Probabilistic Assessment of Extended Detention Basins: Role of Model Parameter Uncertainty

Abstract

This study investigates the role of parameter uncertainty on evaluation of stormwater control measures. Specifically, several formal and informal Bayesian uncertainty analysis techniques are linked with the modified Fair and Geyer model to predict effluent total suspended solids (TSS) concentrations from extended detention basins (EDBs). Moreover, a global sensitivity analysis is performed to identify the most important parameters of the model. The results indicate that TSS removal in EDBs is most sensitive to the particle size distribution and particle density of solids in the runoff entering EDBs. Although formal Bayesian techniques estimated narrower prediction intervals, the inclusion rate of observed data was substantially lower than those estimated using informal methods. These results suggest that formal Bayesian methods may overconstrain the posterior parameter space and hence culminate in underestimation of the uncertainty in pollution removal effects of EDBs. The study reveals that selection of uncertainty analysis methods should be carefully conducted to ensure statistically rigorous and appropriate probabilistic characterization of the performance of stormwater control measures and the accompanying uncertainty in that performance.