Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Wei, Changshou, Zhou, Maosheng, Du, Zhixing, Han, Lijing, and Gao, Hao, 2025. Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms. Scientific Reports, 15(1):6070, doi:10.1038/s41598-025-90363-y.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025NatSR..15.6070W,
author = {{Wei}, Changshou and {Zhou}, Maosheng and {Du}, Zhixing and {Han}, Lijing and {Gao}, Hao},
title = "{Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms}",
journal = {Scientific Reports},
keywords = {3D-CNN, Terrestrial water loading displacement, GRACE, GNSS, Load Green's function, Engineering, Geomatic Engineering},
year = 2025,
month = feb,
volume = {15},
number = {1},
eid = {6070},
pages = {6070},
abstract = "{This work introduces a novel method for estimating hydrological loading
displacement using 3D Convolutional Neural Networks (3D-CNN).
This approach utilizes vertical displacement time series data
from 41 Global Navigation Satellite System (GNSS) stations
across Yunnan Province, China, and its adjacent areas, coupled
with spatiotemporal variations in terrestrial water storage
derived from the Gravity Recovery and Climate Experiment
satellites (GRACE). The 3D-CNN method demonstrates markedly
higher inversion precision compared to conventional load Green's
function inversion techniques. This improvement is evidenced by
substantial reductions in deviations from GNSS observations
across various statistical metrics: the maximum deviation
decreased by 1.34 millimeters, the absolute minimum deviation by
1.47 millimeters, the absolute mean deviation by 79.6\%, and the
standard deviation by 31.4\%. An in-depth analysis of
terrestrial water storage and loading displacement from 2019 to
2022 in Yunnan Province revealed distinct seasonal fluctuations,
primarily driven by dominant annual and semi-annual cycles, and
these periodic signals accounted for over 90\% of the variance.
The spatial distribution of terrestrial water loading
displacement is strongly associated with regional precipitation
patterns, showing smaller amplitudes in the northeast and
northwest and larger amplitudes in the southwest. The research
findings presented in this paper offer a novel perspective on
the spatiotemporal variations of environmental load effects,
particularly those related to the terrestrial water loading
deformation with significant spatial heterogeneity. Accurate
assessment of the effects of terrestrial water loading
displacement (TWLD) is of considerable importance for precise
geodetic observations, as well as for the establishment and
maintenance of high-precision dynamic reference frames.
Furthermore, the development of TWLD model that integrates GRACE
and GNSS data provides valuable data support for the higher-
precision inversion of changes in terrestrial water storage.}",
doi = {10.1038/s41598-025-90363-y},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025NatSR..15.6070W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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