Test application of Bayesian Programming: Adaptive water quality management under uncertainty

• Published in: Advances in water resources Vol. 30; no. 3; pp. 606 - 622
• Main Author: Harrison, Kenneth W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2007; Elsevier Science
• Subjects: Alberta; Bayesian theory; Decision-making under uncertainty; Earth sciences; Earth, ocean, space; Exact sciences and technology; Freshwater; Hydrology. Hydrogeology; Markov chain Monte Carlo; Mathematical programming; Monte Carlo method; pollutants; pollution load; pulp and paper mill effluents; rivers; Stochastic modeling; stochastic processes; Streeter–Phelps water quality model; water pollution; water quality; Alberta; Canada; Athabasca River; Western Canada; Canada, Alberta; Canada, Alberta, Athabasca R; Stochastic modeling; Decision-making under uncertainty; Markov chain Monte Carlo; Streeter–Phelps water quality model; Mathematical programming; Markov chain analysis; bayesian analysis; Monte Carlo analysis; case studies; loading; North America; pollutants; water resources; Streeter-Phelps water quality model; stochastic models; water quality; management; new methods; decision making; strategy; publications
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2006.03.011

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.