.

0 (hodnocen0 x )

(8) Půjčeno:8x 

BK

Chichester : John Wiley & Sons, c2001

xi, 360 s. : il., grafy ; 24 cm

ISBN 0-470-86671-3 (brož.)

Obsahuje bibliografii na s. [324]-353, rejstřík

000022379

Preface ix // 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 14 // 1.6 Runoff Production and Runoff Routeing 17 // 1.7 The Problem of Choosing a Conceptual Model 17 // 1.8 Model Calibration and Validation Issues 19 // 1.9 Key Points from Chapter One 23 // 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 37 // 2.5 Distributed Process Description Based Models 41 // 2.6 Simplified Distributed Models Based on Distribution Functions 44 // 2.7 Recent Developments: What is the Current State of the Art? 45 // 2.8 Key points from Chapter 2 45 // Box 2.1 Linearity, Nonlinearity and Nonstationarity 46 // Box 2.2 The Xinanjiang/Amo/VIC Model 47 // Box 2.3 Control Volumes and Differential Equations 51 // 3 Data for Rainfall-Runoff Modelling 53 // 3.1 Rainfall Data 53 // 3.2 Discharge Data 57 // 3.3 Meteorological Data and the Estimation of Interception and Evapotranspiration 58 // 3.4 Meteorological Data and the Estimation of Snowmelt 63 // 3.5 Distributing Meteorological Data Within a Catchment 64 // 3.6 Other Hydrological Variables 64 // 3.7 Digital Elevation Data 65 // 3.8 Geographical Information and Data Management Systems 69 // 3.9 Remote Sensing Data 70 // 3.10 Key Points from Chapter 3 73 // Box 3.1 The Penman-Monteith Combination Equation for Estimating Evapotranspiration Rates 73 // Box 3.2 Estimating Interception Losses 77 // Box 3.3 Estimating Snowmelt by the Degree-Day Method 80 //

4 Predicting Hydrographs Using Models Based on Data 85 // 4.1 Data Availability and Empirical Modelling 85 // 4.2 Empirical Regression Approaches 86 // 4.3 Transfer Function Models 88 // 4.4 Case Study: DBM Modelling of the CI6 Catchment at Llyn Briane, Wales 93 // 4.5 The ТЕМ Software 96 // 4.6 Nonlinear and Multiple Input Transfer Functions 96 // 4.7 Physical Derivation of Transfer Functions 97 // 4.8 Using Transfer Function Models in Flood Forecasting 102 // 4.9 Empirical Rainfall-Runoff Models Based on Neural Network Concepts 102 // 4.10 Key Points from Chapter 4 105 // Box 4.1 Linear Transfer Function Models 105 // Box 4.2 Use of Transfer Functions to Infer Effective Rainfalls 110 // Box 4.3 Time Variable Estimation of Transfer Function Parameters 111 // 5 Predicting Hydrographs Using Distributed Models Based on Process Descriptions 115 // 5.1 The Physical Basis of Distributed Models 115 // 5.2 Physically Based Rainfall-Runoff Models at the Catchment Scale 124 // 5.3 Case Study: Modelling Flow Processes at Reynolds Creek,Idaho 130 // 5.4 Case Study: Blind Validation Test of the SHE Model on the Rimbaud Catchment, France 132 // 5.5 Simplified Distributed Models 136 // 5.6 Case Study: Modelling Runoff Generation at Walnut Gulch, Arizona 145 // 5.7 Case Study: Modelling the R-5 Catchment at Chichasha, Oklahoma 148 // 5.8 Validation or Evaluation of Distributed Models 150 // 5.9 Discussion of Distributed Models Based on Process Descriptions 152 // 5.10 Key Points from Chapter 5 153 // Box 5.1 Descriptive Equations for Subsurface Flows 154 // Box 5.2 Estimating Infiltration Rates at the Soil Surface 156 // Box 5.3 Solution of Partial Differential Equations: Some Basic Concepts 161 // Box 5.4 Soil Moisture Characteristic Functions for Use in the Richards Equation 166 // Box 5.5 Pedotransfer Functions 170 // Box 5.6 Descriptive Equations for Surface Flows 172 // Box 5.7 Derivation of the Kinematic Wave Equation 176 //

