Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5597-2023
https://doi.org/10.5194/essd-15-5597-2023
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

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Latest update: 23 Jul 2024
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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.
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