Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
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.
• from the NASA Astrophysics Data System • by the DOI System •
@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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