Extreme Rainfall Modeling Using the Generalized Extreme Value Distribution for r-Largest Order Statistics

Information from the abstract

Heavy rainfall is a major driver of floods, flash floods, and landslides, significantly impacting lives, property, and infrastructure. These risks are intensifying under climate change, which increases both the frequency and intensity of extreme rainfall events. This study analyzed the characteristics and assessed the risk of maximum daily cumulative rainfall in Thailand using the Generalized Extreme Value for the r-largest order statistics (GEVr) model. Daily rainfall data from 1996 to 2025 were obtained from NOAA Physical Sciences Laboratory, covering 690 grid points at a spatial resolution of 0.25°. The optimal order r was determined using the strong-stop criterion. Return levels were estimated for return periods of 2, 5, 10, 25, and 50 years. The results indicated that the GEVr model with r =1 adequately described rainfall extremes in 52.90% of the study areas. However, higher orders (r >1) were required in several regions—particularly the Northeast, North, and Southern—to capture multiple extreme rainfall events occurring within the same period. The Gumbel distribution was identified as the most suitable model in 52.75% of the areas. These findings demonstrate that the GEVr model, combined with spatial analysis, provides an effective framework for flood risk assessment, water resource management, and long-term policy planning.

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Related topics: Climate variability and models · Hydrology and Watershed Management Studies · Statistical Distribution Estimation and Applications