.10 Hydraulic habitat indices
5.11 Hydrological indices
5.12 Conclusions
CHAPTER 6: Environmental flow methods
Summary
6.1 Introduction
6.2 Hydrological methods
6.3 Hydraulic rating methods
6.4 Habitat simulation methods
6.5 Frameworks for EFA
6.6 Conclusions
CHAPTER 7: Good modeling practice for EFA
Summary
7.1 Introduction
7.2 Modeling practice
7.3 Behavioral issues in modeling for EFA
7.4 Data‐dependent activities in developing estimation models
7.5 Sampling
7.6 On testing models
7.7 Experimental tests
7.8 Testing models with knowledge
7.9 Testing hydraulic models
7.10 Testing EFMs based on professional judgement
7.11 Testing species distribution models
7.12 Conclusions
CHAPTER 8: Dams and channel morphology
Summary
8.1 Introduction
8.2 Diagnosing the problem and setting objectives
8.3 Managing sediment load
8.4 Specifying morphogenic flows
8.5 Flows for managing vegetation in channels
8.6 Constraints
8.7 Conclusions
CHAPTER 9: Improving the use of existing evidence and expert opinion in environmental flow assessments
Summary
9.1 Introduction
9.2 Overview of proposed method
9.3 Basic principles and background to steps
9.4 Case study: golden perch (
Macquaria ambigua
) in the regulated Goulburn River, southeastern Australia
9.5 Discussion
9.6 Summary
CHAPTER 10: Summary conclusions and recommendations
10.1 Conclusions and recommendations
10.2 A checklist for EFA
Literature cited
Index
End User License Agreement
List of Tables
Chapter 5
Table 5.1 Comparison of production thinking and population thinking in salmon ma...
Table 5.2 “Causal criteria,” or factors to consider in assessing whether a propo...
Table 5.3 AICc 95 % candidate model set and corresponding AICc score and AICc we...
Chapter 7
Table 7.1 Results of logistic regression – McFadden's
ρ
2
is a modification o...
Chapter 8
Table 8.1 Potential objectives of morphogenic flows.
Chapter 9
Table 9.1 Results of the Eco Evidence rapid evidence assessment for eight hypoth...
Table 9.2 First eight rows of the conditional probability table for golden perch...
Table 9.3 Taking advantage of existing models in environmental flow assessments.
List of Illustrations
Chapter 1
Figure 1.1 Thirty‐year running average discharge in the Arroyo Seco River in ce...
Chapter 3
Figure 3.1 Stormwater runoff processes are dominated by subsurface flow in most...
Figure 3.2 Contrasting hyetogaphs (rainfall) and hydrographs (streamflow) for: ...
Figure 3.3 Hydrographs showing gradients from fully Mediterranean (rainfall‐run...
Figure 3.4 Annual peak flows for Mercer Creek, whose 31‐km
2
basin experienced e...
Figure 3.5 Hydrographs for Mercer (urbanized) and Newaukum creeks, expressed as...
Figure 3.6 Downstream (us) and cross‐stream (un) velocity fields at sections sp...
Figure 3.7 Percentage differences between the estimates of water velocity that ...
Figure 3.8 Three sets of three sets of “S” curves compared based on sinuosity a...
Figure 3.9 Alluvial channel form and its principal governing factors. Shading i...
Figure 3.10 Three dimensions of connectivity in river systems: longitudinal (up...
Figure 3.11 Photograph showing comparative growth rates of juvenile Chinook sal...
Figure 3.12 Diurnal temperature fluctuations in surface waters downwelling into...
Chapter 4
Figure 4.1 Typical fishes of North American cold‐water streams. Top left, sculp...
Figure 4.2 Filters (dotted boxed) that determine local fish assemblages (solid ...
Chapter 5
Figure 5.1 The distribution of juvenile coho salmon in a flume. There is a pool...
Figure 5.2 Bar graph of depth at which adult brown trout were observed (left); ...
Figure 5.3 Local density of (a) baetid and (b) heptageniid mayflies across the ...
Figure 5.4 The conceptual model for the Shenton et al. (2011) Bayesian Network....
Figure 5.5 A simple Bayesian Network. The probabilities for each state of encro...
Figure 5.6 A simple Bayesian network showing factors that determine brown trout...
Chapter 6
Figure 6.1 Conceptual plot of dimensionless stream width plotted over dimension...
Figure 6.2 An example of membership functions for low, medium, and high habitat...
Figure 6.3 A conceptual model of the energetic costs and benefits for a drift‐f...
Figure 6.4 Success rate of prey capture versus velocity for rosyside dace at su...
Figure 6.5 Components and model linkages of IFIM.
Figure 6.6 Schematic of sources of error in modeling.
Figure 6.7 Linkage of hydrological data to river cross‐sections: (a) discharge ...
Figure 6.8 Curves showing the decrease in thriving and characteristic assemblag...
Figure 6.9 Conceptual model of the adaptive management cycle. Note that “policy...
Figure 6.10 Work and information flow for the framework presented in this paper...
Figure 6.11 Predicted cover by terrestrial vegetation for four treatments at si...
Chapter 7
Figure 7.1 Comparison of estimates of the seasonal catch in the Dai fishery fro...
Figure 7.2 A plot of simulated velocity predictions and simulated measurements,...
Figure 7.3 Classification tree models for predicting presence versus absence fo...
Figure 7.4 Curves of WUA over discharge for Reach 3, Bridge River, from a study...
Figure 7.5 Scatter plot of predicted versus measured velocity in the Trinity Ri...
Figure 7.6 The relation between fish density and (a) the Habitat Suitability In...
Figure 7.7 The suitability curve for depth used in Guay et al. (2000).
