Articles | Volume 15, issue 12
Data description paper
08 Dec 2023
Data description paper |  | 08 Dec 2023

GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present

Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-315', Anonymous Referee #1, 08 Sep 2023
    • AC1: 'Reply on RC1', Jiabo Yin, 13 Oct 2023
  • RC2: 'Comment on essd-2023-315', Anonymous Referee #2, 10 Oct 2023
    • AC2: 'Reply on RC2', Jiabo Yin, 13 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jiabo Yin on behalf of the Authors (16 Oct 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (22 Oct 2023) by Sander Veraverbeke
AR by Jiabo Yin on behalf of the Authors (25 Oct 2023)  Author's response   Manuscript 
Short summary
This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Final-revised paper