| Preface to the Second Edition |
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| About the Author |
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xvii | |
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xix | |
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1 Down to Basics: Runoff Processes and the Modelling Process |
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1 | (24) |
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1 | (2) |
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3 | (1) |
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1.3 The Modelling Process |
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3 | (3) |
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1.4 Perceptual Models of Catchment Hydrology |
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6 | (7) |
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1.5 Flow Processes and Geochemical Characteristics |
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13 | (2) |
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1.6 Runoff Generation and Runoff Routing |
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15 | (1) |
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1.7 The Problem of Choosing a Conceptual Model |
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16 | (2) |
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1.8 Model Calibration and Validation Issues |
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18 | (3) |
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1.9 Key Points from
Chapter 1 |
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21 | (4) |
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Box 1.1 The Legacy of Robert Elmer Horton (1875-1945) |
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22 | (3) |
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2 Evolution of Rainfall-Runoff Models: Survival of the Fittest? |
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25 | (26) |
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2.1 The Starting Point: The Rational Method |
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25 | (1) |
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2.2 Practical Prediction: Runoff Coefficients and Time Transformations |
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26 | (7) |
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2.3 Variations on the Unit Hydrograph |
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33 | (3) |
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2.4 Early Digital Computer Models: The Stanford Watershed Model and Its Descendants |
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36 | (4) |
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2.5 Distributed Process Description Based Models |
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40 | (2) |
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2.6 Simplified Distributed Models Based on Distribution Functions |
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42 | (1) |
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2.7 Recent Developments: What is the Current State of the Art? |
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43 | (1) |
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2.8 Where to Find More on the History and Variety of Rainfall-Runoff Models |
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43 | (1) |
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2.9 Key Points from
Chapter 2 |
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44 | (7) |
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Box 2.1 Linearity, Nonlinearity and Nonstationarity |
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45 | (1) |
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Box 2.2 The Xinanjiang, ARNO or VIC Model |
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46 | (3) |
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Box 2.3 Control Volumes and Differential Equations |
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49 | (2) |
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3 Data for Rainfall-Runoff Modelling |
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51 | (32) |
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51 | (4) |
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55 | (1) |
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3.3 Meteorological Data and the Estimation of Interception and Evapotranspiration |
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56 | (4) |
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3.4 Meteorological Data and The Estimation of Snowmelt |
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60 | (1) |
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3.5 Distributing Meteorological Data within a Catchment |
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61 | (1) |
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3.6 Other Hydrological Variables |
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61 | (1) |
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3.7 Digital Elevation Data |
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61 | (5) |
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3.8 Geographical Information and Data Management Systems |
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66 | (1) |
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67 | (2) |
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3.10 Tracer Data for Understanding Catchment Responses |
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69 | (1) |
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3.11 Linking Model Components and Data Series |
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70 | (1) |
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3.12 Key Points from
Chapter 3 |
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71 | (12) |
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Box 3.1 The Penman-Monteith Combination Equation for Estimating Evapotranspiration Rates |
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72 | (4) |
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Box 3.2 Estimating Interception Losses |
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76 | (3) |
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Box 3.3 Estimating Snowmelt by the Degree-Day Method |
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79 | (4) |
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4 Predicting Hydrographs Using Models Based on Data |
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83 | (36) |
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4.1 Data Availability and Empirical Modelling |
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83 | (1) |
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4.2 Doing Hydrology Backwards |
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84 | (3) |
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4.3 Transfer Function Models |
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87 | (6) |
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4.4 Case Study: DBM Modelling of the CI6 Catchment at Llyn Briane, Wales |
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93 | (2) |
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4.5 Physical Derivation of Transfer Functions |
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95 | (4) |
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4.6 Other Methods of Developing Inductive Rainfall-Runoff Models from Observations |
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99 | (7) |
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4.7 Key Points from
Chapter 4 |
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106 | (13) |
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Box 4.1 Linear Transfer Function Models |
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107 | (5) |
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Box 4.2 Use of Transfer Functions to Infer Effective Rainfalls |
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112 | (1) |
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Box 4.3 Time Variable Estimation of Transfer Function Parameters and Derivation of Catchment Nonlinearity |
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113 | (6) |
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5 Predicting Hydrographs Using Distributed Models Based on Process Descriptions |
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119 | (66) |
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5.1 The Physical Basis of Distributed Models |
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119 | (9) |
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5.2 Physically Based Rainfall-Runoff Models at the Catchment Scale |
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128 | (7) |
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5.3 Case Study: Modelling Flow Processes at Reynolds Creek, Idaho |
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135 | (3) |
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5.4 Case Study: Blind Validation Test of the SHE Model on the Slapton Wood Catchment |