Figure 7.8 HPI as a function of
λ
(lambda).
Figure 7.9 The contribution of depth to
λ
.
Figure 7.10 HPI as a function of depth for velocities ranging from 0 to 1.2 m s
Figure 7.11 Contours of HPI with substrate size (D
50
) of 4 cm (left), and 16 cm...
Figure 7.12 The relationship between fish densities and values of (a) HSI and (...
Figure 7.13 Observed fish density under clear (a, c, e) and cloudy (b, d, f) co...
Chapter 8
Figure 8.1 Plot of flushing projects from diverse environments showing that suc...
Figure 8.2 Power generation and sediment trapping from dam building in the Sre ...
Figure 8.3 Ratio of shear stress to critical shear stress (
t
ch/
tc
ch) in channel...
Figure 8.4 Hydrographs for morphogenic flow releases from Flix Dam on the Ebro ...
Figure 8.5 Coordination of gravel augmentation at the Lowden Ranch rehabilitati...
Figure 8.6 Hydrographs for the River Spöl for water years 1961–1962, reflecting...
Figure 8.7 Schematic diagram illustrating the process of vegetation encroachmen...
Chapter 9
Figure 9.1 Workflow for the proposed method for modeling ecological responses t...
Figure 9.2 Evidence‐based conceptual model of the processes driving golden perc...
Figure 9.3 Bayesian Belief Network structure for golden perch model.
Guide
Cover
Table of Contents
Begin Reading
Pages
iii
iv
ix
xi
xiii
xiv
xv
1
2
3
4
5
6
7
8
9
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
215
216
217
218
219
220
221
Environmental Flow Assessment
Methods and Applications
John G. Williams
Consultant, Petrolia, California
Peter B. Moyle
University of California, Davis
J. Angus Webb
University of Melbourne
G. Mathias Kondolf
University of California, Berkeley, & Université de Lyon
This edition first published 2019© 2019 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of John G. Williams, Peter B. Moyle, J. Angus Webb and G. Mathias Kondolf to be identified as the authors of this work has been asserted in accordance with law.
Registered Office(s)John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office9600 Garsington Road, Oxford, OX4 2DQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data
Names: Williams, John G., 1941– author. | Moyle, Peter B., author. | Webb, J. Angus, 1971– author. | Kondolf, G. Mathias, author.Title: Environmental flow assessment : methods and applications/ John G. Williams, Peter B. Moyle, J. Angus Webb, G. Mathias Kondolf.Description: Hoboken, NJ : John Wiley & Sons, Inc., 2019. | Includes bibliographical references and index. |Identifiers: LCCN 2018049251 (print) | LCCN 2018056504(ebook) | ISBN 9781119217398(Adobe PDF) | ISBN 9781119217381 (ePub) | ISBN 9781119217367 (hardcover)Subjects: LCSH: Stream measurements. | Stream ecology. |Environmental impact analysis.Classification: LCC GB1201.7 (ebook) | LCC GB1201.7 .W552019 (print) | DDC 551.48/3–dc23LC record available at https://lccn.loc.gov/2018049251
Cover Design: WileyCover Images: © Anton Petrus / Getty Images
About the authors
John G. Williams is a retired consultant with a PhD in physical geography who has published on botany, climatology, hydrology and salmon biology as well as on environmental flow assessment. He has served as an elected director of a water management district in California, and as special master for important litigation regarding environmental flows. He can be reached at [email protected].
Peter B. Moyle is Distinguished Professor Emeritus at the University of California, Davis. He has been working on flows and fish issues since the 1970s. He is particularly proud of his role in designing a flow regime to benefit fish, plants and birds for Putah Creek, near the UCD campus. (See https://watershed.ucdavis.edu/cws‐wfcb‐fish‐conservation‐group.)
J. Angus Webb is an Associate Professor at the University of Melbourne, Australia. He leads the Ecohydraulics laboratory group in the Water, Environment and Agriculture Program within the Melbourne School of Engineering, and is heavily involved in the monitoring, evaluation and adaptive management of environmental flows being delivered under Australia’s Murray–Darling Basin Plan. He was awarded the 2012 Early Career Achievement Award from the Australian Society for Limnology, and the 2013 Award for Building Knowledge in Waterway Management from the Australian River Basin Management Society. (See www.ie.unimelb.edu.au/research/water/)
G. Mathias Kondolf is a fluvial geomorphologist and environmental planner at the University of California Berkeley and a fellow at the Collegium, Institute for Advanced Study at the University of Lyon, France. He works on sustainable river management and restoration, including managing sediment in regulated rivers. (See https://riverlab.berkeley.edu.)
Series foreword
Advancing river restoration and management
The field of river restoration and management has evolved enormously in recent decades, driven largely by increased recognition of ecological values, river functions and ecosystem services. Many conventional river‐management techniques, emphasizing strong structural controls, have proven difficult to maintain over time, resulting in sometimes spectacular failures, and often a degraded river environment. More sustainable results are likely from a holistic framework, which requires viewing the “problem” at a larger catchment scale and involves the application of tools from diverse fields. Success often hinges on understanding the sometimes complex interactions among physical, ecological and social processes.
Thus, effective river restoration and management require nurturing the interdisciplinary conversation, testing and refining of our scientific theories, reducing uncertainties, designing future scenarios for evaluating the best options, and better understanding the divide between nature and culture that conditions human actions. It also implies that scientists should communicate better with managers and practitioners, so that new insights from research can guide management, and so that results from implemented projects can, in turn, inform research directions.
This series provides a forum for “integrative sciences” to improve rivers. It highlights innovative approaches, from the underlying science, concepts, methodologies, new technologies and new practices, to help managers and scientists alike improve our understanding of river processes, and to inform our efforts to steward and restore our fluvial resources better for a more harmonious coexistence of humans with their fluvial environment.
