The urban environment modifies the hydrologic cycle resulting in increased runoff rates, volumes, and peak flows. Green infrastructure, which uses best management practices (BMPs), is a natural system approach used to mitigate the impacts of urbanization onto stormwater runoff. Patterns of stormwater runoff from urban environments are complex, and it is unclear how efficiently green infrastructure will improve the urban water cycle. These challenges arise from issues of scale, the merits of BMPs depend on changes to small‐scale hydrologic processes aggregated up from the neighborhood to the urban watershed. Here, we use a hyper‐resolution (1 m), physically based hydrologic model of the urban hydrologic cycle with explicit inclusion of the built environment. This model represents the changes to hydrology at the BMP scale (~1 m) and represents each individual BMP explicitly to represent response over the urban watershed. Our study varies both the percentage of BMP emplacement and their spatial location for storm events of increasing intensity in an urban watershed. We develop a metric of effectiveness that indicates a nonlinear relationship that is seen between percent BMP emplacement and storm intensity. Results indicate that BMP effectiveness varies with spatial location and that type and emplacement within the urban watershed may be more important than overall percent.

Stormwater represents a complex and dynamic component of the urban water cycle. Hydrologic models have been used to study pre- and post-development hydrology, including green infrastructure. However, many of these models are applied in urban environments with very little formal verification and/or benchmarking. Here we present the results of an intercomparison study between a distributed model (Gridded Surface Subsurface Hydrologic Analysis, GSSHA) and a lumped parameter model (the US Environmental Protection Agency (EPA) Storm Water Management Model, EPA-SWMM) for an urban system. The distributed model scales to higher resolutions, allows for rainfall to be spatially and temporally variable, and solves the shallow water equations. The lumped model uses a non-linear reservoir method to determine runoff rates and volumes. Each model accounts for infiltration, initial abstraction losses, but solves the watershed flow equations in a different way. We use an urban case study with representation of green infrastructure to test the behavior of both models. Results from this case study show that when calibrated, the lumped model is able to represent green infrastructure for small storm events at lower implementation levels. However, as both storm intensity and amount of green infrastructure implementation increase, the lumped model diverges from the distributed model, overpredicting the benefits of green infrastructure on the system. We performed benchmark test cases to evaluate and understand key processes within each model. The results show similarities between the models for the standard cases for simple infiltration. However, as the domain increased in complexity the lumped model diverged from the distributed model. This indicates differences in how the models represent the physical processes and numerical solution approaches used between each. When the distributed model results were used to modify the representation of impermeable surface connections within the lumped model, the results were improved. These results demonstrate how complex, distributed models can be used to improve the formulation of lumped models.