Exploring water fluxes between hydrological model (HM) components is essential to assess and improve model realism. Many classical metrics for HM diagnosis rely solely on streamflow and hence provide limited insights into model performance across processes. This study applies an information theory measure known as “transfer entropy” (TE) to systematically quantify the transfer of information among major HM components. To test and demonstrate the benefits of TE, we use the Framework for Understanding Structural Errors (FUSE) model to mimic and compare four commonly used HM structures, VIC, PRMS, SACRAMENTO, and TOPMODEL, across 671 catchments spanning a variety of hydrologic regimes in the conterminous United States. We explore connections between HM components and catchment landscape characteristics (e.g., climate, topography, soil, and vegetation) and characterize their nonlinear associations using distance correlation and Spearman correlation coefficients. Our results indicate that while the information transferred from precipitation to runoff is similar across model structures (likely as a result of calibration), the information transferred among other components can vary significantly from a FUSE structure to another. We find that aridity, precipitation duration and frequency, snow fraction, mean elevation, forest area, and leaf area index are often significantly associated with TE between the main HM components. We propose that the presence of meaningful nonlinear associations can be used to diagnose process representation in HMs. Our results highlight the necessity to enhance the conventional streamflow‐only calibration approach for a more realistic representation of water dynamics in the models.

中文翻译：

---

通过信息论方法探索大陆规模的水文模型过程连通性

探索水文模型（HM）组件之间的水通量对于评估和改善模型的真实性至关重要。用于HM诊断的许多经典指标仅依赖于流，因此对跨过程的模型性能的了解有限。这项研究应用了一种称为“传递熵”（TE）的信息理论方法，以系统地量化主要HM组件之间的信息传递。为了测试和证明TE的好处，我们使用了理解结构误差框架（FUSE）模型来模拟和比较671个流域，跨各种水文情势的四个常用HM结构，VIC，PRMS，SACRAMENTO和TOPMODEL。最终的美国。我们探索了HM要素与流域景观特征（例如气候，地形，土壤，和植被），并使用距离相关和Spearman相关系数来表征它们的非线性关联。我们的结果表明，虽然从降水到径流的转移信息在各个模型结构之间是相似的（可能是校准的结果），但在其他组件之间转移的信息在FUSE结构和另一个FUSE结构之间可能会有很大差异。我们发现干旱，降水持续时间和频率，降雪分数，平均海拔，森林面积和叶面积指数通常与主要HM要素之间的TE显着相关。我们建议有意义的非线性关联的存在可用于诊断HMs中的过程表示。