A coupled physics-based and machine-learning approach for enhancing daily streamflow simulations in data-scarce regions

Structural uncertainty in physics-based models (PBMs) and the limited generalisability of purely data-driven techniques limit the ability of predicting daily streamflow in catchments in data-scare region. In order to enhance prediction accuracy and geographic transferability, this study proposes a coupled physics-based-machine learning (PBM-ML) framework that combines knowledge of hydrological processes with data-driven learning. The framework was tested in multiple catchments with different hydroclimatic conditions, encompassing basins in Japan and New Zealand. PBM-derived states and fluxes were fed into machine-learning models after a PBM (SWAT) was first calibrated to simulate daily streamflow. The Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R²) were used to evaluate the performance of the model. Coupled PBM-ML models consistently performed better than standalone SWAT in all basins. Testing NSE improved from 0.69-0.76 for SWAT to 0.80-0.89 for coupled models in New Zealand and from 0.67-0.68 to 0.74-0.86 in Japan. SWAT-LSTM had the best prediction ability among the hybrid methods. Regionalization approaches were used to investigate the transferability of the model. The coupled models retained robust performance under partially gauged and fully ungauged conditions. These findings demonstrate that PBM-ML coupling could enhance streamflow prediction and transferability in data-scarce regions.