Abstract
Extreme rainfall poses significant environmental threats with unprecedented consequences to society, particularly in flood-prone regions such as the Sylhet division in the northeastern part of Bangladesh. This study conducts a comprehensive regional frequency analysis of extreme daily rainfall events utilizing advanced statistical techniques, including L-moments, cluster analysis, and Monte Carlo simulations. Clustering methods (Hierarchical Clustering and K-Means Clustering) identified three homogeneous rainfall zones, enabling interpretation of spatial variability. Region 1 (Tajpur, Gobindaganj, Sylhet) exhibited higher annual rainfall and more rainy days, indicative of a persistently wet climate. Region 2 (Zakiganj, Sheola, Lalakhal, Kanairghat) showed intermediate rainfall and seasonal variation, while Region 3 (Bholaganj) experienced irregular and lower rainfall, necessitating localized water management strategies. The Pearson Type 3 (PE3), Generalized Extreme Value (GEV), and Generalized Logistic (GLO) distributions were found to be the most statistically suitable for modeling rainfall extremes for return periods of up to 100 years, although growing uncertainty was observed for longer return periods. This study highlights the importance of integrating regional frequency analysis into flood management, infrastructure planning, and disaster risk reduction, providing a robust framework for data-scarce regions facing increasingly extreme climate conditions.
Proceedings of the International Conference on Civil Engineering Research & Innovations (ICCEI 2025), 12–14 December 2025, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh; Paper ID 994: “Regional Frequency Analysis of Daily Rainfall Extremes Using L-Moments in Sylhet Division, Bangladesh,” Technical Session D4, 13 December 2025, 9:45–11:15 AM, Room CE 2104 (Session Chair: Dr. Md. Aminul Haque, WARPO), In-Person Presentation.
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Md. Jobayer Islam, Sujoy Dey, Nasreen Jahan.
Regional Frequency Analysis of Daily Rainfall Extremes Using L-Moments in Sylhet Division, Bangladesh . Authorea. 17 December 2025.
DOI: https://doi.org/10.22541/au.176582127.74771311/v2
DOI: https://doi.org/10.22541/au.176582127.74771311/v2
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