G. Mathias Kondolf,University of California, Berkeley
Hervé PiégayUniversity of Lyon, CNRS
Preface
In a 2010 review, Arthington et al. remarked that: “There is now wide recognition that a dynamic, variable water regime is required to maintain the native biodiversity and ecological processes characteristic of every river and wetland ecosystem. Yet it remains a challenge to translate this ‘natural flow regime’ paradigm into quantitative environmental flow prescriptions for individual reaches from source to sea” (citations omitted). This book is about methods and approaches for meeting this challenge.
Environmental flow assessment is largely about flow, as the name suggests, but not just about flow. Other biotic and abiotic factors influence flowing water ecosystems, and environmental flow assessment (EFA) needs to take them into account. And, EFA is a social process, probably more than a scientific process. We treat EFA mostly as a kind of applied ecology, but we do not ignore the complications arising from human nature.
People working on EFA have diverse backgrounds, so we expect the same of readers of this book. Some will see themselves primarily as managers, rather than as scientists or engineers, and many will be familiar mainly with one region or even one stream system. Therefore, we have included material that will seem elementary to some readers, mostly to emphasize the variety of stream ecosystems that are the subject of assessments. Similarly, although we expect that many readers will already know a lot about EFA, we have tried to avoid assuming that they do. And, we do not try to be comprehensive. For example, we say little about riparian systems, and almost nothing about estuaries, although dealing with them is an important part of the overall problem. Rather, we try to elaborate an approach or point of view that can be applied generally.
We take a more critical attitude about methods for EFA than other books on the same subject, such as Locke et al. (2008) or Arthington (2012). We make recommendations, but we explain the shortcomings of the methods we recommend, as well as of those we don't. Part of our motivation in writing this book is concern about careless use of models in EFA, and we deal with that at length. Reluctance to criticize others' work is generally an admirable trait, but not in science, where it is part of the job, provided it is not mean‐spirited.
It is an unhappy truth that many scientific papers have been published that should not have been, and many published research findings are false (Ioannidis 2005). There are various reasons for this, and a major one is flawed statistical analyses, especially overreliance on and misuse of statistical significance tests. Ioannidis wrote about the biomedical literature, but the same applies in environmental sciences. For example, Bolker et al. (2009) found problems with 311 of 537 applications of generalized linear mixed models in articles on ecology and evolution, and our impression is that papers on EFAs tend to exhibit a lower level of statistical understanding, and to receive poorer reviewing on statistical matters, than papers in related fields. We discuss and illustrate statistical problems with methods for EFA and related studies, but at a conceptual level, without getting into the technical details.
Geographically, the western USA, and especially California, is overrepresented in the book, as are salmonids. This seems parochial, and it is, but the western USA is highly diverse geographically, salmonids have diverse life‐histories, and most of the literature on EFA deals with salmonids. Since three of us have lived and worked in California for decades, we are more familiar with EFA as it is actually done in California than elsewhere, so our California bias results largely from following the advice to “write what you know.” However, we are broadly familiar with EFA elsewhere, and recommend an approach developed in Australia.
On language, we follow more recent (and more appropriate) usage and refer to “environmental flows” instead of “instream flows,” but we do not intend any change in meaning with this terminology. We have tried to write in plain language, and to avoid overly technical or overblown academic writing such as the following, which we did not make up: “Temporary streams naturally experience flow intermittence and hydrologic discontinuity that act to shape fish community structure,” or worse: “Thus, theoretically, although habitat suitability curves underpinning area‐weighted suitability indices apparently invite the intervention of modeling approaches, the more complex and less‐definite relations between physical habitat and ecological response may reduce this potential, with correspondence at best, treated probabilistically.” Why would anyone who has something to say use such language? We expect that some readers will disagree with some of what we write, but we have tried to write it clearly.
With one exception, separate authorship is not listed for the various chapters, although readers with any sense of language will notice immediately that the writing styles varies. Each chapter has a main author, but each of us has read, commented on, and approved the others. The exception, Chapter 8, Dams and Channel Morphology, was written by fluvial geomorphologist Mathias Kondolf and collaborators from his research group in Lyon, France: Remi Loire, Hervé Piégay, and Jean‐Réné Malavoi, who are thus listed as co‐authors for the chapter.
Overall, our somewhat lofty goal is to give users (and students) of environmental flow methods a better understanding of the tools they are using, and especially where they may fall short. Methods for EFA are constantly evolving, especially analytical tools. Practitioners would be well served to be more critical of existing well‐used methods, and to investigate alternatives coming on line. The more EFAs reflect reality, the more likely they will provide useful information, to the benefit of both flowing‐water ecosystems and human populations that derive so much benefit from them.
Acknowledgements
The ideas presented in Chapter 9 stem largely from development work undertaken in two Australian Research Council Linkage Projects (LP100200170, LP130100174) and eWater Cooperative Research Centre projects. We acknowledge the contributions of the many staff and students involved. We thank Genevieve Smith, in particular, for allowing the use of her Master of Environment research project as the case study presented therein. The material in Chapters 1, 2, 4, and 6 expands on worked funded by the California Energy Commission, Public interest Energy Research Program, through the Center for Watershed Sciences, University of California, Davis. Preparation of Chapter 8 was partly supported by the Collegium, Lyon Institute for Advanced Studies, University of Lyon, and the EURIAS Fellowship Programme and the European Commission (Marie‐Sklodowska‐Curie Actions – COFUND Programme – FP7).
