Abstract Accurately forecasting daily streamflow remains challenging as existing models struggle to balance flexibility for rapid process shifts with physical consistency. Here we present HydroMoE, a dynamic-gated Mixture-of-Experts framework pairing process-based and neural experts for each hydrological subprocess. Meteorology-responsive gates learn to allocate weight between physical and neural experts, advancing hybrid modelling toward interpretable process understanding. Across 550 CAMELS-United States basins, HydroMoE substantially improves daily streamflow prediction. In the independent test period, HydroMoE achieved a median NSE of 0.663 and a median KGE of 0.638, compared with 0.127 and 0.210 for a differentiable process-based baseline and single-module neural ablations. The learned gating weights exhibit physically interpretable patterns: runoff experts are activated during storm events, seasonal cycles align with snowmelt and evapotranspiration dynamics, and spatial transitions correspond to Köppen-Geiger climate zones. Future work may extend this framework to integrate multiple competing physical hypotheses and probabilistic forecasting.
Yuan et al. (Thu,) studied this question.