Probabilistic Hazard Assessment of Extreme Rainfall in Southern Thailand: A Case Study of Ranong Province

Abstract

This study assessed the probabilistic hazard of extreme daily rainfall in Southern Thailand, focusing on Ranong Province as a high-risk case study. Rainfall projections from CMIP6 global climate models under the SSP1-2.6 and SSP3-7.0 scenarios were analyzed. After correcting systematic biases with the Linear Scaling (LS) method, the data were fitted to a Generalized Extreme Value (GEV) distribution. To account for the uncertainty of extreme events beyond historical records, Parametric Bootstrapping Monte Carlo simulations were applied to generate 1,000 synthetic datasets, enabling the estimation of probabilistic hazards and their 95% confidence intervals. The results highlighted Ranong Province as a critical hotspot for extreme precipitation. Under the SSP3-7.0 scenario in the near future (2020–2046), the annual probability of daily rainfall exceeding 300 mm reaches 12.1%. A clear shift in return periods was also observed: a 400-mm rainfall event, which historically had a 100-year return period, was projected to become approximately a 25-year event. These findings, illustrated through spatial hazard maps and probability curves, offered empirical evidence to help engineers and policymakers update infrastructure standards and disaster management plans. However, because these projections depended on specific climate model assumptions and fixed parameters, they must be interpreted with caution regarding model uncertainties.