CHAPTER 1An introduction to environmental flows
Summary
Environmental flows are flows in a river required to sustain aquatic ecosystems and other beneficial uses of free‐flowing rivers. Environmental flow assessment is a general term for studies that can inform management of flows. Such assessments are surprisingly difficult to do right, constrained by the natural variability of the environment through which rivers flow and the diverse needs of organisms that live there. They are also made difficult by social constraints that pit human demands for water against those of the environment, and by aspects of human behavior.
1.1 What are environmental flows?
The 2007 Brisbane Declaration of the 10th International River Symposium and Environmental Flows Conference states that: “Environmental flows describes the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well‐being that depend upon those ecosystems.” We will use this definition, taking “freshwater ecosystems” to include riparian areas. “Instream flows” is an older term that means much the same thing, but we prefer “environmental flows” because it implies a broader view of what should be assessed; instream flow assessments historically have been concerned mainly with the physical environment of only a few species, especially salmonids. We take environmental flow assessment (EFA) to be the process of trying to translate the Brisbane definition into usefully precise estimates of environmental water needs and the effects of modified flows on ecosystems and human well‐being, to inform decisions such as:
Whether to reserve some portion of the flow in a stream for environmental uses, and if so, how much, and on what kind of schedule;
How effects of an existing project on streams or estuaries can be mitigated (or not) by releases of environmental flows or restrictions on water withdrawals;
Whether and how to modify existing water projects to improve environmental conditions;
Whether and how to build a new water project.
Environmental flow assessment is hard to do well. This book is about the scientific and social difficulties with EFA and how to address them as best one can. In this chapter, we first explain why EFA is so difficult, and address problems with the EFA literature.
1.2 Why EFA is so hard; scientific issues
1.2.1 Stream ecosystems are dynamic and open
Twenty‐some years ago, three of the authors of this book participated in a small workshop on environmental flow assessment at the University of California at Davis, which concluded that “…currently no scientifically defensible method exists for defining the instream flows needed to protect particular species of fish or aquatic ecosystems” (Castleberry et al. 1996). Despite major progress with analytical and statistical methods over the last 20 years, especially those described in Chapter 9, we still believe that at best an EFA should be regarded as a first cut, to be implemented within the context of adaptive management. Why is this problem so hard? Scientists have a truly wonderful understanding of the nature of energy and matter, the evolution of the universe, the atomic structure and properties of molecules, the structure and activities of cells, the origin of species and the evolutionary relationships among organisms, and much more. Why, then, is it so hard to assess the consequences of taking some of the water out of a stream, or changing the timing or temperature with which water flows down the stream?
The reasons have been known for some time: ecosystems are open, dynamic systems that are “…in a constant state of flux, usually without long‐term stability, and affected by a series of human and other, often stochastic, factors, many originating outside of the ecosystem itself” (Mangel et al. 1996, p. 356). For such reasons, Healey (1998) argues that questions such as “How much can a river's hydrology be altered without endangering its ecological integrity?” are trans‐scientific, sensu Weinberg (1972); trans‐scientific questions: “… can be stated in the language of science but not answered by the traditional means of science.” These ideas have been restated recently by Harris and Heathwaite (2012) and by Boyd (2012, p. 307): “Predicting the dynamics of real ecosystems – or even of components of these ecosystems – will remain beyond the reach of even the best ecosystem models for the foreseeable future.”
A long‐term study on the South Fork Eel River in Northern California (Box 1.1) illustrates these points. Although the highly predictable seasonality of flow is a major factor structuring the food web in that river, year‐to‐year variation in the timing and magnitude of high‐flow events results in substantial variation in the structure of the food web and its response to mobilization of the bed by high flows; for practical purposes, predictions of the response can only be probabilistic, not deterministic.
Box 1.1 Variable Effects of High Flows on a River Ecosystem
Eighteen years of field observations and five summer field experiments in a coastal California river suggest that hydrologic regimes influence algal blooms and the impacts of fish on algae, cyanobacteria, invertebrates, and small vertebrates. In this Mediterranean climate, rainy winters precede the biologically active summer low‐flow season. Cladophora glomerata, the filamentous green alga that dominates primary producer biomass during summer, reaches peak biomass during late spring or early summer. Cladophora blooms are larger if floods during the preceding winter attained or exceeded “bankfull discharge” (sufficient to mobilize much of the river bed, estimated at 120 m3 s−1). In 9 out of 12 summers preceded by large bed‐scouring floods, the average peak height of attached Cladophora turfs equaled or exceeded 50 cm. In five out of six years when flows remained below bankfull, Cladophora biomass peaked at lower levels. Flood effects on algae were partially mediated through impacts on consumers in food webs. In three experiments [with caged fish] that followed scouring winter floods, juvenile steelhead (Oncorhynchus mykiss) and …[coastal roach, Hesperoleucus venustus] suppressed certain insects and fish fry, affecting persistence or accrual of algae depending on the predator‐specific vulnerabilities of primary consumers [that were] capable of suppressing algae during a given year. During two post‐flood years, these grazers were more vulnerable to small predators (odonates and fish fry, which… [steelhead stocked in the cages always suppressed] …[As a result, the abundant grazers] had adverse effects on algae in those years. During one post‐flood year, all enclosed grazers capable of suppressing algae were consumed by steelhead, which therefore had positive effects on algae. During drought years, when no bed‐scouring winter flows occurred, large armored caddisflies (Dicosmoecus gilvipes) were more abundant during the subsequent summer. In drought‐year experiments, stocked fish had little or no influence on algal standing crops, which increased only when Dicosmoecus were removed from enclosures. Flood scour, by suppressing invulnerable grazers, set the stage for fish‐mediated effects on algae in this river food web. Whether these effects were positive or negative depended on the predator‐specific vulnerabilities of primary consumers that dominated during a given summer. (Power et al. 2008, p. 263 edited for clarity)
As another example, consider the valuable and well‐managed sockeye salmon fishery in Bristol Bay, Alaska, for which long‐term catch records are available for three major fishing districts, corresponding to areas of spawning and rearing habitat. The catch is a good proxy for the number of spawning fish, known since about 1950 (Hilborn et al. 2003). Although there has been little human disturbance in the spawning and rearing areas except for climate change, the relative contributions to the catch from the different districts has varied widely over time, as described by Hilborn et al. (2003, p. 6567):
The stability and sustainability of Bristol Bay sockeye salmon have been greatly influenced by different populations performing well at different times during the last century. Indeed, no one associated with the fishery in the 1950s and 1960s could have imagined that Egegik would produce over 20 million fish in 1 year, nor could they imagine that the Nushagak would produce more than the Kvichak, as it has in the last 4 years. It appears that the resilience of Bristol Bay sockeye is due in large part to the maintenance of all of the diverse life history strategies and geographic locations that comprise the stock. At different times, different geographic regions and different life history strategies have been the major producers. If managers in earlier times had decided to focus management on the most productive runs at the time and had neglected the less productive runs, the biocomplexity that later proved important could have been lost.
