Details

Environmental Modelling


Environmental Modelling

Finding Simplicity in Complexity
2. Aufl.

von: John Wainwright, Mark Mulligan

83,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 22.01.2013
ISBN/EAN: 9781118351482
Sprache: englisch
Anzahl Seiten: 496

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Beschreibungen

<p>Simulation models are an established method used to investigate processes and solve practical problems in a wide variety of disciplines. Central to the concept of this second edition is the idea that environmental systems are complex, open systems. The authors present the diversity of approaches to dealing with environmental complexity and then encourage readers to make comparisons between these approaches and between different disciplines.</p> <p><i>Environmental Modelling: Finding Simplicity in Complexity</i> 2<sup>nd</sup> edition is divided into four main sections:</p> <ol> <li>An overview of methods and approaches to modelling.</li> <li>State of the art for modelling environmental processes</li> <li>Tools used and models for management</li> <li>Current and future developments.</li> </ol> <p>The second edition evolves from the first by providing additional emphasis and material for those students wishing to specialize in environmental modelling. This edition:</p> <ul> <li>Focuses on simplifying complex environmental systems.</li> <li>Reviews current software, tools and techniques for modelling.</li> <li>Gives practical examples from a wide variety of disciplines, e.g. climatology, ecology, hydrology, geomorphology and engineering.</li> <li>Has an associated website containing colour images, links to WWW resources and chapter support pages, including data sets relating to case studies, exercises and model animations.</li> </ul> <p>This book is suitable for final year undergraduates and postgraduates in environmental modelling, environmental science, civil engineering and biology who will already be familiar with the subject and are moving on to specialize in the field. It is also designed to appeal to professionals interested in the environmental sciences, including environmental consultants, government employees, civil engineers, geographers, ecologists, meteorologists, and geochemists.</p>
<p>Preface to the Second Edition xiii</p> <p>Preface to the First Edition xv</p> <p>List of Contributors xvii</p> <p><b>Part I Model Building 1</b></p> <p><b>1 Introduction 3<br /></b><i>John Wainwright and Mark Mulligan</i></p> <p>1.1 Introduction 3</p> <p>1.2 Why model the environment? 3</p> <p>1.3 Why simplicity and complexity? 3</p> <p>1.4 How to use this book 5</p> <p>1.5 The book’s web site 6</p> <p>References 6</p> <p><b>2 Modelling and Model Building 7<br /></b><i>Mark Mulligan and John Wainwright</i></p> <p>2.1 The role of modelling in environmental research 7</p> <p>2.2 Approaches to model building: chickens, eggs, models and parameters? 12</p> <p>2.3 Testing models 16</p> <p>2.4 Sensitivity analysis and its role 18</p> <p>2.5 Errors and uncertainty 20</p> <p>2.6 Conclusions 23</p> <p>References 24</p> <p><b>3 Time Series: Analysis and Modelling 27<br /></b><i>Bruce D. Malamud and Donald L. Turcotte</i></p> <p>3.1 Introduction 27</p> <p>3.2 Examples of environmental time series 28</p> <p>3.3 Frequency-size distribution of values in a time series 30</p> <p>3.4 White noises and Brownian motions 32</p> <p>3.5 Persistence 34</p> <p>3.6 Other time-series models 41</p> <p>3.7 Discussion and summary 41</p> <p>References 42</p> <p><b>4 Non-Linear Dynamics Self-Organization and Cellular Automata Models 45<br /></b><i>David Favis-Mortlock</i></p> <p>4.1 Introduction 45</p> <p>4.2 Self-organization in complex systems 47</p> <p>4.3 Cellular automaton models 53</p> <p>4.4 Case study: modelling rill initiation and growth 56</p> <p>4.5 Summary and conclusions 61</p> <p>4.6 Acknowledgements 63</p> <p>References 63</p> <p><b>5 Spatial Modelling and Scaling Issues 69<br /></b><i>Xiaoyang