Publications related to the GRACE Missions (no abstracts)

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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China

Yang, Rihui, Zhong, Yuqing, Zhang, Xiaoxiang, Maimaitituersun, Aizemaitijiang, and Ju, Xiaohan, 2025. A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China. Remote Sensing, 17(3):493, doi:10.3390/rs17030493.

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@ARTICLE{2025RemS...17..493Y,
       author = {{Yang}, Rihui and {Zhong}, Yuqing and {Zhang}, Xiaoxiang and {Maimaitituersun}, Aizemaitijiang and {Ju}, Xiaohan},
        title = "{A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China}",
      journal = {Remote Sensing},
     keywords = {groundwater storage changes, GRACE, spatial downscaling, RFR, GWR, Jiangsu Province},
         year = 2025,
        month = jan,
       volume = {17},
       number = {3},
          eid = {493},
        pages = {493},
     abstract = "{The Gravity Recovery and Climate Experiment (GRACE) introduces a new
        approach to accurately monitor, in real time, regional
        groundwater resources, which compensates for the limitations of
        traditional hydrological observations in terms of spatiotemporal
        resolution. Currently, observations of groundwater storage
        changes in Jiangsu Province face issues such as low spatial
        resolution, limited applicability of the downscaling models, and
        insufficient water resource observation data. This study based
        on GRACE employs Random Forest Regression (RFR) and
        Geographically Weighted Regression (GWR) methods in order to
        obtain high-resolution information on groundwater storage
        change. The results indicate that among the established 66
        {\texttimes} 158 local GWR models, the coefficient of
        determination (R$^{2}$) ranges from 0.39 to 0.88, with a root
        mean squared error (RMSE) of approximately 2.60 cm. The
        proportion of downscaling models with an R$^{2}$ below 0.5 was
        18.52\%. Similarly, the RFR models trained on the above time
        series grid data achieved an R$^{2}$ of 0.50, with the RMSE
        fluctuating around 1.59 cm. In the results validation, the
        monthly correlation coefficients between the GWR downscaling
        results and the data of measured stations ranged from 0.37 to
        0.66, with 53.33\% of the stations having a coefficient greater
        than 0.5. The seasonal correlation coefficients ranged from 0.41
        to 0.62, with 60\% of the stations exceeding 0.5. The
        correlation coefficients for the RFR downscaling results ranged
        from 0.44 to 0.88, with seasonal correlation coefficients
        ranging from 0.49 to 0.84. Only one station had a correlation
        coefficient below 0.5 for both monthly and seasonal results. In
        the validation of the correlation accuracy between the
        downscaling results and the measured groundwater levels, the
        Random Forest model demonstrated better predictive performance,
        which offers distinct advantages in improving the spatial
        resolution of groundwater storage changes in Jiangsu Province.}",
          doi = {10.3390/rs17030493},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17..493Y},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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