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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-409
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-409
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Aug 2019

Submitted as: research article | 21 Aug 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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

Shufen Pan1, Naiqing Pan1,2, Hanqin Tian1, Pierre Friedlingstein3, Stephen Sitch4, Hao Shi1, Vivek K. Arora5, Vanessa Haverd6, Atul K. Jain7, Etsushi Kato8, Sebastian Lienert9, Danica Lombardozzi10, Catherine Ottle11, Benjamin Poulter12,13, and Sönke Zaehle14 Shufen Pan et al.
  • 1International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36832, USA
  • 2State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
  • 3College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom
  • 4College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, United Kingdom
  • 5Canadian Centre for Climate Modelling and Analysis, Environment Canada, University of Victoria, Victoria, BC, Canada
  • 6CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT 2601, Australia
  • 7Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
  • 8Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan
  • 9Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
  • 10Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80305, USA.
  • 11LSCE-IPSL-CNRS, Orme des Merisiers, 91191, Gif-sur-Yvette, France
  • 12NASA Goddard Space Flight Center, Biospheric Science Laboratory, Greenbelt, MD 20771, USA
  • 13Department of Ecology, Montana State University, Bozeman, MT 59717, USA
  • 14Max Planck Institute for Biogeochemistry, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany

Abstract. 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) and 0.38 mm yr−2, respectively. In contrast, the ensemble mean of LSMs showed no statistically significant change (0.23 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.

Shufen Pan et al.
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