Title:
Environmental modelling, software and decision support : state of the art and new perspective
Series:
Developments in integrated environmental assessment ; 3
Publication Information:
Amsterdam, The Netherlands : Elsevier Science, 2008
Physical Description:
xvi, 369 p. : ill., maps ; 25 cm
ISBN:
9780080568867
Subject Term:
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010191134 | GE45.D37 E585 2008 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Table of Contents
| Preface |
| 1 Modelling and Software as Instruments for Advancing Sustainability |
| Summary |
| 1.1 Introduction |
| 1.2 Aims of the Summit |
| 1.3 The role of modelling and software |
| 1.4 Common problems in modelling |
| 1.5 Current state of the art and future challenges in modelling |
| 1.5.1 Generic issues |
| 1.5.2 Sectoral issues |
| 1.6 Conclusions References |
| 2 Good Modelling Practice |
| Summary |
| 2.1 Introduction |
| 2.2 Key components of good modelling practice |
| 2.2.1 Model purpose |
| 2.2.2 Model evaluation |
| 2.2.3 Performance measures |
| 2.2.4 Stating and testing model assumptions |
| 2.2.5 Ongoing model testing and evaluation |
| 2.3 Model transparency and dissemination |
| 2.3.1 Terminology |
| 2.3.2 Reporting |
| 2.3.3 Model dissemination |
| 2.4 A definition of good modelling practice |
| 2.5 Progress towards good modelling practice |
| 2.6 Recommendations |
| References. |
| 3 Bridging the Gaps between Design and Use: Developing Tools to Support Environmental Management and Policy |
| Summary |
| 3.1 A gap between design and use? |
| 3.2 Decision and information support tool review |
| 3.3 Supporting organisational decision making |
| 3.4 Supporting participatory and collaborative decision making |
| 3.5 The nature and extent of the gap |
| 3.6 Good practice guidelines for involving users in development |
| 3.6.1tKnow the capabilities and limitations of DIST technologies |
| 3.6.2tFocus on process not product |
| 3.6.3tUnderstand roles, responsibilities and requirements |
| 3.6.4tWork collaboratively |
| 3.6.5tBuild and maintain trust and credibility |
| 3.7 Conclusions |
| References |
| 4 Complexity and Uncertainty: Rethinking the Modelling Activity |
| Summary |
| 4.1 Introduction |
| 4.2 Uncertainty: causes and manifestations |
| 4.2.1 Causes of uncertainty |
| 4.2.2 Manifestation of uncertainty |
| 4.3 A conceptual approach to deal with uncertainty and complexity in modelling |
| 4.3.1 Prediction |
| 4.3.2 Exploratory analysis |
| 4.3.3 Communication |
| 4.3.4 Learning |
| 4.4 Examples |
| 4.4.1 Prediction: model use in the development of the US clean air mercury rule |
| 4.4.2 Exploratory analysis: microeconomic modelling of land use change in a coastal zone area |
| 4.4.3 Communication: modelling water quality at different scales and different levels of complexity |
| 4.4.4 Learning: modelling for strategic river planning in the Maas, the Netherlands |
| 4.5 Conclusions |
| 4.5.1 Models for prediction purposes |
| 4.5.2 Models for exploratory purposes |
| 4.5.3 Models for communication purposes |
| 4.5.4 Models for learning purposes |
| References |
| 5 Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions |
| Summary |
| 5.1 Introduction |
| 5.2 Environmental Decision-Making Process |
| 5.3 Sources of Uncertainty |
| 5.4 Progress, Challenges and Future Directions |
| 5.4.1 Risk-based assessment criteria |
| 5.4.2 Uncertainty in human input |
| 5.4.3 Computational efficiency |
| 5.4.4 Integrated software frameworks for decision making under uncertainty |
| 5.5 Conclusions |
| References |
| 6 Environmental Policy Aid under Uncertainty |
| Summary |
| 6.1 Introduction |
| 6.2 Factors influencing perceptions of uncertainty |
| 6.3 Uncertainty in decision models |
| 6.4 Uncertainty in practical policy making |
| 6.5 Reducing uncertainty through innovative policy interventions |
| 6.6 Discussion and conclusions |
| References |
| 7 Integrated Modelling Frameworks for Environmental Assessment and Decision Support |
| Summary |
| 7.1 Introduction |
| 7.1.1 A first definition |
| 7.1.2 Why do we develop new frameworks? |
| 7.1.3 A more insightful definition |
| 7.2 A generic architecture for EIMFs |
| 7.2.1 A vision |
| 7.3 Knowledge representation and management |
| 7.3.1 Challenges for knowledge-based environmental modelling |
| 7.4 Model Engineering |
| 7.4.1 Component-based modelling |
| 7.4.2 Distributed modelling |
| 7.5 Driving and supporting the modelling process |
| 7.5.1 The experimental frame |
| 7.6 Conclusions |
| References |
| 8 I |
