Thu Oct 08, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Xu, Keke, Zhu, Yongzhen, Liu, Xianglei, Zheng, Wei, Li, Huanxu, Zhao, Jiaqi, and Chen, Mengchao, 2026. Temporal Gap Filling and Model–Based Spatial Downscaling of GRACE–Based Groundwater–Storage Anomalies Using Gaussian Process and Random Forest Models. Remote Sensing, 18(16):2702, doi:10.3390/rs18162702.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026RemS...18.2702X,
author = {{Xu}, Keke and {Zhu}, Yongzhen and {Liu}, Xianglei and {Zheng}, Wei and {Li}, Huanxu and {Zhao}, Jiaqi and {Chen}, Mengchao},
title = "{Temporal Gap Filling and Model-Based Spatial Downscaling of GRACE-Based Groundwater-Storage Anomalies Using Gaussian Process and Random Forest Models}",
journal = {Remote Sensing},
keywords = {groundwater-storage anomalies, GRACE, groundwater depletion, machine learning, North China},
year = 2026,
month = aug,
volume = {18},
number = {16},
eid = {2702},
pages = {2702},
abstract = "{What are the main findings? A sequential Gaussian Process--Random Forest
workflow was used for temporal gap filling and model-based
spatial downscaling of GRACE-based groundwater-storage
anomalies. The resulting 1 km gridded estimates showed temporal
agreement with groundwater-level anomalies (R = 0.88) and
identified persistent groundwater depletion in northern Henan. A
sequential Gaussian Process--Random Forest workflow was used for
temporal gap filling and model-based spatial downscaling of
GRACE-based groundwater-storage anomalies. The resulting 1 km
gridded estimates showed temporal agreement with groundwater-
level anomalies (R = 0.88) and identified persistent groundwater
depletion in northern Henan. What are the implications of the
main findings? The workflow improves the temporal continuity and
spatial representation of GRACE-based groundwater-storage
estimates without implying independently observed groundwater
information at the 1 km scale. The approach is potentially
applicable to other regions, subject to local calibration,
hydrogeological conditions, and validation with independent
observations. The workflow improves the temporal continuity and
spatial representation of GRACE-based groundwater-storage
estimates without implying independently observed groundwater
information at the 1 km scale. The approach is potentially
applicable to other regions, subject to local calibration,
hydrogeological conditions, and validation with independent
observations. Groundwater-storage anomalies (GWSA) derived from
the Gravity Recovery and Climate Experiment (GRACE) mission
provide valuable information for regional groundwater
monitoring. Improving the spatial representation and temporal
continuity of GRACE-derived GWSA is important for supporting
groundwater assessment at subregional scales. A sequential
framework combining Gaussian Process (GP) temporal gap filling
and Random Forest (RF) spatial downscaling was developed for
GWSA reconstruction in Henan Province, China, during 2002--2022.
The GP model was used to reconstruct missing observations and
characterize temporal variations, while the RF model
statistically redistributed the GRACE-based GWSA signal using
multi-source hydroclimatic predictors. The resulting dataset
comprises model-derived GWSA estimates on a 1 km output grid
constrained by the coarse spatial support of GRACE observations
and the relationships learned from the auxiliary variables.
Therefore, the 1 km grid spacing should not be interpreted as an
independent 1 km resolving capability for groundwater-storage
variations. Agreement with the parent GRACE-based GWSA product
was used to assess coarse-scale reconstruction consistency
rather than independent fine-scale accuracy. Comparison with
groundwater-level anomalies from 63 monitoring wells yielded a
correlation coefficient of 0.88, indicating temporal agreement
at the sampled locations. Because the groundwater-level
observations were not converted into storage anomalies using
specific yield, this comparison does not establish absolute GWSA
accuracy or independently validate the model-derived fine-scale
spatial patterns. The reconstructed estimates revealed
pronounced spatial heterogeneity in groundwater-storage changes,
with persistent depletion concentrated in northern Henan, where
groundwater decline rates exceeded 20 mm yr$^{-1}$. Overall, the
framework improved the temporal continuity and spatial
representation of GRACE-based groundwater-storage estimates
while retaining the fundamental spatial constraints of satellite
gravimetry. The results demonstrate the potential of integrating
GRACE observations, machine learning, and multi-source Earth
observation data to support regional groundwater assessment.}",
doi = {10.3390/rs18162702},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2702X},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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