Author(s): Moonyoung Lee; Heejin An; Seol Jeon; Siyeon Kim; Kichul Jung; Myoung-Jin Um; Daeryong Park

Linked Author(s): Moonyoung Lee, Daeryong Park

Keywords: Nalytical probabilistic model; Rainfall event characteristics; Lognormal distribution; Storm Water Management Model

Abstract: Stormwater management facilities are installed to control urban stormwater quantity and quality. This study deals with an analytical probabilistic (APM) approach for the design of the facilities to control urban flooding. The APM approach has been proposed as a substitution for the continuous simulation approach that is most reliable, but exhausting and time-consuming to organize the simulation. Most of APM studies have shown that the one-parameter exponential distribution for rainfall event characteristics and applied to North America. The annual rainfall distribution in South Korea, unlike that in North America, is relatively concentrated from July to August. Therefore, it is necessary to determine the most appropriate probability distribution for the rainfall event characteristics in South Korea. This study developed a suitable APM to simulate runoff event volumes in South Korea and verified by comparison with the continuous simulation model. The rainfall event characteristics, such as rainfall event volume, duration, and interevent time are investigated for 30-year rainfall data for Seoul station in South Korea. It confirmed that the probability density function (PDF) of the lognormal distribution best fits the histogram of rainfall event characteristics. The APM is developed by deriving the PDF of the runoff event volume with the lognormal distribution. In the derivation process, the rainfall-runoff transformation type considers the perviousness and imperviousness of the study areas. A frequency analysis was subsequently performed based on the runoff event volume obtained by the APM to verify the suitability of the model. The runoff event volumes in the APM were compared with the runoff event volumes simulated by the Storm Water Management Model (SWMM) from the United States Environmental Protection Agency (US EPA). The exceedance probabilities and return periods of the developed APM with lognormal distribution are very close to SWMM results. However, the return periods of APM are overestimated and the adjustment needs to the model to match the SWMM results. Acknowledgments: This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1007447 and 2019R1F1A1060028), and has been performed as Project No Open Innovation R&D (21-BC-002) supported by K-water.

DOI: https://doi.org/10.3850/IAHR-39WC2521711920221655

Year: 2022