Appraisal of Steady-State Stormwater Control Measure Pollutant Removal Models within a Dynamic Stormwater Routing Framework

In this study, three different stormwater control measures (SCMs) pollutant removal models, consisting of the linear regression model, modified Fair and Geyer (MFG) k-C* were simulated under both steady-state dynamic hydraulic conditions to evaluate how applying those models a modeling framework will affect outputs. Uncertainty SCM was also evaluated using Monte Carlo (MC) first-order variance estimation (FOVE) approaches. The calibrated data from International Stormwater Best Management Practices (BMP) database assuming for each event, then applied with variable dictated by runoff generated watershed model. model same results conditions. However, MFG underestimated 20%–90% terms uncertainty, FOVE method prediction intervals that smaller than MC method, 95th percentile outputs generally being 5%–20% lower FOVE, compared MC. partially compensated conditions, such assumptions very similar modeling.