Publications related to the GRACE Missions (no abstracts)

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Improved grid–based semi–centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning

Anjaneyulu, Roniki, Garai, Nabakishor, Abhishek, and Zhao, Wenpeng, 2026. Improved grid–based semi–centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning. Journal of Geodesy, 100(8):67, doi:10.1007/s00190-026-02098-x.

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@ARTICLE{2026JGeod.100...67A,
       author = {{Anjaneyulu}, Roniki and {Garai}, Nabakishor and {Abhishek} and {Zhao}, Wenpeng},
        title = "{Improved grid-based semi-centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning}",
      journal = {Journal of Geodesy},
     keywords = {Global water storage, GRACE, Reconstruction, Machine learning, Trend to variability ratio, Sustainable water resources, Earth Sciences, Physical Geography and Environmental Geoscience},
         year = 2026,
        month = jul,
       volume = {100},
       number = {8},
          eid = {67},
        pages = {67},
     abstract = "{Gravity Recovery and Climate Experiment (GRACE)-based terrestrial water
        storage (TWS){\textemdash}an essential climate
        variable{\textemdash}has revolutionized our understanding of
        global climate systems, terrestrial water cycles, glacier mass
        balance, and sea level changes over the past two decades.
        However, the data brevity of GRACE hinders a holistic
        understanding of the hydrological cycle and water fluxes, and
        the available reconstructed data have several limitations. Here,
        we leverage a suite of machine learning models, namely,
        Artificial Neural Network, Random Forest (RF), and Long Short-
        Term Memory (LSTM), to reconstruct the grid-based global TWS
        from 1980 to 2024 and validate with GRACE-based observations.
        Further, we examine the secular trends and variability of the
        45-year long-term TWSA data using a recently proposed metric,
        namely the trend to variability ratio (TVR). The results show
        that three models performed well in over 88\% of the study area,
        with RF as the best-performing model covering 48.5\% of the
        area, followed by Long Short-Term Memory (LSTM) (27.9\%) and
        Artificial Neural Network (ANN) (23.6\%). Global TWS experienced
        five multiyear depletion events, with an exceptional water loss
        of 34.41 Gt during April 2011─January 2013, and has been
        continually below-average since 2012, primarily concentrated in
        regions with high aridity, heavy ice mass loss, extreme climatic
        shifts, and extensive human activities. TVR identifies
        significant water losses and gains in the majority of hotspot
        regions, which would have been otherwise overlooked in
        conventional linear trend analysis. The study offers crucial
        insights into identifying long-term water depletion areas,
        enabling proactive planning, prioritizing interventions, and
        sustainable water resource allocation for decision-makers.}",
          doi = {10.1007/s00190-026-02098-x},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JGeod.100...67A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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