The objective of stormwater detention basins is to capture stormwater runoff to reduce and delay peak flow and to improve the water quality. These objectives can be improved upon by actively controlling the outflow of the basins rather than traditional passive outflow structures. There are studies demonstrating the performance of the active controls that respond in real-time to basin hydraulics, detention time, and rainfall forecasts. We hypothesize that the performance of these active controls can be improved upon by incorporating real-time water quality data streams into the control algorithm. Furthermore, we hypothesize that performance of these active controls also depends on hydrologic variability, perturbing the highly dynamic rainfall-runoff process. Here, these hypotheses are tested using a numerical modeling framework evaluating the systemslevel reliability of passive and active control of stormwater basin outflow using a Monte Carlo method. The numerical modeling is performed in EPA-SWMM urban hydrologic model driven by stochastic rainfall time-series generated from the Modified Bartlett-Lewis Rectangular Pulses Model. Water quality-informed real-time active control algorithms are developed, tested, and demonstrated to result in a clear improvement over the traditional passive (no control) systems and other storage-based active controls for water and suspended sediment capture. Duration curve analysis showed that both water level-and water quality-informed control performance varied for different storm return periods and this variability could partly be attributed to the fraction of time the valve is closed. In addition, control performance was sensitive to rainfall variability, generally decreasing as storms become less frequent and more intense. Therefore, control system performance may depend on seasonal and longer timescale variability in climate and rainfall-runoff processes. We anticipate this study to be a starting point to incorporate theories of reliability to assess detention basin and conveyance network performance under more complex real-time control algorithms and failure modes.

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