The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability.

Graphical abstract showing climate projections, SWAT-based hydrological modeling, and LSTM machine learning used to predict future river flow changes from rainfall and temperature inputs. The transboundary Upper Meghna Basin (UMB), shared by Bangladesh and India, is very susceptible to climate change due to its complex region characterized by its mountainous origins and deltaic terrain. Cities in the northeastern districts of Bangladesh are particularly vulnerable to climate change, especially in the context of flash floods and monsoon floods. The lack of upstream data limits the performance and efficiency of traditional hydrological models in this basin. To overcome these constraints, this study integrates a deep learning-based Long Short-Term Memory (LSTM) model with CMIP6 models and compares results with a process-based Soil and Water Assessment Tool (SWAT) model. LSTM model outperformed SWAT in hydrological performances, achieving a higher R2 of 0.95 and 0.91 against 0.86 and 0.89, and lower errors (RMSE) for calibration and validation. Annual streamflow will be increased by 7.1% (3.0%–20.2%) and 5.3% (3.1%–14.3%) by the mid-century (2031–2060), and 10.9% (6.9%–20.5%) and 15.0% (6.6%–27.3%) by the end of the century (2071–2100) under SSP2-4.5 and SSP3-7.0, respectively. Monthly low flows in dry season and peak flows in pre-and post-monsoon may rise as much as 30.8% (−3.4%–60.8%), 14.5% (−2.5%–36.3%), and 17.0% (−5.2%–45.0%) by the mid-century under SSP3-7.0.

Graphical abstract summarizing the methodological workflow of the study. The Brahmaputra River Basin (BRB) is expected to experience marked impacts, with climate change likely to alter low-flow conditions, ecological flows, water availability, and drought dynamics. This study applies the SWAT hydrological model to assess potential changes in low flows, ecological flow thresholds, and drought frequency using the Streamflow Drought Index (SDI) under CMIP6 climate scenarios. Low flow exhibits a declining trend for the far future and an increasing trend for the near future, with a 100-year return period. Under SSP3-7.0 in the near future, flows may increase by up to 13% (uncertainty range: 10 to 28%), whereas far-future ensemble projections indicate a potential decline of 8% (uncertainty range: −58 to 11%), which reflects significant inter-model spread. Standardized Discharge Index (SDI) analysis reveals more frequent wet and dry extremes than in the historical baseline, particularly in the far future. Additionally, Ecological flows exhibit a declining trend across all scenarios, with reductions of approximately 2.3% (range: −4.4 to −2.3%) under SSP3-7.0 in the near future. These findings highlight the need for adaptive water management strategies that address both hydrological extremes and the inherent uncertainties of future climate projections.