Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 6
Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 6
Data description paper
 | 
13 Jun 2025
Data description paper |  | 13 Jun 2025

Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data

Nehar Mandal, Prabal Das, and Kironmala Chanda

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Short summary
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating terrestrial water storage anomalies (TWSAs). TWSAs are reconstructed (BNML_TWSA) with grid-specific leader models (among four machine learning models) from January 1960 to December 2022 to generate a continuous global gridded dataset. The uncertainty in the reconstructed BNML_TWSA product is also assessed in terms of standard error.
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