Environmental Modelling, Software and Decision Support

Environmental Modelling, Software and Decision Support

State of the Art and New Perspective

1st Edition - September 11, 2008

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  • Editors: Anthony J. Jakeman, Alexey A. Voinov, Andrea E. Rizzoli, Serena H. Chen
  • Hardcover ISBN: 9780080568867
  • eBook ISBN: 9780080915302

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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


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

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
    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
    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
    3.6.1 Know the capabilities and limitations of DIST technologies
    3.6.2 Focus on process not product
    3.6.3 Understand roles, responsibilities and requirements
    3.6.4 Work collaboratively
    3.6.5 Build and maintain trust and credibility
    3.7 Conclusions
    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
    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
    6. Environmental Policy Aid under Uncertainty.
    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
    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
    8. Intelligent Environmental Decision Support Systems. Summary.
    8.1 Introduction
    8.1.1 Complexity of environmental systems
    8.1.2 New tools for a new paradigm
    8.2 Intelligent environmental decision support systems
    8.2.1 IEDSS development
    8.3 About uncertainty management
    8.4 Temporal reasoning
    8.4.1 Featuring the problem
    8.4.2 Approaches to temporal reasoning
    8.4.3 Case-based reasoning for temporal reasoning
    8.5 Geographic information and spatial reasoning
    8.5.1 Understanding spatial reasoning
    8.5.2 Kriging and variants
    8.5.3 Representing change/time steps/feedback loops
    8.5.4 Middleware, blackboards and communication protocols
    8.5.5 Multiagent systems
    8.6 Evaluation of IEDSS and benchmarking
    8.6.1 Benchmarking
    8.7 Conclusions and future trends
    9. Formal Scenario Development for Environmental Impact Assessment Studies. Summary.
    9.1 Introduction
    9.2 Terminology and background
    9.2.1 Terminology
    9.2.2 Characteristics of scenarios
    9.3 A formal approach to scenario development
    9.3.1 Scenario definition
    9.3.2 Scenario construction
    9.3.3 Scenario analysis
    9.3.4 Scenario assessment
    9.3.5 Risk management
    9.4 Monitoring and post-audits
    9.5 Discussions and future directions
    9.5.1 Uncertainty issues
    9.5.2 Potential obstacles to formal scenario development
    9.5.3 Future recommendations
    10. Free and Open Source Geospatial Tools for Environmental Modelling and Management. Summary.
    10.1 Introduction
    10.2 Platform
    10.3 Software stack
    10.3.1 Geospatial software stacks
    10.3.2 System software
    10.3.3 Geospatial data processing libraries
    10.3.4 Data serving
    10.3.5 User Interface
    10.3.6 End-user applications
    10.4 Workflows for environmental modelling and management
    10.4.1 Case 1 – Cartographic map production
    10.4.2 Case 2 – Web-based mapping
    10.4.3 Case 3 – Numerical Simulation
    10.4.4 Case 4 – Environmental management
    10.5 Discussion
    10.6 Conclusion
    11. Modelling and Monitoring Environmental Outcomes in Adaptive Management. Summary.
    11.1 Adaptive management and feedback control
    11.2 Shared and distinct features of the management and control problems
    11.3 Adaptivity
    11.3.1 Limitations of feedback and motivation for adaptivity
    11.3.2 Adaptive control and its failings
    11.4 Problems in adaptive management and some tools from other fields
    11.4.1 A short list of problems in adaptive management
    11.4.2 “Difficulties in developing acceptable predictive models”
    11.4.3 Robustness to poor prediction via model predictive control
    11.4.4 Adaptive management and Bayesian analysis
    11.4.5 “Conflicts regarding ecological values and management goals”
    11.4.6 “Inadequate attention to nonscientific information”
    11.4.7 “Unwillingness by agencies to implement long-term policies”
    11.5 Open challenges for adaptive management
    11.5.1 Characterisation of uncertainty
    11.5.2 Matching the model to system characteristics
    11.5.3 Bottom-up and top-down modelling
    11.6 Conclusions preceding the workshop
    Appendix: Summary of workshop discussion
    12 Data Mining for Environmental Systems
    12.1 Introduction
    12.2 Data mining techniques
    12.2.1 Preprocessing: data cleaning, outlier detection, missing value treatment, transformation and creation of variables
