Abstract
Abstract. 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.
Highlights
Terrestrial evapotranspiration (ET) is the sum of the water lost to the atmosphere from plant tissues via transpiration and that lost from the land surface elements, including soil, plants and open water bodies, through evaporation
In most regions of the Amazon Basin, the mean ET of remote sensing physical models is more than 200 mm yr−1 higher than the mean ET of land surface models (LSMs) and machine-learning methods
The uncertainty of ET estimates from LSMs is large in the Amazon Basin, where the standard deviation of LSM estimates is more than 2 times larger than that of benchmark estimates
Summary
Terrestrial evapotranspiration (ET) is the sum of the water lost to the atmosphere from plant tissues via transpiration and that lost from the land surface elements, including soil, plants and open water bodies, through evaporation. Processes controlling ET play a central role in linking the energy (latent heat), water (moisture flux) and carbon cycles (photosynthesis–transpiration trade-off) in the Earth system. ET is coupled with the carbon dioxide (CO2) exchange between the canopy and the atmosphere through vegetation photosynthesis. These linkages make ET an important variable in both short-term numerical weather forecasts and longterm climate predictions. In order to enhance our predictive understanding of the Earth system and sustainability, it is essential to accurately assess land surface ET in a changing global environment
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