Environmental Flow Assessment Methods and Applications

by Williams, John G.; Moyle, Peter B.; Webb, J. Angus; Kondolf, G. Mathias

• ISBN13: 9781119217367
• ISBN10: 1119217369
• eBook ISBN(s): 9781119217381
• Edition: 1st
• Format: Hardcover
• Copyright: 2019-06-10
• Publisher: Wiley-Blackwell
• More Book Details

Summary

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.

Author Biography

John G. Williams, PhD, is an independent scientist who has written influential papers on environmental flow assessment, as well as on a monograph on Chinook salmon, steelhead, and their habitats in California’s Central Valley. He served two terms as an elected director of a powerful water management district and was special master for an important legal case concerning environmental flows.

Peter B. Moyle, PhD, is Distinguished Professor Emeritus in the Department of Wildlife, Fish, and Conservation Biology and the Center for Watershed Sciences, University of California Davis. He has been studying the effects of altered flows on fish since the 1970s.

J. Angus Webb, PhD, is a senior lecturer in the Water, Environment and Agriculture Program within the Melbourne School of Engineering, University of Melbourne. He has particular expertise in the monitoring, evaluation, and adaptive management of environmental flow programs.

G. Mathias Kondolf, PhD, is a Professor in the Departments of Landscape Architecture & Environmental Planning and Geography, University of California Berkeley, and fellow of the Collegium, Institute of Advanced Studies, University of Lyon. His expertise is in fluvial geomorphology, environmental planning, and river restoration.

