Test application of Bayesian Programming: Adaptive water quality management under uncertainty |
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Authors: | Kenneth W Harrison |
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Institution: | Department of Civil and Environmental Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, United States |
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Abstract: | A new method, Bayesian Programming (BP), developed by Harrison Harrison KW. Multi-stage decision-making under uncertainty and stochasticity: Bayesian Programming. Adv Water Resour, submitted for publication] is tested on a case study involving optimal adaptive management of a river basin. The case study considers anew the process of permitting pulp mills on the Athabasca River in Alberta, Canada. The problem has characteristics common to many environmental management problems. There is uncertainty in the water quality response to pollutant loadings that will not be completely resolved with monitoring and the resolution of this uncertainty is impeded by the stochastic behavior of the water quality system. A two-stage adaptive management process is optimized with BP. Based on monitoring data collected after implementation of the first-stage decision, the uncertainties are updated prior to the second decision stage using Bayesian analysis. The worth of this two-stage adaptive management approach to this problem and the worth of monitoring are evaluated. Conclusions are drawn on the general practicality of BP for adaptive management. Potential strategies are outlined for extending the BP approach to secure further benefits of adaptive management. |
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Keywords: | Decision-making under uncertainty Stochastic modeling Markov chain Monte Carlo Mathematical programming Streeter&ndash Phelps water quality model |
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