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.

Flood susceptibility mapping (FSM) has become a key area of research in hydrology and disaster management, providing critical insights for identifying vulnerable zones and supporting flood ...

The Sylhet district in Bangladesh experiences frequent flooding due to its geographic setting and heavy monsoon rainfall, further intensified by human-driven activities, including rapid urban expansion, land-use/land-cover alterations, and river encroachment, which collectively cause significant damage to communities, infrastructure, and agriculture. This study aims to delineate flood risk in the district by integrating flood hazard and vulnerability maps using an enhanced GIS-AHP (Analytical Hierarchy Process) approach that incorporates 14 hazard and 8 vulnerability factors. Employing AHP, influencing weights were assigned to hazard and vulnerability defining factors, thereafter mapped in GIS to assess the spatial characteristics of the flood hazard index (FHI) and flood vulnerability index (FVI), thus collectively producing the flood risk index (FRI). Validation of the FHI map was conducted through Receiver Operating Characteristic curve analysis, yielding an Area Under the Curve of 0.858. The results also indicated a high level of correspondence with the flood inundation maps for 2004 and 2022. The FVI map was validated by qualitative analysis, confirming its contextual reliability. The assessment reveals that moderate flood hazard dominates across the district, with upazilas like Companiganj, Gowainghat, Bishwanath, and Balaganj having moderate hazard levels above 80%. However, vulnerability levels are higher in densely populated settlement areas, particularly in Sylhet Sadar, Golapganj, Dakshin Surma, and Biswanath upazilas. Moreover, 74.26% of Sylhet falls within the moderate-risk category. The high-risk zones are mainly clustered in Sylhet Sadar, Biswanath, Dakshin Surma, and Golapganj, where over 40% of the land area is classified as high-risk. The FRI map’s performance was further evaluated using recent flood event information, revealing that flooded areas mostly correspond to moderate- and high-risk zones.

Cities face significant flooding risks due to dense populations and valuable assets, making comprehensive flood risk assessment crucial for effective management. This study evaluates floodi...

Flood is one of the predominant natural hazards, inducing catastrophic impacts on lives, structures, and agriculture in low-elevated and riverine areas like Feni, Bangladesh, highly susceptible to flooding (flash and riverine floods), amplified by intense and prolonged rainfall in the monsoon. This study aims to assess flood-causing factors, risk profile by susceptibility maps, contrasting multi-criteria decision-making (MCDM) models, and sensitivity analysis for the Feni district. The novel aspect includes a comparison of eight MCDM models (subjective, objective, and ranking-based) for flood susceptibility mapping using traditional and DeLong-bootstrap receiver operating characteristic (ROC) curves, sensitivity, and confusion matrix analyses. This study explores twelve fundamental hydro-meteorological factors (Elevation, Slope, Aspect, Curvature, Drainage density, Topographic wetness index, Rainfall, Normalized difference vegetation index, Distance from river, Topographic position index, Land use land cover, and Stream power index), where each factor illustrated statistical significance according to a multicollinearity test applying MCDM methods: Analytical Hierarchy Process (AHP), Fuzzy AHP (FAHP), Analytic Network Process (ANP), Decision Making Trial and Evaluation Laboratory (DEMATEL), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIse Kriterijumska Optimizacijai Kompromisno Resenje (VIKOR), Entropy, and Evaluation Based on Distance from Average Solution (EDAS). The results indicate that 28-62.5% of the area is moderately susceptible to flooding, 0.22-16.84% is very highly susceptible, as assessed by the accuracy using the ROC curve and the area under the curve (AUC) value. The traditional ROC analysis yielded AUC values of 0.93, 0.93, 0.93, 0.93, 0.82, 0.70, 0.92, and 0.85 for AHP, FAHP, ANP, DEMATEL, TOPSIS, VIKOR, Entropy, and EDAS, respectively. Among these methods, DEMATEL, a subjective MCDM approach, exhibited the best predictive performance, achieving the highest AUC (0.935) when further validated using the DeLong bootstrap test, sensitivity, and confusion matrix analysis with supporting data from the Feni District. This study facilitates a novel methodology for flood susceptibility assessment in data-deficient Feni and analogous regions, guiding policymakers and governments for sustainable flood management and increasing the community's resilience.