Synthesis of global actual evapotranspiration from 1982 to 2019

Elnashar, Abdelrazek; Wang, Linjiang; Wu, Bingfang; Zhu, Weiwei; Zeng, Hongwei

doi:https://doi.org/10.5194/essd-13-447-2021

Articles | Volume 13, issue 2

https://doi.org/10.5194/essd-13-447-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-13-447-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 2

Data description paper

|

16 Feb 2021

Data description paper |

| 16 Feb 2021

Synthesis of global actual evapotranspiration from 1982 to 2019

Abdelrazek Elnashar, Linjiang Wang, Bingfang Wu, Weiwei Zhu, and Hongwei Zeng

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Final revised paper (published on 16 Feb 2021)
Preprint (discussion started on 15 Jul 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments on “Synthesis of Global Actual Evapotranspiration from 1982 to 2019”', Anonymous Referee #1, 04 Aug 2020
- AC1: 'Synthesis of Global Actual Evapotranspiration from 1982 to 2019', Abdelrazek Elnashar, 24 Aug 2020
RC2: 'Conclusion', Rafat Ali, 22 Dec 2020
- AC2: 'Reply on RC2', Abdelrazek Elnashar, 31 Dec 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Abdelrazek Elnashar on behalf of the Authors (31 Dec 2020)

ED: Publish as is (12 Jan 2021) by Kirsten Elger

AR by Abdelrazek Elnashar on behalf of the Authors (13 Jan 2021) Manuscript

Short summary

Based on a site-pixel validation and comparison of different global evapotranspiration (ET) products, this paper aims to produce a synthesized ET which has a minimum level of uncertainty over as many conditions as possible from 1982 to 2019. Through a high-quality flux eddy covariance (EC) covering the globe, PML, SSEBop, MOD16A2105, and NTSG ET products were chosen to create the new dataset. It agreed well with flux EC ET and can be used without other datasets or further assessments.