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
