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Publication . Article . Other literature type . 2020

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

Shufen Pan; Naiqing Pan; Hanqin Tian; Pierre Friedlingstein; Stephen Sitch; Hao Shi; Vivek K. Arora; +10 Authors
Open Access

Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1(454 mm yr−1)to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p  0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

Subjects by Vocabulary

Microsoft Academic Graph classification: Relative stability Canopy Environmental science Arid Satellite Evapotranspiration Remote sensing (archaeology) Water stress Physical model Climatology

Library of Congress Subject Headings: lcsh:Technology lcsh:T lcsh:Environmental technology. Sanitary engineering lcsh:TD1-1066 lcsh:Geography. Anthropology. Recreation lcsh:G lcsh:Environmental sciences lcsh:GE1-350


General Earth and Planetary Sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 530 Physics

Funded by
NSF| INFEWS: U.S.-China: Integrated systems modeling for sustainable FEW nexus under multi-factor global changes: Innovative comparison between Yellow River and Mississippi River Basins
  • Funder: National Science Foundation (NSF)
  • Project Code: 1903722
  • Funding stream: Directorate for Geosciences | Division of Earth Sciences
EC| 4C
Climate-Carbon Interactions in the Current Century
  • Funder: European Commission (EC)
  • Project Code: 821003
  • Funding stream: H2020 | RIA
Validated by funder
SNSF| Die Entmündigung wegen psychisch und sozial abwei- chendem Verhalten de lege lata et ferenda nach dem Recht des ZGB und der USA.
  • Funder: Swiss National Science Foundation (SNSF)
  • Project Code: 20020
  • Funding stream: Careers | Fellowships | Fellowships for prospective researchers
NSF| Collaborative Research: EaSM2--Wildfires and Regional Climate Variability - Mechanisms, Modeling, and Prediction
  • Funder: National Science Foundation (NSF)
  • Project Code: 1243232
  • Funding stream: Directorate for Geosciences | Division of Atmospheric and Geospace Sciences