GRACE and GRACE-FO Related Publications (no abstracts)

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Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation

Li, Fupeng, Springer, Anne, Kusche, Jürgen, Gutknecht, Benjamin D., and Ewerdwalbesloh, Yorck, 2025. Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation. Water Resources Research, 61(2):2024WR037926, doi:10.1029/2024WR037926.

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@ARTICLE{2025WRR....6137926L,
       author = {{Li}, Fupeng and {Springer}, Anne and {Kusche}, J{\"u}rgen and {Gutknecht}, Benjamin D. and {Ewerdwalbesloh}, Yorck},
        title = "{Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation}",
      journal = {Water Resources Research},
         year = 2025,
        month = feb,
       volume = {61},
       number = {2},
        pages = {2024WR037926},
     abstract = "{Hydrological Models face limitations in simulating the water cycle due
        to deficiencies in process representation and such problems also
        weaken their forecasting skills. Here, we use Machine Learning
        (ML) to forecast the Gravity Recovery and Climate Experiment
        (GRACE) derived total water storage anomaly (TWSA) up to 1 year
        ahead over Europe with near real-time meteorological
        observations as predictors. Subsequently, we assimilate the
        forecasted and GRACE TWSA into the Community Land Model (CLM) to
        enhance its performance in both reanalysis and forecast. As
        found in five hindcast experiments, ML forecasted TWSA for the
        following year fits quite well to the actual GRACE observations
        over Europe, with an average correlation of 0.91, 0.92, and 0.94
        in the Iberian peninsula, Danube, and Volga basins. Validation
        by observations and reanalysis data suggests that assimilating
        forecasted TWSA can improve CLM's capacity to forecast not only
        hydrological states but also hydrological droughts.
        Additionally, ML forecasted TWSA is a viable alternative to
        GRACE data in terms of enhancing hydrological forecasting on
        seasonal to annual scales through Data assimilation (DA). We
        also highlight the contribution of GRACE DA for generating a CLM
        based TWSA reanalysis that overcomes deficiencies of purely
        model-based TWSA. This study suggests that seasonal drought or
        water resource forecasting services might not only consider to
        integrate GRACE TWSA but would also benefit from constraining
        models with ML-forecasted TWSA. At shorter timescales, such
        forecasts could also be useful in the quick-look analysis of
        near real-time TWSA processing as is suggested for upcoming
        satellite gravity missions.}",
          doi = {10.1029/2024WR037926},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025WRR....6137926L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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