Green roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration-validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling-Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration-validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other noncalibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof.Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity.Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.

Green roofs are a popular form of sustainable drainage infrastructure. They provide many environmental benefits, such as reducing peak urban stormwater runoff by enabling retention and evapotranspiration similar to natural conditions. Each green roof has unique hydrologic behavior based on physical properties of its growth medium, types of vegetation, structural design, and climate. To improve the application of green roof technology at a site, there is a need to predict stormwater mitigation for several designs before commencing green roof construction. The Storm Water Management Model (SWMM) includes a low impact development control module which makes it possible to model the hydrologic performance of a green roof by directly defining the physical characteristics of its layers. In this study we compare the outputs of the SWMM model with hydrologic performance data from a large extensive green roof in Syracuse, NY from April 2017 to October 2017. Our objectives are to evaluate the performance of SWMM as a long-term modeling software appropriate for predicting the hydrologic performance of a green roof, and to explore changing parameters that might improve hydrologic performance when designing future green roofs. It is expected that this work will help designers of green roofs in climates similar to those of Central NY. In the future, more extensive hydrologic data will be obtained to enable better assessment of SWMM as a tool to help design green roofs.

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