Satellite Evidence for Increasing in Terrestrial Evapotranspiration over the Contiguous United States from 2001 to 2022

Author:

Liu Lu1,Yao Yunjun1ORCID,Li Yufu2ORCID,Xie Zijing1,Ning Jing1,Yu Ruiyang1ORCID,Fan Jiahui1,Kan Yixi3,Zhang Luna1,Xu Jia4ORCID

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

2. Jincheng Meteorological Administration, Jincheng 048026, China

3. School of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China

4. Department of Infrastructure Engineering, Faculty of Engineering & IT, University of Melbourne, Melbourne, VIC 3010, Australia

Abstract

Evapotranspiration (ET) is a key process in the eco-hydrological cycle of a basin and a reliable indicator of climate change. However, the spatiotemporal alterations of ET in the contiguous United States (CONUS) over the recent two decades remain largely uncertain. In this study, we used the recently proposed Priestley–Taylor (PT)-SinRH model to estimate the ET of CONUS during 2001–2022 based on satellite and reanalysis data. The results showed that the PT-SinRH model yielded superior performance at eddy covariance (EC) sites, and the root-mean-square error (RMSE) ranged from 6.0 to 33.5 W/m2, the Kling–Gupta efficiency (KGE) ranged from 0.22 to 0.66. The annual mean value of ET in CONUS from 2001 to 2022, estimated by the PT-SinRH model, was 42.54 W/m2, and the spatial pattern of seasonal and annual ET variations increased from west to east. From 2001 to 2022, seasonal and annual ET of CONUS showed linear trends, with an average increase of 0.76 W/m2/da (p < 0.05). The ET in the east of CONUS exhibited a rate of increase at 1.45 W/m2/da, and the ET in the west of CONUS exhibited a rate of increase at 0.42 W/m2/da (p < 0.05). Importantly, our analysis of ET trends highlights that the change of precipitation (P) and normalized difference vegetation index (NDVI) exerts a significant impact on the change of ET over CONUS.

Funder

Natural Science Fund of China

Open Research Program of the International Research Center of Big Data for Sustainable Development Goals

Publisher

MDPI AG

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