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E-raamat: Environmental Modelling: Finding Simplicity in Complexity

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Edited by (King's College London, UK), Edited by (Durham University UK)
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  • Ilmumisaeg: 22-Jan-2013
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118366103
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Environmental Modelling: Finding Simplicity in ComplexitySuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Jan-2013
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118366103
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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.

Environmental Modelling: Finding Simplicity in Complexity 2nd edition is divided into four main sections:





An overview of methods and approaches to modelling. State of the art for modelling environmental processes Tools used and models for management Current and future developments.

The second edition evolves from the first by providing additional emphasis and material for those students wishing to specialize in environmental modelling. This edition:





Focuses on simplifying complex environmental systems. Reviews current software, tools and techniques for modelling. Gives practical examples from a wide variety of disciplines, e.g. climatology, ecology, hydrology, geomorphology and engineering. 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.

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.

Arvustused

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.  (European Journal of Soil Science, 1 December 2013)

Summing Up: Recommended.  Graduate students, researchers/faculty, and professionals/practitioners.  (Choice, 1 January 2014)

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.  (Environmental Engineering and Management Journal, 1 April 2013)

Preface to the Second Edition xiii
Preface to the First Edition xv
List of Contributors
xvii
PART I MODEL BUILDING
1(150)
1 Introduction
3(4)
John Wainwright
Mark Mulligan
1.1 Introduction
3(1)
1.2 Why model the environment?
3(1)
1.3 Why simplicity and complexity?
3(2)
1.4 How to use this book
5(1)
1.5 The book's web site
6(1)
References
6(1)
2 Modelling and Model Building
7(20)
Mark Mulligan
John Wainwright
2.1 The role of modelling in environmental research
7(5)
2.2 Approaches to model building: chickens, eggs, models and parameters?
12(4)
2.3 Testing models
16(2)
2.4 Sensitivity analysis and its role
18(2)
2.5 Errors and uncertainty
20(3)
2.6 Conclusions
23(4)
References
24(3)
3 Time Series: Analysis and Modelling
27(18)
Bruce D. Malamud
Donald L. Turcotte
3.1 Introduction
27(1)
3.2 Examples of environmental time series
28(2)
3.3 Frequency-size distribution of values in a time series
30(2)
3.4 White noises and Brownian motions
32(2)
3.5 Persistence
34(7)
3.6 Other time-series models
41(1)
3.7 Discussion and summary
41(4)
References
42(3)
4 Non-Linear Dynamics, Self-Organization and Cellular Automata Models
45(24)
David Favis Mortlock
4.1 Introduction
45(2)
4.2 Self-organization in complex systems
47(6)
4.3 Cellular automaton models
53(3)
4.4 Case study: modelling rill initiation and growth
56(5)
4.5 Summary and conclusions
61(2)
4.6 Acknowledgements
63(6)
References
63(6)
5 Spatial Modelling and Scaling Issues
69(22)
Xiaoyang Zhang
Nick A. Drake
John Wainwright
5.1 Introduction
69(1)
5.2 Scale and scaling
70(1)
5.3 Causes of scaling problems
71(1)
5.4 Scaling issues of Input parameters and possible solutions
72(4)
5.5 Methodology for scaling physically based models
76(6)
5.6 Scaling land-surface parameters for a soil-erosion model: a case study
82(2)
5.7 Conclusion
84(7)
References
87(4)
6 Environmental Applications of Computational Fluid Dynamics