    12.2.2 Data reduction and projection
    12.2.3 Visualisation
    12.2.4 Clustering and density estimation
    12.2.5 Classification and regression methods
    12.2.6 Association analysis
    12.2.7 Artificial Neural Networks
    12.2.8 Other techniques
    12.2.9 Spatial and temporal aspects of environmental data mining
    12.3 Guidelines for good data mining practice
    12.3.1 Integrated approaches
    12.4 Software - existing and under development
    12.5 Conclusions and challenges for data mining of environmental systems
    13. Generic Simulation Models for Facilitating Stakeholder Involvement in Water Resources Planning and Management: a Comparison, Evaluation, and Identification of Future Needs
    13.1 Introduction
    13.2 Model characteristics and comparisons
    13.3 Stakeholder Involvement
    13.4 Enhancing non-expert modelling accessibility
    13.5 Reaching out to younger generations
    13.6 The current state of the art - results of workshop discussion
    13.6.1 On detail and complexity
    13.6.2 On stakeholder participation and shared vision modelling
    13.6.3 On applied technology
    13.6.4 On development and continuity
    13.6.5 On content
    13.7 Overall conclusion
    14. Computational Air Quality Modelling. Summary.
    14.1 Introduction
    14.2 The purpose of air quality modelling
    14.3 Urban air quality information and forecasting systems
    14.4 Integrated modelling
    14.5 Air quality modelling for environment and health risk assessments
    14.6 Air quality modelling as a natural part of climate change modelling
    14.7 Scales of the processes/models and scale-interaction aspects
    14.8 Chemical schemes and aerosol treatment
    14.9 Real-time air quality modelling
    14.10 Internet and information technologies for air quality modelling
    14.11 Application category examples
    15. State of the Art in Methods and Software for the Identification, Resolution and Apportionment of Contamination Sources. Summary.
    15.1 Introduction
    15.2 Data sets
    15.3 Models and Methods
    15.3.1 Principal Component Analysis and Factor Analysis
    15.3.2 Alternatives to PCA based methods
    15.3.3 Other Related Techniques
    15.4 Some Applications
    15.4.1 Combined Aerosol Trajectory Tools
    15.4.2 Source identification in southern California by nonparametric regression
    15.4.3 Comparison between PMF and PCA-MLRA performance
    15.5 Conclusions
    16. Regional Models of Intermediate Complexity (Remics) – A New Direction in Integrated Landscape Modelling. Summary.
    16.1 Why do we need better models on a landscape scale?
    16.2 The way forward
    16.3 Landscape models
    16.3.1 Selection of landscape indicators
    16.3.2 REMICs
    16.3.3 Hybrid models
    16.3.4 Complexity in landscape modelling
    16.4 A sample modelling tool
    16.5 Conclusions
    17. Challenges in Earth System Modelling: Approaches and Applications. Summary.
    17.1 Introduction
    17.2 Key challenges (1)
    17.2.1 Atmosphere modelling
    17.2.2 Land modelling
    17.2.3 Ocean modelling
    17.3 Key challenges (2)
    17.3.1 Overall discussion
    17.3.2 Biogeochemical modelling needs
    17.3.3 Methodologies for employing output from earth system models
    17.4 Conclusions
    18. Uncertainty and Sensitivity Issues in Process-Based Models of Carbon and Nitrogen Cycles in Terrestrial Ecosystems. Summary.
    18.1 Introduction
    18.2 Uncertainty
    18.2.1 Uncertainty in measurements
    18.2.2 Model uncertainty
    18.2.3 Scenario uncertainty and scaling
    18.3 Model validation
    18.4 Sensitivity analysis
    18.5 Conclusions
    19. Model-Data Fusion in the Studies of Terrestrial Carbon Sink. Summary.
    19.1 Introduction
    19.2 The major obstacles
    19.3 The solutions
    19.3.1 The use of FLUXNET data
    19.3.2 The use of atmospheric CO2 concentration measurements
    19.3.3 The use of remote sensing data
    19.4 The way forward
    20. Building a Community Modelling and Information Sharing Culture. Summary.
    20.1 Introduction
    20.2 Open Source and Hacker Culture
    20.3 Knowledge sharing and Intellectual Property Rights
    20.4 Software Development and Collaborative Research
    20.5 Open Source Software vs. Community Modelling
    20.6 Pros and Cons of Open Source Modelling
    20.7 Open Data
    20.8 Teaching
    20.9 Conclusions and Recommendations

Product details

  • No. of pages: 384
  • Language: English
  • Copyright: © Elsevier Science 2008
  • Published: September 11, 2008
  • Imprint: Elsevier Science
  • Hardcover ISBN: 9780080568867
  • eBook ISBN: 9780080915302

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

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