Accurate evapotranspiration (ET) estimation plays a central role in better understanding the allocation of water resources in a time of increasing scarcity; however, ET estimation remains many challenges. To enhance the accuracy of ET prediction, this study proposes an enhanced gross primary productivity (GPP)-based Priestley–Taylor algorithm (GPP-PT) that uses GPP to compute fractional vegetation cover (f_V). In terms of soil moisture fraction (f_SM), it is described by using either diurnal temperature (DT) or soil moisture index (SWI), conducting two variations of the proposed model (i.e., hereafter called GPP-DT and GPP-SWI, respectively). These two improved algorithms were compared with their previous models, the GPP-DT with a modified satellite-based Priestley–Taylor model (MS-PT), and the GPP-SWI with a soil water index (SWI)-based Priestley–Taylor model (SWI-PT). Datasets from 42 flux towers covering different land cover types were used to investigate the performance of these algorithms. The GPP-DT algorithm was found to be superior to the MS-PT model, with 12.60% and 10.42% reductions in the root mean square error (RMSE) and mean absolute error (MAE), respectively, and with 9.05% and 2.19% increases in the determination coefficient (R²) and index of agreement (IOA), respectively. Similarly, the GPP-SWI model yielded RMSE and MAE reductions of 10.95% and 10.67%, respectively, and R² and IOA increases of 8.88% and 3.72%, respectively, compared to the SWI-PT model. In the direct comparison between the two newly proposed models, the GPP-DT model performed better in shrubland and forest, whereas the GPP-SWI model performed more efficiently in grassland. Sensitivity analysis found that soil moisture was more sensitive to both evaporation and transpiration than DT in most land cover types, and the GPP had a stable relationship with transpiration in different biomes. The newly improved GPP-PT models were robust and effective for estimating ET and might thus be used as a reliable input for hydrological models.

中文翻译：

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基于生态生理约束的总初级生产力预测蒸散发研究进展

在日益稀缺的时期，准确的蒸发蒸腾 (ET) 估算在更好地了解水资源分配方面发挥着核心作用；然而，ET 估计仍然存在许多挑战。为了提高 ET 预测的准确性，本研究提出了一种基于增强型总初级生产力 (GPP) 的 Priestley-Taylor 算法 (GPP-PT)，该算法使用 GPP 计算植被覆盖率 ( f _V )。以土壤水分分数（f _SM)，它是通过使用昼夜温度 (DT) 或土壤湿度指数 (SWI) 来描述的，对所提出的模型进行两种变体（即，以下分别称为 GPP-DT 和 GPP-SWI）。这两种改进的算法与之前的模型进行了比较，GPP-DT 与基于卫星的 Priestley-Taylor 模型 (MS-PT) 的改进，以及 GPP-SWI 与基于土壤水分指数 (SWI) 的 Priestley-Taylor 模型(SWI-PT)。来自覆盖不同土地覆盖类型的 42 个通量塔的数据集用于研究这些算法的性能。发现 GPP-DT 算法优于 MS-PT 模型，均方根误差 (RMSE) 和平均绝对误差 (MAE) 分别降低了 12.60% 和 10.42%，分别降低了 9.05% 和 2.19%决定系数的增加 ( R ²) 和协议指数 (IOA)。同样，与 SWI-PT 模型相比，GPP-SWI 模型的 RMSE 和 MAE 分别降低了 10.95% 和 10.67%，R ²和 IOA 分别增加了 8.88% 和 3.72%。在两个新提出的模型之间的直接比较中，GPP-DT 模型在灌木丛和森林中表现更好，而 GPP-SWI 模型在草地上表现更有效。敏感性分析发现，在大多数土地覆盖类型中，土壤水分对蒸发和蒸腾的敏感性均高于DT，并且GPP与不同生物群落的蒸腾具有稳定的关系。新改进的 GPP-PT 模型在估算 ET 方面稳健有效，因此可用作水文模型的可靠输入。