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Research paper | Open Access

Factors driving surface deformations in plain area of eastern Zhengzhou City, China

Zi-jun Zhuo1,2, Dun-yu Lv1,3( ), Shu-ran Meng1,3, Jian-yu Zhang1,3, Song-bo Liu1,3, Cui-ling Wang1,3
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
China University of Geosciences (Beijing), Beijing 100083, China
Key Laboratory of Quaternary Chronology and Hydro-Environmental Evolution, China Geological Survey (CGS) & Hebei Province, Shijiazhuang 050061, 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.

Keywords

PS-InSARGIS spatial analysisGeographical detector modelDegree of contribution of a driving factorSpatially stratified heterogeneity

References

 

Chen BB, Gong LH, Chen Y, et al. 2020. Land subsidence and its relation with groundwater aquifers in Beijing Plain of China. The Science of the Total Environment, 735: 139111. DOI:10.1016/j.scitotenv.2020.139111.
Crossref Google Scholar
 

Chen M, Tomas R, Li ZH, et al. 2016. Imaging land subsidence induced by groundwater extraction in Beijing (China) using satellite radar interferometry. Remote Sensing, 8(6): 468. DOI:10.3390/rs8060468.
Crossref Google Scholar
 

Cheng R, Zhu L, Zhou JH, et al. 2021. Spatio-Temporal heterogeneity and driving factors of land subsidence in middle lower part of Chaobai River alluvial fan. Journal of Jilin University (Earth Science Edition), 51(4): 1182−1192. (in Chinese) DOI:10.13278/j.cnkijjuese.20200047.
Crossref Google Scholar
 
Chen Y, Chen S, Li J, et al. High precision extraction of surface deformation information based on principal component spatiotemporal analysis and time-series InSAR: Taking Xuzhou as an Example. Journal of Geo-information Science, 1−16. (in Chinese)
 

Ciampalini A, Raspini F, Solari L, et al. 2016. PSInSAR analysis in the Pisa Urban Area (Italy): A case study of subsidence related to stratigraphical factors and urbanization. Remote Sensing, 8(6): 120. DOI:10.3390/rs802012.
Crossref Google Scholar
 

Dong SC, Samsonov S, Yin HW, et al. 2018. Two-dimensional ground deformation monitoring in Shanghai based on SBAS and MSBAS InSAR methods. Journal of Earth Science, 29(4): 960−968. DOI:10.1007/s12583-017-0955-x.
Crossref Google Scholar
 

Ferretti A, Prati C, Rocca F, et al. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2202−2212. DOI:10.1109/36.868878.
Crossref Google Scholar
 

Ferretti A, Prati C, Rocca F, et al. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8−20. DOI:10.1109/36.898661.
Crossref Google Scholar
 

Guo HP, Bai JB, Zhang YQ, et al. 2017. The evolution characteristics and mechanism of the land subsidence in typical areas of the North China Plain. Geology in China, 44(6): 1115−1127. (in Chinese) DOI:10.12029/gc20170606.
Crossref Google Scholar
 
Guan L, Tang W, Dai HY, et al. Monitoring ground subsidence in Zhengzhou City based on SBAS-InSAR technology. Beijing Surveying and Mapping, 33(4): 462-467. (in Chinese)
 

Guo XH, Li JX. 2007. The necessity of land subsidence monitoring in Zhengzhou City. The Chinese Journal of Geolocical Hazard and Control, 18(1): 147−148. (in Chinese) DOI:10.16031/.cnki.issn.1003-8035.2007.01.035.
Crossref Google Scholar
 

Jia SM, Wang HG, Zhao SS, et al. 2007. A tentative study of the mechanism of land subsidence in Beijing. City Geology, 2(1): 20−26. (in Chinese) DOI:10.3969/j.issn.1007-1903.2007.01.005.
Crossref Google Scholar
 

Lei KC, Luo Y, Chen BB, et al. 2016. Distribution characteristics and influence factors of land subsidence in Beijing area. Geology in China, 43(6): 2216−2228. (in Chinese) DOI:10.12029/gc20160628.
Crossref Google Scholar
 

Lei KC, Ma FS, Luo Y, et al. 2022. Main subsidence layers and deformation characteristics in Being Plain at present in. Journal of Engineering Geology, 30(2): 442−458. (in Chinese) DOI:10.13544/i.cnki.jeg.2021-0238.
Crossref Google Scholar
 

Li GE, Zhou YH. 2017. Study on fusion methods of InSAR, Leveling and GPS data. Bulletin of Surveying and Mapping, 486(9): 78−82. (in Chinese) DOI:10.13474/j.cnki.11-2246.2017.0292.
Crossref Google Scholar
 

Li HH, Hou ZD, Li SJ, et al. 2022. Analysis of characteristics and causes of land subsidence in Changzhou by time series InSAR. Journal of Geodesy and Geodynamics, 42(1): 54−58. DOI:10.14075/j.jgg.2022.01.011.
Crossref Google Scholar
 

Li HJ, Zhang YW, Yang YQ, et al. 2018. Spatial-emporal distribution characteristics and causation analysis of land subsidence in three northern counties area of Langfang I. Science Technology and Engineering, 18(11): 23−30. (in Chinese) DOI:10.3969/j.issn.1671-1815.2018.11.003.
Crossref Google Scholar
 

Liu HW, Du D, Xu JB, et al. 2018. Characteristics and affecting factors of land subsidence identification based on PSInSAR measures in Shandong Peninsula Blue-Yellowy Overlapping Economic Zone. Geology in China, 45(6): 1116−1127. (in Chinese) DOI:10.12029/gc20180603.
Crossref Google Scholar
 