91(20)
N.G. Wright
D.M. Hargreaves
6.1 Introduction
91(1)
6.2 CFD fundamentals
92(5)
6.3 Applications of CFD in environmental modelling
97(7)
6.4 Conclusions
104(7)
References
106(5)
7 Data-Based Mechanistic Modelling and the Emulation of Large Environmental System Models
111(22)
Peter C. Young
David Leedal
7.1 Introduction
111(2)
7.2 Philosophies of science and modelling
113(1)
7.3 Statistical identification, estimation and validation
113(2)
7.4 Data-based mechanistic (DBM) modelling
115(2)
7.5 The statistical tools of DBM modelling
117(1)
7.6 Practical example
117(5)
7.7 The reduced-order modelling of large computer-simulation models
122(1)
7.8 The dynamic emulation of large computer-simulation models
123(5)
7.9 Conclusions
128(5)
References
129(4)
8 Stochastic versus Deterministic Approaches
133(18)
Philippe Renard
Andres Alcolea
David Ginsbourger
8.1 Introduction
133(2)
8.2 A philosophical perspective
135(2)
8.3 Tools and methods
137(6)
8.4 A practical illustration in Oman
143(3)
8.5 Discussion
146(5)
References
148(3)
PART II THE STATE OF THE ART IN ENVIRONMENTAL MODELLING
151(182)
9 Climate and Climate-System Modelling
153(12)
L.D. Danny Harvey
9.1 The complexity
153(1)
9.2 Finding the simplicity
154(5)
9.3 The research frontier
159(1)
9.4 Online material
160(5)
References
163(2)
10 Soil and Hillslope (Eco)Hydrology
165(18)
Andrew J. Baird
10.1 Hillslope e-c-o-hydrology?
165(4)
10.2 Tyger, tyger
169(3)
10.3 Nobody loves me, everybody hates me
172(4)
10.4 Memories
176(2)
10.5 I'll avoid you as long as I can?
178(1)
10.6 Acknowledgements
179(4)
References
180(3)
11 Modelling Catchment and Fluvial Processes and their Interactions
183(24)
Mark Mulligan
John Wainwright
11.1 Introduction: connectivity in hydrology
183(1)
11.2 The complexity
184(12)
11.3 The simplicity
196(5)
11.4 Concluding remarks
201(6)
References
201(6)
12 Modelling Plant Ecology
207(14)
Rosie A. Fisher
12.1 The complexity
207(2)
12.2 Finding the simplicity
209(3)
12.3 The research frontier
212(1)
12.4 Case study
213(4)
12.5 Conclusions
217(1)
12.6 Acknowledgements
217(4)
References
218(3)
13 Spatial Population Models for Animals
221(14)
George L. W. Perry
Nick R. Bond
13.1 The complexity: introduction
221(1)
13.2 Finding the simplicity: thoughts on modelling spatial ecological systems
222(5)
13.3 The research frontier: marrying theory and practice
227(1)
13.4 Case study: dispersal dynamics in stream ecosystems
228(2)
13.5 Conclusions
230(2)
13.6 Acknowledgements
232(3)
References
232(3)
14 Vegetation and Disturbance
235(18)
Stefano Mazzoleni
Francisco Rego
Francesco Giannino
Christian Ernest Vincenot
Gian Boris Pezzatti
Colin Legg
14.1 The system complexity: effects of disturbance on vegetation dynamics
235(2)
14.2 The model simplification: simulation of plant growth under grazing and after fire
237(3)
14.3 New developments in ecological modelling
240(2)
14.4 Interactions of fire and grazing on plant competition: field experiment and modelling applications
242(5)
14.5 Conclusions
247(1)
14.6 Acknowledgements
248(5)
References
248(5)
15 Erosion and Sediment Transport: Finding Simplicity in a Complicated Erosion Model
253(14)
Richard E. Brazier
15.1 The complexity
253(1)
15.2 Finding the simplicity
253(1)
15.3 WEPP - The Water Erosion Prediction Project
254(2)
15.4 MIRSED - a Minimum Information Requirement version of WEPP
256(2)
15.5 Data requirements
258(1)
15.6 Observed data describing erosion rates
259(1)
15.7 Mapping predicted erosion rates
259(3)
15.8 Comparison with published data
262(2)
15.9 Conclusions
264(3)
References
264(3)
16 Landslides, Rockfalls and Sandpiles
267(10)
Stefan Hergarten
References
275(2)
17 Finding Simplicity in Complexity in Biogeochemical Modelling
277(14)
Hordur V. Haraldsson
Harald Sverdrup