Zhang Nick A. Drake and John Wainwright</i></p> <p>5.1 Introduction 69</p> <p>5.2 Scale and scaling 70</p> <p>5.3 Causes of scaling problems 71</p> <p>5.4 Scaling issues of input parameters and possible solutions 72</p> <p>5.5 Methodology for scaling physically based models 76</p> <p>5.6 Scaling land-surface parameters for a soil-erosion model: a case study 82</p> <p>5.7 Conclusion 84</p> <p>References 87</p> <p><b>6 Environmental Applications of Computational Fluid Dynamics 91<br /></b><i>N.G. Wright and D.M. Hargreaves</i></p> <p>6.1 Introduction 91</p> <p>6.2 CFD fundamentals 92</p> <p>6.3 Applications of CFD in environmental modelling 97</p> <p>6.4 Conclusions 104</p> <p>References 106</p> <p><b>7 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models 111<br /></b><i>Peter C. Young and David Leedal</i></p> <p>7.1 Introduction 111</p> <p>7.2 Philosophies of science and modelling 113</p> <p>7.3 Statistical identification, estimation and validation 113</p> <p>7.4 Data-based mechanistic (DBM) modelling 115</p> <p>7.5 The statistical tools of DBM modelling 117</p> <p>7.6 Practical example 117</p> <p>7.7 The reduced-order modelling of large computer-simulation models 122</p> <p>7.8 The dynamic emulation of large computer-simulation models 123</p> <p>7.9 Conclusions 128</p> <p>References 129</p> <p><b>8 Stochastic versus Deterministic Approaches 133<br /></b><i>Philippe Renard, Andres Alcolea and David Ginsbourger</i></p> <p>8.1 Introduction 133</p> <p>8.2 A philosophical perspective 135</p> <p>8.3 Tools and methods 137</p> <p>8.4 A practical illustration in Oman 143</p> <p>8.5 Discussion 146</p> <p>References 148</p> <p><b>Part II The State of The Art in Environmental Modelling 151</b></p> <p><b>9 Climate and Climate-System Modelling 153<br /></b><i>L.D. Danny Harvey</i></p> <p>9.1 The complexity 153</p> <p>9.2 Finding the simplicity 154</p> <p>9.3 The research frontier 159</p> <p>9.4 Online material 160</p> <p>References 163</p> <p><b>10 Soil and Hillslope (Eco)Hydrology 165<br /></b><i>Andrew J. Baird</i></p> <p>10.1 Hillslope e-c-o-hydrology? 165</p> <p>10.2 Tyger tyger<i>. . .</i> 169</p> <p>10.3 Nobody loves me everybody hates me<i>. . .</i> 172</p> <p>10.4 Memories 176</p> <p>10.5 I’ll avoid you as long as I can? 178</p> <p>10.6 Acknowledgements 179</p> <p>References 180</p> <p><b>11 Modelling Catchment and Fluvial Processes and their Interactions 183<br /></b><i>Mark Mulligan and John Wainwright</i></p> <p>11.1 Introduction: connectivity in hydrology 183</p> <p>11.2 The complexity 184</p> <p>11.3 The simplicity 196</p> <p>11.4 Concluding remarks 201</p> <p>References 201</p> <p><b>12 Modelling Plant Ecology 207<br /></b><i>Rosie A. Fisher</i></p> <p>12.1 The complexity 207</p> <p>12.2 Finding the simplicity 209</p> <p>12.3 The research frontier 212</p> <p>12.4 Case study 213</p> <p>12.5 Conclusions 217</p> <p>12.6 Acknowledgements 217</p> <p>References 218</p> <p><b>13 Spatial Population Models for Animals 221<br /></b><i>George L.W. Perry and Nick R. Bond</i></p> <p>13.1 The complexity: introduction 221</p> <p>13.2 Finding the simplicity: thoughts on modelling spatial ecological systems 222</p> <p>13.3 The research frontier: marrying theory and practice 227</p> <p>13.4 Case study: dispersal dynamics in stream ecosystems 228</p> <p>13.5 Conclusions 230</p> <p>13.6 Acknowledgements 232</p> <p>References 232</p> <p><b>14 Vegetation and Disturbance 235<br /></b><i>Stefano Mazzoleni, Francisco Rego, Francesco Giannino Christian Ernest Vincenot, Gian Boris Pezzatti and Colin Legg</i></p> <p>14.1 The system complexity: effects of disturbance on vegetation dynamics 235</p> <p>14.2 The model simplification: simulation of plant growth under grazing and after fire 237</p> <p>14.3 New developments in ecological modelling 240</p> <p>14.