| Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences | Host |
| Groundwater Science and Engineering Limited | Publish |
| Citation: |
Zhuo Zi-jun, Lv Dun-yu, Meng Shu-ran, Zhang Jian-yu, Liu Song-bo, Wang Cui-ling. 2023. Factors driving surface deformations in plain area of eastern Zhengzhou City, China. Journal of Groundwater Science and Engineering, 11(4): 347-364. doi: 10.26599/JGSE.2023.9280028
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Factors driving surface deformations in plain area of eastern Zhengzhou City, China
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Abstract
With the rapid socio-economic development and urban expansion, land subsidence has emerged as a major environmental issue, impeding the high-quality development of the plain area in eastern Zhengzhou City, Henan Province, China. However, effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations. In order to accurately identify the dominant factor driving surface deformations in the study area, this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar (PS-InSAR) technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020. The acquired data was verified using leveling data. Subsequently, GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors. The findings are as follows: Finally, the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions. The findings are as follows: (1) Surface deformations in the study area are dominated by land subsidence, concentrated mainly in Zhongmu County, with a deformation rate of −12.5–−37.1 mm/a. In contrast, areas experiencing surface uplift are primarily located downtown, with deformation rates ranging from 0 mm to 8 mm; (2) Groundwater level, lithology, and urban construction exhibit strong spatial correlations with cumulative deformation amplitude; (3) Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations, with a contributive degree of 0.5328. The contributive degrees of driving factors are significantly enhanced through interactions. Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area. Their total contributive degree increases to 0.5722 after interactions, establishing them as the primary factors influencing surface deformation patterns in the study area. The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area, as well as contributing to research on the underlying mechanisms.
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References
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Zhuo Zi-jun, Lv Dun-yu, Meng Shu-ran, Zhang Jian-yu, Liu Song-bo, Wang Cui-ling. 2023. Factors driving surface deformations in plain area of eastern Zhengzhou City, China. Journal of Groundwater Science and Engineering, 11(4): 347-364. doi: 10.26599/JGSE.2023.9280028
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Figure 1.
Geographic location of the study area and the scope of Sentinel-1A images
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Figure 2.
Hydrogeological cross sections
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Figure 3.
Spatial and temporal baselines of Sentinel-1A images
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Figure 4.
Distribution of leveling measurement points, PS points, and benchmark of the leveling measurement
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Figure 5.
Verification results of InSAR monitoring
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Figure 6.
Spatial distribution of surface deformations rate in the study area
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Figure 7.
Groundwater exploitation in the study area
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Figure 8.
Elevation contours of groundwater level and cumulative deformation amplitude in the study area in 2019
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Figure 9.
Spatial distribution of cohesive soil thickness and cumulative deformation amplitude
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Figure 10.
Land use types in the study area during 2010‒2019 (1: Construction land; 2: Cultivated land; 3: Water body; 4: Grassland; 5: Forest land)
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Figure 11.
Evolutionary relationships between construction land and zones with land subsidence based on equal-fan analysis
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Figure 12.
Spatial distribution of driving factors
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Figure 13.
Detection results of driving factor interaction