Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Chen, Jun, Wang, Linsong, Chen, Chao, and Peng, Zhenran, 2025. Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai–Tibet Plateau Using Deep Learning and Multi-Source Data. Remote Sensing, 17(8):1333, doi:10.3390/rs17081333.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025RemS...17.1333C,
author = {{Chen}, Jun and {Wang}, Linsong and {Chen}, Chao and {Peng}, Zhenran},
title = "{Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai{\textendash}Tibet Plateau Using Deep Learning and Multi-Source Data}",
journal = {Remote Sensing},
keywords = {GRACE, terrestrial water storage anomalies, downscaling, gated recurrent unit, Qinghai{\textendash}Tibet Plateau},
year = 2025,
month = apr,
volume = {17},
number = {8},
eid = {1333},
pages = {1333},
abstract = "{The Qinghai{\textendash}Tibet Plateau (QTP), a critical hydrological
regulator for Asia through its extensive glacier systems, high-
altitude lakes, and intricate network of rivers, exhibits
amplified sensitivity to climate-driven alterations in
precipitation regimes and ice mass balance. While the Gravity
Recovery and Climate Experiment (GRACE) and its Follow-On
(GRACE-FO) missions have revolutionized monitoring of
terrestrial water storage anomalies (TWSAs) across this
hydrologically sensitive region, spatial resolution limitations
(3{\textdegree}, equivalent to
\raisebox{-0.5ex}\textasciitilde300 km) constrain process-scale
analysis, compounded by mission temporal discontinuity (data
gaps). In this study, we present a novel downscaling framework
integrating temporal gap compensation and spatial refinement to
a 0.25{\textdegree} resolution through Gated Recurrent Unit
(GRU) neural networks, an architecture optimized for univariate
time series modeling. Through the assimilation of multi-source
hydrological parameters (glacier mass flux,
cryosphere{\textendash}precipitation interactions, and land
surface processes), the GRU-based result resolves nonlinear
storage dynamics while bridging inter-mission observational
gaps. Grid-level implementation preserves mass conservation
principles across heterogeneous topographies, successfully
reconstructing seasonal-to-interannual TWSA variability and also
its long-term trends. Comparative validation against GRACE
mascon solutions and process-based hydrological models
demonstrates enhanced capacity in resolving sub-basin
heterogeneity. This GRU-derived high-resolution TWSA is
especially valuable for dissecting local variability in areas
such as the Brahmaputra Basin, where complex water cycling can
affect downstream water security. Our study provides
transferable methodologies for mountainous hydrogeodesy analysis
under evolving climate regimes. Future enhancements through
physics-informed deep learning and next-generation
climatology{\textendash}hydrology{\textendash}gravimetry synergy
(e.g., observations and models) could further constrain
uncertainties in extreme elevation zones, advancing the
predictive understanding of Asia's water tower sustainability.}",
doi = {10.3390/rs17081333},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.1333C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner