Accurately predicting stream water temperature at large scales requires modeling techniques that account for various physical, chemical, and biological processes that influence water temperature. One common approach is to use process-based hydrodynamic models that simulate water movement throughout a river basin. In this study, we assessed whether enhancing the Soil and Water Assessment Tool (SWAT) by incorporating a semi-Lagrangian formulation that explicitly accounts for various thermal processes improves its ability to predict stream water temperature. A basin-scale evaluation of the modified module, conducted in the Gunpowder Falls River and San Antonio River Basins using data from 2012 to 2022, showed that the modified model consistently demonstrated strong predictive capabilities for daily stream water temperature with R2 and KGE values ranging from 0.71 to 0.91, and 0.77 to 0.92, respectively. It also more accurately captured the monthly and seasonal variations in stream temperature compared to the default formulation, which is simply a function of air temperature. We recommend accounting for thermal processes using a semi-Lagrangian approach wherever these variables are available to improve the SWAT’s ability to predict stream temperature.

中文翻译：

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使用半拉格朗日公式改进 SWAT 中基于过程的溪流水温预测


准确预测大尺度河流水温需要建模技术，这些技术要考虑影响水温的各种物理、化学和生物过程。一种常见的方法是使用基于过程的水动力学模型来模拟整个流域的水运动。在这项研究中，我们评估了通过纳入明确考虑各种热过程的半拉格朗日公式来增强土壤和水评估工具 （SWAT） 是否能提高其预测溪流水温的能力。使用 2012 年至 2022 年的数据在火药瀑布河和圣安东尼奥河流域对修改后的模块进行了流域规模的评估，结果表明，修改后的模型始终表现出对日溪流水温的强大预测能力，R2 和 KGE 值分别为 0.71 至 0.91 和 0.77 至 0.92。与默认公式相比，它还更准确地捕获了溪流温度的月度和季节性变化，默认公式只是空气温度的函数。我们建议在这些变量可用的地方使用半拉格朗日方法考虑热过程，以提高 SWAT 预测溪流温度的能力。