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Preface to the Second Edition xiii
Preface to the First Edition xv
List of Contributors xvii
Part I Model Building 1
1 Introduction 3John Wainwright and Mark Mulligan
1.1 Introduction 3
1.2 Why model the environment? 3
1.3 Why simplicity and complexity? 3
1.4 How to use this book 5
1.5 The book's web site 6
References 6
2 Modelling and Model Building 7Mark Mulligan and John Wainwright
2.1 The role of modelling in environmental research 7
2.2 Approaches to model building: chickens, eggs, models and parameters? 12
2.3 Testing models 16
2.4 Sensitivity analysis and its role 18
2.5 Errors and uncertainty 20
2.6 Conclusions 23
References 24
3 Time Series: Analysis and Modelling 27Bruce D. Malamud and Donald L. Turcotte
3.1 Introduction 27
3.2 Examples of environmental time series 28
3.3 Frequency-size distribution of values in a time series 30
3.4 White noises and Brownian motions 32
3.5 Persistence 34
3.6 Other time-series models 41
3.7 Discussion and summary 41
References 42
4 Non-Linear Dynamics Self-Organization and Cellular Automata Models 45David Favis-Mortlock
4.1 Introduction 45
4.2 Self-organization in complex systems 47
4.3 Cellular automaton models 53
4.4 Case study: modelling rill initiation and growth 56
4.5 Summary and conclusions 61
4.6 Acknowledgements 63
References 63
5 Spatial Modelling and Scaling Issues 69Xiaoyang Zhang Nick A. Drake and John Wainwright
5.1 Introduction 69
5.2 Scale and scaling 70
5.3 Causes of scaling problems 71
5.4 Scaling issues of input parameters and possible solutions 72
5.5 Methodology for scaling physically based models 76
5.6 Scaling land-surface parameters for a soil-erosion model: a case study 82
5.7 Conclusion 84
References 87
6 Environmental Applications of Computational Fluid Dynamics 91N.G. Wright and D.M. Hargreaves
6.1 Introduction 91
6.2 CFD fundamentals 92
6.3 Applications of CFD in environmental modelling 97
6.4 Conclusions 104
References 106
7 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models 111Peter C. Young and David Leedal
7.1 Introduction 111
7.2 Philosophies of science and modelling 113
7.3 Statistical identification, estimation and validation 113
7.4 Data-based mechanistic (DBM) modelling 115
7.5 The statistical tools of DBM modelling 117
7.6 Practical example 117
7.7 The reduced-order modelling of large computer-simulation models 122
7.8 The dynamic emulation of large computer-simulation models 123
7.9 Conclusions 128
References 129
8 Stochastic versus Deterministic Approaches 133Philippe Renard, Andres Alcolea and David Ginsbourger
8.1 Introduction 133
8.2 A philosophical perspective 135
8.3 Tools and methods 137
8.4 A practical illustration in Oman 143
8.5 Discussion 146
References 148
Part II The State of The Art in Environmental Modelling 151
9 Climate and Climate-System Modelling 153L.D. Danny Harvey
9.1 The complexity 153
9.2 Finding the simplicity 154
9.3 The research frontier 159
9.4 Online material 160
References 163
10 Soil and Hillslope (Eco)Hydrology 165Andrew J. Baird
10.1 Hillslope e-c-o-hydrology? 165
10.2 Tyger tyger. . . 169
10.3 Nobody loves me everybody hates me. . . 172
10.4 Memories 176
10.5 I'll avoid you as long as I can? 178
10.6 Acknowledgements 179
References 180
11 Modelling Catchment and Fluvial Processes and their Interactions 183Mark Mulligan and John Wainwright
11.1 Introduction: connectivity in hydrology 183
11.2 The complexity 184
11.3 The simplicity 196
11.4 Concluding remarks 201
References 201
12 Modelling Plant Ecology 207Rosie A. Fisher
12.1 The complexity 207
12.2 Finding the simplicity 209
12.3 The research frontier 212
12.4 Case study 213
12.5 Conclusions 217
12.6 Acknowledgements 217
References 218
13 Spatial Population Models for Animals 221George L.W. Perry and Nick R. Bond
13.1 The complexity: introduction 221
13.2 Finding the simplicity: thoughts on modelling spatial ecological systems 222
13.3 The research frontier: marrying theory and practice 227
13.4 Case study: dispersal dynamics in stream ecosystems 228
13.5 Conclusions 230
13.6 Acknowledgements 232
References 232
14 Vegetation and Disturbance 235Stefano Mazzoleni, Francisco Rego, Francesco Giannino Christian Ernest Vincenot, Gian Boris Pezzatti and Colin Legg
14.1 The system complexity: effects of disturbance on vegetation dynamics 235
