中文版 | English
Title

A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China

Author
Corresponding AuthorChen, Kejie
Publication Years
2022
DOI
Source Title
EISSN
2072-4292
Volume14
Abstract
Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Indexed By
EI ; SCI
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
Funding: This research was financially supported by the Open Foundation of the United Laboratory of Numerical Earthquake Forecasting (Grant No: 2021LNEF01) and the National Natural Science Foundation of China (Grant Nos: 42074024 and 41672192).
WOS Accession No
WOS:000816594600001
Publisher
EI Accession Number
20222512261426
EI Keywords
Deformation ; Global positioning system ; Loading ; Time series analysis ; Wavelet transforms
ESI Classification Code
Materials Handling Methods:691.2 ; Mathematical Transformations:921.3 ; Mathematical Statistics:922.2
Data Source
EV Compendex
Citation statistics
Cited Times [WOS]:3
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/411572
DepartmentDepartment of Earth and Space Sciences
Affiliation
1.Department of Earth and Space Sciences, South University of Science and Technology of China, Shenzhen; 518055, China
2.College of Resource Environment and Tourism, Capital Normal University, Beijing; 100048, China
3.Institute of Earthquake Science, China Earthquake Administration, Beijing; 100036, China
First Author AffilicationDepartment of Earth and Space Sciences
Corresponding Author AffilicationDepartment of Earth and Space Sciences
First Author's First AffilicationDepartment of Earth and Space Sciences
Recommended Citation
GB/T 7714
Hu, Shunqiang,Chen, Kejie,Zhu, Hai,et al. A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China[J]. Remote Sensing,2022,14.
APA
Hu, Shunqiang.,Chen, Kejie.,Zhu, Hai.,Xue, Changhu.,Wang, Tan.,...&Zhao, Qian.(2022).A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China.Remote Sensing,14.
MLA
Hu, Shunqiang,et al."A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China".Remote Sensing 14(2022).
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