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