Publications related to the GRACE Missions (no abstracts)

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Machine Learning Approaches for Terrestrial Water Storage Assessment in Coastal Lowland Aquifer System Using GRACE/GRACE–FO Satellite Data (2003–2023)

Jahan, Md Nasrat, Yarbrough, Lance D., Ghaffari, Zahra, and Yasarer, Hakan, 2026. Machine Learning Approaches for Terrestrial Water Storage Assessment in Coastal Lowland Aquifer System Using GRACE/GRACE–FO Satellite Data (2003–2023). Remote Sensing, 18(11):1680, doi:10.3390/rs18111680.

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@ARTICLE{2026RemS...18.1680J,
       author = {{Jahan}, Md Nasrat and {Yarbrough}, Lance D. and {Ghaffari}, Zahra and {Yasarer}, Hakan},
        title = "{Machine Learning Approaches for Terrestrial Water Storage Assessment in Coastal Lowland Aquifer System Using GRACE/GRACE-FO Satellite Data (2003--2023)}",
      journal = {Remote Sensing},
     keywords = {GRACE mascon, terrestrial water storage anomaly, downscaling, machine learning model},
         year = 2026,
        month = may,
       volume = {18},
       number = {11},
          eid = {1680},
        pages = {1680},
     abstract = "{What are the main findings? Developed a high-resolution
        (\raisebox{-0.5ex}\textasciitilde800 m) GRACE/GRACE-FO-based
        terrestrial water storage (TWS) dataset for the Coastal Lowland
        Aquifer System (CLAS) using machine learning downscaling.
        Artificial Neural Network (ANN) outperformed Random Forest (RF)
        and Deep Neural Network (DNN) in capturing spatiotemporal TWS
        variability (2003--2023). Developed a high-resolution
        (\raisebox{-0.5ex}\textasciitilde800 m) GRACE/GRACE-FO-based
        terrestrial water storage (TWS) dataset for the Coastal Lowland
        Aquifer System (CLAS) using machine learning downscaling.
        Artificial Neural Network (ANN) outperformed Random Forest (RF)
        and Deep Neural Network (DNN) in capturing spatiotemporal TWS
        variability (2003--2023). What are the implications of the main
        findings? Enables fine-scale monitoring of TWS dynamics in data-
        scarce coastal aquifer systems. Supports improved water resource
        management and climate adaptation planning across CLAS regions.
        Enables fine-scale monitoring of TWS dynamics in data-scarce
        coastal aquifer systems. Supports improved water resource
        management and climate adaptation planning across CLAS regions.
        The Gravity Recovery and Climate Experiment (GRACE) mascon data
        relies on minor gravitational field variations to map
        terrestrial water storage anomaly (TWSA). However, the coarse
        spatial resolution of three degrees by three degrees restricts
        their application for evaluating small-scale changes in water
        storage. To address this challenge, in this study, GRACE and
        GRACE Follow-On (GRACE-FO) data from 2003 to 2023 were
        downscaled to 800-m resolution across the Coastal Lowland
        Aquifer System (CLAS) in Texas, Louisiana, Mississippi, Alabama,
        and Florida. This downscaling used machine learning (ML) models,
        including Random Forest (RF), Artificial Neural Network (ANN),
        and Deep Neural Network (DNN). These models incorporated
        variables such as anomalies in total precipitation (APT), mean
        temperature (ATM), normalized difference vegetation index
        (ANDVI), evapotranspiration (AET) from 2003 to 2023, Shuttle
        Radar Topography Mission DEM, slope angle, soil type, and
        lithology to generate monthly 800-m TWSA maps. The ANN model
        showed strong predictive performance (R$^{2}$ = 0.869--0.989 with
        low RMSE), although the DNN achieved slightly better statistical
        accuracy and spatial evaluation metrics; however, ANN was
        selected for its more realistic and spatially consistent outputs
        regionally. Building on this improved spatial resolution,
        analysis of the downscaled TWSA data from 2003 to 2023
        identified an overall declining trend in water storage. Trend
        analysis using linear regression shows that the western
        CLAS{\textemdash}particularly the Gulf Coast aquifer in Texas
        and western Louisiana{\textemdash}experiences the strongest
        depletion, with rates of {\ensuremath{-}}0.30 and
        {\ensuremath{-}}0.17 cm/year in Zones 1 and 2, respectively,
        with Zone 1 being statistically significant. In contrast, the
        eastern CLAS shows relatively stable conditions, with weak, non-
        significant increases (+0.05 to +0.18 cm/year), likely
        reflecting natural variability rather than sustained long-term
        gain. Therefore, ML-based downscaling of GRACE data enables
        high-resolution TWS assessment and provides a framework for
        future extraction of groundwater storage anomalies (GWSA),
        supporting improved groundwater management.}",
          doi = {10.3390/rs18111680},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.1680J},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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