Markov Chain Model for Seasonal-Water Quality Management

Abstract

A nonstationary Markov chain approach to designing seasonal risk-equivalent water-quality management strategies is presented in this paper. Unlike many existing models for determining seasonal waste load allocations, this approach considers a wider range of possible low-flow conditions than those in the observed historical record. It explicitly incorporates the probabilistic relationships between low-flow data at adjacent gauging stations. The approach is demonstrated for the management of biochemical oxygen demanding (BOD) waste on the Willamette River in Oregon. The results of this application are compared with those of an existing model for seasonal risk-equivalent waste management. The treatment strategies derived for the Markov chain approach are similar, but slightly more conservative than those for the alternative program. Moreover, the ability of these treatment strategies to achieve an acceptable probability of water-quality violation is potentially less sensitive to the length of the historical record.