Zi-jun Zhuo; Dun-yu Lv; Shu-ran Meng; Jian-yu Zhang; Song-bo Liu; Cui-ling Wang

doi:10.26599/JGSE.2023.9280028

doi: 10.26599/JGSE.2023.9280028

^{1, 2},
^{1, 3, ,},
^{1, 3},
^{1, 3},
^{1, 3},
^{1, 3}

计量
- 文章访问数: 649
- HTML全文浏览量: 277
- PDF下载量: 133
- 被引次数: 0
出版历程
- 收稿日期: 2023-01-30
- 录用日期: 2023-05-10
- 网络出版日期: 2023-12-10
- 刊出日期: 2023-12-31

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

Zi-jun Zhuo^{1, 2},
Dun-yu Lv^{1, 3
, ,},
Shu-ran Meng^{1, 3},
Jian-yu Zhang^{1, 3},
Song-bo Liu^{1, 3},
Cui-ling Wang^{1, 3}

1.
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
2.
China University of Geosciences (Beijing), Beijing 100083, China
3.
Key Laboratory of Quaternary Chronology and Hydro-Environmental Evolution, China Geological Survey (CGS) & Hebei Province, Shijiazhuang 050061, China

More Information

Corresponding author: lvdunyu@foxmail.com

摘要

- /
- /
- /
- /
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.
- PS-InSAR /
- GIS spatial analysis /
- Geographical detector model /
- Degree of contribution of a driving factor /
- Spatially stratified heterogeneity

HTML全文

Figure 1. Geographic location of the study area and the scope of Sentinel-1A images

下载: 全尺寸图片幻灯片

Figure 2. Hydrogeological cross sections

Notes：(Q_h^a1: Holocene alluvium; Q_p^3a1: Upper Pleistocene alluvium; Q_p^2a1: Middle Pleistocene alluvium; Q_p^1a1+1: Lower Pleistocene alluvium; N¹: Upper section of the Neogene)

下载: 全尺寸图片幻灯片

Figure 3. Spatial and temporal baselines of Sentinel-1A images

下载: 全尺寸图片幻灯片

Figure 4. Distribution of leveling measurement points, PS points, and benchmark of the leveling measurement

下载: 全尺寸图片幻灯片

Figure 5. Verification results of InSAR monitoring

下载: 全尺寸图片幻灯片

Figure 6. Spatial distribution of surface deformations rate in the study area

下载: 全尺寸图片幻灯片

Figure 7. Groundwater exploitation in the study area

下载: 全尺寸图片幻灯片

Figure 8. Elevation contours of groundwater level and cumulative deformation amplitude in the study area in 2019

下载: 全尺寸图片幻灯片

Figure 9. Spatial distribution of cohesive soil thickness and cumulative deformation amplitude

下载: 全尺寸图片幻灯片

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)

下载: 全尺寸图片幻灯片

Figure 11. Evolutionary relationships between construction land and zones with land subsidence based on equal-fan analysis

下载: 全尺寸图片幻灯片

Figure 12. Spatial distribution of driving factors

下载: 全尺寸图片幻灯片

Figure 13. Detection results of driving factor interaction

Note: ^# indicates the two-factor enhancement type, ^* indicates the non-linear enhancement type

下载: 全尺寸图片幻灯片

Table 1. Data types, description, and sources

Data type	Data description	Data resources
Satellite images	Sentinel-1A satellite images (January 2018‒March 2020)	ESA European Space Agency (https://viewer.esaworldcover.org/worldcover)
Geographical information data	Land use data (resolution: 30 m, date: 2011‒2019)	GeoData Institute (Yang and Huang, 2021)(https://essd.copernicus.org/articles/13/3907/2021/)
	Digital elevation model (resolution: 30 m)	Resource and Environment Science and Data Center of CAS (https://www.resdc.cn)
	Vector data of urban road network and buildings (resolution: 30 m)	Open Street Map website (https://www.openstreetmap.org)
Geological environment data	Cohesive soil thickness	Urban geological survey program Geological safety inspection and risk assessment sampling of Zhengzhou
	Groundwater level monitoring data (date: January 2018‒March 2020）
	Leveling data (date: January 2018-March 2020)

