Environmental Modelling, Software and Decision Support
State of the Art and New Perspective
- 1st Edition, Volume 3 - November 11, 2008
- Latest edition
- Editors: Anthony J. Jakeman, Alexey A. Voinov, Andrea E. Rizzoli, Serena H. Chen
- Language: English
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… Read more
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.
- 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
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
3.6.1 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
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. 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
References.
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
References.
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
References.
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
References.
12 Data Mining for Environmental Systems
Summary.
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
References.
13. Generic Simulation Models for Facilitating Stakeholder Involvement in Water Resources Planning and Management: a Comparison, Evaluation, and Identification of Future Needs
Summary.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References
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
3.6.1 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
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. 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
References.
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
References.
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
References.
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
References.
12 Data Mining for Environmental Systems
Summary.
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
References.
13. Generic Simulation Models for Facilitating Stakeholder Involvement in Water Resources Planning and Management: a Comparison, Evaluation, and Identification of Future Needs
Summary.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References.
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
References
- Edition: 1
- Latest edition
- Volume: 3
- Published: November 11, 2008
- Language: English
AV
Alexey A. Voinov
Affiliations and expertise
Johns Hopkins University and Fellow at Gund Institute for Ecological Economics, USA
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