Performance Modeling of Storm Water Best Management Practices with Uncertainty Analysis

Abstract

The performance of storm water best management practices (BMPs) contains many uncertainties that make predicting BMP performance difficult. The objective of this study is to build a BMP performance model that incorporates uncertainty analysis and to evaluate this model using observed total suspended solids (TSS) from detention basin data sets in the International Stormwater BMP Database. The representative storage-treatment performance model, the k-C* model, was chosen to represent BMP performance. Its input parameters, influent event mean concentration (EMC) (Cin), and the areal removal rate constant (k) are considered with the uncertainty analysis. To estimate the variance associated with k, the prediction interval method is applied to the linear regression equation relating hydraulic loading rate (q) to k. To estimate the variance of Cin, observed Cin data in the BMP database are used. This study assumes that both Cin and k can be represented by lognormal distributions. The probability density function...