Thu Oct 08, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Youm, Kookhyoun, Seo, Ki–Weon, Kim, Jae–Seung, Chen, Jianli, and Wilson, Clark R., 2026. Improved GNSS–Based Estimates of Terrestrial Water Storage in California Through Suppression of Non–Elastic Loading Signals. Water Resources Research, 62(9):e2026WR044758, doi:10.1029/2026WR044758.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026WRR....6244758Y,
author = {{Youm}, Kookhyoun and {Seo}, Ki--Weon and {Kim}, Jae--Seung and {Chen}, Jianli and {Wilson}, Clark R.},
title = "{Improved GNSS-Based Estimates of Terrestrial Water Storage in California Through Suppression of Non-Elastic Loading Signals}",
journal = {Water Resources Research},
keywords = {vertical displacement, inversion, TWS, GNSS, noise correction},
year = 2026,
month = sep,
volume = {62},
number = {9},
eid = {e2026WR044758},
pages = {e2026WR044758},
abstract = "{Accurate estimation of terrestrial water storage (TWS) variations is
important for understanding climate change and managing water
resources. Satellite gravimetry has been a unique tool for this
purpose, but has inherently low spatial resolution. Dense
networks of the Global Navigation Satellite System (GNSS)
receivers may provide a higher resolution alternative by
observing crustal deformation due to changing water mass loads.
However, GNSS observations contain noise and other residual
signals from various sources that are unrelated to elastic
loading, which is troublesome in TWS applications. In this
study, we estimated monthly TWS changes on a 0.25{\textdegree}
grid in California from January 2019 to December 2023 using GNSS
data, after developing a method to separate elastic loading
signals through a Combined Empirical Orthogonal Function (CEOF)
analysis with Gravity Recovery and Climate Experiment Follow--On
(GRACE--FO). The corrected GNSS data were used to estimate TWS
change via a regularized linear inversion, revealing clear
seasonal variations and long--term decline from January 2019 to
October 2022, followed by an abrupt increase associated with
atmospheric river events from October 2022 to March 2023. GNSS--
based estimates are validated with in situ lake water elevation
data, and provide additional spatial detail compared to GRACE--
FO mass concentration solutions and hydrological model
estimates. These results improve the potential of GNSS--based
TWS estimates in regional hydrological analyses, water resource
management, and climate change assessment.}",
doi = {10.1029/2026WR044758},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026WRR....6244758Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Thu Oct 08, F. Flechtner