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• W86301657 abstract "The credibility of the scientific methodology of numerical models and their adequacy to form the basis of public policy decisions have been frequently challenged. The first part of this chapter aims to address the issue of model reliability by evaluating the current state of aquatic biogeochemical modeling. We provide evidence that there is still considerable controversy among modelers and the resource managers about how to develop, evaluate, and interpret mathematical models. Our arguments are that (1) models are not always developed in a consistent manner, clearly stated purpose, and predetermined acceptable model performance level, and (2) the potential users select models without properly assessing their technical value. The second part of this presentation argues that the development of novel methods for rigorously assessing the uncertainty underlying model predictions should be a top priority of the modeling community. Striving for novel uncertainty analysis tools, we introduce Bayesian calibration of process-based models as a methodological advancement that warrants consideration in aquatic ecosystem research. This modeling framework combines the advantageous features of both process-based and statistical approaches, that is, mechanistic understanding that remains within the bounds of data-based parameter estimation. The incorporation of mechanism improves the confidence in predictions made for a variety of conditions, whereas the statistical methods provide an empirical basis for parameter value selection and allow for realistic estimates of predictive uncertainty. Other advantages of the Bayesian approach include the ability to sequentially update beliefs as new knowledge is available, and the consistency with the scientific process of progressive learning and the policy practice of adaptive management. Finally, we illustrate some of the anticipated benefits from the Bayesian calibration framework, well suited for stakeholders and policy makers when making environmental management decisions, using the Hamilton Harbour – a eutrophic system in Ontario, Canada – as a case study." @default.
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• W86301657 date "2011-01-01" @default.
• W86301657 modified "2023-09-27" @default.
• W86301657 title "Integration of Bayesian Inference Techniques with Mathematical Modeling" @default.
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• W86301657 doi "https://doi.org/10.1016/b978-0-12-374711-2.00910-4" @default.
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