Climate-driven streamflow and extreme flow projections using machine learning in the Brahmaputra basin

ABSTRACT This study quantifies the impact of projected climate change on the daily streamflow of the Brahmaputra River (Bahadurabad outlet, Bangladesh) using four data-driven models: support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Trained on 1981–2008 data with rainfall and temperature as predictors and tested on 2009–2014 (Bi-LSTM and RF outperformed with R2 ≈ 0.90), the models were forced with bias-corrected projections from 13 CMIP6 GCMs under six composite scenarios (coolest to wettest) for the 2030s, 2050s, and 2080s. Key findings indicate a significant alteration of the hydrograph, characterized by an earlier monsoon rise, higher July–August flow plateaus (∼50,000–70,000 m3/s by the 2080s under warm/wet scenarios), and a slower recession. Projections also show intensifying extremes, with median monsoon monthly maxima reaching ∼66,000–90,000 m3/s by the 2080s, model-dependent. Crucially, the analysis reveals a trend toward greater seasonal variability, where the wet season becomes wetter while the dry season may become even drier, particularly under the driest and coolest scenarios. Mean annual flow changes by the 2080s range from −1% (coolest) to +50% (wettest). The results unanimously project stronger, longer monsoon flows and amplified peaks, and substantially extended flood risk, particularly under warmer and wetter futures.