Accurate estimation of evapotranspiration (ET) is crucial for understanding water and carbon cycles, and water resource management. Direct ET measurements are expensive and technically challenging. A common indirect estimation method involves multiplying potential evapotranspiration (PET) by a soil water stress function (β). However, a soil water stress function with static parameters (βS) was generally used in models, neglecting the variation in soil moisture availability under different atmospheric and vegetation conditions. Additionally, although various linear and nonlinear forms of β have been developed, comparisons of their performance in ET estimation using large observational datasets remain limited. In this study, we evaluate ET estimation using three widely used forms of β (linear, exponential and sigmoid) across 135 global eddy covariance sites, and propose a soil water stress function with dynamic parameters (βD), which adjusts β based on variations in atmospheric demand (PET) and vegetation conditions (leaf area index, LAI). Subsequently, we compare the ET estimates from βS and βD with observed ET. Results show that the performance of ET estimation using the exponential function outperforms the linear and sigmoid functions, with mean NSE values of 0.38, 0.25, and 0.34, respectively. Furthermore, the βD method (mean NSE = 0.58) significantly improves ET estimation accuracy compared to the βS method (mean NSE = 0.33), particularly for vegetation types such as MF and DBF. Vegetation is the key determinant of the difference between βD and βS. Specifically, the βS method tends to overestimate ET at low intensities and underestimate ET at high intensities, especially in regions with high vegetation variability. Our findings underscore the importance of incorporating dynamic characteristics of β, particularly in areas with significant vegetation changes.

中文翻译：

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动态土壤水分胁迫函数改进了植被变化显著区域的蒸散估计

准确估计蒸散量 （ET） 对于了解水和碳循环以及水资源管理至关重要。直接 ET 测量成本高昂且技术上具有挑战性。一种常见的间接估计方法涉及将潜在蒸散量 （PET） 乘以土壤水分胁迫函数 （β）。然而，模型中通常使用具有静态参数 （βS） 的土壤水分胁迫函数，忽略了不同大气和植被条件下土壤水分有效性的变化。此外，尽管已经开发了各种线性和非线性形式的 β，但它们在使用大型观测数据集进行 ET 估计的性能比较仍然有限。在这项研究中，我们使用三种广泛使用的β形式（线性、指数和 S 形）在 135 个全球涡流协方差站点中评估 ET 估计，并提出了一个具有动态参数 （βD） 的土壤水分胁迫函数，该函数根据大气需求 （PET） 和植被条件（叶面积指数，LAI）的变化调整β。随后，我们将 βS 和 βD 的 ET 估计值与观察到的 ET 进行比较。结果表明，使用指数函数的 ET 估计性能优于线性函数和 S 形函数，平均 NSE 值分别为 0.38、0.25 和 0.34。此外，与 βS 方法（平均 NSE = 0.33）相比，βD 方法（平均 NSE = 0.58）显著提高了 ET 估计的准确性，特别是对于 MF 和 DBF 等植被类型。植被是 βD 和 βS 之间差异的关键决定因素。具体来说，βS 方法倾向于高估低强度下的 ET 和高强度下的 ET，尤其是在植被变化较大的地区。 我们的研究结果强调了纳入β动态特征的重要性，尤其是在植被变化显著的地区。