下载: 导出CSV

Table 2. The interaction types and corresponding criteria

Criterion	Interaction type
q(X₁∩X₂)<Min(q(X₁),q(X₂))	Nonlinear weakening
Min(q(X₁),q(X₂))<q(X₁∩X₂)<Max(q(X₁),q(X₂))	Nonlinear weakening of a single factor
q(X₁∩X₂)> Max(q(X₁),q(X₂))	Enhancement of two factors
q(X₁∩X₂)=q(X₁)+q(X₂)	Independent
q(X₁∩X₂)>q(X₁)+q(X₂)	Nonlinear enhancement

下载: 导出CSV

Table 3. Criteria for developmental degrees of surface deformations

Surface deformation grading	Severe	Moderate	Weak	Slight
Deformation rate/mm·a⁻¹	＜−18	−8 to −18	0 to −8	＞0
Cumulative deformation amplitude/mm	＜−30	−30 to −20	−10 to 0	＞0
*Note: for surface deformation rates and cumulative deformation amplitude, “+” denotes land uplift and “−” denotes land subsidence.

下载: 导出CSV

Table 4. Statistics of surface deformation rates in the study area

Statistics	Deformation rate/mm·a⁻¹	Cumulative deformation amplitude/mm
Statistics	Deformation rate/mm·a⁻¹	Stage I: January 2018 to February 2019	Stage II: January 2019 to March 2020
Minimum	−37.1	−33.82	−40.36
Maximum	+8	+7.29	+8.71
Average	−5.66	−4.84	−5.01

下载: 导出CSV

Table 5. Statistics of changes in the area of zones with different developmental degrees of surface deformations from January 2018 to March 2020

Developmental degree of surface deformations /mm·a⁻¹	Stage Ⅰ: January 2018 to February 2019		Stage Ⅱ: February 2019 to March 2020		January 2018 to March 2020
Developmental degree of surface deformations /mm·a⁻¹	Area /km²	Proportion /%	Area /km²	Proportion /%	Area /km²	Proportion /%
Severe (＜ −18）	16.24	0.74	26.34	1.20	21.24	0.97
Moderate (−8 to −18）	337.59	15.38	432.42	19.70	385.07	17.54
Weak (0 to −8)	1,225.25	55.82	1,086.53	49.50	1,155.81	52.66
Slight (＞0 )	615.92	28.06	649.72	29.60	632.88	28.83

下载: 导出CSV

Table 6. Statistics of the increased area of construction land and the area of zones with different cumulative deformation amplitudes from January 2018 to March 2020

Direction	Increased area of construction land /km²	Area of a zone with different cumulative deformation amplitudes/km²				Direction	Increased area of construction land /km²	Area of zones with a cumulative deformation amplitude /km²
Direction	Increased area of construction land /km²	> −30	−20 to −30	0 to −10	Total	Direction	Increased area of construction land /km²	> 30	20 to 30	0 to 10	Total
N	23.4	4.098	12.294	24.588	40.98	S	34.8	3.884	11.652	23.304	38.84
NNE	29.75	5.565	16.695	33.39	55.65	SSW	34.53	4.463	13.389	26.778	44.63
NE	50.67	28.753	31.255	22.502	82.51	SW	28.26	1.101	3.303	6.606	11.01
NEE	115.46	263.1	131.55	131.55	438.5	SWW	28.85	0.793	2.379	4.758	7.93
E	123.91	114.10	190.17	76.068	380.3	W	27.96	2.756	8.268	16.536	27.56
SEE	98.88	33.527	100.581	201.162	335.2	NWW	22.4	4.451	13.353	26.706	44.51
SE	66.5	7.674	23.022	46.044	76.74	NW	45.56	28.648	55.944	51.888	136.48
SSE	37.51	1.361	4.083	8.166	13.61	NNW	25.72	5.454	16.362	32.724	54.54