17.1 Introduction to models
277(1)
17.2 The basic classification of models
278(1)
17.3 A `good' and a `bad' model
278(1)
17.4 Dare to simplify
279(1)
17.5 Sorting
280(2)
17.6 The basic path
282(1)
17.7 The process
283(1)
17.8 Biogeochemical models
283(5)
17.9 Conclusion
288(3)
References
288(3)
18 Representing Human Decision-Making in Environmental Modelling
291(18)
James D.A. Millington
John Wainwright
Mark Mulligan
18.1 Introduction
291(3)
18.2 Scenario approaches
294(3)
18.3 Economic modelling
297(3)
18.4 Agent-based modelling
300(4)
18.5 Discussion
304(5)
References
305(4)
19 Modelling Landscape Evolution
309(24)
Peter van der Beek
19.1 Introduction
309(1)
19.2 Model setup and philosophy
310(3)
19.3 Geomorphic processes and model algorithms
313(5)
19.4 Model testing and calibration
318(3)
19.5 Coupling of models
321(1)
19.6 Model application: some examples
321(3)
19.7 Conclusions and outlook
324(9)
References
327(6)
PART III MODELS FOR MANAGEMENT
333(118)
20 Models Supporting Decision-Making and Policy Evaluation
335(14)
Mark Mulligan
20.1 The complexity: making decisions and implementing policy in the real world
335(6)
20.2 The simplicity: state-of-the-art policy-support systems
341(4)
20.3 Addressing the remaining barriers
345(2)
20.4 Conclusions
347(1)
20.5 Acknowledgements
347(2)
References
347(2)
21 Models in Policy Formulation and Assessment: The WadBOS Decision-Support System
349(16)
Guy Engelen
21.1 Introduction
349(1)
21.2 Functions of WadBOS
350(1)
21.3 Decision-support systems
351(1)
21.4 Building the integrated model
351(3)
21.5 The integrated WadBOS model
354(5)
21.6 The toolbase
359(1)
21.7 The database
359(1)
21.8 The user-interface
360(2)
21.9 Discussion and conclusions
362(1)
21.10 Acknowledgments
363(2)
References
363(2)
22 Soil Erosion and Conservation
365(14)
Mark A. Nearing
22.1 The problem
365(2)
22.2 The approaches
367(2)
22.3 The contributions of modelling
369(6)
22.4 Lessons and implications
375(1)
22.5 Acknowledgements
376(3)
References
376(3)
23 Forest-Management Modelling
379(20)
Mark J. Twery
Aaron R. Weiskittel
23.1 The issue
379(1)
23.2 The approaches
379(4)
23.3 Components of empirical models
383(3)
23.4 Implementation and use
386(4)
23.5 Example model
390(1)
23.6 Lessons and implications
390(9)
References
391(8)
24 Stability and Instability in the Management of Mediterranean Desertification
399(16)
John B. Thornes
24.1 Introduction
399(1)
24.2 Basic propositions
400(3)
24.3 Complex interactions
403(5)
24.4 Climate gradient and climate change
408(1)
24.5 Implications
409(1)
24.6 Plants
410(1)
24.7 Lessons and implications
411(4)
References
411(4)
25 Operational European Flood Forecasting
415(20)
Hannah Cloke
Florian Pappenberger
Jutta Thielen
Vera Thiemig
25.1 The problem: providing early flood warning at the European scale
415(1)
25.2 Flood forecasting at the European scale: the approaches
416(6)
25.3 The European Flood Alert System (EFAS)
422(7)
25.4 Lessons and implications
429(6)
References
430(5)
26 Assessing Model Adequacy
435(16)
Michael Goldstein
Allan Seheult
Ian Vernon
26.1 Introduction
435(1)
26.2 General issues in assessing model adequacy
435(3)
26.3 Assessing model adequacy for a fast rainfall-runoff model
438(8)
26.4 Slow computer models
446(3)
26.5 Acknowledgements
449(2)
References
449(2)
PART IV CURRENT AND FUTURE DEVELOPMENTS
451(14)
27 Pointers for the Future
453(12)
John Wainwright
Mark Mulligan
27.1 What have we learned?
453(6)
27.2 Research directions
459(1)
27.3 Technological directions
459(4)
27.4 Is it possible to find simplicity in complexity?
463(2)
References
463(2)
Index 465
Editors

John Wainwright, Department of Geography, Durham University, UK

Mark Mulligan, Department of Geography, King's College London, UK
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