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Uz, Metehan, Akyilmaz, Orhan, and Shum, C. K., 2024. Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States. Journal of Hydrology, 645:132194, doi:10.1016/j.jhydrol.2024.132194.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024JHyd..64532194U,
author = {{Uz}, Metehan and {Akyilmaz}, Orhan and {Shum}, C.~K.},
title = "{Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States}",
journal = {Journal of Hydrology},
keywords = {GRACE, GRACE(‑FO), Deep learning neural networks, Temporal downscaling, Daily terrestrial water storage anomalies},
year = 2024,
month = dec,
volume = {645},
eid = {132194},
pages = {132194},
abstract = "{The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn
(GRACE(‑FO)) satellites have been monitoring Earth's changes in
terrestrial water storage (TWS) or surficial mass changes at
monthly sampling and a spatial scale longer than
{\ensuremath{\sim}}330 km (half wavelength) over the past two
decades. At monthly sampling or revisit time, the use of
satellite gravimetry is difficult to effectively monitor abrupt
extreme weather events which are high-frequency, including the
climate-induced hurricanes/cyclones, flash floods and droughts.
The majority of the contemporary studies have focused on
satellite gravimetry spatial downscaling, and not on reducing
the temporal resolution of Earth's mass change. Here, we
developed a Deep Learning (DL) algorithm to downscale monthly
GRACE/GRACE(‑FO) Mass Concentration (Mascon) TWS anomaly (TWSA)
solutions to daily sampling over the Contiguous United States
(CONUS), with the aim of monitoring rapidly evolving natural
hazard episodes. The simulative performance of the DL algorithm
is validated by comparing the modeling to an independent
observation and the land hydrology model (LHM) predicted TWSA.
To confirm that our daily and monthly simulations captured the
climatic variations, we first compared our simulations with El
Ni{\~n}o/La Ni{\~n}a Southern Oscillation (ENSO) circulation
system index, which has a dominant climatological and
socioeconomic impact across CONUS, and results reveal high
correlations which are statistically significant. Next, we
assessed the feasibilities to detect long- and short-term
variations in the TWSA signals triggered by hydrological
extremes, including the 2011 and 2019 Missouri River Floods, the
August 2017 Atlantic Hurricane Harvey landfalls in Texas, the
2012{\textendash}2017 drought in California, and the flash
drought in the Northern Great Plains in 2017. Additional
validation results using independent in situ observations reveal
that our DL-aided gravimetry downscaled daily simulations are
capable of elucidating hazards and water cycle evolutions at
high temporal resolution.}",
doi = {10.1016/j.jhydrol.2024.132194},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024JHyd..64532194U},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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