PREDICTING THE HYPOXIC-VOLUME IN CHESAPEAKE BAY WITH THE STREETER-PHELPS MODEL: A BAYESIAN APPROACH

摘要

Hypoxia is a long-standing threat to the integrity of the Chesapeake Bay ecosystem. In this study, we introduce a Bayesian framework that aims to guide the parameter estimation of a Streeter-Phelps model when only hypoxic volume data are available. We present a modeling exercise that addresses a hypothetical scenario under which the only data available are hypoxic volume estimates. To address the identification problem of the model, we formulated informative priors based on available literature information and previous knowledge from the system. Our analysis shows that the use of hypoxic volume data results in reasonable predictive uncertainty, although the variances of the marginal posterior parameter distributions are usually greater than those obtained from fitting the model to dissolved oxygen (DO) profiles. Numerical experiments of joint parameter estimation were also used to facilitate the selection of more parsimonious models that effectively balance between complexity and performance. Parameters with relatively stable posterior means over time and narrow uncertainty bounds were considered as temporally constant, while those with time varying posterior patterns were used to accommodate the interannual variability by assigning year-specific values. Finally, our study offers prescriptive guidelines on how this model can be used to address the hypoxia forecasting in the Chesapeake Bay area.