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<p>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.</p>

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A dynamic-gated Mixture-of-Experts framework improves and interprets daily streamflow simulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Wenrui Yuan,
  • Shi HU,
  • Chesheng Zhan,
  • Zhonghui Lin

摘要

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.