Mon Aug 24, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Nasiri, Mahsa, Fatolazadeh, Farzam, and Goïta, Kalifa, 2026. Multi–Resolution Estimation of GNSS–Based Terrestrial Water Storage Changes Using Green's Function and Slepian Basis Function Methods. Remote Sensing, 18(14):2392, doi:10.3390/rs18142392.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026RemS...18.2392N,
author = {{Nasiri}, Mahsa and {Fatolazadeh}, Farzam and {Go{\"i}ta}, Kalifa},
title = "{Multi-Resolution Estimation of GNSS-Based Terrestrial Water Storage Changes Using Green's Function and Slepian Basis Function Methods}",
journal = {Remote Sensing},
keywords = {GNSS, GRACE, terrestrial water storage, Green's function, Slepian basis functions},
year = 2026,
month = jul,
volume = {18},
number = {14},
eid = {2392},
pages = {2392},
abstract = "{What are the main findings? Based on the 2839 GNSS stations dataset from
September 2010 to September 2024, GNSS-derived TWS changes show
strong agreement with GRACE in basins with coherent hydrological
signals and dense station coverage, particularly in snow-
dominated mountainous regions. Decreasing grid spacing from
1{\textdegree} to 0.25{\textdegree} generally reduces agreement
with GRACE due to noise amplification and insufficient
observational constraints in most basins; however, notable
exceptions exist in some basins. Based on the 2839 GNSS stations
dataset from September 2010 to September 2024, GNSS-derived TWS
changes show strong agreement with GRACE in basins with coherent
hydrological signals and dense station coverage, particularly in
snow-dominated mountainous regions. Decreasing grid spacing from
1{\textdegree} to 0.25{\textdegree} generally reduces agreement
with GRACE due to noise amplification and insufficient
observational constraints in most basins; however, notable
exceptions exist in some basins. What are the implications of
the main findings? The effective spatial resolution of GNSS-
based TWS inversion is controlled by GNSS network density,
hydrological signal coherence, and regularization, rather than
nominal grid spacing. Green's function method better captures
localized variability in dense networks, while Slepian basis
functions offer more stable and spatially coherent solutions.
The effective spatial resolution of GNSS-based TWS inversion is
controlled by GNSS network density, hydrological signal
coherence, and regularization, rather than nominal grid spacing.
Green's function method better captures localized variability in
dense networks, while Slepian basis functions offer more stable
and spatially coherent solutions. Terrestrial water storage
(TWS) is an important indicator of the hydrological cycle. In
this study, GNSS-derived TWS changes across the contiguous
United States and southern Canada were estimated using Green's
function (GF) and Slepian basis function (SBF) methods at
1{\textdegree}, 0.5{\textdegree}, and 0.25{\textdegree} grid
spacings based on 2839 GNSS stations between September 2010 and
September 2024. The results were evaluated against GRACE/GRACE-
FO over 13 major river basins. Both methods captured the
dominant seasonal variability with strong agreement in snow-
dominated basins. Increasing resolution to 0.25{\textdegree}
reduced agreement in most basins; however, exceptions exist in
some cases. While regions with sparse station density suffer
from noise amplification due to insufficient observational
constraints, densely instrumented regions can reflect localized
variability. Overall, the correlations range between
{\ensuremath{-}}0.37 and 0.92, while the RMSE vary from 3.40 cm
to 15.08 cm between GNSS-derived and GRACE TWS changes. In the
Columbia River basin, correlations reached 0.92 (GF) and 0.88
(SBF) at 1{\textdegree} resolution, and decreased to 0.86 and
0.82 at 0.25{\textdegree}, respectively. The Atlantic Ocean
Seaboard showed near-zero correlations across all resolutions,
indicating spatially heterogeneous signals. In the Pacific Ocean
Seaboard, RMSE at 0.25{\textdegree} reached 12.86 cm (GF) versus
8.93 cm (SBF), reflecting GF's greater sensitivity to localized
variations at finer scales. This highlights that effective
resolution depends on station density, hydrological signal
coherence, and regularization rather than grid spacing alone.}",
doi = {10.3390/rs18142392},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2392N},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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