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138 | (2) |
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5.5 Simplified Distributed Models |
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140 | (8) |
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5.6 Case Study: Distributed Modelling of Runoff Generation at Walnut Gulch, Arizona |
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148 | (3) |
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5.7 Case Study: Modelling the R-5 Catchment at Chickasha, Oklahoma |
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151 | (3) |
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5.8 Good Practice in the Application of Distributed Models |
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154 | (1) |
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5.9 Discussion of Distributed Models Based on Continuum Differential Equations |
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155 | (2) |
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5.10 Key Points from
Chapter 5 |
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157 | (28) |
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Box 5.1 Descriptive Equations for Subsurface Flows |
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158 | (2) |
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Box 5.2 Estimating Infiltration Rates at the Soil Surface |
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160 | (6) |
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Box 5.3 Solution of Partial Differential Equations: Some Basic Concepts |
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166 | (5) |
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Box 5.4 Soil Moisture Characteristic Functions for Use in the Richards Equation |
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171 | (4) |
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Box 5.5 Pedotransfer Functions |
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175 | (2) |
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Box 5.6 Descriptive Equations for Surface Flows |
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177 | (4) |
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Box 5.7 Derivation of the Kinematic Wave Equation |
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181 | (4) |
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6 Hydrological Similarity, Distribution Functions and Semi-Distributed Rainfall-Runoff Models |
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185 | (46) |
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6.1 Hydrological Similarity and Hydrological Response Units |
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185 | (2) |
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6.2 The Probability Distributed Moisture (PDM) and Grid to Grid (G2G) Models |
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187 | (3) |
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190 | (8) |
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6.4 Case Study: Application of TOPMODEL to the Saeternbekken Catchment, Norway |
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198 | (5) |
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203 | (1) |
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6.6 Semi-Distributed Hydrological Response Unit (HRU) Models |
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204 | (3) |
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6.7 Some Comments on the HRU Approach |
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207 | (1) |
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6.8 Key Points from
Chapter 6 |
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208 | (23) |
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Box 6.1 The Theory Underlying TOPMODEL |
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210 | (9) |
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Box 6.2 The Soil and Water Assessment Tool (SWAT) Model |
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219 | (5) |
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Box 6.3 The SCS Curve Number Model Revisited |
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224 | (7) |
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7 Parameter Estimation and Predictive Uncertainty |
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231 | (58) |
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7.1 Model Calibration or Conditioning |
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231 | (2) |
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7.2 Parameter Response Surfaces and Sensitivity Analysis |
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233 | (6) |
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7.3 Performance Measures and Likelihood Measures |
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239 | (2) |
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7.4 Automatic Optimisation Techniques |
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241 | (2) |
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7.5 Recognising Uncertainty in Models and Data: Forward Uncertainty Estimation |
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243 | (1) |
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7.6 Types of Uncertainty Interval |
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244 | (1) |
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7.7 Model Calibration Using Bayesian Statistical Methods |
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245 | (2) |
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7.8 Dealing with Input Uncertainty in a Bayesian Framework |
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247 | (2) |
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7.9 Model Calibration Using Set Theoretic Methods |
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249 | (3) |
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7.10 Recognising Equifinality: The GLUE Method |
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252 | (6) |
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7.11 Case Study: An Application of the GLUE Methodology in Modelling the Saeternbekken MINIFELT Catchment, Norway |
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258 | (3) |
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7.12 Case Study: Application of GLUE Limits of Acceptability Approach to Evaluation in Modelling the Brue Catchment, Somerset, England |
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261 | (4) |
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7.13 Other Applications of GLUE in Rainfall-Runoff Modelling |
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265 | (1) |
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7.14 Comparison of GLUE and Bayesian Approaches to Uncertainty Estimation |
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266 | (1) |
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7.15 Predictive Uncertainty, Risk and Decisions |
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267 | (1) |
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7.16 Dynamic Parameters and Model Structural Error |
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268 | (1) |
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7.17 Quality Control and Disinformation in Rainfall-Runoff Modelling |
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269 | (5) |
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7.18 The Value of Data in Model Conditioning |
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274 | (1) |
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7.19 Key Points from
Chapter 7 |
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274 | (15) |
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Box 7.1 Likelihood Measures for use in Evaluating Models |
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276 | (7) |
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Box 7.2 Combining Likelihood Measures |
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283 | (1) |
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Box 7.3 Defining the Shape of a Response or Likelihood Surface |
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284 | (5) |
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8 Beyond the Primer: Models for Changing Risk |
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289 | (24) |
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8.1 The Role of Rainfall-Runoff Models in Managing Future Risk |
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289 | (1) |
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8.2 Short-Term Future Risk: Flood Forecasting |
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290 | (1) |
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8.3 Data Requirements for Flood Forecasting |
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291 | (2) |
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8.4 Rainfall-Runoff Modelling for Flood Forecasting |
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293 | (4) |
