Publications related to the GRACE Missions (no abstracts)

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Spatial Downscaling of GRACE Groundwater Storage Based on DTW Distance Clustering and an Analysis of Its Driving Factors

Xue, Huazhu, Wang, Hao, Dong, Guotao, and Li, Zhi, 2025. Spatial Downscaling of GRACE Groundwater Storage Based on DTW Distance Clustering and an Analysis of Its Driving Factors. Remote Sensing, 17(14):2526, doi:10.3390/rs17142526.

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@ARTICLE{2025RemS...17.2526X,
       author = {{Xue}, Huazhu and {Wang}, Hao and {Dong}, Guotao and {Li}, Zhi},
        title = "{Spatial Downscaling of GRACE Groundwater Storage Based on DTW Distance Clustering and an Analysis of Its Driving Factors}",
      journal = {Remote Sensing},
     keywords = {GRACE, groundwater storage, spatial downscaling, spatial clustering, random forest, driving factors},
         year = 2025,
        month = jul,
       volume = {17},
       number = {14},
          eid = {2526},
        pages = {2526},
     abstract = "{High-resolution groundwater storage is essential for effective regional
        water resource management. While Gravity Recovery and Climate
        Experiment (GRACE) satellite data offer global coverage, the
        coarse spatial resolution (0.25{\textendash}0.5{\textdegree})
        limits the data's applicability at regional scales. Traditional
        downscaling methods often fail to effectively capture spatial
        heterogeneity within regions, leading to reduced model
        performance. To overcome this limitation, a zoned downscaling
        strategy based on time series clustering is proposed. A K-means
        clustering algorithm with dynamic time warping (DTW) distance,
        combined with a random forest (RF) model, was employed to
        partition the Hexi Corridor region into relatively homogeneous
        subregions for downscaling. Results demonstrated that this
        clustering strategy significantly enhanced downscaling model
        performance. Correlation coefficients rose from 0.10 without
        clustering to above 0.84 with K-means clustering and the RF
        model, while correlation with the groundwater monitoring well
        data improved from a mean of 0.47 to 0.54 in the first subregion
        (a) and from 0.40 to 0.45 in the second subregion (b). The
        driving factor analysis revealed notable differences in dominant
        factors between subregions. In the first subregion (a),
        potential evapotranspiration (PET) was found to be the primary
        driving factor, accounting for 33.70\% of the variation. In the
        second subregion (b), the normalized difference vegetation index
        (NDVI) was the dominant factor, contributing 29.73\% to the
        observed changes. These findings highlight the effectiveness of
        spatial clustering downscaling methods based on DTW distance,
        which can mitigate the effects of spatial heterogeneity and
        provide high-precision groundwater monitoring data at a 1 km
        spatial resolution, ultimately improving water resource
        management in arid regions.}",
          doi = {10.3390/rs17142526},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.2526X},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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