Environmental Flow Assessment: Methods and Applications

ISBN-13: 9781119217367 / Angielski / Twarda / 2019 / 240 str.

John G. Williams; G. Mathias Kondolf; Peter B. Moyle

Spis treści

About the authors ixSeries foreword xiPreface xiiiAcknowledgements xv1 An introduction to environmental flows 1Summary 11.1 What are environmental flows? 11.2 Why EFA is so hard; scientific issues 21.2.1 Stream ecosystems are dynamic and open 21.2.2 Fish evolve 31.2.3 Streams adjust 41.2.4 Climate changes 41.2.5 Populations vary 51.2.6 Habitat selection is conditional 51.2.7 Spatial and temporal scales matter 51.3 Why EFA is so hard: social issues 61.3.1 Social objectives evolve 61.3.2 Science and dispute resolution 71.3.3 Water is valuable 71.3.4 Managers or clients often want the Impossible 71.4 Why EFA is so hard: problems with the literature 81.5 Why EFA is so hard: limitations of models and objective methods 81.5.1 Models and environmental flow assessment 81.5.2 Objective and subjective methods 91.6 Conclusions 92 A brief history of environmental flow assessments 11Summary 112.1 Introduction 112.2 The legal basis for environmental flows 122.3 The scope of environmental flow assessments 132.4 Methods for quantifying environmental flows 142.5 Conclusions 20Note 203 A primer on flow in rivers and streams 21Summary 213.1 Introduction 213.2 Precipitation and runoff 223.3 Flow regimes 223.3.1 Describing or depicting flow regimes 223.3.2 Variation in flow regimes across climates and regions 253.3.3 Anthropogenic changes in flow regimes 283.3.4 Hydrologic classifications 293.4 Spatial patterns and variability within streams 303.4.1 Spatial complexity of flow within stream channels 303.4.2 The variety of channel forms 313.4.3 Lateral connectivity with floodplain and off-channel water bodies 333.4.4 Bed topography and hyporheic exchange 363.5 Managing environmental flows 373.6 Conclusions 384 Life in and around streams 39Summary 394.1 Introduction 394.2 Structure of stream ecosystems 404.2.1 Across-channel gradients 404.2.2 Upstream-downstream gradient 414.3 Adaptations of stream organisms 434.3.1 Morphological adaptations 434.3.2 Physiological adaptations 444.3.3 Behavioral adaptations 454.4 Adapting to extreme flows 464.5 Synthesis 474.6 Environmental flows and fish assemblages 474.7 Conclusions 495 Tools for environmental flow assessment 51Summary 515.1 Introduction 515.2 Descriptive tools 525.2.1 Graphical tools and images 525.2.2 Stream classifications 535.2.3 Habitat Classifications 545.2.4 Species classifications 555.2.5 Methods classifications 555.3 Literature reviews 555.4 Experiments 565.4.1 Flow experiments 565.4.2 Laboratory experiments 565.4.3 Thought experiments 565.5 Long-term monitoring 585.6 Professional opinion 595.7 Causal criteria 605.8 Statistics 605.8.1 Sampling 615.8.2 Sampling methods 615.8.3 Hypothesis testing 615.8.4 Model selection and averaging 625.8.5 Resampling algorithms 625.9 Modeling 635.9.1 Abundance-environment relations 645.9.2 Habitat association models 655.9.3 Drift-foraging models 655.9.4 Capability models 665.9.5 Bayesian networks 665.9.6 Hierarchical Bayesian models 695.9.7 Dynamic occupancy models 705.9.8 State-dependent life-history models and dynamic energy budget models 715.9.9 Hydraulic models 715.9.10 Hydrological models 725.9.11 Temperature models 725.9.12 Sediment transport models 725.9.13 Other uses of models in EFA 735.10 Hydraulic habitat indices 735.11 Hydrological indices 755.12 Conclusions 756 Environmental flow methods 77Summary 776.1 Introduction 776.1.1 Hydrologic, habitat rating, habitat simulation, and holistic methods 786.1.2 Top-down and bottom-up approaches 786.1.3 Sample-based methods and whole-system methods 786.1.4 Standard-setting and incremental approaches 796.1.5 Micro-, meso-, and river-, scale methods 796.1.6 Opinion-based and model-based methods 796.2 Hydrological methods 806.2.1 The tennant method and its relatives 806.2.2 Indicators of hydraulic alteration (IHA) 816.3 Hydraulic rating methods 826.4 Habitat simulation methods 836.4.1 Habitat association models 846.4.2 Bioenergetic or drift-foraging models 886.5 Frameworks for EFA 926.5.1 Instream flow incremental methodology (IFIM) 926.5.2 Downstream response to imposed flow transformation (DRIFT) 956.5.3 Ecological limits of hydraulic alteration (ELOHA) 976.5.4 Adaptive management 1026.5.5 Evidence-based EFA 1046.6 Conclusions 1077 Good modeling practice for EFA 109Summary 1097.1 Introduction 1097.2 Modeling practice 1107.2.1 What are the purposes of the modeling? 