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A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX).

Peyman Saemian1, Mohammad J Tourian2, Karim Douch3

  • 1Institute of Geodesy, University of Stuttgart, Stuttgart, Germany. peyman.saemian@gis.uni-stuttgart.de.

Scientific Data
|January 29, 2026
PubMed
Summary

ML-TWiX provides a new global dataset of monthly total water storage anomalies (TWSA) from 1980-2012. This extends the satellite record, aiding long-term climate and hydrological research.

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Area of Science:

  • Earth Science
  • Hydrology
  • Climate Science

Background:

  • Satellite missions like GRACE offer valuable data on total water storage anomalies (TWSA), but their limited duration restricts long-term climate and hydrological studies.
  • Extending the TWSA record into the pre-GRACE era is crucial for comprehensive analysis of global water resources and climate variability.

Purpose of the Study:

  • To develop ML-TWiX, a global dataset of monthly TWSA from 1980 to 2012, extending the observational record prior to the GRACE mission.
  • To utilize machine learning models to reconstruct TWSA, integrating hydrological and land surface model simulations.
  • To provide a unified TWSA product with uncertainty estimates for diverse scientific applications.

Main Methods:

  • Employed an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression.
  • Trained models on global hydrological and land surface simulations to reconstruct monthly TWSA on a 0.5° × 0.5° grid.
  • Combined outputs from individual models via ensemble averaging to generate a unified dataset with uncertainty quantification.

Main Results:

  • Successfully reconstructed a continuous global dataset of monthly TWSA from 1980 to 2012.
  • Validated the ML-TWiX dataset against independent data sources, including satellite laser ranging and sea level budget estimates.
  • The resulting dataset offers spatially explicit uncertainty estimates.

Conclusions:

  • ML-TWiX provides a valuable, extended record of global TWSA, crucial for long-term climate and hydrological research.
  • The dataset supports a wide range of applications, including water resource assessment and understanding climate-driven hydrological changes.
  • Machine learning offers a powerful approach for reconstructing historical climate and hydrological variables.