Synthesis of global actual evapotranspiration from 1982 to 2019

被引:114
作者
Elnashar, Abdelrazek [1 ,2 ,3 ]
Wang, Linjiang [1 ,2 ]
Wu, Bingfang [1 ,2 ]
Zhu, Weiwei [1 ]
Zeng, Hongwei [1 ,2 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[3] Cairo Univ, Fac African Postgrad Studies, Dept Nat Resources, Giza 12613, Egypt
基金
中国国家自然科学基金;
关键词
SURFACE-ENERGY-BALANCE; TERRESTRIAL EVAPOTRANSPIRATION; CLIMATE-CHANGE; MULTISCALE VALIDATION; LAND EVAPORATION; WATER-RESOURCES; SYSTEM SEBS; DATA SET; PRODUCTS; MODEL;
D O I
10.5194/essd-13-447-2021
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman-Monteith-Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021).
引用
收藏
页码:447 / 480
页数:34
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