Rainfall-runoff modelling: the primer (2nd Ed.)

Rainfall-Runoff Modelling: The Primer and Beyond is the second edition of this popular and authoritative text, first published in 2001. The book provides both a primer for the novice and detailed descriptions of techniques for more advanced practitioners, covering rainfall-runoff models and their practical applications. This new edition extends these aims to include additional chapters dealing with prediction in ungauged basins, predicting residence time distributions, predicting the impacts of change and the next generation of hydrological models. Giving a comprehensive summary of available techniques based on established practices and recent research the book offers a thorough and accessible overview of the area.

Rainfall-Runoff Modelling: The Primer and Beyond focuses on predicting hydrographs using models based on data and on representations of hydrological process. Dealing with the history of the development of rainfall-runoff models, uncertainty in mode predictions, good and bad practice and ending with a look at how to predict future catchment hydrological responses this book provides an essential underpinning of rainfall-runoff modelling topics.

• Fully revised and updated version of this highly popular text
• Suitable for both novices in the area and for more advanced users and developers
• Written by a leading expert in the field

Guide to internet sources for rainfall-runoff modelling software

Preface to the Second Edition xiii

About the Author xvii

List of Figures xix

1 Down to Basics: Runoff Processes and the Modelling Process 1

1.1 Why Model? 1

1.2 How to Use This Book 3

1.3 The Modelling Process 3

1.4 Perceptual Models of Catchment Hydrology 6

1.5 Flow Processes and Geochemical Characteristics 13

1.6 Runoff Generation and Runoff Routing 15

1.7 The Problem of Choosing a Conceptual Model 16

1.8 Model Calibration and Validation Issues 18

1.9 Key Points from Chapter 1 21

Box 1.1 The Legacy of Robert Elmer Horton (1875-1945) 22

2 Evolution of Rainfall-Runoff Models: Survival of the Fittest? 25

2.1 The Starting Point: The Rational Method 25

2.2 Practical Prediction: Runoff Coefficients and Time Transformations 26

2.3 Variations on the Unit Hydrograph 33

2.4 Early Digital Computer Models: The Stanford Watershed Model and Its Descendants 36

2.5 Distributed Process Description Based Models 40

2.6 Simplified Distributed Models Based on Distribution Functions 42

2.7 Recent Developments: What is the Current State of the Art? 43

2.8 Where to Find More on the History and Variety of Rainfall-Runoff Models 43

2.9 Key Points from Chapter 2 44

Box 2.1 Linearity, Nonlinearity and Nonstationarity 45

Box 2.2 The Xinanjiang, ARNO or VIC Model 46

Box 2.3 Control Volumes and Differential Equations 49

3 Data for Rainfall-Runoff Modelling 51

3.1 Rainfall Data 51

3.2 Discharge Data 55

3.3 Meteorological Data and the Estimation of Interception and Evapotranspiration 56

3.4 Meteorological Data and The Estimation of Snowmelt 60

3.5 Distributing Meteorological Data within a Catchment 61

3.6 Other Hydrological Variables 61

3.7 Digital Elevation Data 61

3.8 Geographical Information and Data Management Systems 66

3.9 Remote-sensing Data 67

3.10 Tracer Data for Understanding Catchment Responses 69

3.11 Linking Model Components and Data Series 70

3.12 Key Points from Chapter 3 71

Box 3.1 The Penman-Monteith Combination Equation for Estimating Evapotranspiration Rates 72

Box 3.2 Estimating Interception Losses 76

Box 3.3 Estimating Snowmelt by the Degree-Day Method 79

4 Predicting Hydrographs Using Models Based on Data 83

4.1 Data Availability and Empirical Modelling 83

4.2 Doing Hydrology Backwards 84

4.3 Transfer Function Models 87

4.4 Case Study: DBM Modelling of the CI6 Catchment at Llyn Briane, Wales 93

4.5 Physical Derivation of Transfer Functions 95

4.6 Other Methods of Developing Inductive Rainfall-Runoff Models from Observations 99

4.7 Key Points from Chapter 4 106

Box 4.1 Linear Transfer Function Models 107

Box 4.2 Use of Transfer Functions to Infer Effective Rainfalls 112

Box 4.3 Time Variable Estimation of Transfer Function Parameters and Derivation of Catchment Nonlinearity 113

5 Predicting Hydrographs Using Distributed Models Based on Process Descriptions 119

5.1 The Physical Basis of Distributed Models 119

5.2 Physically Based Rainfall-Runoff Models at the Catchment Scale 128

5.3 Case Study: Modelling Flow Processes at Reynolds Creek, Idaho 135

5.4 Case Study: Blind Validation Test of the SHE Model on the Slapton Wood Catchment 138

5.5 Simplified Distributed Models 140

5.6 Case Study: Distributed Modelling of Runoff Generation at Walnut Gulch, Arizona 148

5.7 Case Study: Modelling the...