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

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.

doi:10.5194/hess-24-1485-2020

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

Journal Article · Tue Mar 31 00:00:00 EDT 2020 · Hydrology and Earth System Sciences (Online)

DOI:https://doi.org/10.5194/hess-24-1485-2020· OSTI ID:1607800

Pan, Shufen; Pan, Naiqing;

;

; Sitch, Stephen; Shi, Hao; Arora, Vivek K.; Haverd, Vanessa;

;

; Lombardozzi, Danica;

; Ottlé, Catherine;

;

; Running, Steven W.

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 × 10⁴ km³ yr^–1) to 617.1 mm yr^–1 (6.87 × 10⁴ km³ 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, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr^–2, 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 600 mm 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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Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0016323

OSTI ID:: 1607800

Alternate ID(s):: OSTI ID: 1802925

Journal Information:: Hydrology and Earth System Sciences (Online), Journal Name: Hydrology and Earth System Sciences (Online) Vol. 24 Journal Issue: 3; ISSN 1607-7938

Publisher:: European Geosciences Union (EGU)Copyright Statement

Country of Publication:: Germany

Language:: English

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