Liu P, Li Q, Li Z, et al. 2016. Anatomy of subsidence in Tianjin from time series InSAR. Remote Sensing, 8(3): 266. DOI:10.3390/rs8030266.
Crossref Google Scholar
 

Liu YY, Yan X, Liu B, et al. 2022. Characterizing spatiotemporal patterns of land subsidence after the South-to-North Water Diversion Project based on Sentinel-1 InSAR observations in the Eastern Beijing Plain. Remote Sensing, 14(5810): 5810. DOI:10.3390/rs14225810.
Crossref Google Scholar
 

Motagh M, Shamshiri R, Haghighi HM, et al. 2017. Quantifying groundwater exploitation induced subsidence in the Rafsanjan Plain, southeastern Iran, using InSAR time-series and in situ measurements. Engineering Geology, 218: 134−151. DOI:10.1016/j.enggeo.2017.01.011.
Crossref Google Scholar
 

Pan D, Wang JH, Wang BC, et al. 2020. Subsidence in Zhengzhou City study on distribution characteristics and mechanism of land. Journal of Shandong Agricultural University (Natural Science Edition), 51(4): 660−662. (in Chinese) DOI:10.3969/iissn.1000-2324.2020.04.015.
Crossref Google Scholar
 
Peng JN. 2008. Research on GIS spatial analysis method. M. S. thesis. Ji Lin: Jilin University .
 

Qu FF, Zhang Q, Lu Z, et al. 2014. Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis. Remote Sensing of Environment, 155: 366−376. DOI:10.1016/j.rse.2014.09.008.
Crossref Google Scholar
 

Shi YS, Shi DH, Cao XY, et al. 2018. Impacting factors and temporal and spatial differentiation of land subsidence in Shanghai. Sustainability, 10(9): 3146. DOI:10.3390/su10093146.
Crossref Google Scholar
 

Tomás R, Herrera G. 2010. A ground subsidence study based on DInSAR data: Calibration of soil parameters and subsidence prediction in Murcia City (Spain). Engineering Geology, 111(1): 19−30. DOI:10.1016/j.enggeo.2009.11.004.
Crossref Google Scholar
 

Wang C, Wang YB, Zhou CD, et al. 2018. The influence of urban expansion of Tongzhou on land subsidence. Journal of Capital Normal University (Natural Science Edition), 39(4): 68−74. (in Chinese) DOI:10.19789/j.1004-9398.2018.04.013.
Crossref Google Scholar
 

Wang JF, Xu CD. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116−134. (in Chinese) DOI:10.11821/d1xb201701010.
Crossref Google Scholar
 
Xu CJ, Zhang CY. 2009. Crustal deformation measurement and data processing. Wuhan: Wuhan University Press. (in Chinese)
 

Yang HF, Meng RF, Bao XL, et al. 2022. Assessment of water level threshold for groundwater restoration and over-exploitation remediation the Beijing-Tianjin-Hebei Plain. Journal of Groundwater Science and Engineering, 10(2): 113−127. DOI:10.19637/j.cnki.2305-7068.2022.02.002.
Crossref Google Scholar
 

Yang J, Huang X. 2021. The 30 m annual land cover and its dynamics in China from 1990 to 2019. Earth System Science Data Discussions, 13(8): 3907−3925. DOI:10.5194/essd-13-3907-2021.
Crossref Google Scholar
 

Ye YC. 2021. Temporal and spatial characteristics analysis and prediction of ground subsidence along Zhengzhou Metro based on time series InSAR. M. S. thesis. Zheng Zhou: Zhengzhou University: 2216−2228. (in Chinese) DOI:10.27466/d.cnki.gzzdu.2021.005288.
Crossref Google Scholar
 
Yu HR, Gong HL, Chen BB, et al. The advance and consideration of land subsidence in Beijing-Tianjin-Hebei region. Science of Surveying and Mapping, 45(4): 125-133. (in Chinese)
 

Zhang Y, Liu YF, Liu Y, et al. 2022. Spatia-Temproal variation characteristics and geographic detection mechanism of land subsidence in Wuhan City from 2007 to 2019. Geomatics and Information Science of Wuhan University, 47(9): 1486−1497. (in Chinese) DOI:10.13203/j.whugis20210143.
Crossref Google Scholar
 

Zhao YW, Wang XY, Liu CL. et al. 2020. Finite-difference model of land subsidence caused by cluster loads in Zhengzhou, China. Journal of Groundwater Science and Engineering, 8(1): 43−56. DOI:10.19637/j.cnki.2305-7068.2020.01.005.
Crossref Google Scholar
 

Zhou CD, Gong HL, Zhang YQ, et al. 2018. Spatiotemporal evolution of land subsidence in the Beijing Plain 2003–2015 using Persistent Scatterer Interferometry (PSI) with Multi-Source SAR data. Remote Sensing, 10(4): 552. DOI:10.3390/rs10040552.
Crossref Google Scholar
 

Zhou CF, Gong HL, Chen BB, et al. 2019. Quantifying the contribution of multiple factors to land subsidence in the Beijing Plain, China with machine learning technology. Geomorphology, 335: 48−61. DOI:10.1016/j.geomorph.2019.03.017.
Crossref Google Scholar
Journal of Groundwater Science and Engineering
Volume 11 Issue 4,
December 2023
Pages 347-364
DOI: 10.26599/JGSE.2023.9280028
Cite this article:
Zhuo Z-j, Lv D-y, Meng S-r, et al. Factors driving surface deformations in plain area of eastern Zhengzhou City, China. Journal of Groundwater Science and Engineering, 2023, 11(4): 347-364. https://doi.org/10.26599/JGSE.2023.9280028

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Received: 30 January 2023
Accepted: 10 May 2023
Published: 10 December 2023
2305-7068/© 2023 Journal of Groundwater Science and Engineering Editorial Office

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)
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