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8.5 Case Study: Flood Forecasting in the River Eden Catchment, Cumbria, England |
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297 | (2) |
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8.6 Rainfall-Runoff Modelling for Flood Frequency Estimation |
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299 | (3) |
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8.7 Case Study: Modelling the Flood Frequency Characteristics on the Skalka Catchment, Czech Republic |
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302 | (3) |
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8.8 Changing Risk: Catchment Change |
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305 | (2) |
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8.9 Changing Risk: Climate Change |
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307 | (2) |
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8.10 Key Points from
Chapter 8 |
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309 | (4) |
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Box 8.1 Adaptive Gain Parameter Estimation for Real-Time Forecasting |
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311 | (2) |
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9 Beyond the Primer: Next Generation Hydrological Models |
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313 | (16) |
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9.1 Why are New Modelling Techniques Needed? |
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313 | (2) |
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9.2 Representative Elementary Watershed Concepts |
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315 | (3) |
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9.3 How are the REW Concepts Different from Other Hydrological Models? |
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318 | (1) |
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9.4 Implementation of the REW Concepts |
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318 | (2) |
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9.5 Inferring Scale-Dependent Hysteresis from Simplified Hydrological Theory |
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320 | (1) |
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9.6 Representing Water Fluxes by Particle Tracking |
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321 | (3) |
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9.7 Catchments as Complex Adaptive Systems |
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324 | (1) |
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9.8 Optimality Constraints on Hydrological Responses |
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325 | (2) |
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9.9 Key Points from
Chapter 9 |
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327 | (2) |
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10 Beyond the Primer: Predictions in Ungauged Basins |
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329 | (14) |
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10.1 The Ungauged Catchment Challenge |
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329 | (1) |
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330 | (1) |
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10.3 The MOPEX Initiative |
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331 | (1) |
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10.4 Ways of Making Predictions in Ungauged Basins |
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331 | (1) |
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10.5 PUB as a Learning Process |
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332 | (1) |
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10.6 Regression of Model Parameters Against Catchment Characteristics |
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333 | (2) |
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10.7 Donor Catchment and Pooling Group Methods |
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335 | (1) |
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10.8 Direct Estimation of Hydrograph Characteristics for Constraining Model Parameters |
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336 | (2) |
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10.9 Comparing Regionalisation Methods for Model Parameters |
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338 | (1) |
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10.10 HRUs and LSPs as Models of Ungauged Basins |
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339 | (1) |
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10.11 Gauging the Ungauged Basin |
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339 | (2) |
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10.12 Key Points from
Chapter 10 |
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341 | (2) |
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11 Beyond the Primer: Water Sources and Residence Times in Catchments |
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343 | (26) |
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11.1 Natural and Artificial Tracers |
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343 | (2) |
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11.2 Advection and Dispersion in the Catchment System |
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345 | (1) |
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11.3 Simple Mixing Models |
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346 | (1) |
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11.4 Assessing Spatial Patterns of Incremental Discharge |
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347 | (1) |
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11.5 End Member Mixing Analysis (EMMA) |
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347 | (1) |
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11.6 On the Implications of Tracer Information for Hydrological Processes |
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348 | (1) |
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11.7 Case Study: End Member Mixing with Routing |
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349 | (4) |
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11.8 Residence Time Distribution Models |
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353 | (4) |
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11.9 Case Study: Predicting Tracer Transport at the Gardsjon Catchment, Sweden |
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357 | (2) |
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11.10 Implications for Water Quality Models |
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359 | (1) |
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11.11 Key Points from
Chapter 11 |
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360 | (9) |
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Box 11.1 Representing Advection and Dispersion |
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361 | (4) |
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Box 11.2 Analysing Residence Times in Catchment Systems |
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365 | (4) |
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12 Beyond the Primer: Hypotheses, Measurements and Models of Everywhere |
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369 | (12) |
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12.1 Model Choice in Rainfall-Runoff Modelling as Hypothesis Testing |
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369 | (2) |
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12.2 The Value of Prior Information |
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371 | (1) |
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12.3 Models as Hypotheses |
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372 | (2) |
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12.4 Models of Everywhere |
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374 | (1) |
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12.5 Guidelines for Good Practice |
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375 | (1) |
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12.6 Models of Everywhere and Stakeholder Involvement |
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376 | (1) |
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12.7 Models of Everywhere and Information |
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377 | (1) |
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12.8 Some Final Questions |
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378 | (3) |
| Appendix A Web Resources for Software and Data |
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381 | (6) |
| Appendix B Glossary of Terms |
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387 | (10) |
| References |
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397 | (52) |
| Index |
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449 | |
Lisainfo e-raamatute kohta