Abstract. Accurate global terrestrial evapotranspiration (ET) estimation is essential to better understand Earth’s energy and water cycles. Although several global ET products exist, recent studies indicate that ET estimates exhibit high uncertainty. With the increasing trend of extreme climate hazards (e.g., droughts and heat waves), accurate ET estimation under extreme conditions remains challenging. To overcome these challenges, we used 3-hour and 0.25° Global Land Data Assimilation System (GLDAS) datasets (net radiation, land surface temperature (LST), and air temperature) and three-temperature (3T) model, without resistance and parameter calibration, in global terrestrial ET product development. The results demonstrated that the 3T model-based ET product agreed well with both global eddy covariance (EC) tower (root mean square error (RMSE) = 24.9 mm month^-1) and basin-scale water balance observations (RMSE =116.0 mm yr^-1). The 3T model-based global terrestrial ET product was comparable to other common ET products, i.e., MOD16, P-LSH, PML, GLEAM, GLDAS, and Fluxcom, retrieved from various models, but the 3T model performed better under extreme weather conditions in croplands than did the GLDAS, attaining 9.0–20 % RMSE reduction. The proposed ET product could provide periodic and large-scale information to support water cycle-related studies. The dataset is freely available at the Science Data Bank (http://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022).

中文翻译：

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基于三温模型、输入参数少、无需标定的全球陆地蒸发量产品

摘要。准确的全球陆地蒸发量 (ET) 估计对于更好地了解地球的能量和水循环至关重要。尽管存在几种全球 ET 产品，但最近的研究表明，ET 估计具有很高的不确定性。随着极端气候灾害（例如干旱和热浪）的增加趋势，在极端条件下准确估算 ET 仍然具有挑战性。为了克服这些挑战，我们使用了 3 小时和 0.25° 全球陆地数据同化系统 (GLDAS) 数据集（净辐射、地表温度 (LST) 和气温）和三温 (3T) 模型，没有阻力和参数校准，在全球陆地 ET 产品开发中。结果表明，基于 3T 模型的 ET 产品与全局涡协方差 (EC) 塔（均方根误差 (RMSE) = 24.^-1 ) 和流域尺度的水平衡观测 (RMSE =116.0 mm yr ^-1 )。基于 3T 模型的全球陆地 ET 产品与其他常见的 ET 产品（即 MOD16、P-LSH、PML、GLEAM、GLDAS 和 Fluxcom）相当，从各种模型中检索到，但 3T 模型在极端天气条件下表现更好农田比 GLDAS 减少 9.0-20% RMSE。拟议的 ET 产品可以提供定期和大规模的信息，以支持与水循环相关的研究。该数据集可在科学数据库免费获得 (http://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022)。