下载: 导出CSV

Table 7. Driving factors selected for the geographical detector model

Type	Driving factor	Indicator introduction
Urban contribution	Building density (X₁)	Reflecting building loads and the intensity of engineering activity
Urban contribution	Road density (X₂)
Groundwater exploitation	Groundwater level in the first aquifer group (X₃)	Reflecting the intensity of the groundwater exploitation in the study area
Groundwater exploitation	Groundwater level in the second aquifer group (X₄)
Lithology	Cohesive soil thickness of the first aquifer group (X₅)	Reflecting the elastic and plastic deformation capacities of soil mass
Lithology	Cohesive soil thickness of the second aquifer group (X₆)

下载: 导出CSV

Table 8. Detection results of driving factors

Driving factor	X₁	X₂	X₃	X₄	X₅	X₆
Value q	0.0067^**	0.0983^**	0.2674^**	0.5382^**	0.0396^**	0.0713
Value p	0.00	0.00	0.00	0.00	0.00	0.00
Note: ^** denotes the significant enhancement of the interpretation degree of a driving factor.

下载: 导出CSV

参考文献(37)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Wang JF, Xu CD. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116−134. (in Chinese) DOI: 10.11821/d1xb201701010.

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.

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.

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.

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.

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.

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.

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.

[1]	Edmealem Temesgen, Demelash Wendmagegnehu Goshime, Destaw Akili2023: Determination of groundwater potential distribution in Kulfo-Hare watershed through integration of GIS, remote sensing, and AHP in Southern Ethiopia, Journal of Groundwater Science and Engineering, 11, 249-262. doi: 10.26599/JGSE.2023.9280021
[2]	Dun Wang, Li-xin Pei, Li-zhong Zhang, Xi-wen Li, Ze-heng Chen, Yue-hu Zhou2023: Water resource utilization characteristics and driving factors in the Hainan Island, Journal of Groundwater Science and Engineering, 11, 191-206. doi: 10.26599/JGSE.2023.9280017
[3]	Temesgen Mekuriaw Manderso, Yitbarek Andualem Mekonnen, Tadege Aragaw Worku2023: Application of GIS based analytical hierarchy process and multicriteria decision analysis methods to identify groundwater potential zones in Jedeb Watershed, Ethiopia, Journal of Groundwater Science and Engineering, 11, 221-236. doi: 10.26599/JGSE.2023.9280019
[4]	Xiu-bo Sun, Chang-lai Guo, Jing Zhang, Jia-quan Sun, Jian Cui, Mao-hua Liu2023: Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis, Journal of Groundwater Science and Engineering, 11, 37-46. doi: 10.26599/JGSE.2023.9280004
[5]	Dr Muthamilselvan A, Anamika Sekar, Emmanuel Ignatius2022: Identification of groundwater potential in hard rock aquifer systems using Remote Sensing, GIS and Magnetic Survey in Veppanthattai, Perambalur, Tamilnadu, Journal of Groundwater Science and Engineering, 10, 367-380. doi: 10.19637/j.cnki.2305-7068.2022.04.005
[6]	Niway Wondesen Fikade, Molla Dagnachew Daniel, Lohani Tarun Kumar2022: Holistic approach of GIS based Multi-Criteria Decision Analysis (MCDA) and WetSpass models to evaluate groundwater potential in Gelana watershed of Ethiopia, Journal of Groundwater Science and Engineering, 10, 138-152. doi: 10.19637/j.cnki.2305-7068.2022.02.004
