As heavy precipitation events increase in intensity and frequency, urban flooding wreaks havoc on energy and water services, transportation hubs, housing, education, and employment.
Modeling flooding in urban watersheds is challenging because of the spatial variations and interactions between land cover, landscape characteristics, and precipitation that affect the hydrologic response. A new paper in the Journal of Hydrologic Engineering, “Modeling Storm Sewer Networks and Urban Flooding in Roanoke, Virginia, with SWMM and GSSHA,” by Conrad E. Brendel, Randel L. Dymond, and Marcus F. Aguilar, presents a case study describing the development and evaluation of both a semidistributed and a fully distributed model to address urban flooding issues in a medium-sized urbanized area.
Abstract
Modeling urban flooding is challenging because of complex spatial variations and interactions between precipitation, land cover, and drainage networks. This paper presents a case study of the development of two hydrology and hydraulics models – the semidistributed stormwater management model (SWMM) and the fully distributed gridded surface/subsurface hydrologic analysis (GSSHA) model – to simulate the hydrologic response of two neighboring urban watersheds with large storm sewer networks in the city of Roanoke, Virginia. Both models were calibrated and validated for the two watersheds based on nine events (May-October 2018), and the models were assessed on their ability to replicate measured stream discharge and storm sewer flow depths. The findings from the study indicate that both models reasonably capture the observed hydrologic responses but that each model offers unique benefits. Overall, SWMM’s value to the city is its ability to provide detailed information regarding the hydraulic conditions within the city’s storm sewer network, whereas GSSHA’s value to the city is its ability to predict the duration and spatial extent of flooding in two dimensions.
Read the full paper in the ASCE Library: https://doi.org/10.1061/(ASCE)HE.1943-5584.0002021