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications 242</p> <p>14.5 Conclusions 247</p> <p>14.6 Acknowledgements 248</p> <p>References 248</p> <p><b>15 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model 253<br /></b><i>Richard E. Brazier</i></p> <p>15.1 The complexity 253</p> <p>15.2 Finding the simplicity 253</p> <p>15.3 WEPP – The Water Erosion Prediction Project 254</p> <p>15.4 MIRSED – a Minimum Information Requirement version of WEPP 256</p> <p>15.5 Data requirements 258</p> <p>15.6 Observed data describing erosion rates 259</p> <p>15.7 Mapping predicted erosion rates 259</p> <p>15.8 Comparison with published data 262</p> <p>15.9 Conclusions 264</p> <p>References 264</p> <p><b>16 Landslides Rockfalls and Sandpiles 267<br /></b><i>Stefan Hergarten</i></p> <p>References 275</p> <p><b>17 Finding Simplicity in Complexity in Biogeochemical Modelling 277<br /></b><i>H</i><i>ördur V. Haraldsson and Harald Sverdrup</i></p> <p>17.1 Introduction to models 277</p> <p>17.2 The basic classification of models 278</p> <p>17.3 A ‘good’ and a ‘bad’ model 278</p> <p>17.4 Dare to simplify 279</p> <p>17.5 Sorting 280</p> <p>17.6 The basic path 282</p> <p>17.7 The process 283</p> <p>17.8 Biogeochemical models 283</p> <p>17.9 Conclusion 288</p> <p>References 288</p> <p><b>18 Representing Human Decision-Making in Environmental Modelling 291<br /></b><i>James D.A. Millington, John Wainwright and Mark Mulligan</i></p> <p>18.1 Introduction 291</p> <p>18.2 Scenario approaches 294</p> <p>18.3 Economic modelling 297</p> <p>18.4 Agent-based modelling 300</p> <p>18.5 Discussion 304</p> <p>References 305</p> <p><b>19 Modelling Landscape Evolution 309<br /></b><i>Peter van der Beek</i></p> <p>19.1 Introduction 309</p> <p>19.2 Model setup and philosophy 310</p> <p>19.3 Geomorphic processes and model algorithms 313</p> <p>19.4 Model testing and calibration 318</p> <p>19.5 Coupling of models 321</p> <p>19.6 Model application: some examples 321</p> <p>19.7 Conclusions and outlook 324</p> <p>References 327</p> <p><b>Part III Models for Management 333</b></p> <p><b>20 Models Supporting Decision-Making and Policy Evaluation 335<br /></b><i>Mark Mulligan</i></p> <p>20.1 The complexity: making decisions and implementing policy in the real world 335</p> <p>20.2 The simplicity: state-of-the-art policy-support systems 341</p> <p>20.3 Addressing the remaining barriers 345</p> <p>20.4 Conclusions 347</p> <p>20.5 Acknowledgements 347</p> <p>References 347</p> <p><b>21 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System 349<br /></b><i>Guy Engelen</i></p> <p>21.1 Introduction 349</p> <p>21.2 Functions of WadBOS 350</p> <p>21.3 Decision-support systems 351</p> <p>21.4 Building the integrated model 351</p> <p>21.5 The integrated WadBOS model 354</p> <p>21.6 The toolbase 359</p> <p>21.7 The database 359</p> <p>21.8 The user-interface 360</p> <p>21.9 Discussion and conclusions 362</p> <p>21.10 Acknowledgments 363</p> <p>References 363</p> <p><b>22 Soil Erosion and Conservation 365<br /></b><i>Mark A. Nearing</i></p> <p>22.1 The problem 365</p> <p>22.2 The approaches 367</p> <p>22.3 The contributions of modelling 369</p> <p>22.4 Lessons and implications 375</p> <p>22.5 Acknowledgements 376</p> <p>References 376</p> <p><b>23 Forest-Management Modelling 379<br /></b><i>Mark J. Twery and Aaron R. Weiskittel</i></p> <p>23.1 The issue 379</p> <p>23.2 The approaches 379</p> <p>23.3 Components of empirical models 383</p> <p>23.4 Implementation and use 386</p> <p>23.5 Example model 390</p> <p>23.6 Lessons and implications 390</p> <p>References 391</p> <p><b>24 Stability and Instability in the Management of Mediterranean Desertification 399<br /></b><i>John B. Thornes</i></p> <p>24.1 Introduction 399</p> <p>24.2 Basic propositions 400</p> <p>24.3 Complex interactions 403</p> <p>24.4 Climate gradient and climate change 408</p> <p>24.5 Implications 409</p> <p>24.6 Plants 410</p> <p>24.7 Lessons and implications 411</p> <p>References 411</p> <p><b>25 Operational European Flood Forecasting 415<br /></b><i>Hannah Cloke, Florian Pappenberger, Jutta Thielen and Vera Thiemig</i></p> <p>25.1 The problem: