A Diagnostic Analysis of Low-Impact Development Simulations with SWMM

Abstract

Green infrastructure (GI) aims to mitigate the impacts of imperviousness by storing and slowing the flow of water through urbanized landscapes. Green roofs, one type of GI, are widely simulated using the Storm Water Management Model (SWMM) but are rarely evaluated using diagnostic analyses. In this study, we utilize frugal diagnostic analyses to investigate potential sources of nonlinearity, uncertainty, and equifinality within SWMM applied to a particular case study: event-based modeling of a large green roof in Syracuse, New York. Our findings highlight the major sources of uncertainty in SWMM—inputs, parameters, structural equations, and reconciling differences between simulated outputs versus observed variables—and demonstrate that more complex diagnostic analysis is necessary to fully understand the fundamental drivers of, and interactions among, sources of uncertainty. When assessing diagnostics in terms of outputs, we achieved strong agreement between simulated and observed runoff but were not able to replicate observed storage time series during simulation. This suggests that common approaches to calibrate only to wet times may misrepresent key hydrologic storages and fluxes within the model.