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.

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health.

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Antibiotic resistance is a public health threat that is exacerbated by the dispersion of antibiotic resistance genes (ARGs) into aquatic environments. Urban stormwater runoff has been recognized as a source...