Environmental Modelling, Software and Decision Support book cover

Environmental Modelling, Software and Decision Support

State of the art and new perspective

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.

Audience
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

Hardbound, 384 Pages

Published: October 2008

Imprint: Elsevier

ISBN: 978-0-08-056886-7

Contents

  • Preface1. Modelling and Software as Instruments for Advancing Sustainability. Summary1.1 Introduction1.2 Aims of the Summit1.3 The role of modelling and software1.4 Common problems in modelling1.5 Current state of the art and future challenges in modelling1.5.1 Generic issues1.5.2 Sectoral issues1.6 ConclusionsReferences2. Good Modelling Practice. Summary2.1 Introduction2.2 Key components of good modelling practice2.2.1 Model purpose2.2.2 Model evaluation2.2.3 Performance measures2.2.4 Stating and testing model assumptions2.2.5 Ongoing model testing and evaluation2.3 Model transparency and dissemination2.3.1 Terminology 2.3.2 Reporting2.3.3 Model dissemination2.4 A definition of good modelling practice2.5 Progress towards good modelling practice2.6 Recommendations References.3. Bridging the Gaps between Design and Use: Developing Tools to Support Environmental Management and Policy. Summary3.1 A gap between design and use?3.2 Decision and information support tool review3.3 Supporting organisational decision making3.4 Supporting participatory and collaborative decision making3.5 The nature and extent of the gap3.6 Good practice guidelines for involving users in development3.6.1 Know the capabilities and limitations of DIST technologies3.6.2 Focus on process not product3.6.3 Understand roles, responsibilities and requirements3.6.4 Work collaboratively3.6.5 Build and maintain trust and credibility3.7 ConclusionsReferences.4. Complexity and Uncertainty: Rethinking the Modelling Activity. Summary.4.1 Introduction4.2 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 rule4.4.2 Exploratory analysis: microeconomic modelling of land use change in a coastal zone area4.4.3 Communication: modelling water quality at different scales and different levels of complexity4.4.4 Learning: modelling for strategic river planning in the Maas, the Netherlands4.5 Conclusions4.5.1 Models for prediction purposes4.5.2 Models for exploratory purposes4.5.3 Models for communication purposes4.5.4 Models for learning purposesReferences.5. Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions. Summary.5.1 Introduction5.2 Environmental Decision-Making Process5.3 Sources of Uncertainty5.4 Progress, Challenges and Future Directions5.4.1 Risk-based assessment criteria5.4.2 Uncertainty in human input5.4.3 Computational efficiency5.4.4 Integrated software frameworks for decision making under uncertainty5.5 ConclusionsReferences.6. Environmental Policy Aid under Uncertainty.Summary.6.1 Introduction6.2 Factors influencing perceptions of uncertainty6.3 Uncertainty in decision models6.4 Uncertainty in practical policy making6.5 Reducing uncertainty through innovative policy interventions6.6 Discussion and conclusionsReferences.7. Integrated Modelling Frameworks for Environmental Assessment and Decision Support. Summary.7.1 Introduction7.1.1 A first definition7.1.2 Why do we develop new frameworks?7.1.3 A more insightful definition7.2 A generic architecture for EIMFs7.2.1 A vision7.3 Knowledge representation and management7.3.1 Challenges for knowledge-based environmental modelling7.4 Model Engineering7.4.1 Component-based modelling7.4.2 Distributed modelling7.5 Driving and supporting the modelling process7.5.1 The experimental frame7.6 ConclusionsReferences.8. Intelligent Environmental Decision Support Systems. Summary.8.1 Introduction8.1.1 Complexity of environmental systems8.1.2 New tools for a new paradigm8.2 Intelligent environmental decision support systems8.2.1 IEDSS development8.3 About uncertainty management 8.4 Temporal reasoning 8.4.1 Featuring the problem8.4.2 Approaches to temporal reasoning8.4.3 Case-based reasoning for temporal reasoning8.5 Geographic information and spatial reasoning8.5.1 Understanding spatial reasoning8.5.2 Kriging and variants8.5.3 Representing change/time steps/feedback loops8.5.4 Middleware, blackboards and communication protocols8.5.5 Multiagent systems8.6 Evaluation of IEDSS and benchmarking8.6.1 Benchmarking8.7 Conclusions and future trendsReferences.9. Formal Scenario Development for Environmental Impact Assessment Studies. Summary.9.1 Introduction9.2 Terminology and background 9.2.1 Terminology9.2.2 Characteristics of scenarios9.3 A formal approach to scenario development9.3.1 Scenario definition9.3.2 Scenario construction9.3.3 Scenario analysis9.3.4 Scenario assessment9.3.5 Risk management9.4 Monitoring and post-audits9.5 Discussions and future directions9.5.1 Uncertainty issues9.5.2 Potential obstacles to formal scenario development9.5.3 Future recommendationsReferences.10. Free and Open Source Geospatial Tools for Environmental Modelling and Management. Summary.10.1 Introduction10.2 Platform10.3 Software stack10.3.1 Geospatial software stacks10.3.2 System software10.3.3 Geospatial data processing libraries10.3.4 Data serving10.3.5 User Interface10.3.6 End-user applications10.4 Workflows for environmental modelling and management10.4.1 Case 1 – Cartographic map production10.4.2 Case 2 – Web-based mapping10.4.3 Case 3 – Numerical Simulation10.4.4 Case 4 – Environmental management10.5 Discussion10.6 ConclusionReferences.