Table of Contents

About the authors ix

Series foreword xi

Preface xiii

Acknowledgements xv

1 An introduction to environmental flows 1

Summary 1

1.1 What are environmental flows? 1

1.2 Why EFA is so hard; scientific issues 2

1.2.1 Stream ecosystems are dynamic and open 2

1.2.2 Fish evolve 3

1.2.3 Streams adjust 4

1.2.4 Climate changes 4

1.2.5 Populations vary 5

1.2.6 Habitat selection is conditional 5

1.2.7 Spatial and temporal scales matter 5

1.3 Why EFA is so hard: social issues 6

1.3.1 Social objectives evolve 6

1.3.2 Science and dispute resolution 7

1.3.3 Water is valuable 7

1.3.4 Managers or clients often want the Impossible 7

1.4 Why EFA is so hard: problems with the literature 8

1.5 Why EFA is so hard: limitations of models and objective methods 8

1.5.1 Models and environmental flow assessment 8

1.5.2 Objective and subjective methods 9

1.6 Conclusions 9

2 A brief history of environmental flow assessments 11

Summary 11

2.1 Introduction 11

2.2 The legal basis for environmental flows 12

2.3 The scope of environmental flow assessments 13

2.4 Methods for quantifying environmental flows 14

2.5 Conclusions 20

Note 20

3 A primer on flow in rivers and streams 21

Summary 21

3.1 Introduction 21

3.2 Precipitation and runoff 22

3.3 Flow regimes 22

3.3.1 Describing or depicting flow regimes 22

3.3.2 Variation in flow regimes across climates and regions 25

3.3.3 Anthropogenic changes in flow regimes 28

3.3.4 Hydrologic classifications 29

3.4 Spatial patterns and variability within streams 30

3.4.1 Spatial complexity of flow within stream channels 30

3.4.2 The variety of channel forms 31

3.4.3 Lateral connectivity with floodplain and off‐channel water bodies 33

3.4.4 Bed topography and hyporheic exchange 36

3.5 Managing environmental flows 37

3.6 Conclusions 38

4 Life in and around streams 39

Summary 39

4.1 Introduction 39

4.2 Structure of stream ecosystems 40

4.2.1 Across‐channel gradients 40

4.2.2 Upstream–downstream gradient 41

4.3 Adaptations of stream organisms 43

4.3.1 Morphological adaptations 43

4.3.2 Physiological adaptations 44

4.3.3 Behavioral adaptations 45

4.4 Adapting to extreme flows 46

4.5 Synthesis 47

4.6 Environmental flows and fish assemblages 47

4.7 Conclusions 49

5 Tools for environmental flow assessment 51

Summary 51

5.1 Introduction 51

5.2 Descriptive tools 52

5.2.1 Graphical tools and images 52

5.2.2 Stream classifications 53

5.2.3 Habitat Classifications 54

5.2.4 Species classifications 55

5.2.5 Methods classifications 55

5.3 Literature reviews 55

5.4 Experiments 56

5.4.1 Flow experiments 56

5.4.2 Laboratory experiments 56

5.4.3 Thought experiments 56

5.5 Long‐term monitoring 58

5.6 Professional opinion 59

5.7 Causal criteria 60

5.8 Statistics 60

5.8.1 Sampling 61

5.8.2 Sampling methods 61

5.8.3 Hypothesis testing 61

5.8.4 Model selection and averaging 62

5.8.5 Resampling algorithms 62

5.9 Modeling 63

5.9.1 Abundance–environment relations 64

5.9.2 Habitat association models 65

5.9.3 Drift‐foraging models 65

5.9.4 Capability models 66

5.9.5 Bayesian networks 66

5.9.6 Hierarchical Bayesian models 69

5.9.7 Dynamic occupancy models 70

5.9.8 State‐dependent life‐history models and dynamic energy budget models 71

5.9.9 Hydraulic models 71

5.9.10 Hydrological models 72

5.9.11 Temperature models 72

5.9.12 Sediment transport models 72

5.9.13 Other uses of models in EFA 73

5.10 Hydraulic habitat indices 73

5.11 Hydrological indices 75

5.12 Conclusions 75

6 Environmental flow methods 77

Summary 77

6.1 Introduction 77

6.1.1 Hydrologic, habitat rating, habitat simulation, and holistic methods 78

6.1.2 Top‐down and bottom‐up approaches 78

6.1.3 Sample‐based methods and whole‐system methods 78

6.1.4 Standard‐setting and incremental approaches 79

6.1.5 Micro‐, meso‐, and river‐, scale methods 79

6.1.6 Opinion‐based and model‐based methods 79

6.2 Hydrological methods 80

6.2.1 The tennant method and its relatives 80

6.2.2 Indicators of hydraulic alteration (IHA) 81

6.3 Hydraulic rating methods 82

6.4 Habitat simulation methods 83

6.4.1 Habitat association models 84

6.4.2 Bioenergetic or drift‐foraging models 88

6.5 Frameworks for EFA 92

6.5.1 Instream flow incremental methodology (IFIM) 92

6.5.2 Downstream response to imposed flow transformation (DRIFT) 95

6.5.3 Ecological limits of hydraulic alteration (ELOHA) 97

6.5.4 Adaptive management 102

6.5.5 Evidence‐based EFA 104

6.6 Conclusions 107

7 Good modeling practice for EFA 109

Summary 109

7.1 Introduction 109

7.2 Modeling practice 110

7.2.1 What are the purposes of the modeling? 110

7.2.2 How should you think about the natural system being assessed? 111

7.2.3 What data are or will be available, and how good are they? 111

7.2.4 How will the available budget be distributed over modeling efforts or between modeling and data collection, or between the assessment and subsequent monitoring? 112

7.2.5 How will the uncertainty in the results of the modeling be estimated and communicated? 112

7.2.6 How will the model and model development be documented? 113

7.2.7 How will the models be tested? 113

7.2.8 How good is good enough to be useful? 113

7.2.9 Who will use the results of the modeling, and how will they be used? 113

7.2.10 Do you really need a model? 113

7.3 Behavioral issues in modeling for EFA 114

7.4 Data‐dependent activities in developing estimation models 115

7.5 Sampling 118

7.5.1 General considerations 118

7.5.2 Spatial scale issues in sampling 119

7.5.3 Cleaning data sets 119

7.6 On testing models 120

7.6.1 The purpose of testing models 120

7.6.2 Why testing models can be hard 120

7.6.3 The problem with validation 120

7.6.4 The limited utility of significance tests 121

7.6.5 Tests should depend on the nature of the method being applied 122

7.6.6 Models should be tested multiple ways 122

7.6.7 The importance of plausibility 123

7.6.8 The importance of testing models with independent data 123

7.6.9 The quality of the data limits the quality of the tests 123

7.6.10 The importance of replication 123

7.6.11 Models should be tested against other models 123

7.7 Experimental tests 126

7.7.1 Flow experiments 126

7.7.2 Behavioral carrying‐capacity tests 128

7.7.3 Virtual ecosystem experiments 128

7.8 Testing models with knowledge 129

7.9 Testing hydraulic models 129

7.10 Testing EFMs based on professional judgement 130

7.11 Testing species distribution models 131

7.11.1 Goodness of fit 132

7.11.2 Prevalence 132

7.11.3 Imperfect detection 133

7.11.4 Spatial scale and other complications 133

7.12 Conclusions 141

Note 142

8 Dams and channel morphology 143

Summary 143

8.1 Introduction 143

8.2 Diagnosing the problem and setting objectives 145

8.3 Managing sediment load 146

8.3.1 Existing dams 146

8.3.2 Proposed dams 147

8.3.3 Obsolete dams 150

8.4 Specifying morphogenic flows 152

8.4.1 Three common approaches to specifying morphogenic flows 152

8.4.2 Clear objectives needed 153

8.4.3 Magnitude 153

8.4.4 Duration 155

8.4.5 The hydrograph 155

8.4.6 Seasonality 156

8.4.7 Recurrence 158

8.5 Flows for managing vegetation in channels 159

8.6 Constraints 159

8.6.1 Minimizing cost of foregone power production and other uses of water 159

8.6.2 Preserving spawning gravels 160

8.6.3 Preventing flooding and bank erosion 161

8.7 Conclusions 161

9 Improving the use of existing evidence and expert opinion in environmental flow assessments 163

Summary 163

9.1 Introduction 163

9.2 Overview of proposed method 164

9.3 Basic principles and background to steps 165

9.3.1 Literature as a basis of an evidence‐based conceptual model 165

9.3.2 Translate the conceptual model into the structure of a Bayesian belief network 166

9.3.3 Quantify causal relationships in the BBN using formal expert elicitation 166

9.3.4 Update causal relationships using empirical data 166

9.4 Case study: golden perch (Macquaria ambigua) in the regulated Goulburn River, southeastern Australia 168

9.4.1 Evidence‐based conceptual model of golden perch responses to flow variation 168

9.4.2 Bayesian belief network structure of the golden perch model 168

9.4.3 Expert‐based quantification of effects of flow and non‐flow drivers on golden perch 169

9.4.4 Inclusion of monitoring data to update the golden perch BBN 171

9.5 Discussion 172

9.5.1 Improved use of knowledge from the literature 172

9.5.2 Improving the basis of Bayesian networks for environmental flows 173

9.5.3 Hierarchical Bayesian methods as best practice 174

9.5.4 Piggy‐backing on existing knowledge 175

9.5.5 Resourcing improved practice 175

9.5.6 Accessibility of methods 176

9.6 Summary 176

10 Summary conclusions and recommendations 177

10.1 Conclusions and recommendations 177

10.1.1 Confront uncertainty and manage adaptively 177

10.1.2 Methods for EFA 178

10.1.3 Recommendations on monitoring 180

10.1.4 Recommendations for assessments 181

10.2 A checklist for EFA 182

Literature cited 185

Index 215