Hilborn et al. (2003) were thinking of fisheries management, but the same point would apply to managing the freshwater habitat in these regions; there have been major geographical shifts in productivity in this undisturbed habitat, and no one knows why.
1.2.2 Fish evolve
We are used to thinking of evolution as a slow process, but this is not always the case. Stearns and Hendry (2004) wrote that: “A major shift in evolutionary biology in the last quarter century is due to the insight that evolution can be very rapid when populations containing ample genetic variation encounter strong selection (citations omitted).” It is now clear that significant evolution can occur within the time spans commonly considered in EFA, and fish populations may respond to changes in the environment in unexpected ways. For example, in several California rivers, releases of cold water from the lower levels of reservoirs have created have good habitat for large trout. The steelhead populations in these rivers apparently have evolved toward a resident life‐history in response (Williams 2006). Where hatcheries “mitigate” for habitat lost above dams, salmonids evolve greater fitness for reproduction in hatcheries, and lower fitness for reproducing in rivers (Myers et al. 2004; Araki et al. 2007; Christie et al. 2014); significant domestication can occur in a single generation (Christie et al. 2016). If hatchery fish mix with naturally spawning fish in the river below the dam, the population of naturally spawning fish below the dam that can be supported by a given flow regime will be reduced as fitness declines.
1.2.3 Streams adjust
Alluvial or partially alluvial streams create their own channels. Anything that substantially changes flow or sediment transport in a stream, such as a new dam, will provoke geomorphic adjustments in channel size and form that will change the physical habitat, compromising assessments based on the pre‐project habitat.
1.2.4 Climate changes
Long‐term climate records and paleoclimatic data from tree rings and other sources show that climates have always varied over decades and centuries, and now greenhouse gas emissions are driving rapid change. One predictable change, already evident in flow data, is more winter runoff and less snowmelt runoff in mountain streams. Precipitation may increase or decrease, depending upon the region, and may become more variable. Thus, the amount and temporal distribution of water available to be allocated between instream and consumptive uses will change, as will the temperature of the water. Methodologically, climate change confounds analytical methods that assume that the statistical properties of flow data will be stationary, i.e. not change over time (Milly et al. 2008). Predicting climate change at any particular location is even more difficult than predicting global change (Deser et al. 2012), so uncertainty about climate will add substantially to the uncertainties already faced in EFAs.
Even without major human influences, climates and flow regimes vary substantially over time, especially in arid and semi‐arid regions, as shown by a plot of the 30‐year running average discharge in the Arroyo Seco River in California. (Figure 1.1). Thus, the particular period of record that is available for analysis can make a major difference (Williams 2017). Probably the most famous example of this is the Colorado River Compact of 1922, which allocated the water from the Colorado River among the various states of the USA in the basin. The allocation was based on unusually high flows in the early twentieth century, and so seriously over‐allocated water from the river, as noted by the National Research Council (2007, pp. 99, 103):
Figure 1.1 Thirty‐year running average discharge in the Arroyo Seco River in central California. There has been no significant development in the basin. Data from the USGS gage 11152000.
Source: John Williams.
From the vantage point of the early 21st century, there is now a greater appreciation that the roughly 100 years of flow data within the Lees Ferry gage record represents a relatively small window of time of a system that is known to fluctuate considerably on scales of decades and centuries. (p. 99). … Long‐term Colorado River mean flows calculated over these periods of hundreds of years are significantly lower than both the mean of the Lees Ferry gage record upon which the Colorado River Compact was based and the full 20th century gage record (citation).
1.2.5 Populations vary
Populations of fish and other aquatic organisms can be highly variable in time and space (e.g. Dauwalter et al. 2009), even in stable stream environments (e.g. Elliott 1994). This makes it hard to determine population trends or whether changes in flows have done any good or harm (Korman and Higgins 1997; Williams et al. 1999). This is particularly true for anadromous fish, populations of which may be strongly affected by ocean conditions that vary from year to year (e.g. Lindley et al. 2009). Within short sections of streams, abundance can vary strongly over periods of days (e.g. Bélanger and Rodríguez 2002), so assessments of habitat quality based on fish density can be unstable.
1.2.6 Habitat selection is conditional
Environmental flow assessments are often based on the assumptions that providing more of the kind of habitat where fish are found will increase the population of fish. The assumption may be sound, provided that it is tempered by biological understanding, by appropriate choice of spatial scale in the assessment, and by the recognition that habitat selection is conditional; in other words, fish can only select habitat that is available to them, and habitat selection at fine spatial scales can be affected by many factors, including habitat at coarser spatial scales, population density, competition, season, water temperature, cloud cover, and even discharge (Chapter 7). It is also necessary to consider how much of a particular kind of habitat a population of a given size needs, and to recognize that other factors altogether may determine abundance. Habitats affect populations through their effects on births, deaths, growth, and migration.
