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Environmental Modelling, Software and Decision Support - 1st Edition - ISBN: 9780080568867, 9780080915302

Environmental Modelling, Software and Decision Support, Volume 3

1st Edition

State of the Art and New Perspective

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Editors: Anthony J. Jakeman Alexey A. Voinov Andrea E. Rizzoli Serena H. Chen
Hardcover ISBN: 9780080568867
eBook ISBN: 9780080915302
Imprint: Elsevier Science
Published Date: 1st October 2008
Page Count: 384
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Table of Contents

Preface

  1. Modelling and Software as Instruments for Advancing Sustainability. Summary
  2. 1 Introduction
  3. 2 Aims of the Summit
  4. 3 The role of modelling and software
  5. 4 Common problems in modelling
  6. 5 Current state of the art and future challenges in modelling
  7. 5.1 Generic issues
  8. 5.2 Sectoral issues
  9. 6 Conclusions References
  10. Good Modelling Practice. Summary
  11. 1 Introduction
  12. 2 Key components of good modelling practice
  13. 2.1 Model purpose
  14. 2.2 Model evaluation
  15. 2.3 Performance measures
  16. 2.4 Stating and testing model assumptions
  17. 2.5 Ongoing model testing and evaluation
  18. 3 Model transparency and dissemination
  19. 3.1 Terminology
  20. 3.2 Reporting
  21. 3.3 Model dissemination
  22. 4 A definition of good modelling practice
  23. 5 Progress towards good modelling practice
  24. 6 Recommendations
    References.
  25. Bridging the Gaps between Design and Use: Developing Tools to Support Environmental Management and Policy. Summary
  26. 1 A gap between design and use?
  27. 2 Decision and information support tool review
  28. 3 Supporting organisational decision making
  29. 4 Supporting participatory and collaborative decision making
  30. 5 The nature and extent of the gap
  31. 6 Good practice guidelines for involving users in development
  32. 6.1 Know the capabilities and limitations of DIST technologies
  33. 6.2 Focus on process not product
  34. 6.3 Understand roles, responsibilities and requirements
  35. 6.4 Work collaboratively
  36. 6.5 Build and maintain trust and credibility
  37. 7 Conclusions References.
  38. Complexity and Uncertainty: Rethinking the Modelling Activity. Summary.
  39. 1 Introduction
  40. 2 Uncertainty: causes and manifestations
  41. 2.1 Causes of uncertainty
  42. 2.2 Manifestation of uncertainty
  43. 3 A conceptual approach to deal with uncertainty and complexity in modelling
  44. 3.1 Prediction
  45. 3.2 Exploratory analysis
  46. 3.3 Communication
  47. 3.4 Learning
  48. 4 Examples
  49. 4.1 Prediction: model use in the development of the US clean air mercury rule
  50. 4.2 Exploratory analysis: microeconomic modelling of land use change in a coastal zone area
  51. 4.3 Communication: modelling water quality at different scales and different levels of complexity
  52. 4.4 Learning: modelling for strategic river planning in the Maas, the Netherlands
  53. 5 Conclusions
  54. 5.1 Models for prediction purposes
  55. 5.2 Models for exploratory purposes
  56. 5.3 Models for communication purposes
  57. 5.4 Models for learning purposes References.
  58. Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions. Summary.
  59. 1 Introduction
  60. 2 Environmental Decision-Making Process
  61. 3 Sources of Uncertainty
  62. 4 Progress, Challenges and Future Directions
  63. 4.1 Risk-based assessment criteria
  64. 4.2 Uncertainty in human input
  65. 4.3 Computational efficiency
  66. 4.4 Integrated software frameworks for decision making under uncertainty
  67. 5 Conclusions References.
  68. Environmental Policy Aid under Uncertainty. Summary.
  69. 1 Introduction
  70. 2 Factors influencing perceptions of uncertainty
  71. 3 Uncertainty in decision models
  72. 4 Uncertainty in practical policy making
  73. 5 Reducing uncertainty through innovative policy interventions
  74. 6 Discussion and conclusions References.
  75. Integrated Modelling Frameworks for Environmental Assessment and Decision Support. Summary.
  76. 1 Introduction
  77. 1.1 A first definition
  78. 1.2 Why do we develop new frameworks?
  79. 1.3 A more insightful definition
  80. 2 A generic architecture for EIMFs
  81. 2.1 A vision
  82. 3 Knowledge representation and management
  83. 3.1 Challenges for knowledge-based environmental modelling
  84. 4 Model Engineering
  85. 4.1 Component-based modelling
  86. 4.2 Distributed modelling
  87. 5 Driving and supporting the modelling process
