Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Used in Comprehensive Calculation
2.1.1. CORS Network Data
2.1.2. Atmosphere Pressure Data
2.1.3. MSLA Data
2.1.4. GLDAS Model
2.2. GRACE Data and Post-Processing
2.3. Method
3. Results and Analysis
4. Comparison between CORS and GRACE Results
5. Conclusions
- The effect of terrestrial water load on the crust vertical deformation in southeast Zhejiang from 2015 to 2017 reaches the centimeter scale, the amplitude changes from −1.8 cm to 2.4 cm, and the seasonal variation is obvious. In addition, among the three kinds of environmental load, the effect on crustal vertical deformation of atmospheric load is the greatest, at −7~7 mm;
- The spatial distribution of the terrestrial water load effect in the study area from 2015 to 2017 takes the ladder form from the inland to coastal regions, and considering most of the 38 CORS sites, the amplitude change in the west is higher than that in the east. The surface vertical deformation caused by groundwater load change in the east–west–south–north–central sub-regions shows obvious fluctuations;
- The vertical deformation of terrestrial water load based on the comprehensive calculation of the CORS network can reflect spatial–temporal characteristics more precisely than GRACE. The signal strength of GRACE’s monitoring results has no significant effect on the spatial distribution, while the results derived from the CORS network show that a stronger signal in the west than in the east. As regards temporal distribution, the amplitude change in GRACE’s monitoring results over three years is significantly smaller than that of CORS, and there are significant differences between the two sets of results in individual months, especially in November 2015 and January and February 2016;
- GRACE’s monitoring results contain a two-month phase delay compared with the CORS network at all the 38 CORS sites. After correcting the phase delay, the correlation coefficient between the two results was significantly improved. This indicates that CORS station can respond to the vertical displacement of terrestrial water load in a more timely manner than GRACE. GRACE is limited by its spatial resolution, and it struggles to identify details at such spatial scales as those employed in the study area; there is little distinction between sites in the GRACE data. This is the essence of the value of the GNSS, as it helps us to see local effects that may not be captured by GRACE.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Experimental Results of Other CORS Stations
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Parameter | Processing Mode |
---|---|
Sampling interval data | 15 s |
Satellite cut-off elevation angle (°) | 10 |
Baseline processing mode | BASELINE |
Ionosphere delay model | LC_AUTCLN |
Satellite clock error model | Precise clock offset and orbit products of IGS |
Tropospheric model | Saastamoinen + GPT2w + estimation |
Solar radiation pressure model | ECOMC model |
Solid tide model | IERS2010 |
Ocean tide model | FES2004(otl_FES2004.grid) |
Atmospheric mapping function | VMF1 |
Inertial framework | J2000 |
Framework of prior coordinates | ITRF2014 |
PCO/PCV | IGS14 atx |
Ambiguity resolution | LAMBDA method |
IGS station priori coordinates | The coordinates under ITRF2008 published by SOPAC |
Site | Longitude and Latitude | Site | Longitude and Latitude | Site | Longitude and Latitude |
---|---|---|---|---|---|
AIRA | 130.5995 31.8240 | ARTU | 58.5604 56.4298 | BJFS | 115.8924 39.6086 |
DAEJ | 127.3744 36.3994 | LHAZ | 91.1040 29.6573 | PIMO | 121.0777 14.6357 |
POL2 | 74.6942 42.6797 | SHAO | 121.2004 31.0996 | TCMS | 120.9873 24.7979 |
WUHN | 87.6006 43.8079 | URUM | 114.3572 30.5316 | YSSK | 142.7167 47.0297 |
Site Name | Weight | Site Name | Weight | Site Name | Weight | Site Name | Weight |
---|---|---|---|---|---|---|---|
DONT | 0.8 | LUOY | 1.0 | SHAT | 1.0 | YAYA | 1.0 |
FDIQ | 0.8 | PANA | 0.8 | SHNQ | 1.0 | YONK | 1.0 |
JHYW | 1.0 | PCHQ | 0.8 | SNYN | 1.0 | YUEQ | 1.0 |
JINH | 1.0 | PCJM | 0.8 | SUIC | 1.0 | ZHRQ | 1.0 |
JINX | 1.0 | QINT | 1.0 | TAIZ | 1.0 | ZJCN | 1.0 |
JNJZ | 1.0 | QIYU | 1.0 | WENC | 0.8 | ZJJN | 1.0 |
JSAN | 1.0 | QNYN | 1.0 | XIAG | 1.0 | ZJWL | 1.0 |
LHAI | 1.0 | QUZH | 1.0 | XNJU | 1.0 | ZJXJ | 1.0 |
LISH | 1.0 | QZLY | 1.0 | YANT | 1.0 | ZJYH | 1.0 |
LONQ | 1.0 | RUIA | 1.0 |
Station Name | Value | Station Name | Value | Station Name | Value | Station Name | Value |
---|---|---|---|---|---|---|---|
FDIQ | 0.69 | JHYW | 0.64 | JINH | 0.68 | JINX | 0.68 |
JNJZ | 0.71 | JSAN | 0.72 | LISH | 0.70 | LONQ | 0.71 |
LUOY | 0.71 | PANA | 0.64 | PCHQ | 0.74 | PCJM | 0.73 |
QINT | 0.68 | QIYU | 0.72 | QNYN | 0.74 | QUZH | 0.72 |
QZLY | 0.69 | RUIA | 0.68 | SHAT | 0.67 | SHNQ | 0.71 |
SNYN | 0.71 | SUIC | 0.71 | WENC | 0.69 | XIAG | 0.68 |
YANT | 0.67 | YAYA | 0.69 | YONK | 0.66 | ZHRQ | 0.71 |
ZJCN | 0.68 | ZJJN | 0.71 | ZJYH | 0.71 | DONT | 0.67 |
LHAI | 0.64 | TAIZ | 0.64 | XNJU | 0.66 | YUEQ | 0.65 |
ZJWL | 0.64 | ZJXJ | 0.66 |
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Li, W.; Dong, J.; Wang, W.; Wen, H.; Liu, H.; Guo, Q.; Yao, G.; Zhang, C. Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang. Sensors 2021, 21, 7699. https://doi.org/10.3390/s21227699
Li W, Dong J, Wang W, Wen H, Liu H, Guo Q, Yao G, Zhang C. Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang. Sensors. 2021; 21(22):7699. https://doi.org/10.3390/s21227699
Chicago/Turabian StyleLi, Wanqiu, Jie Dong, Wei Wang, Hanjiang Wen, Huanling Liu, Qiuying Guo, Guobiao Yao, and Chuanyin Zhang. 2021. "Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang" Sensors 21, no. 22: 7699. https://doi.org/10.3390/s21227699
APA StyleLi, W., Dong, J., Wang, W., Wen, H., Liu, H., Guo, Q., Yao, G., & Zhang, C. (2021). Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang. Sensors, 21(22), 7699. https://doi.org/10.3390/s21227699