Preprints
https://doi.org/10.5194/essd-2021-456
https://doi.org/10.5194/essd-2021-456
01 Mar 2022
 | 01 Mar 2022
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang

Abstract. Land evapotranspiration (ET) is a key element of Earth’s water-carbon system. Accurate estimation of global land ET is essential for better understanding of land-atmosphere interaction. Past decades have witnessed the generation of various ET products. However, the widely used products still contain inherent uncertainty induced by forcing inputs and imperfect model parameterizations. In addition, direct evaluation of ET products is not feasible due to the lack of sufficient global in-situ observations, which hinders our usage and assimilation. Hence, merging a global dataset as reliable benchmark and exploring evaluation method for ET products are of great importance. The aims of our study were as followed: (1) to design and validate a collocation-based method for ET merging; (2) to generate a long-term (1981–2020) ET product employing ERA5, FLUXCOM, PMLV2, GLDAS and GLEAM at 0.1°–8Daily and 0.25°-Daily resolutions. The produced Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE) was then compared with others at point and regional scales. At the point scale, the results showed that the CAMELE performed well over different vegetation coverage. The accuracy of CAMELE was validated against in-situ observations with Pearson Correlation of 0.68, 0.62 and root mean square error of 0.84 and 1.03 mm/d on average over 0.1° and 0.25°, respectively. In terms of Kling-Gupta Efficiency, CAMELE ET obtained results superior (mean 0.52) to the second best ERA5 (mean 0.44) at 0.1° basis. For global comparison, the spatial distribution of multi-year average and annual variation were in consistent with others. Our merged product revealed increased ET in South Asia, Northwest Australia, and decreases in Amazon Plain and Congo Basin. The CAMELE products is freely available at https://doi.org/10.5281/zenodo.6283239 (Li et al., 2021).

Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-456', Jianzhi Dong, 09 Mar 2022
    • AC1: 'Reply on RC1', Changming li, 07 Jun 2022
  • RC2: 'Comment on essd-2021-456', Anonymous Referee #2, 29 Apr 2022
    • AC2: 'Reply on RC2', Changming li, 07 Jun 2022
  • AC1: 'Reply on RC1', Changming li, 07 Jun 2022
  • AC2: 'Reply on RC2', Changming li, 07 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-456', Jianzhi Dong, 09 Mar 2022
    • AC1: 'Reply on RC1', Changming li, 07 Jun 2022
  • RC2: 'Comment on essd-2021-456', Anonymous Referee #2, 29 Apr 2022
    • AC2: 'Reply on RC2', Changming li, 07 Jun 2022
  • AC1: 'Reply on RC1', Changming li, 07 Jun 2022
  • AC2: 'Reply on RC2', Changming li, 07 Jun 2022
Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang

Data sets

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data Changming Li, Hanbo Yang https://doi.org/10.5281/zenodo.6283239

Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang

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Short summary
A long-term (1980–2020) global ET product is generated based on a collocation-based merging method. The produced Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE) performed well over different vegetation coverage against in-situ data. For global comparison, the spatial distribution of multi-year average and annual variation were in consistent with inputs.The CAMELE products is freely available at https://doi.org/10.5281/zenodo.6283239 (Li et al., 2021).
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