Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr(-1) (6.56 x 10(4) km(3) yr(-1)) to 617.1 mm yr(-1) (6.87 x 10(4) km(3) yr(-1)). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr(-2) with a significance level of p < 0.05 and 0.38 mm yr(-2) with a significance level of p 0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600mm yr(-1). Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model-data fusion will advance our predictive understanding of global terrestrial ET.

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Author Pan, Shufen
Pan, Naiqing
Tian, Hanqin
Friedlingstein, Pierre
Sitch, Stephen
Shi, Hao
Arora, Vivek K.
Haverd, Vanessa
Jain, Atul K.
Kato, Etsushi
Lienert, Sebastian
Lombardozzi, Danica
Nabel, Julia E. M. S.
Ottlé, Catherine
Poulter, Benjamin
Zaehle, Sönke
Running, Steven W.
Publisher UCAR/NCAR - Library
Publication Date 2020-03-31T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Resource Version N/A
Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:35:20.504082
Metadata Record Identifier edu.ucar.opensky::articles:23234
Metadata Language eng; USA
Suggested Citation Pan, Shufen, Pan, Naiqing, Tian, Hanqin, Friedlingstein, Pierre, Sitch, Stephen, Shi, Hao, Arora, Vivek K., Haverd, Vanessa, Jain, Atul K., Kato, Etsushi, Lienert, Sebastian, Lombardozzi, Danica, Nabel, Julia E. M. S., Ottlé, Catherine, Poulter, Benjamin, Zaehle, Sönke, Running, Steven W.. (2020). Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7862kn0. Accessed 19 March 2025.

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