1.2.7 Spatial and temporal scales matter
The response times of the resources of concern complicate EFAs. Biotic communities may take decades to respond detectably to management actions, or the response may change over time. For example, the population of Sacramento River spring Chinook salmon initially increased after the construction of Shasta Dam (Eicher 1976), but later collapsed (Williams 2006), probably because of interbreeding with fall Chinook salmon. This problem is particularly acute for fish that use spatially dispersed and distinct habitats over the course of their life cycles, when only some of the habitats are affected by the actions.
Even if the inquiry concerns physical habitat, response times may still present problems. Events such as scouring floods that seem to destroy habitat in the short term may create other habitat, such as deep pools, in the long term. Anything that substantially changes sediment transport in a stream, such as a new dam that blocks sediment transport or modifies flows, will provoke geomorphic adjustments in channel size and form that will change the physical habitat.
Spatial scales also matter, for example in assessments of habitat selection (Cooper et al. 1998; Welsh and Perry 1998; Tullos et al. 2016). Factors that seem to drive habitat selection at a fine spatial scale may explain relatively little at a coarser spatial scale (Fausch et al. 2002; Durance et al. 2006; Bouchard and Boisclair 2008). As an additional complication, organisms can select habitat at multiple scales. In a classic observational study, Bachman (1984, p. 9) wrote that:
The mean home‐range size of 53 wild brown trout was 15.6 m2 (SE, 1.7) as determined from minimum‐convex polygons encompassing 95 % of the scan sighting of each fish each year. … Typically, foraging sites were in front of a submerged rock, or on top of but on the downward‐sloping rear surface of a rock … From there the fish had an unobstructed view of oncoming drift. While a wild brown trout was in such a site, its tail beat was minimal … indicating that little effort was required to maintain a stationary position even though the current only millimeters overhead was as high as 60–70 cm s−1. Most brown trout could be found in one of several such sites day after day, and it was not uncommon to find a fish using many of the same sites for three consecutive years.
Thus, the trout selected habitat on a scale of centimeters with respect to the rock, on a scale of meters with respect to incoming drift, and a scale of tens of meters with respect to home range; further study might have shown selection of home ranges on a scale of hundreds or thousands of meters.
1.3 Why EFA is so hard: social issues
1.3.1 Social objectives evolve
Like ecosystems, societies are not stable equilibrium systems; social attitudes and objectives also evolve, as do environmental laws and regulations, and the evolution is rapid relative to the duration of major water‐development projects. We are old enough to remember the resurgence of environmental concern in the 1960s that laid the basis for much of current environmental law in the USA, such as the Clean Water Act, the Endangered Species Act, and the National Environmental Policy Act. Environmental concerns also affected judicial decisions. For example, in 1971, in Marks v. Whitney (6 Cal.3d 251), a decision about tidelands in Tomales Bay, the California Supreme Court broadened the uses that are protected by the Public Trust to include providing environments for birds and marine life, and scientific study. This decision did not come from abstract legal reasoning, but rather from the political mood of the time. In pertinent part, the decision states that:
Public trust easements are traditionally defined in terms of navigation, commerce and fisheries. They have been held to include the right to fish, hunt, bathe, swim, to use for boating and general recreation purposes the navigable waters of the state, and to use the bottom of the navigable waters for anchoring, standing, or other purposes (citations omitted). The public has the same rights in and to tidelands. … The public uses to which tidelands are subject are sufficiently flexible to encompass changing public needs. In administering the trust the state is not burdened with an outmoded classification favoring one mode of utilization over another (citations omitted). There is a growing public recognition that one of the most important public uses of tidelands – a use encompassed within the tidelands trust – is the preservation of those lands in their natural state, so that they may serve as units for scientific study, as open space, and as environments which produce food and habitat for birds and marine life, and which favorably affect the scenery and climate of the area. …
This broadening of trust uses was extended to navigable lakes and streams and their tributaries in 1983 in National Audubon Society v. Superior Court (33 Cal.3d 419), concerning environmental flows in Rush Creek, a tributary to Mono Lake. The Audubon decision and the environmental attitudes it reflected also gave new life to existing legislation affecting environmental flows, such as Fish and Game Code sec. 5937, discussed in Chapter 2. Changing social attitudes also change the practical effect of environmental laws. Monticello Dam on Putah Creek in California releases water for re‐diversion 10 km downstream. These releases support a trout fishery, which, together with recreational uses of the reservoir, was long thought to meet any environmental obligations arising from the project, including Fish and Game Code sec. 5937. Over time, however, native fishes that were formerly regarded as “trash fish” came to be valued, and litigation resulted in revised environmental flow releases to protect them (Moyle et al. 1998).
Similar changes have developed elsewhere, although the nature and pace of the change has varied among nations and regions. South Africa, for example, experienced sudden advances in the relevant law and methods for EFA in the euphoric period after Nelson Mandela ushered in a peaceful end to apartheid. Together with scientists in Australia, where semi‐arid conditions made methods such as the physical habitat simulation system (PHABSIM) clearly unsuitable, South African scientists developed holistic methods (Arthington et al. 1992a). These were applied in Australia when the need for multi‐state planning in the Murray–Darling basin, underscored by the Millennium Drought, brought about major changes in water law that called for shifting allocations of water from consumptive uses to the environment (Skinner and Langford 2013).
