Environmental Decision-Making in the Face of Uncertainty: A Bayesian approach
In water quality management, mathematical models are used to evaluate management alternatives, and to support the policy making process. Environmental models involve substantial uncertainty which can be very critical when striving to identify polluters, to direct the use of taxpayers' dollars, and to determine management strategies that have considerable social and economic implications. This research project aims to address the urgent need for novel policy analysis tools that can effectively support environmental management by combining environmental modelling with Bayesian inference techniques. One of the most degraded Canadian freshwater ecosystems (Bay of Quinte) will be used as a case study. Some of the anticipated benefits from this research, such as the quantification of uncertainty in model predictions, alignment with the policy practice of adaptive management, and expression of model outputs as probability distributions will be useful for stakeholders and policy makers when making decisions for sustainable environmental management.