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Use of green stormwater infrastructure (GSI) to mitigate urban runoff impacts has grown substantially in recent decades, but municipalities often lack an integrated approach to prioritize areas for implementation, demonstrate compelling evidence of catchment-scale improvements, and communicate stormwater program effectiveness. We present a method for quantifying runoff reduction benefits associated with distributed GSI that is designed to align with the spatial scale of information required by urban stormwater implementation. The model was driven by a probabilistic representation of rainfall events to estimate annual runoff and reductions associated with distributed GSI for various design storm levels. Raster-based calculations provide estimates on a 30-m grid, preserving unique combinations of drainage factors that drive runoff production, hydrologic storage, and infiltration benefits of GSI. The model showed strong correspondence with aggregated continuous runoff data from a set of urbanized catchments in Salinas, California, USA, over a three-year monitoring period and output sensitivity to the storm drain network inputs. Because the model runs through a web browser and the parameterization is based on readily available spatial data, it is suitable for nonmodeling experts to rapidly update GSI features, compare alternative implementation scenarios, track progress toward urban runoff reduction goals, and demonstrate regulatory compliance. [ABSTRACT FROM AUTHOR]

Urban stormwater runoff is among the most significant sources of trash delivery to waterways, degrading aquatic habitats and contributing to oceanic trash gyres across the globe. Municipal water quality permits that require elimination of trash inputs to stormwater systems employ visual trash assessments on city streets to demonstrate litter reduction progress. We present a novel method to increase the utility of these assessments by quantifying their degree of certainty at a granular spatial scale via Bayesian credibility intervals. Using data collected in the City of Salinas, California, we illustrate how the outputs can be used to determine effective trash controls and prioritize areas for management actions. Spatial dependence was incorporated to the uncertainty estimates within individual stormwater drainages and results were interpolated to adjacent parcels. After 3–6 observation periods over 20 months, we found approximately 30% of the city area showed minimal litter accumulation at an 80% certainty level. The outputs provide a practical alternative for cities to determine compliance with stormwater trash regulations, update understanding of trash accumulation patterns, and iteratively adjust sampling designs in response to new observations. The methods described have been implemented as a web-based geospatial decision support tool to help stormwater managers target implementation actions and report progress to regulators. • An approach is presented to quantify uncertainty associated with assessments of urban trash loading to stormwater systems. • Outputs can be used to track trash mitigation effectiveness and prioritize areas for action. • The approach has been incorporated into web-based geospatial stormwater management platform. [ABSTRACT FROM AUTHOR]