Mon Feb 16, F. Flechtner
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Uz, Metehan, Atman, Kazım Gökhan, Akyılmaz, Orhan, and Shum, C. K., 2026. A dynamic soft–constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage. Journal of Hydrology, 668:135015, doi:10.1016/j.jhydrol.2026.135015.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026JHyd..66835015U,
author = {{Uz}, Metehan and {Atman}, Kaz{\i}m G{\"o}khan and {Aky{\i}lmaz}, Orhan and {Shum}, C.~K.},
title = "{A dynamic soft-constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage}",
journal = {Journal of Hydrology},
keywords = {Soft constraint paradigm, Mass conservation, GRACE and GRACE follow-on gravimetry, Deep learning-aided spatial downscaling, Terrestrial water storage anomalies},
year = 2026,
month = apr,
volume = {668},
eid = {135015},
pages = {135015},
abstract = "{The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On
(GRACE-FO) satellite gravimetry missions have contributed
significantly to our knowledge of variations in Earth's
Terrestrial Water Storage anomalies (TWSA) throughout the last
two decades. However, the ability to quantifying
hydrometeorological and other climate/weather episodes is
hindered by limitations in the current TWSA spatiotemporal
resolutions at monthly sampling and approximately coarser than
300 km. In this study, we used Deep Learning (DL) approach that
is specifically developed for accurate and effective spatial
downscaling of TWSA time series from NASA's Jet Propulsion
Laboratory (JPLM). Each TWSA maps of JPLM are downscaled from
300 km to 50 km spatial resolution spanning from April 2002
through December 2022 by using inherent spatiotemporal
correlations of WaterGAP Hydrology Model (WGHM) TWSA. For this
purpose, a novel dynamic soft-constrained loss function is
introduced and applied that adaptively balances while optimizing
the TWSA signal with low-resolution JPLM observations against
high-resolution spatial patterns derived from the WGHM
hydrological model and ERA5 inputs. Internal validation shows
that while the downscaled TWSA preserves basin-averaged temporal
dynamics (trends, seasonality) from JPLM, the correlations and
spectral analyses show it successfully incorporates WGHM TWSA's
high-resolution spatial variability. External validation of
downscaled TWSA products also demonstrates their ability to
capture El Ni{\~n}o Southern Oscillation (ENSO)-driven
interannual variability, glacial mass loss trends, spectral
consistency with Soil Moisture Active Passive (SMAP) satellite-
derived surface soil moisture at high-resolution band and
similar predictive skill against previous studies. Furthermore,
the validation against groundwater well observations indicates
that the downscaled TWSA effectively represents the spatial
patterns of long-term groundwater depletion in heavily stressed
aquifers and significantly enhancing the spatial localization of
depletion or recharging signals relative to the coarse-
resolution JPLM TWSA.}",
doi = {10.1016/j.jhydrol.2026.135015},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026JHyd..66835015U},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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