Flood Risk Assessment Using Extreme Value Copulas in Khong Watershed Northeastern, Thailand

• Published in: Lobachevskii journal of mathematics Vol. 45; no. 12; pp. 6286 - 6295
• Main Authors: Chomphuwiset, Prapawan, Phoophiwfa, Tossapol, Prahadchai, Thanawan, Busababodhin, Piyapatr
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.12.2024; Springer Nature B.V
• Subjects: Algebra; Analysis; Bivariate analysis; Correlation coefficients; Extreme values; Flood control; Flood management; Geometry; Likelihood ratio; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Performance evaluation; Probability Theory and Stochastic Processes; Rainfall; Risk analysis; Watersheds; Thailand; bivariate generalized extreme value distribution; mean residual life plot; drought; return period; peak over threshold; Kendall tau; bivariate generalized Pareto distribution; extreme value copulas; 2010 Mathematics Subject Classification: 62H05, 62G32, 60G70
• ISSN: 1995-0802; 1818-9962
• DOI: 10.1134/S199508022460777X

Floods are one of the most frequent causes of damage to agricultural products. In this research, we analyzed employing bivariate approaches on weekly moving cumulative rainfall (WMCR) data from 1990 to 2023 in Khong Watershed Northeastern, Thailand. The analysis was based on nine extreme value copulas (EVC) derived from the bivariate generalized Pareto distribution (BGPD) model, and using a mean residual life (MRL) plot for choosing a threshold for each. The assessment of the spatial relationship between two areas was evaluated through the Kendall tau correlation coefficient. The research utilized the Likelihood Ratio test (LRT) to evaluate the performance of joint probabilities and XV-CIC to identify the best-fitting EVC models. These selected models were subsequently used to calculate return periods for flood risk in the areas. The result revealed that symmetric models were effective in modeling dependence functions, and the highest flood risk probabilities were in the Loie, Nongkai, and Udon Thani Provinces. Therefore, bivariate risk analysis provides valuable decision-making support for the region’s design and management strategies for flood defenses.