Computational Statistics & Data Analysis
Sensitivity analysis of model output: an investigation of new techniques
Computational Statistics & Data Analysis
Steps to implement Bayesian input distribution selection
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Sharing scientific models in environmental applications
Proceedings of the 2002 ACM symposium on Applied computing
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
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Traditionally, sensitivity and uncertainty analyses in environmental modelling contribute quantitative descriptions of the relative importances of individual parameters and processes, highlighting areas of significant contributors to the overall uncertainties of model predictions and giving markers for areas requiring substantial improvement (perhaps through directed experimental work). Both sensitivity and uncertainty analysis require extensive re-sampling from input data and simulation of model response and there is a large and growing literature concerning their use in environmental modelling. There is however growing interest in the sensitivity of predictions to model structure, and in evaluating the contribution of model structural uncertainty to the overall uncertainty within the general framework of sensitivity analysis (Draper, 1995, Beven, 1993, Beven and Binley, 1992). Sensitivity and uncertainty analysis contribute to all stages of model development, testing and assessment and their impact on model reliability and validity will be described. After a general introduction to sensitivity and uncertainty analysis and discussion of their contributions, some applications of sensitivity analysis to several environmental modelling studies will be presented.