1.3.2 Science and dispute resolution
Environmental flow assessments almost always occur within the context of disputes over water, and the resolution of these disputes will involve trade‐offs and balancing, and often negotiation. For this reason, the main publication on the use of the Instream Flow Incremental Methodology (Bovee et al. 1998) deals extensively with negotiation and dispute resolution. We do not deal with these aspects of the flow‐setting process in this review, since we are not experts in them, although we recognize that effective negotiation and dispute resolution are critical aspects of protecting environmental flows. However, it is also important to keep in mind that science and dispute‐resolution are separate endeavors that have different rules for settling questions.
Distinctions among human activities often break down in the details, but generally, science settles questions by testing hypotheses or models with data. Procedures for doing this may be generally agreed upon, but they are always subject to criticism, alternatives can always be put forward, and conclusions are always subject to change in light of new evidence. In legal or political disputes, on the other hand, questions can also be settled by the parties agreeing to an answer, and in legal disputes this answer may be final, at least for the parties involved, regardless of new evidence that may emerge. For example, the parties in a dispute over water may agree that the results of a study of part of the stream in question will be taken as representative of the whole. This will not wash in science. Science and dispute‐resolution both have major roles in EFA, but it is important to keep them separate.
In the regulatory world, disputes are supposed to be resolved, which requires that decisions be made in reasonable time. This produces a tension between science and dispute‐resolution. Adaptive management, discussed in Chapter 6, can be viewed as a way to reduce this tension, but it will not do away with it.
1.3.3 Water is valuable
Because water is valuable, disputes over the allocation between environmental and other uses are often intense. Mark Twain allegedly said that “Whiskey is for drinking; water is for fighting over,” and, even if the quote is not authentic, the comment rings true. If consultants or agency staff on one side of a dispute see their job simply as furthering the interests of their client or employer, then consultants and staff on the other side have little choice but to do the same, resulting in “combat biology.” Something similar results from the tendency of people in a dispute, as social animals, to see their side as in the right, and to accept the opinions of others on their side as correct, with opinions of those on the other side as suspect at best. It is hard to conduct a dispassionate assessment in these circumstances.
1.3.4 Managers or clients often want the impossible
In disputes, properly describing uncertainty can be problematic. Scientists working on EFA normally work for someone else, usually a manager in an agency or consulting firm, or sometimes a specific client, and often in the context of disputes over water. Often, the manager or client will want more definitive results than the state of the science allows. Experience shows that there are scientists who will provide such results, or even the particular results desired, and this presents yet another difficulty for those wanting to do honest work. We wish we had a solution for this problem, but we do not.
A related “real world” problem for EFA is that specialists in the field may build a career around one method or another, and become personally invested in it. They are then resistant to criticisms of the method that cannot be accommodated by minor changes in it. As Upton Sinclair famously wrote, “It is difficult to get a man to understand something, when his salary depends on his not understanding it.”
Lest this recitation of difficulties seem too gloomy, we reiterate Healey's (1998) point that people do know quite a lot about fish and riverine ecosystems. We do have a lot of background knowledge and analytical tools with which to think about environmental flow assessment. The rub, however, is that we cannot do a good job of EFA without clear thinking, and clear thinking is as hard to do as it is essential. Therefore, we should do the best we can, be clear about what we did and did not do and why, and try to work in an adaptive framework that will allow changes in management as new information and understanding become available.
1.4 Why EFA is so hard: problems with the literature
There are several literatures on environmental flow assessments or on matters highly relevant to them. It is common to distinguish peer‐reviewed journals from agency or consulting reports, but there are also important distinctions among peer‐reviewed journals. Roughly, there is a more academically oriented literature, largely in ecological or hydrologic journals, and a more applied fisheries literature, with surprisingly little overlap between them. There are also many relevant papers in journals on geomorphology, engineering, and statistics, and a large literature on habitat selection in wildlife journals. Unfortunately, there are now also “pay to publish” journals that will print almost anything. Even among legitimate journals, these distinctions matter, because the quality of the reviewing tends to vary. Generally, the reviewing for the academically more prestigious journals is more rigorous, but the reviewers for these journals may not be as familiar with the details of a particular topic as reviewers for the relevant specialty journals. The distinction that really matters is whether journal articles or agency reports are based on good logic, methods, and evidence.
Peer review is an important part of scientific quality control, but it is far from perfect and many deeply flawed articles are published. Ioannidis (2005) described this problem for biomedical research in an influential article entitled “Why most published research findings are false,” and the problem has received considerable attention since. For example, the Open Science Collaboration (2015) recently reported that replication of the work reported in 100 psychology articles from leading journals showed that most reported findings were not substantiated. There are various reasons for this unfortunate state of affairs, including conscious or unconscious bias by the investigators, and misuse of statistical methods (the latter is a common one). We are not statisticians, but we often see obvious statistical problems with papers dealing with EFA. The upshot is that even the scientific literature needs to be approached carefully and critically, and those of us who are not experts at statistics should cultivate good relations with people who are. Even apart from statistical issues, we should read the literature with the question “Why should I believe that?” always in mind. Skepticism is particularly justified in reading the EFA literature, as the history of EFA shows.
1.5 Why EFA is so hard: limitations of models and objective methods
1.5.1 Models and environmental flow assessment
Models are essential tools for environmental flow assessment, but are often misused (Chapter 7). The proper use of models is to help people think, even for well‐studied physical systems. Consider weather forecasting. The National Weather Service forecasters in our area base their forecasts on the results of three and sometimes four different models, using their knowledge of how well each model handles particular kinds of weather, and the plausibility of each model result. Proper use of models requires a good understanding of the model, the data at hand, the system being modeled, and the questions being addressed. A remark by the economist Thomas Piketty seems applicable to ecosystems: “Models can contribute to clarifying logical relationships between particular assumptions and conclusions but only by oversimplifying the real world to an extreme point. Models can play a useful role but only if one does not overestimate the meaning of this kind of abstract operation” (Piketty 2015, p. 70). Inevitably, models embody simplifications of the world, based on the aspects of the world that we (or someone) believe are important for the problem at hand. That is, we model the way that we think the world works, but we should remember that the world has no obligation to work that way. The invaluable thing that models do is to show us the logical consequences of our thinking, or, for estimation models, to show us how well the data support our thinking.
