Assessment of rainfall trends and extremes in the southeastern Chittagong region of Bangladesh using multiple statistical approaches

Understanding rainfall trends and extremes is crucial for anticipating and mitigating the impacts of hydrometeorological hazards. This study aims to provide insights into rainfall variability and extremes in Bangladesh’s southeastern Chittagong region, a hydrologically understudied area prone to significant hydrometeorological hazards, including floods and landslides. The analysis utilizes yearly maximum rainfall data from nine stations over 35 years, spanning from 1988 to 2023. The Mann–Kendall, modified Mann–Kendall trend model, and Sen’s slope estimator were applied for trend analysis. Different probability distribution functions (PDFs), including Normal, Log-Normal, Pearson Type-III, Log Pearson Type-III, Generalized Extreme Value, and Gumbel, were applied to estimate return periods (RPs). To identify the best-fit PDF, three goodness-of-fit tests, Chi-Square, Kolmogorov–Smirnov, and Anderson–Darling, were performed. Every station demonstrated a decreasing trend, except for Patia-CL325, which showed an increasing trend. Of the nine stations, three displayed statistically significant trends. The Generalized Extreme Value (GEV) distribution was the best fit for five stations, namely Amtali-CL301, Anowara-CL302, Chittagong-CL306, Nazirhat-CL324, and Patia-CL325. The Log-Normal distribution was the best fit for two stations, Narayanhat-CL323 and Rangamati-CL328. Meanwhile, the Normal distribution best fitted Rangunia-CL301, and the Log Pearson Type-III best fitted for Fatikchhari-CL301. This indicates that GEV was the most appropriate for frequency analysis in the region. The frequency analysis aligned with the results of trend analysis and also revealed that Patia-CL325 exhibited a larger variation in rainfall, with a 906 mm difference, while Anowara-CL302 showed a smaller variation, with a 66 mm difference, when considering the 500-year RP and the 5% and 95% confidence intervals. This study’s outcomes will improve the understanding of present rainfall trends and offer crucial insights for predicting future hydrometeorological extremes in the region.