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 problems11.3 Adaptivity11.3.1 Limitations of feedback and motivation for adaptivity11.3.2 Adaptive control and its failings11.4 Problems in adaptive management and some tools from other fields11.4.1 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 control11.4.4 Adaptive management and Bayesian analysis11.4.5 “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 management11.5.1 Characterisation of uncertainty11.5.2 Matching the model to system characteristics11.5.3 Bottom-up and top-down modelling11.6 Conclusions preceding the workshopAppendix: Summary of workshop discussionReferences.12 Data Mining for Environmental SystemsSummary.12.1 Introduction 12.2 Data mining techniques 12.2.1 Preprocessing: data cleaning, outlier detection, missing value treatment, transformation and creation of variables12.2.2 Data reduction and projection12.2.3 Visualisation12.2.4 Clustering and density estimation12.2.5 Classification and regression methods12.2.6 Association analysis12.2.7 Artificial Neural Networks12.2.8 Other techniques12.2.9 Spatial and temporal aspects of environmental data mining12.3 Guidelines for good data mining practice12.3.1 Integrated approaches12.4 Software - existing and under development12.5 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 comparisons13.3 Stakeholder Involvement13.4 Enhancing non-expert modelling accessibility13.5 Reaching out to younger generations13.6 The current state of the art - results of workshop discussion13.6.1 On detail and complexity13.6.2 On stakeholder participation and shared vision modelling13.6.3 On applied technology13.6.4 On development and continuity13.6.5 On content13.7 Overall conclusionReferences.14. Computational Air Quality Modelling. Summary.14.1 Introduction14.2 The purpose of air quality modelling14.3 Urban air quality information and forecasting systems14.4 Integrated modelling 14.5 Air quality modelling for environment and health risk assessments14.6 Air quality modelling as a natural part of climate change modelling14.7 Scales of the processes/models and scale-interaction aspects14.8 Chemical schemes and aerosol treatment14.9 Real-time air quality modelling14.10 Internet and information technologies for air quality modelling14.11 Application category examplesReferences.15. State of the Art in Methods and Software for the Identification, Resolution and Apportionment of Contamination Sources. Summary.15.1 Introduction15.2 Data sets15.3 Models and Methods15.3.1 Principal Component Analysis and Factor Analysis 15.3.2 Alternatives to PCA based methods 15.3.3 Other Related Techniques 15.4 Some Applications15.4.1 Combined Aerosol Trajectory Tools 15.4.2 Source identification in southern California by nonparametric regression15.4.3 Comparison between PMF and PCA-MLRA performance 15.5 ConclusionsReferences.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 forward16.3 Landscape models16.3.1 Selection of landscape indicators16.3.2 REMICs16.3.3 Hybrid models16.3.4 Complexity in landscape modelling16.4 A sample modelling tool16.5 ConclusionsReferences.17. Challenges in Earth System Modelling: Approaches and Applications. Summary.17.1 Introduction17.2 Key challenges (1)17.2.1 Atmosphere modelling17.2.2 Land modelling17.2.3 Ocean modelling17.3 Key challenges (2)17.3.1 Overall discussion17.3.2 Biogeochemical modelling needs17.3.3 Methodologies for employing output from earth system models 17.4 ConclusionsReferences.18. Uncertainty and Sensitivity Issues in Process-Based Models of Carbon and Nitrogen Cycles in Terrestrial Ecosystems. Summary.18.1 Introduction18.2 Uncertainty18.2.1 Uncertainty in measurements18.2.2 Model uncertainty18.2.3 Scenario uncertainty and scaling18.3 Model validation18.4 Sensitivity analysis18.5 ConclusionsReferences.19. Model-Data Fusion in the Studies of Terrestrial Carbon Sink. Summary.19.1 Introduction19.2 The major obstacles19.3 The solutions19.3.1 The use of FLUXNET data19.3.2 The use of atmospheric CO2 concentration measurements19.3.3 The use of remote sensing data19.4 The way forwardReferences.20. Building a Community Modelling and Information Sharing Culture. Summary.20.1 Introduction20.2 Open Source and Hacker Culture 20.3 Knowledge sharing and Intellectual Property Rights20.4 Software Development and Collaborative Research20.5 Open Source Software vs. Community Modelling20.6 Pros and Cons of Open Source Modelling20.7 Open Data20.8 Teaching20.9 Conclusions and RecommendationsReferences

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