Comparative Analysis of HEC-HMS and Temporal Fusion Transformer for Streamflow Prediction under Climate Change Scenarios in the Swat River Basin

Climate change is expected to significantly alter hydrological regimes in mountainous river basins, increasing uncertainty in future streamflow and flood risk. This study assesses historical and future streamflow dynamics of the Swat River Basin, Pakistan. A comparative framework was adopted using a physically based semi-distributed model (HEC-HMS) and a data-driven deep learning model, the Temporal Fusion Transformer (TFT). Eleven downscaled CMIP6 Global Climate Models (GCMs) were first evaluated using Recursive Feature Elimination coupled with a Random Forest algorithm, resulting in the selection of three top-performing models: INM-CM4-8, MRI-ESM-2-0, and GFDL-ESM4. Historical simulations showed that while HEC-HMS performed satisfactorily in reproducing seasonal flows and runoff volumes, it underestimated extreme flood peaks. In contrast, the TFT model demonstrated very high predictive accuracy and effectively captured extreme events, including the catastrophic 2010 flood. Future projections (2020–2100) under SSP2-4.5 and SSP5-8.5 scenarios indicate a substantial increase in peak flows, particularly under high-emission pathways, with several extreme events projected to occur in the near future. Overall, the results highlight the limitations of conventional hydrological models in simulating non-linear extremes and demonstrate the strong potential of advanced machine-learning models for climate change impact assessment and flood risk management in data-scarce mountainous basins.