Bayesian calibration and benefits for environmental management

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In water quality assessment and management, mathematical models are used to understand ecological processes, to predict aquatic ecosystem dynamics, to evaluate management alternatives, and to support the policy making process. Environmental models involve substantial uncertainty contributed by both model structure and parameters. The question of model uncertainty is important because models are used to identify polluters, to direct the use of research dollars, and to determine management strategies that have considerable social and economic implications. Erroneous model outputs and failure to account for uncertainty could provide misleading results and misallocation of the limited resources during the costly implementation of alternative environmental management schemes. This research program aims to address the urgent need for novel modelling tools that can effectively support environmental management by combining environmental mathematical modelling with Bayesian analysis. The basic hypothesis is that the Bayesian approach provides a convenient methodology in which past experience along with present ecological information form the basis for more realistic predictions of future ecosystem response. The main focus for the examination of the Bayesian techniques will be placed on lake eutrophication models, but the proposed framework can be easily transferred to a wide variety of disciplines (e.g., hydrology, ecotoxicology, air pollution). Three of the most degraded Canadian large lakes (Ontario, Erie, and Winnipeg) will be used as case studies. Some of the anticipated benefits from this research, such as the control of uncertainty in model predictions, optimization of the sampling design of monitoring programs, alignment with the policy practice of adaptive management (or "learning by doing"), and expression of model outputs as probability distributions will be particularly useful for stakeholders and policy makers when making decisions for sustainable environmental management.