Publications related to the GRACE Missions (no abstracts)

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Assimilation of in-situ groundwater level data into the obtained groundwater storage from GRACE and GLDAS for spatial downscaling

Mahbuby, Hany and Eshagh, Mehdi, 2025. Assimilation of in-situ groundwater level data into the obtained groundwater storage from GRACE and GLDAS for spatial downscaling. Journal of Hydrology, 661:133604, doi:10.1016/j.jhydrol.2025.133604.

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@ARTICLE{2025JHyd..66133604M,
       author = {{Mahbuby}, Hany and {Eshagh}, Mehdi},
        title = "{Assimilation of in-situ groundwater level data into the obtained groundwater storage from GRACE and GLDAS for spatial downscaling}",
      journal = {Journal of Hydrology},
     keywords = {Data assimilation, Downscaling, Groundwater storage, Optimisation, Terrestrial water storage, Variance factor},
         year = 2025,
        month = nov,
       volume = {661},
          eid = {133604},
        pages = {133604},
     abstract = "{Groundwater storage (GWS) is a crucial source of drinking water and
        agricultural supply. The effects of climate change, such as
        global warming, drought, and reduced rainfall, make it
        increasingly difficult to replenish depleted groundwater.
        Therefore, accurately monitoring changes in GWS is of paramount
        importance. The twin Gravity Recovery and Climate Experiment
        (GRACE) satellites provide a valuable tool for estimating
        monthly GWS changes when combined with hydrological models,
        though their low spatial resolution poses a challenge. To
        achieve higher resolution, terrestrial water storage (TWS)
        changes derived from GRACE must be downscaled by integrating
        outputs from hydrological models and in-situ data. In this
        study, three types of data were utilised to estimate GWS changes
        in Tehran at a high resolution. Monthly TWS changes from GRACE
        mascon solutions were combined with outputs from the Global Land
        Data Assimilation System (GLDAS) to estimate monthly GWS changes
        at a 0.25-degree grid. Subsequently, high-resolution groundwater
        level (GWL) changes from in-situ wells were combined with GWS
        changes using optimum interpolation (OI), a data assimilation
        (DA) method. Using the Bayesian method, it is shown that
        downscaling to a resolution of 0.05 degrees is meaningful. This
        DA method is suitable for combining two datasets with differing
        locations, amplitudes, and statistical characteristics.
        Additionally, solving an inequality constrained optimisation
        problem for optimal variance factor estimation was proposed. To
        assess the accuracy, several Piezomteric wells excluded from the
        DA process and were used to validate the results. Our numerical
        study showed that the Root Mean Square Error (RMSE) between the
        excluded wells and the assimilated GWS changes was reduced. The
        average RMSE before and after DA was 3.10 cm and 1.95 cm,
        respectively, demonstrating that the downscaling method improved
        the accuracy by 37 \%, with a higher correlation to the
        validation wells.}",
          doi = {10.1016/j.jhydrol.2025.133604},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..66133604M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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