Evaluation of a hydrology and run-off BMP model in SUSTAIN on a commercial area and a public park in South Korea

Abstract

Adapting best management practices (BMPs) is influenced by target reduction efficiency BMP size, and BMP type. The System for Urban Storm water Treatment and Analysis INtegration (SUSTAIN) model was evaluated to determine optimal size and type of BMP with monitoring results from a commercial area and a public park in Korea. The hydrology model in SUSTAIN was tested in a commercial area (impervious area: 85%) and a public park (impervious area: 36%) in South Korea. A sensitivity analysis revealed that the significant parameters for total flow were impervious area Manning’s roughness (IMPN) and saturated hydraulic conductivity (HYDCON); and those for peak flow were IMPN, Manning’s roughness of conduit (ROUGH) and HYDCON. The observed average run-off ratios of the two study sites were 0.59 and 0.30 for the commercial area and the public park, respectively. In contrast, the simulated average run-off ratios were 0.53 and 0.22, respectively. The SUSTAIN hydrology model was also evaluated statistically by comparing observed and simulated run-off. In a commercial area, R2, root mean square error, and Nash–Sutcliffe efficiency were 0.68, 10.98, and 0.46, respectively, whereas the public park yielded 0.74, 1.97, and 0.62, respectively. After calibrating the model, the BMP options of SUSTAIN (i.e. bioretention, dry pond, and wet pond) were utilized to test run-off reduction capability with 11 mm of retaining run-off depth from the commercial area and 3 mm from the public park. Monitoring data showed that 11 and 3 mm run-off storage ensured about a 50% reduction of run-off from the commercial area and the public park, respectively. In the commercial area, average reduction rates were identically all 43.0% for bioretention, dry pond, and wet pond, respectively, and those for the public park were 49.6, 57.6, and 53.5%, respectively. Overall, the BMP function of SUSTAIN seemed to be reasonable for reducing run-off and could be used to design BMP to meet a target reduction goal where monitoring data does not exist.