Analysis of Urban Flooding Impacts Based on Predicted Precipitation Uncertainty

This study aims to analyze the impact of rainfall prediction uncertainty on urban flooding by focusing on the Dorimcheon basin in Seoul during the heavy rainfall event in the metropolitan area on August 8, 2022. Using AWS observed rainfall data provided by the Korea Meteorological Administration as the baseline, the study evaluated the rainfall prediction performance of two predictive rainfall datasets (LDAPS and MAPLE), estimated manhole overflow volumes, and conducted flood simulations based on these overflow volumes. The results show that the predicted rainfall by LDAPS exhibited an NSE of –0.482 and a PBIAS of 87.692, indicating a significant underestimation. Meanwhile, MAPLE demonstrated an NSE of 0.668 and a PBIAS of –4.176, suggesting an overestimation but achieving quantitatively superior performance. The flood simulation results revealed that LDAPS-based predictions matched AWS-based results with a 5.2% hit rate, whereas MAPLE achieved a hit rate of 91.9%, along with an additional 0.856 km² of flooded area. This study highlights that uncertainty in predictive rainfall datasets significantly impacts urban flood prediction accuracy, emphasizing the necessity of calibration for predictive rainfall data to improve flood prediction reliability.

Funding
This research was supported by the Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT. (No. 2022M3D7A1090338)