Uncertainties in Environmental Modelling and Consequences for Policy Making / Edition 1by Philippe Baveye
Pub. Date: 05/15/2009
Publisher: Springer Netherlands
Mathematical modelling has become in recent years an essential tool for the prediction of environmental change and for the development of sustainable policies. Yet, many of the uncertainties associated with modelling efforts appear poorly understood by many, especially by policy makers. This book attempts for the first time to cover the full range of issues related
Mathematical modelling has become in recent years an essential tool for the prediction of environmental change and for the development of sustainable policies. Yet, many of the uncertainties associated with modelling efforts appear poorly understood by many, especially by policy makers. This book attempts for the first time to cover the full range of issues related to model uncertainties, from the subjectivity of setting up a conceptual model of a given system, all the way to communicating the nature of model uncertainties to non-scientists and accounting for model uncertainties in policy decisions. Theoretical chapters, providing background information on specific steps in the modelling process and in the adoption of models by end-users, are complemented by illustrative case studies dealing with soils and global climate change. All the chapters are authored by recognized experts in their respective disciplines, and provide a timely and uniquely comprehensive coverage of an important field.
- Springer Netherlands
- Publication date:
- NATO Science for Peace and Security Series C: Environmental Security
- Edition description:
- Product dimensions:
- 9.21(w) x 6.14(h) x 0.86(d)
Table of ContentsPreface. Contributors. Theme I. Model Conceptualization. Spatially Explicit Versus Lumped Models In Catchment Hydrology Experiences From Two Case Studies; H. Bormann Et Al.- Cellular Automata Modelling Of Environmental Systems; S. Straface And G. Mendicino.- Agent-Based Modeling Of Socio-Economic Processes Related To The Environment: Example Of Land-Use Change; J. G. Polhill.- Theme II. Verification Of Models. Interval Analysis And Verification Of Mathematical Models; T. Csendes.- Stochastic Arithmretic And Verification Of Mathematical Models; J.-M. Chesneaux, et al.- Theme III. Calibration And Sensitivity Analysis. Model Calibration/Parameter Estimation Techniques And Conceptual Model Error; P.Gaganis.- User Subjectivity In Monte Carlo Modelling Of Pesticide Exposure; M. Trevisan.- Recommended Practices In Global Sensitivity Analysis; A. Saltelli et al.- Theme IV. Evaluation Of Models. Incoherence Of The Generalized Likelihood Uncertainty Estimation (GLUE) Methodology And The Equifinality Thesis; E.Todini.- Theme V. Communicating Modelling Results And Uncertainties. Communicating Uncertainty To Policy Makers; A. Patt.- Communicating Scientific Uncertainty For Decision Making About Co2 Storage; P.M. Hogan.- Theme VI. Decision Making On The Basis Of Model Prediction. Approaches To Handling Uncertainty When Setting Environmental Exposure Standards; E. Budtz-Jørgensen et al.- Case Study I. Soil Carbon Dynamics. Sources Of Uncertainty In Future Soil Organic Carbon Storage; Chr. Jones And Pete Falloon.- Uncertainties Related To The Temperature Sensitivity Of Soil Carbon Decomposition; M.J.I. Briones.- Case Study II. Global Climate Change. Media Representational Practices In The Anthropocene Era; M.T. Boykoff.-The Stern Review And The Uncertainties In The Economics Of Climate Change; V. Peña.- Case Study III. Natural Attenuation Of Contamimants And Risk Assessment. Phytotechnologies: HowPlants And Bacteria Work Together; S. Shilev et al.- Subject Index.
and post it to your social network
Most Helpful Customer Reviews
See all customer reviews >