An Evaluation of the Likelihood of Rainfall-induced Inundation Using the Impact-Based Forecast Model with Heavy Rainfall Damage Data

  In recent years, the intensity and frequency of hazardous weather events, such as heavy rainfall, have been increasing due to the influence of climate change. However, even under similar severe weather conditions, outcomes can vary depending on temporal and topographical features. Therefore, it is important to be aware of the potential impacts when facing the risk of sudden weather disasters and to take proactive measures.

  From August 8th to 10th, 2022, South Korea experienced heavy rainfall, with hourly rainfall exceeding 100mm and daily rainfall exceeding 100-300mm. This resulted in widespread flooding of roads, surrounding the residential and commercial areas, leading to over 5,000 evacuees and 14 fatalities over the Metropolitan area. The damages were significant due to the intense rainfall within a short period. The Korea Meteorological Administration (KMA) has constructed a database of damages caused by rainfall, including this heavy rainfall case. Rainfall damages were collected from the National Fire Agency, the National Disaster Management System (NDMS), and newspapers. Following that, meteorological data (wind speed, temperature, and accumulated precipitation) from Automatic Weather Stations (AWS) was incorporated. Based on this database, the damages caused by heavy rainfall were classified and analyzed.

  The Impact-Based Forecast model for heavy rainfall has been developed to provide region-specific likelihoods of rainfall-induced inundation. This study examined the predicted results from several numerical weather prediction models for heavy rainfall in this case. It is expected that the results of this study will contribute to the production and provision of preemptive rainfall-induced impact information for disaster preparedness.