Integrating SWAT and machine learning for streamflow simulation and runoff sensitivity in the Tawi watershed
Accurate streamflow simulation is essential for water resource management in data-scarce Himalayan watersheds, where hydro-climatic variability and land-use changes influence hydrological processes. This study evaluates the performance of the Soil and Water Assessment Tool (SWAT) and machine learning (ML) models, Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), for simulating streamflow in the Tawi River watershed using hydroclimatic data from 2000 to 2020. SWAT performance was assessed using statistical indicators and hydrograph analysis. Despite a negative PBIAS (− 30.92%) during calibration (2002–2014), the model showed satisfactory performance (R2 = 0.80, NSE = 0.67) and improved validation results (2015–2020) (R2 = 0.85, NSE = 0.77, PBIAS = − 12.50%). Machine learning models were trained on historical data and evaluated over the overlap period (2015–2020). XGBoost (R2 = 0.77, RMSE = 24.34 m3/s) and Random Forest (R2 = 0.74) outperformed SVR and ANN, which showed lower accuracy and underestimation of peak flows. Comparative analysis indicates that SWAT provides physically interpretable and consistent simulations of streamflow dynamics, whereas machine learning models, particularly XGBoost and RF, offer efficient data-driven predictions with strong capability in capturing nonlinear relationships. Sensitivity analysis revealed rainfall as the dominant control on streamflow (εRF up to 4.93), while temperature showed a comparatively weaker influence. Overall, the results demonstrate that process-based and data-driven approaches provide complementary strengths for streamflow simulation; however, the extended simulations (2021–2050) represent statistical or stationary projections and should not be interpreted as climate-driven future forecasts.