Spatial Dependence Analysis of Weekly Moving Cumulative Rainfall for Flood Risk Assessment - Prapawan Chomphuwiset; Tossapol Phoophiwfa; Wanlop Kannika; Palakorn Seenoi; Sujitta Suraphee; Jeong-Soo Park; Piyapatr Busababodhin (Atmosphere, 2023)
Climate change has intensified the frequency and severity of extreme weather events, necessitating a nuanced understanding of flood patterns for effective risk management. This study examines flood risk in the Chi watershed, Thailand, using Weekly Moving Cumulative Rainfall (WMCR) data from 1990 to 2021. We employ extreme value copula analysis to assess spatial dependence between meteorological stations in the watershed. Nine bivariate generalized extreme value (BGEV) models were evaluated using the Akaike Information Criterion (AIC) and the Likelihood Ratio test (LRT) to ensure model robustness. The BGEV model revealed higher tail dependence among stations near the bay of the watershed. We also calculated the flood recurrence period to estimate flood events’ frequency and potential severity. Stations ST5 (Khon Kaen), ST6 (Tha Phra Khon Kaen), and ST8 (Maha Sarakham) were identified as potential hotspots, with higher probabilities of experiencing extreme rainfall of approximately 200 (mm.) during the rainy season. These findings provide valuable insights for flood management and mitigation strategies in the Chi watershed and offer a methodological framework adaptable to other regions facing similar challenges.