1107.2.2 How should you think about the natural system being assessed? 1117.2.3 What data are or will be available, and how good are they? 1117.2.4 How will the available budget be distributed over modeling efforts or between modeling and data collection, or between the assessment and subsequent monitoring? 1127.2.5 How will the uncertainty in the results of the modeling be estimated and communicated? 1127.2.6 How will the model and model development be documented? 1137.2.7 How will the models be tested? 1137.2.8 How good is good enough to be useful? 1137.2.9 Who will use the results of the modeling, and how will they be used? 1137.2.10 Do you really need a model? 1137.3 Behavioral issues in modeling for EFA 1147.4 Data-dependent activities in developing estimation models 1157.5 Sampling 1187.5.1 General considerations 1187.5.2 Spatial scale issues in sampling 1197.5.3 Cleaning data sets 1197.6 On testing models 1207.6.1 The purpose of testing models 1207.6.2 Why testing models can be hard 1207.6.3 The problem with validation 1207.6.4 The limited utility of significance tests 1217.6.5 Tests should depend on the nature of the method being applied 1227.6.6 Models should be tested multiple ways 1227.6.7 The importance of plausibility 1237.6.8 The importance of testing models with independent data 1237.6.9 The quality of the data limits the quality of the tests 1237.6.10 The importance of replication 1237.6.11 Models should be tested against other models 1237.7 Experimental tests 1267.7.1 Flow experiments 1267.7.2 Behavioral carrying-capacity tests 1287.7.3 Virtual ecosystem experiments 1287.8 Testing models with knowledge 1297.9 Testing hydraulic models 1297.10 Testing EFMs based on professional judgement 1307.11 Testing species distribution models 1317.11.1 Goodness of fit 1327.11.2 Prevalence 1327.11.3 Imperfect detection 1337.11.4 Spatial scale and other complications 1337.12 Conclusions 141Note 1428 Dams and channel morphology 143Summary 1438.1 Introduction 1438.2 Diagnosing the problem and setting objectives 1458.3 Managing sediment load 1468.3.1 Existing dams 1468.3.2 Proposed dams 1478.3.3 Obsolete dams 1508.4 Specifying morphogenic flows 1528.4.1 Three common approaches to specifying morphogenic flows 1528.4.2 Clear objectives needed 1538.4.3 Magnitude 1538.4.4 Duration 1558.4.5 The hydrograph 1558.4.6 Seasonality 1568.4.7 Recurrence 1588.5 Flows for managing vegetation in channels 1598.6 Constraints 1598.6.1 Minimizing cost of foregone power production and other uses of water 1598.6.2 Preserving spawning gravels 1608.6.3 Preventing flooding and bank erosion 1618.7 Conclusions 1619 Improving the use of existing evidence and expert opinion in environmental flow assessments 163Summary 1639.1 Introduction 1639.2 Overview of proposed method 1649.3 Basic principles and background to steps 1659.3.1 Literature as a basis of an evidence-based conceptual model 1659.3.2 Translate the conceptual model into the structure of a Bayesian belief network 1669.3.3 Quantify causal relationships in the BBN using formal expert elicitation 1669.3.4 Update causal relationships using empirical data 1669.4 Case study: golden perch (Macquaria ambigua) in the regulated Goulburn River, southeastern Australia 1689.4.1 Evidence-based conceptual model of golden perch responses to flow variation 1689.4.2 Bayesian belief network structure of the golden perch model 1689.4.3 Expert-based quantification of effects of flow and non-flow drivers on golden perch 1699.4.4 Inclusion of monitoring data to update the golden perch BBN 1719.5 Discussion 1729.5.1 Improved use of knowledge from the literature 1729.5.2 Improving the basis of Bayesian networks for environmental flows 1739.5.3 Hierarchical Bayesian methods as best practice 1749.5.4 Piggy-backing on existing knowledge 1759.5.5 Resourcing improved practice 1759.5.6 Accessibility of methods 1769.6 Summary 17610 Summary conclusions and recommendations 17710.1 Conclusions and recommendations 17710.1.1 Confront uncertainty and manage adaptively 17710.1.2 Methods for EFA 17810.1.3 Recommendations on monitoring 18010.1.4 Recommendations for assessments 18110.2 A checklist for EFA 182Literature cited 185Index 215

O autorze

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.