Spatial and Temporal Extreme Modeling of Daily Maximum Precipitation Based on a Generalized Additive Model

South Korea experiences significant regional variation in precipitation due to its unique topographical features. Over the years, the intensification of summer rainfall concentration has led to recurring damage from floods and torrential downpours. To mitigate such impacts, the Korea Meteorological Administration monitors precipitation using observed data from weather stations and estimated values for non-observed locations. The Generalized Extreme Value (GEV) distribution is commonly employed to model annual maximum precipitation, enabling the calculation of return levels that serve as foundational data for flood prevention. This study aims to estimate the spatially generalized additive GEV distribution of daily maximum precipitation using data from 54 Automatic Synoptic Observation System (ASOS) stations between 1972 and 2024. Spatial elements (latitude, longitude, altitude) and temporal elements (year) were incorporated into the model. The location, scale, and shape parameters of the GEV distribution were estimated using the maximum likelihood method, with smoothing functions accounting for spatial and temporal factors. The results indicate that the location and scale parameters, influenced by latitude and longitude, are relatively lower in central regions, while the shape parameter, influenced by altitude, shows similar trends. Furthermore, return levels for 50-year and 100-year return periods are notably higher in mountainous regions. Goodness-of-fit tests, such as the Anderson-Darling test, were performed on the GEV distributions of 53 ASOS stations, excluding one. However, 12 stations located in island regions, high-altitude areas, or regions affected by typhoons exhibited distributions that were difficult to explain spatially. These findings are expected to aid in the development of efficient water resource management strategies and regional flood prevention measures based on the distribution characteristics of precipitation.