Evapotranspiration (ET) is a critical component of the hydrological cycle, and its spatiotemporal prediction and interpretation are essential for managing agricultural water resources in river basins. However, both physics-based models (PBM) and data-driven models (DDM) have inherent limitations in watershed ET modeling and mechanistic interpretation, while their coupling provides a potential solution by integrating reliable hydrological physical mechanisms with robust nonlinear learning capabilities. This study developed an interpretable coupl...