6 Hydrological Similarity and Distribution Function Rainfall-Runoff Models 179 // 6.1 Hydrological Similarity and Hydrological Response Units 179 // 6.2 The Probability Distributed Moisture Model (PDM) 180 // 6.3 Hydrological Response Unit Models 182 // 6.4 TOPMODEL 187 // 6.5 Case Study: Application of TOPMODEL to the Saeternbekken Catchment, Norway 197 // 6.6 TOPKAPI 200 // 6.7 Key Points from Chapter 6 202 // Box 6.1 The SCS Curve Number Model Revisited 203 // Box 6.2 The Theory Underlying TOPMODEL 208 // 7 Parameter Estimation and Predictive Uncertainty 217 // 7.1 Parameter Estimation and Predictive Uncertainty 217 // 7.2 Parameter Response Surfaces and Sensitivity Analysis 219 // 7.3 Performance Measures and Likelihood Measures 223 // 7.4 Automatic Optimization Techniques 226 // 7.5 Recognizing Uncertainty in Models and Data: Reliability Analysis 229 // 7.6 Model Calibration Using Set Theoretic Methods 231 // 7.7 Recognizing Equifinality: The GLUE Method 234 // 7.8 Case Study: An Application of the GLUE Methodology in Modelling the Saeternbekken MINIFELT Catchment, Norway 240 // 7.9 Dealing with Equifinality in Rainfall-Runoff Modelling 244 // 7.10 Predictive Uncertainty and Risk 247 // 7.11 Key Points from Chapter 7 247 // Box 7.1 Likelihood Measures for Use in Evaluating Models 248 // Box 7.2 Combining Likelihood Measures 253 // 8 Predicting Floods 255 // 8.1 Data Requirements for Real-Time Prediction 256 // 8.2 Rainfall-Runoff Modelling for Flood Forecasting 259 // 8.3 The Lambert ISO Model 260 // 8.4 Adaptive Transfer Function Models for Real-Time Forecasting 261 // 8.5 Case Study: A Real-Time Forecasting System for the Town of Dumfries 262 // 8.6 Methods for Flood Inundation in Real Time 264 // 8.7 Flood Frequency Prediction Using Rainfall-Runoff Models 265 // 8.8 Case Study: Modelling the Flood Frequency Characteristics of the Wye Catchment, Wales 270 // 8.9 Flood Frequency Estimation Including Snowmelt Events 271 //

8.10 Hydrological Similarity and Flood Frequency Estimation 272 // 8.11 Key Points from Chapter 8 273 // Box 8.1 Adaptive Gain Parameter Estimation for Real-Time Forecasting 274 // 9 Predicting the Effects of Change 277 // 9.1 Predicting the Impacts of Land Use Change 279 // 9.2 Case Study: Predicting the Impacts of Fire and Logging on the Melbourne Water Supply Catchments 284 // 9.3 Predicting the Impacts of Climate Change 285 // 9.4 Case Study: Modelling the Impact of Climate Change on Flood Frequency in the Wye Catchment 293 // 9.5 Key Points from Chapter 9 294 // 10 Revisiting the Problem of Model Choice 297 // 10.1 Model Choice in Rainfall-Runoff Modelling as Hypothesis Testing 297 // 10.2 The Value of Prior Information 300 // 10.3 The Ungauged Catchment Problem 301 // 10.4 Changing Parameter Values and Predictive Uncertainty 302 // 10.5 Predictive Uncertainty and Model Validation 303 // 10.6 Final Comments: An Uncertain Future? 304 // Appendix A Demonstration Software 307 // Appendix В Glossary of Terms 315 // References 323 // Index 355