Flood prediction is difficult in urban areas because only sparse gauge data and radar data of low accuracy are usually used to analyze flooding and inundation. Sub-basins of urban areas are extremely small, so rainfall data of high spatial resolution are required for analyzing complex drainage systems with high spatial variability. This study aimed to produce three types of quantitative precipitation estimation (QPE) products using rainfall data that was derived from 190 gauges, including the new high-density rain-gauge network operated by the SK Planet company, and the automated weather stations of the Korea Meteorological Administration, along with weather radar data. This study also simulated urban runoff for the Gangnam District of Seoul, South Korea, using the obtained QPE products to evaluate hydraulic and hydrologic impacts according to three rainfall fields. The accuracy of this approach was assessed in terms of the amount and spatial distribution of rainfall in an urban area. The QPE products provided highly accurate results and simulations of peak runoff and overflow phenomena. They also accurately described the spatial variability of the rainfall fields. Overall, the integration of high-density gauge data with radar data proved beneficial for quantitative rainfall estimation.

This study presents the development of a statistical flash flood risk index model, which is currently operating in research mode for flash flood risk forecasting in ungauged mountainous areas. The grid-based statistical flash flood risk index, with temporal and spatial resolutions of 1 h and 1 km, respectively, has been developed to simulate the flash flood risk index leading to flash flood casualties using hourly rainfall, surface flow, and soil water content in the previous 6 h. The statistical index model employs factor analysis and multi-linear regression to analyze its gridded hydrological components that are obtained from the TOPMODEL-based Land Atmosphere Transfer Scheme (TOPLATS). The performance of the developed index model has been evaluated in estimating flash flooding in ungauged mountain valleys and small streams. Numerical results show that the approach simulated 38 flash flood catastrophes in the Seoul Capital Region with 71% accuracy; therefore, this approach is potentially adequate for flash flood risk forecasting.