This manuscript outlines a novel approach to predicting green stormwater infrastructure (GSI) performance by utilizing continuous simulations with the Environmental Protection Agency's Stormwater Management Model (SWMM). Simulations using continuous rain data were used to estimate the long‐term (annual) benefits of implementing bioretention cells (BRCs) at selected sites instead of focusing on their performance during large events (e.g., 10 yr storm) for which these practices were not designed. In addition, the simulations (66 total) provided estimates of potential runoff volume reduction (22%–76% of runoff volume) for comparing BRC implementation on a wide range of soil types (saturated hydraulic conductivity 0.07–18.18 cm/h). This study indicates that soils (Types A, B, and C) with saturated hydraulic conductivity of 0.3 cm/h can result in a minimum of 50% reduction in annual runoff. For soils with hydraulic conductivities of less than 0.3 cm/h, the soil property, effective suction at the wetting front (Sf), was an important indicator of infiltration rate since those soils in the “higher” range of effective suction at the wetting front, resulted in substantially higher infiltration. The models for this work were built and tested based on the geographic information system (GIS) analysis of readily available parcel data (e.g., geographic location, soil, and area) and remote sensing data (e.g., site imperviousness) for Lucas County, Ohio. It was estimated that over 60% of the developed land in Lucas County, OH is suitable for installing bioretention cells, which could significantly (>70%) reduce stormwater runoff from parking lots. This transferable approach can be used to identify preferred sites for installing green stormwater infrastructure, where the hydrologic performance benefits are maximized. © 2016 American Institute of Chemical Engineers Environ Prog, 36: 557–564, 2017

In this study, water depth measurements were collected in storm water infrastructure during rain events using a pressure based water level sensor. Irregularities within the measured water level datasets required data smoothing to prepare the observed data for calibration. A rainfall-runoff model was created using a proprietary version of the U.S. Environmental Protection Agency's Storm Water Management Model, PCSWMM, to predict the performance of recently implemented green stormwater infrastructure with respect to runoff at the site. The PCSWMM model calibration was accomplished by comparing water level data collected on site to the PCSWMM output data produced by the uncalibrated model. Nash-Sutcliffe efficiency was used to assess the performance of the calibration procedure. Sensitivity analyses of the estimated parameters were performed to assess the impacts of the model parameters on overall model output. The overarching objective of the study was to demonstrate the value of inexpensive and readily available real-time pressure based water level sensor data to calibrate a PCSWMM model.