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<p>We present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5<sup>°</sup> × 0.5<sup>°</sup> global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Peyman Saemian,
  • Mohammad J. Tourian,
  • Karim Douch,
  • James Foster,
  • Junyang Gou,
  • David Wiese,
  • Amir AghaKouchak,
  • Nico Sneeuw

摘要

We present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5° × 0.5° global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment.