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Integrated decision support tools are needed to investigate the tradeoffs of stormwater control measures (SCMs) and determine the optimal suite of SCMs based on the needs of watersheds. In this study, an urbanized watershed undergoing infill development (the Berkeley neighborhood located in Denver, CO, USA) was modeled using a modified version of the U.S. Environmental Protection Agency’s (EPA) System for Urban Stormwater Treatment and Analysis IntegratioN (SUSTAIN). The primary goal was to compare the relative performance between green and grey SCMs, use optimizations and a planning-level approach to assist in decision-making, and discuss how stakeholder and community preferences can shift which SCMs are optimal for the watershed. Green and grey SCMs have variable hydrologic performance based on design and function, and both offer benefits that may be important to decision makers. Our results showed that infiltration trenches and underground infiltration were optimal for reducing flow volumes while vegetated swales and underground detention were optimal for pollutant concentration reduction. Stakeholders value both of these benefits and so the optimal stormwater solution in the Berkeley neighborhood included a mix of green and grey SCMs. Determining the optimal SCMs while considering tradeoffs in costs and associated benefits was complex and multifaceted. Modeling results such as those presented here are critical for informing stakeholders’ decision-making process.

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Misrepresentation of sensitive siting and routing parameters for low impact development (LID) affects hydrologic model outputs, stormwater infrastructure design, and regulatory compliance. ABSTRACT: Cities are experiencing new growth through infill development, or "redevelopment," where lower density land uses are redeveloped to high density with increased impervious surfaces. Cities need revised stormwater criteria to manage increases in stormwater runoff and flooding from redevelopment via low impact development (LID). Watershed-scale hydrologic modeling in the Storm Water Management Model (SWMM) can help policy makers revise regulations to adapt to redevelopment. However, LID modeling is still relatively new and the understanding of model sensitivity to certain parameters is lacking. In particular, LID siting and routing parameters such as outflow routing from, area treated by, and placement of LID are not well studied. Using a case study of a redeveloping neighborhood in Denver, Colorado, we tested 32 configurations of these parameters in a calibrated, SWMM for PC (PCSWMM) model. We found some configurations lead to counteracting model processes that result in similar runoff reduction across a variety of configurations, suggesting to policy makers that reduction targets may be met in a myriad of ways. The relative sensitivity of runoff volume to area treated and LID placement was found to be on average 3.0 and 11.2 times higher than other model parameters. Given this sensitivity, practitioners and modelers should consider LID siting and routing parameters in hydrologic modeling efforts that inform regulations.

Predictions of urban runoff are heavily reliant on semi‐distributed models, which simulate runoff at subcatchment scales. These models often use “effective” model parameters that average across the small‐scale heterogeneity. Here we quantify the error in model prediction that arises when the optimal calibrated value of effective parameters changes with model forcing. The uncertainty this produces, which we refer to as “calibration parameter transfer uncertainty,” can undermine the usefulness of important applications of urban hydrologic models, for example, to predict the hydrologic response to novel climate or development scenarios. Using the urban hydrologic model SWMM (“Stormwater Management Model”) as a case study, we quantify the transferability of two calibrated effective parameters: subcatchment “width” and “connected impervious area.” Through numerical experiments, we simulate overland flow across a highly simplified synthetic urban landscape subject to a range of scenarios (combinations of storm events, soil types, and impervious areas). For each scenario, we calibrate SWMM “width” and “connected impervious area” parameters to the outcomes of a distributed model. We find that the calibrated values of these parameters vary with soil, storm, and land cover forcing. This variation across forcing parameters can result in prediction errors up to a magnitude of 60% when a calibrated SWMM is used to predict runoff following changes in climate and land cover. Such calibration transfer uncertainty is largely unaccounted for in urban hydrologic modeling. These results point to a need for additional research to determine how to use urban hydrologic models to make robust predictions across future conditions.

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