1.5.2 Objective and subjective methods
A few decades ago, it was common for scientists to promote “objective” methods for analyzing problems, generally by applying some numerical model. This conceit has largely been given up, in the face of persuasive arguments that modeling always involves subjective choices. For example, Brenden et al. (2008) used regression trees analysis to develop a classification system for stream segments in Michigan, partly out of concern that a classification based on expert opinion could be hard to defend. However, they deliberately selected a similarity threshold of 0.6, largely because it generated a system “that had good agreement with a previously completed expert‐opinion delineation of stream segments” (p. 1622). In modeling for EFAs, there are always subjective choices about what to include in the model and how to do so (Kondolf et al. 2000). Subjectivity will enter into EFAs, whether we want it to or not; the question is whether the subjectivity will be recognized and taken into account.
Just as science should inform EFAs, EFAs should inform science. That is, studies conducted for EFA should be so conducted as to add to the general body of knowledge, and there should be feedback regarding questions and uncertainties that loom large in assessments and may be amenable to traditional scientific inquiry. Thus, the reasoning and assumptions underlying: environmental flow models (EFMs) should be stated explicitly, as should the reasoning underlying environmental flow decisions. In particular, it should be possible to tell what kinds of evidence or new understanding would justify a change in the assessment or the decision.
1.6 Conclusions
For at least two reasons, environmental flow assessment is not just science: the main question it asks may be trans‐scientific, and usually the question is asked in the context of dispute‐resolution. These qualities are not unique to EFA, but rather apply to ecosystem management generally, which has been called a “wicked problem” accordingly (DeFries and Nagendra (2017). Science can and should inform environmental flow assessment, and EFMs should be consistent with scientific practice. However, the limits to what science can contribute should be recognized. Ecosystems are enormously complicated, and it is not realistic to expect that standard methods can be devised by which EFA can be successfully accomplished without good data, careful thought and informed judgment.
CHAPTER 2A brief history of environmental flow assessments
Summary
Attempts to quantify flow needs for fish and the environment have long followed two approaches: embodying existing knowledge into some method, and developing new understanding of the species or ecosystem in question. Methods have tended to become increasingly elaborate with the development and continued improvement of computers. However, while holistic methods have garnered considerable interest in recent years, their complexity and cost has resulted in renewed interest in simple but conservative hydrological methods that can be applied widely and quickly.
2.1 Introduction
The brief history given here emphasizes the early period of environmental flow assessment (EFA), in the mid‐twentieth century, which is not well described in the recent literature. It also emphasizes California, since we are most familiar with developments here, and California was in the forefront of developing methods for EFA, but we try to cover the main trends generally. The review of environmental flow methods in Chapter 6 elaborates some of the points touched on here.
Broadly, there have long been two responses to the challenge of EFA. One is using existing knowledge to specify what flows should be, based either on professional opinion or on some model. As an example, Menchen (1978, p. 4) used assumptions about the area of spawning habitat required to produce a given number of Chinook salmon, and simple statistical analyses between spring flows and adult returns, to specify flow targets for the Tuolumne River, a tributary to the San Joaquin River in California:
Spawning gravel area requirements for runs of 32 000 to 52 000 spawners are fairly straightforward. An area of 1 000 000 square feet will accommodate 32 000 spawners on the Tuolumne. Fifty thousand spawners require 1 562 000 square feet. … outflows during March through June of around 4 000 cfs are required to produce 32 000 adults and of around 7 500 cfs are required for runs of 50 000 adults.
The other approach is developing better scientific understanding of aquatic ecosystems. As an early example, the state of California began an interagency Delta Fish and Wildlife Protection Study to clarify the environmental consequences of a major state water project. According to the abstract of the first report of this work (Kelley 1965):
The Delta Fish and Wildlife Protection Study was organized in 1961 to investigate the effects of future water development on fish and wildlife resources dependent upon the Sacramento–San Joaquin River estuary, and to recommend measures to protect and enhance these resources. The investigations described in this bulletin were designed to answer a number of specific questions relevant to water development plans and also to start us toward an understanding of the estuary's ecology. The bulletin describes the results of about 2 years of collecting and 1 year of analysis on zooplankton, zoobenthos, and fishes of the middle or bay portion of this estuary and on zooplankton and zoobenthos of the upper portion that is known as the Delta.
Unfortunately, the State of California has restricted this ecological approach mainly to the Delta, but it has been pursued elsewhere, for example the studies in the state of Michigan, discussed in Chapter 6. The rationale for this approach was nicely stated by Anderson et al. (2006, p. 317): “To many, the research agenda we are proposing will appear similar to that of much of ‘basic’ aquatic ecology. This is no accident; we contend that successfully providing for [environmental flow needs] in streams and rivers requires understanding how these systems work.” It seems to us that both kinds of response are needed, and, as argued in later chapters, can be reconciled by adaptive management.
2.2 The legal basis for environmental flows
Environmental flow assessment presupposes a way to act on the results, so a logical precursor to EFA is some way to protect environmental flows. The evolution of such protection has varied among and within states and nations, depending largely on the relation between the supply and demand for water, and also on the state of economic development and on legal systems. Again, California was in the forefront, at least on paper.
