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.

The Surma River Basin in northeastern Bangladesh is highly vulnerable to recurrent flash and riverine floods, yet discharge-based flood frequency estimates remain scarce. This study addresses that gap by analyzing 40 years (1985–2024) of annual maximum discharge from two key stations: Kanairghat (SW266) and Sylhet (SW267). Nine probability distributions were evaluated using Product Moments and L-moments, with goodness-of-fit tests (Chi-Square, Kolmogorov-Smirnov, Anderson-Darling) guiding model selection. At Kanairghat, the Generalized Extreme Value distribution with L-moments (GEV-LM) ranked first, capturing the heavy upper tail of flash-driven regimes. At Sylhet, the Log-Pearson Type-III with L-moments (LP3-LM) was optimal, reflecting downstream attenuation by Haor wetlands and channel sedimentation. Trend analysis revealed contrasting stationarity: Kanairghat exhibited a declining signal with a structural break around 1995, while Sylhet remained stationary. Non-stationary GEV modeling projected the 100-year flood at Kanairghat to decrease by roughly 22% by 2030 relative to 1985, underscoring the need to reassess stationary design values. Bootstrap confidence intervals and tail sensitivity analysis showed substantially wider uncertainty bounds at the upstream station. Design water levels consistently exceeded observed rating-curve ranges, indicating that current infrastructure may be under-designed for moderate return periods. The principal methodological contribution is a five-component uncertainty framework that simultaneously quantifies model, estimator, stationarity, sampling, and tail sensitivity uncertainty—a combination not previously applied to discharge-based flood frequency analysis in Bangladesh, offering a fuller characterization of design flood uncertainty than single-component assessments typically provide, within the two-station scope analyzed here. The methodology is directly applicable to other flash-flood-prone regions of Bangladesh, supporting more resilient hydraulic design and flood risk planning.

Riverbank erosion is a significant and recurring hydro-morphological hazard in Bangladesh, particularly along the Jamuna River, one of the most dynamic braided rivers in South Asia. This study aims to assess the spatiotemporal dynamics of riverbank erosion and accretion patterns and evaluate their impact on navigability in the Dewanganj and Islampur upazilas of Jamalpur district. Utilizing 30 years of Landsat satellite imagery (1995–2024), including geospatial tools such as Google Earth Engine and ArcGIS, and morphological data, the study analyzes the extension of lateral and vertical erosion and accretion, bank-line shifts, and river morphology changes. In the results, it is found that lateral migration of the river is very extensive, and during the study period (1995–2024), about 55,436 hectares of land have been eroded, and 53,496 hectares have been accreted, indicating persistent lateral instability. Despite this balance, both satellite imagery and cross-sectional analysis indicate a relative dominance of accretion in recent years, contributing to rising sedimentation and declining river depth, and resulting in a narrowing of navigation routes. Field observations corroborate satellite-derived findings and highlight socioeconomic consequences, such as rising transportation costs (40–60%), longer trip times (60–70%), and greater dredging demands, particularly during the dry season. The study evaluates river dynamics and navigability constraints, showing how well satellite remote sensing, morphological analysis, and field surveys may be integrated. The method used provides a strong framework for river management and navigation planning in sediment-rich alluvial systems and can be extended to other braided rivers in Bangladesh, including the Teesta, Dharla, and Lower Padma.

To predict natural events such as intense precipitation, flooding, and landslides, analyzing trends in rainfall patterns and conducting frequency analysis using the most suitable probability distribution function (PDF) is essential. The study aims to perform trend analysis and conduct frequency analysis after determining the best-fit PDF based on 35 years of annual rainfall data from 1988 to 2023 for nine stations in the Chittagong division, Bangladesh. Sen’s Slope Estimator and the Mann-Kendall trend model were used for trend analysis. Several PDFs, such as Gumbel, Generalized Extreme Value, Pearson Type-III, Log Pearson Type-III, Log-Normal, and Normal, were used, and return periods were estimated. Three goodness-of-fit tests—the Anderson-Darling, Chi-Square, and Kolmogorov-Smirnov—are performed to determine the best-fit PDF. All stations showed a declining trend, except CL325 (which exhibited an upward trend). Three of the stations showed statistically significant trend lines, while the remaining stations were not. Among the nine stations, the Generalized Extreme Value (GEV) distribution is the best fit for five, the Log-Normal for two, the Normal for one, and the Log Pearson Type-III for another, indicating that GEV is the most suitable for rainfall frequency analysis. For the return period, CL325 showed more variation in rainfall, whereas CL302 showed less variation. The findings of this study will enhance the understanding of current rainfall patterns and provide valuable insights into predicting future hydrological extremes, such as intense precipitation and flooding, in the region.