Assessing the dynamics of Bhasan Char is very crucial, as the Government of Bangladesh (GoB) has recently selected the island as the accommodation of the FDMN. This article critically evaluates the spatiotemporal morphological variations due to erosion, accretion, and subsurface deformation of the island through multi-temporal geospatial and geophysical data analysis, groundwater quality-quantity, and also determines the nature and rate of changes from 2003 to 2020. This is the first study in this island on which multi-temporal Landsat Satellite Imagery and seismic data have been used with geospatial techniques with Digital Shoreline Analysis System (DSAS) and petrel platform, respectively. The analysis of satellite images suggests that the island first appeared in 2003 in the Bay of Bengal, then progressively evolved to the present stable condition. Significant changes have taken place in the morphological and geographical conditions of the island since its inception. Since 2012, the island has been constantly accreted by insignificant erosion. It receives tidally influenced fluvial sediments from the Ganges-Brahmaputra-Meghna (GBM) river system and the sedimentary accretion, in this case, is higher than the erosion due to relatively weaker wave action and longshore currents. It has gained approximately 68 km² area, mostly in the northern part and because of erosion in the south. Although the migration of the Bhasan Char was ubiquitous during 2003–2012, it has been concentrated in a small area to the east since 2018. The net shoreline movements (NSM) suggest that the length of the shoreline enlarged significantly by around 39 km in 2020 from its first appearance. Seismic and GPS data clearly indicate that the island is located on the crest of a slowly uplifting low-amplitude anticline, which may result in a stable landform around the island. Based on the analysis of historical data, it has been assessed that the current configuration of Bhasan Char would not be severely affected by 10–15-foot-high cyclone. Therefore, FDMN rehabilitation here might be safer that would be a good example for future geo-environmental assessment for any areas around the world for rehabilitation of human in remote and vulnerable island. The findings of this research will facilitate the government’s decision to rehabilitate FDMN refugees to the island and also contribute to future research in this area.

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.