Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with an ML algorithm known as long short-term memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash–Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e. NSE < 0). However, all models performed poorly in catchments with extended ...

中文翻译：

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机器学习辅助的混合模型可以改善整个美国本土各流域的水流模拟

物理过程的不完整表示通常会导致基于过程的（PB）水文模型出现结构错误。机器学习（ML）算法可以减少流建模错误，但不会强制实现物理一致性。结果，如果用于提供未来的气候和土地利用模式不在训练数据范围内的未来水文气候预测，则机器学习算法可能是不可靠的。在这里，我们测试了通过将PB模型输出与称为长短期记忆（LSTM）网络的ML算法集成而构建的混合模型，这些混合模型具有模拟531个流域的流量的能力，这些流代表了整个美国的不同条件。通过Nash-Sutcliffe效率（NSE）衡量的混合模型的模型性能相对于独立的PB和LSTM模型有所提高。更重要的是，PB模型完全失效（即NSE <0）时，混合模型在集水区提供了最大的改进。但是，所有模型在集水区扩展的情况下表现都较差。