providing early flood warning at the European scale 415</p> <p>25.2 Flood forecasting at the European scale: the approaches 416</p> <p>25.3 The European Flood Alert System (EFAS) 422</p> <p>25.4 Lessons and implications 429</p> <p>References 430</p> <p><b>26 Assessing Model Adequacy 435<br /></b><i>Michael Goldstein Allan Seheult and Ian Vernon</i></p> <p>26.1 Introduction 435</p> <p>26.2 General issues in assessing model adequacy 435</p> <p>26.3 Assessing model adequacy for a fast rainfall-runoff model 438</p> <p>26.4 Slow computer models 446</p> <p>26.5 Acknowledgements 449</p> <p>References 449</p> <p><b>Part IV Current and Future Developments 451</b></p> <p><b>27 Pointers for the Future 453<br /></b><i>John Wainwright and Mark Mulligan</i></p> <p>27.1 What have we learned? 453</p> <p>27.2 Research directions 459</p> <p>27.3 Technological directions 459</p> <p>27.4 Is it possible to find simplicity in complexity? 463</p> <p>References 463</p> <p>Index 465</p>
<p>“Those caveats aside, this book will provide an interesting and stimulating read for scientists with some familiarity with modelling who want to extend their understanding and to see how modelling has been usefully applied across a very wide range of problems in environmental science.”  (<i>European Journal of Soil Science</i>, 1 December 2013)</p> <p>“Summing Up: Recommended.  Graduate students, researchers/faculty, and professionals/practitioners.”  (<i>Choice</i>, 1 January 2014)</p> <p>“To conclude, the book offers important information on how to use models to develop our understanding of the processes that form the environment around us.”  (<i>Environmental Engineering and Management Journal</i>, 1 April 2013)</p>
<p>Editors <p><b>John Wainwright</b>, <i>Department of Geography, Durham University, UK</i> <p><b>Mark Mulligan</b>, <i>Department of Geography, King's College London, UK</i>
<p>Second Edition</br> <b>Environmental Modelling</b></br> Finding Simplicity in Complexity <p>Simulation models are an established method used to investigate processes and solve practical problems in a wide variety of disciplines. Central to the concept of this second edition is the idea that environmental systems are complex, open systems. The authors present the diversity of approaches to dealing with environmental complexity and then encourage readers to make comparisons between these approaches and between different disciplines. <p><i>Environmental Modelling: Finding Simplicity in Complexity, Second Edition</i>, is divided into four main sections: <ul> <p><b>1. An overview of methods and approaches to modelling.</b></br> <b>2. State of the art for modelling environmental processes.</b></br> <b>3. Tools used and models for management.</b></br> <b>4. Current and future developments.</b></br> </ul> <p>The second edition evolves from the first by providing additional emphasis and material for those students wishing to specialize in environmental modelling. <p>This book is suitable for final year undergraduates and postgraduates in environmental modelling, environmental science, civil engineering and biology who will already be familiar with the subject and are moving on to specialize in the field. It is also designed to appeal to professionals interested in the environmental sciences, including environmental consultants, government employees, civil engineers, geographers, ecologists, meteorologists, and geochemists. <p>This second edition: <ul> <li>Focuses on simplifying complex environmental systems.</li> <li>Reviews current software, tools and techniques for modelling.</li> <li>Gives practical examples from a wide variety of disciplines, e.g. climatology, ecology, hydrology, geomorphology and engineering.</li> <li>Has an associated website containing colour images, links to WWW resources and chapter support pages, including data sets relating to case studies, exercises and model animations.</li> </ul>

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