  88. 5.1 The experimental frame
  89. 6 Conclusions References.
  90. Intelligent Environmental Decision Support Systems. Summary.
  91. 1 Introduction
  92. 1.1 Complexity of environmental systems
  93. 1.2 New tools for a new paradigm
  94. 2 Intelligent environmental decision support systems
  95. 2.1 IEDSS development
  96. 3 About uncertainty management
  97. 4 Temporal reasoning
  98. 4.1 Featuring the problem
  99. 4.2 Approaches to temporal reasoning
  100. 4.3 Case-based reasoning for temporal reasoning
  101. 5 Geographic information and spatial reasoning
  102. 5.1 Understanding spatial reasoning
  103. 5.2 Kriging and variants
  104. 5.3 Representing change/time steps/feedback loops
  105. 5.4 Middleware, blackboards and communication protocols
  106. 5.5 Multiagent systems
  107. 6 Evaluation of IEDSS and benchmarking
  108. 6.1 Benchmarking
  109. 7 Conclusions and future trends References.
  110. Formal Scenario Development for Environmental Impact Assessment Studies. Summary.
  111. 1 Introduction
  112. 2 Terminology and background
  113. 2.1 Terminology
  114. 2.2 Characteristics of scenarios
  115. 3 A formal approach to scenario development
  116. 3.1 Scenario definition
  117. 3.2 Scenario construction
  118. 3.3 Scenario analysis
  119. 3.4 Scenario assessment
  120. 3.5 Risk management
  121. 4 Monitoring and post-audits
  122. 5 Discussions and future directions
  123. 5.1 Uncertainty issues
  124. 5.2 Potential obstacles to formal scenario development
  125. 5.3 Future recommendations References.
  126. Free and Open Source Geospatial Tools for Environmental Modelling and Management. Summary.
  127. 1 Introduction
  128. 2 Platform
  129. 3 Software stack
  130. 3.1 Geospatial software stacks
  131. 3.2 System software
  132. 3.3 Geospatial data processing libraries
  133. 3.4 Data serving
  134. 3.5 User Interface
  135. 3.6 End-user applications
  136. 4 Workflows for environmental modelling and management
  137. 4.1 Case 1 – Cartographic map production
  138. 4.2 Case 2 – Web-based mapping
  139. 4.3 Case 3 – Numerical Simulation
  140. 4.4 Case 4 – Environmental management
  141. 5 Discussion
  142. 6 Conclusion References.
  143. Modelling and Monitoring Environmental Outcomes in Adaptive Management. Summary.
  144. 1 Adaptive management and feedback control
  145. 2 Shared and distinct features of the management and control problems
  146. 3 Adaptivity
  147. 3.1 Limitations of feedback and motivation for adaptivity
  148. 3.2 Adaptive control and its failings
  149. 4 Problems in adaptive management and some tools from other fields
  150. 4.1 A short list of problems in adaptive management
  151. 4.2 “Difficulties in developing acceptable predictive models”
  152. 4.3 Robustness to poor prediction via model predictive control
  153. 4.4 Adaptive management and Bayesian analysis
  154. 4.5 “Conflicts regarding ecological values and management goals”
  155. 4.6 “Inadequate attention to nonscientific information”
  156. 4.7 “Unwillingness by agencies to implement long-term policies”
  157. 5 Open challenges for adaptive management
  158. 5.1 Characterisation of uncertainty
  159. 5.2 Matching the model to system characteristics
  160. 5.3 Bottom-up and top-down modelling
  161. 6 Conclusions preceding the workshop Appendix: Summary of workshop discussion References. 12 Data Mining for Environmental Systems Summary.
  162. 1 Introduction
  163. 2 Data mining techniques
  164. 2.1 Preprocessing: data cleaning, outlier detection, missing value treatment, transformation and creation of variables
  165. 2.2 Data reduction and projection
  166. 2.3 Visualisation
  167. 2.4 Clustering and density estimation
  168. 2.5 Classification and regression methods
  169. 2.6 Association analysis
  170. 2.7 Artificial Neural Networks
  171. 2.8 Other techniques
  172. 2.9 Spatial and temporal aspects of environmental data mining
  173. 3 Guidelines for good data mining practice
  174. 3.1 Integrated approaches
  175. 4 Software - existing and under development
  176. 5 Conclusions and challenges for data mining of environmental systems References.
  177. Generic Simulation Models for Facilitating Stakeholder Involvement in Water Resources Planning and Management: a Comparison, Evaluation, and Identification of Future Needs Summary.
  178. 1 Introduction
  179. 2 Model characteristics and comparisons
  180. 3 Stakeholder Involvement
  181. 4 Enhancing non-expert modelling accessibility
  182. 5 Reaching out to younger generations