[7]	GUI Chun-lei, WANG Zhen-xing, MA Rong, ZUO Xue-feng2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN, Journal of Groundwater Science and Engineering, 9, 1-11. doi: 10.19637/j.cnki.2305-7068.2021.01.001
[8]	ZHOU Hao, WU Yong, HUANG Feng, TANG Xue-fang2021: Experimental simulation and dynamic model analysis of Cadmium (Cd) release in soil affected by rainfall leaching in a coal-mining area, Journal of Groundwater Science and Engineering, 9, 65-72. doi: 10.19637/j.cnki.2305-7068.2021.01.006
[9]	Abdelhakim LAHJOUJ, Abdellah EL HMAIDI, Karima BOUHAFA2020: Spatial and statistical assessment of nitrate contamination in groundwater: Case of Sais Basin, Morocco, Journal of Groundwater Science and Engineering, 8, 143-157. doi: 10.19637/j.cnki.2305-7068.2020.02.006
[10]	Yacob T Tesfaldet, Avirut Puttiwongrak, Tanwa Arpornthip2020: Spatial and temporal variation of groundwater recharge in shallow aquifer in the Thepkasattri of Phuket, Thailand, Journal of Groundwater Science and Engineering, 8, 10-19. doi: 10.19637/j.cnki.2305-7068.2020.01.002
[11]	Bahrami Mehdi, Khaksar Elmira, Khaksar Elahe2020: Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran), Journal of Groundwater Science and Engineering, 8, 230-243. doi: 10.19637/j.cnki.2305-7068.2020.03.004
[12]	Negar Fathi, Mohammad Bagher Rahnama, Mohammad Zounemat Kermani2020: Spatial analysis of groundwater quality for drinking purpose in Sirjan Plain, Iran by fuzzy logic in GIS, Journal of Groundwater Science and Engineering, 8, 67-78. doi: 10.19637/j.cnki.2305-7068.2020.01.007
[13]	T K G P Ranasinghe, R U K Piyadasa2019: Visualizing the spatial water quality of Bentota, Sri Lanka in the presence of seawater intrusion, Journal of Groundwater Science and Engineering, 7, 340-353. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.005
[14]	Nouayti Abderrahime, Khattach Driss, Hilali Mohamed, Nouayti Nordine2019: Mapping potential areas for groundwater storage in the High Guir Basin (Morocco):Contribution of remote sensing and geographic information system, Journal of Groundwater Science and Engineering, 7, 309-322. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.002
[15]	JIANG Ti-sheng, QU Ci-xiao, WANG Ming-yu, SUN Yan-wei, HU Bo, CHU Jun-yao2017: Analysis on temporal and spatial variations of groundwater hydrochemical characteristics in the past decade in southern plain of Beijing, China, Journal of Groundwater Science and Engineering, 5, 235-248.
[16]	WANG Ji-ning, MENG Yong-hui2016: Characteristics analysis and model prediction of sea-salt water intrusion in lower reaches of the Weihe River, Shandong Province, China, Journal of Groundwater Science and Engineering, 4, 149-156.
[17]	GUO Li-jun, YAN Ya-ya, GUO Li-na, MA Jin-long, LV Ming-yu2016: GIS-based spatial and temporal changes of land occupation caused by mining activities-a study in eastern part of Hubei Province, Journal of Groundwater Science and Engineering, 4, 60-68.
[18]	LIU Jun, CHENG Jian-mei, JIANG Fang-yuan2015: Methodological study of coastal geological hazard assessment based on GIS, Journal of Groundwater Science and Engineering, 3, 77-85.
[19]	GAO Zong-jun, ZHU Zhen-hui, LIU Xiao-di, XU Yan-lan2014: The Formation and Model of High Fluoride Groundwater and In-situ Dispelling Fluoride Assumption in Gaomi City of Shandong Province, Journal of Groundwater Science and Engineering, 2, 34-39.
[20]	Feng-long Zhang, Fu-li Qi, Shou-cheng Lu, Yong-li Li2013: Analysis of the Water Factor with the Major Environmental Issues in the Sanjiang Plain, Journal of Groundwater Science and Engineering, 1, 28-32.