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Review Article

Linking observation, modelling and satellite-based estimation of global land evapotranspiration

, , , , &
Pages 94-127 | Received 09 Jan 2020, Accepted 09 Mar 2020, Published online: 27 Apr 2020
 

ABSTRACT

Evapotranspiration (ET) is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance. In this paper, the exited actual ET main datasets in global scale, and the global ET modeling and estimates were focused on discussion. The Source energy balance (SEB) models, empirical models and other process-based models are summarized. Accuracy for ET estimates by SEB models highly depends on accurate surface temperature retrieval, and SEB models are hard to apply in large heterogeneous surface. The Penman–Monteith (PM) equations are thought to be with considerable sound mechanism. However, it involves large number of parameters, which are not all global available. A simplified PM equation by Priestley and Taylor (PT) is found to perform well on well-watered surface. For both PM and PT equations in estimating ET, the key is to consider the constraint from surface resistance primarily water stress. Empirical models are simple but the accuracy of which highly depends on training samples. Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs; However, finding the most proper way to estimate global ET remains problematic. Several reasons for this issue are also analyzed in this review.

Acknowledgments

This work was funded by the CAS Strategic Priority Research Program (No. XDA19030402) and the National Natural Science Foundation of China (No. 31671585, 41871253). We are very grateful to the editor and anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the CAS Strategic Priority Research CAS [No. XDA19030402]; National Natural Science Foundation of China [31671585, 41871253].