  183. 6 The current state of the art - results of workshop discussion
  184. 6.1 On detail and complexity
  185. 6.2 On stakeholder participation and shared vision modelling
  186. 6.3 On applied technology
  187. 6.4 On development and continuity
  188. 6.5 On content
  189. 7 Overall conclusion References.
  190. Computational Air Quality Modelling. Summary.
  191. 1 Introduction
  192. 2 The purpose of air quality modelling
  193. 3 Urban air quality information and forecasting systems
  194. 4 Integrated modelling
  195. 5 Air quality modelling for environment and health risk assessments
  196. 6 Air quality modelling as a natural part of climate change modelling
  197. 7 Scales of the processes/models and scale-interaction aspects
  198. 8 Chemical schemes and aerosol treatment
  199. 9 Real-time air quality modelling
  200. 10 Internet and information technologies for air quality modelling
  201. 11 Application category examples References.
  202. State of the Art in Methods and Software for the Identification, Resolution and Apportionment of Contamination Sources. Summary.
  203. 1 Introduction
  204. 2 Data sets
  205. 3 Models and Methods
  206. 3.1 Principal Component Analysis and Factor Analysis
  207. 3.2 Alternatives to PCA based methods
  208. 3.3 Other Related Techniques
  209. 4 Some Applications
  210. 4.1 Combined Aerosol Trajectory Tools
  211. 4.2 Source identification in southern California by nonparametric regression
  212. 4.3 Comparison between PMF and PCA-MLRA performance
  213. 5 Conclusions References.
  214. Regional Models of Intermediate Complexity (Remics) – A New Direction in Integrated Landscape Modelling. Summary.
  215. 1 Why do we need better models on a landscape scale?
  216. 2 The way forward
  217. 3 Landscape models
  218. 3.1 Selection of landscape indicators
  219. 3.2 REMICs
  220. 3.3 Hybrid models
  221. 3.4 Complexity in landscape modelling
  222. 4 A sample modelling tool
  223. 5 Conclusions References.
  224. Challenges in Earth System Modelling: Approaches and Applications. Summary.
  225. 1 Introduction
  226. 2 Key challenges (1)
  227. 2.1 Atmosphere modelling
  228. 2.2 Land modelling
  229. 2.3 Ocean modelling
  230. 3 Key challenges (2)
  231. 3.1 Overall discussion
  232. 3.2 Biogeochemical modelling needs
  233. 3.3 Methodologies for employing output from earth system models
  234. 4 Conclusions References.
  235. Uncertainty and Sensitivity Issues in Process-Based Models of Carbon and Nitrogen Cycles in Terrestrial Ecosystems. Summary.
  236. 1 Introduction
  237. 2 Uncertainty
  238. 2.1 Uncertainty in measurements
  239. 2.2 Model uncertainty
  240. 2.3 Scenario uncertainty and scaling
  241. 3 Model validation
  242. 4 Sensitivity analysis
  243. 5 Conclusions References.
  244. Model-Data Fusion in the Studies of Terrestrial Carbon Sink. Summary.
  245. 1 Introduction
  246. 2 The major obstacles
  247. 3 The solutions
  248. 3.1 The use of FLUXNET data
  249. 3.2 The use of atmospheric CO2 concentration measurements
  250. 3.3 The use of remote sensing data
  251. 4 The way forward References.
  252. Building a Community Modelling and Information Sharing Culture. Summary.
  253. 1 Introduction
  254. 2 Open Source and Hacker Culture
  255. 3 Knowledge sharing and Intellectual Property Rights
  256. 4 Software Development and Collaborative Research
  257. 5 Open Source Software vs. Community Modelling
  258. 6 Pros and Cons of Open Source Modelling
  259. 7 Open Data
  260. 8 Teaching
  261. 9 Conclusions and Recommendations References

Description

The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field.

Key Features

  • State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers
  • Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage
  • Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions

Readership

Researchers and postgraduates in environmental modelling, natural resource management, environmental assessment and planning, environmental decision making, atmospheric and air pollution modelling, informatics, decision support systems, global change and earth system modelling, carbon and nitrogen cycling


Details

No. of pages:
384
Language:
English
Copyright:
© Elsevier Science 2008
Published:
1st October 2008
Imprint:
Elsevier Science
Hardcover ISBN:
9780080568867
eBook ISBN:
9780080915302

Ratings and Reviews


About the Editors

Anthony J. Jakeman

Alexey A. Voinov

Affiliations and Expertise

Johns Hopkins University and Fellow at Gund Institute for Ecological Economics, USA 3

Andrea E. Rizzoli

Serena H. Chen