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A Multi-Scale Comprehensive Evaluation for Nine Evapotranspiration Products Across Mainland China Under Extreme Climatic Conditions

Long Qian, Lifeng Wu, Ning Dong, Tianjin Dai, Xingjiao Yu, Xuqian Bai, Qiliang Yang, Xiaogang Liu, Junying Chen and Zhitao Zhang ()
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Long Qian: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Lifeng Wu: School of Soil and Water Conversation, Jiangxi University of Water Resources and Electric Power, Nanchang 330099, China
Ning Dong: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Tianjin Dai: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Xingjiao Yu: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Xuqian Bai: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Qiliang Yang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xiaogang Liu: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Junying Chen: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Zhitao Zhang: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China

Agriculture, 2025, vol. 15, issue 18, 1-30

Abstract: Accurate quantification of evapotranspiration (ET) is crucial for agricultural water management and climate change adaptation, especially in global warming and extreme climate events. Despite the availability of various ET products, their applicability across different scales and climatic conditions has not been comprehensively verified. This study evaluates nine ET products at grid, basin, and site scales in China from 2003 to 2014 under varying climatic conditions, including extreme temperatures, vapor pressure deficit (VPD), and drought. The main results are as follows: (1) At the grid scale, all products except the MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid (MOD16A2) product showed high consistency, with the Global Land Evaporation Amsterdam Model V4.2a (GLEAM) product exhibiting the highest comparability. The three-cornered hat (TCH) method revealed that GLEAM and the Synthesized Global Actual Evapotranspiration Dataset (Syn) had low uncertainties in multiple basins, while the Reliability Ensemble Averaging (REA) product and Penman–Monteith–Leuning Evapotranspiration V2 (PMLv2) product had the smallest uncertainties in the Songhua River and Hai River Basins. (2) At the basin scale, ET products were closely aligned with water-balance-based ET (WB-ET), with GLEAM achieving the smallest root mean square error (RMSE) (22.94 mm/month). (3) At the site scale, accuracy decreased significantly under extreme climatic conditions, with the coefficient of determination (R 2 ) dropping from about 0.60 to below 0.30 and the mean absolute error (MAE) increasing by 110.30% (extreme high temperatures) and 101.40% (extreme high VPD). Drought conditions caused slight instability in ET estimations, with MAE increasing by approximately 12.00–40.00%. (4) Finally, using a small number of daily ET products as inputs for machine learning models, such as random forest (RF), greatly improved ET estimation, with R 2 reaching 0.91 overall and 0.81 under extreme conditions. GLEAM was the most important product for RF in ET estimation. This study provides essential guidance for selecting and improving ET products to enhance agricultural water-use efficiency and sustainable irrigation.

Keywords: evapotranspiration; ET products; extreme climatic conditions; interpretable machine learning; multi-scale analysis; agriculture; water balance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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