14.2 The model simplification: simulation of plant growth under grazing and after fire 237
14.3 New developments in ecological modelling 240
14.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications 242
14.5 Conclusions 247
14.6 Acknowledgements 248
References 248
15 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model 253Richard E. Brazier
15.1 The complexity 253
15.2 Finding the simplicity 253
15.3 WEPP - The Water Erosion Prediction Project 254
15.4 MIRSED - a Minimum Information Requirement version of WEPP 256
15.5 Data requirements 258
15.6 Observed data describing erosion rates 259
15.7 Mapping predicted erosion rates 259
15.8 Comparison with published data 262
15.9 Conclusions 264
References 264
16 Landslides Rockfalls and Sandpiles 267Stefan Hergarten
References 275
17 Finding Simplicity in Complexity in Biogeochemical Modelling 277Hördur V. Haraldsson and Harald Sverdrup
17.1 Introduction to models 277
17.2 The basic classification of models 278
17.3 A 'good' and a 'bad' model 278
17.4 Dare to simplify 279
17.5 Sorting 280
17.6 The basic path 282
17.7 The process 283
17.8 Biogeochemical models 283
17.9 Conclusion 288
References 288
18 Representing Human Decision-Making in Environmental Modelling 291James D.A. Millington, John Wainwright and Mark Mulligan
18.1 Introduction 291
18.2 Scenario approaches 294
18.3 Economic modelling 297
18.4 Agent-based modelling 300
18.5 Discussion 304
References 305
19 Modelling Landscape Evolution 309Peter van der Beek
19.1 Introduction 309
19.2 Model setup and philosophy 310
19.3 Geomorphic processes and model algorithms 313
19.4 Model testing and calibration 318
19.5 Coupling of models 321
19.6 Model application: some examples 321
19.7 Conclusions and outlook 324
References 327
Part III Models for Management 333
20 Models Supporting Decision-Making and Policy Evaluation 335Mark Mulligan
20.1 The complexity: making decisions and implementing policy in the real world 335
20.2 The simplicity: state-of-the-art policy-support systems 341
20.3 Addressing the remaining barriers 345
20.4 Conclusions 347
20.5 Acknowledgements 347
References 347
21 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System 349Guy Engelen
21.1 Introduction 349
21.2 Functions of WadBOS 350
21.3 Decision-support systems 351
21.4 Building the integrated model 351
21.5 The integrated WadBOS model 354
21.6 The toolbase 359
21.7 The database 359
21.8 The user-interface 360
21.9 Discussion and conclusions 362
21.10 Acknowledgments 363
References 363
22 Soil Erosion and Conservation 365Mark A. Nearing
22.1 The problem 365
22.2 The approaches 367
22.3 The contributions of modelling 369
22.4 Lessons and implications 375
22.5 Acknowledgements 376
References 376
23 Forest-Management Modelling 379Mark J. Twery and Aaron R. Weiskittel
23.1 The issue 379
23.2 The approaches 379
23.3 Components of empirical models 383
23.4 Implementation and use 386
23.5 Example model 390
23.6 Lessons and implications 390
References 391
24 Stability and Instability in the Management of Mediterranean Desertification 399John B. Thornes
24.1 Introduction 399
24.2 Basic propositions 400
24.3 Complex interactions 403
24.4 Climate gradient and climate change 408
24.5 Implications 409
24.6 Plants 410
24.7 Lessons and implications 411
References 411
25 Operational European Flood Forecasting 415Hannah Cloke, Florian Pappenberger, Jutta Thielen and Vera Thiemig
25.1 The problem: providing early flood warning at the European scale 415
25.2 Flood forecasting at the European scale: the approaches 416
25.3 The European Flood Alert System (EFAS) 422
25.4 Lessons and implications 429
References 430
26 Assessing Model Adequacy 435Michael Goldstein Allan Seheult and Ian Vernon
26.1 Introduction 435
26.2 General issues in assessing model adequacy 435
26.3 Assessing model adequacy for a fast rainfall-runoff model 438
26.4 Slow computer models 446
26.5 Acknowledgements 449
References 449
Part IV Current and Future Developments 451
27 Pointers for the Future 453John Wainwright and Mark Mulligan
27.1 What have we learned? 453
27.2 Research directions 459
27.3 Technological directions 459
27.4 Is it possible to find simplicity in complexity? 463
References 463
Index 465
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