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Journal of Groundwater Science and Engineering 2023, 11(1): 68-80 https://doi.org/10.26599/JGSE.2023.9280007
Research Article | Open Access | Issue | Published: 20 March 2023

Influence of underground space development mode on the groundwater flow field in Xiong’an new area

Show Author's Information Yi-hang Gao1,2,3,  Jun-hui Shen2,3( ),  Lin Chen4( ),  Xiao Li4,  Shuang Jin5,  Zhen Ma1,  Qing-hua Meng1
Tianjin Geological Survey Center, China Geological Survey, Tianjin 300000, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
Shenyang Geological Survey Center, China Geological Survey, Shenyang 110034, China
Fifth Geological Brigade, Hebei Bureau of Geology and Mineral Resources, Tangshan 063000, Hebei Province, China
Keywords:
Xiong’an new area, Groundwater flow field, Underground space, GMS
Cite this article:
Gao Y-h, Shen J-h, Chen L, et al. Influence of underground space development mode on the groundwater flow field in Xiong’an new area. Journal of Groundwater Science and Engineering, 2023, 11(1): 68-80. https://doi.org/10.26599/JGSE.2023.9280007
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Abstract Full text About this article

The degree and scale of underground space development are growing with the continuous advancement of urbanization in China. The lack of research on the change of the groundwater flow field before and after the development of underground space has led to various problems in the process of underground space development and operation. This paper took the key development zone of the Xiong’an New Area as the study area, and used the Groundwater modeling system software (GMS) to analyse the influence on the groundwater flow field under the point, line, and surface development modes. The main results showed that the underground space development would lead to the expansion and deepening of the cone of depression in the aquifer. The groundwater level on the upstream face of the underground structure would rise, while the water level on the downstream face would drop. The “line” concurrent development has the least impact on the groundwater flow field, and the maximum rise of water level on the upstream side of the underground structure is expected to be approximately 3.05 m. The “surface” development has the greatest impact on the groundwater flow field, and the maximum rise of water level is expected to be 7.17 m.


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About this article

Influence of underground space development mode on the groundwater flow field in Xiong’an new area

Show Author's information Yi-hang Gao1,2,3, Jun-hui Shen2,3( ), Lin Chen4( ), Xiao Li4, Shuang Jin5, Zhen Ma1, Qing-hua Meng1
Tianjin Geological Survey Center, China Geological Survey, Tianjin 300000, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
Shenyang Geological Survey Center, China Geological Survey, Shenyang 110034, China
Fifth Geological Brigade, Hebei Bureau of Geology and Mineral Resources, Tangshan 063000, Hebei Province, China

Abstract

The degree and scale of underground space development are growing with the continuous advancement of urbanization in China. The lack of research on the change of the groundwater flow field before and after the development of underground space has led to various problems in the process of underground space development and operation. This paper took the key development zone of the Xiong’an New Area as the study area, and used the Groundwater modeling system software (GMS) to analyse the influence on the groundwater flow field under the point, line, and surface development modes. The main results showed that the underground space development would lead to the expansion and deepening of the cone of depression in the aquifer. The groundwater level on the upstream face of the underground structure would rise, while the water level on the downstream face would drop. The “line” concurrent development has the least impact on the groundwater flow field, and the maximum rise of water level on the upstream side of the underground structure is expected to be approximately 3.05 m. The “surface” development has the greatest impact on the groundwater flow field, and the maximum rise of water level is expected to be 7.17 m.

Keywords: Xiong’an new area, Groundwater flow field, Underground space, GMS

References(45)

Attard G, Rossier Y, Eisenlohr L. 2017. Underground structures increasing the intrinsic vulnerability of urban groundwater: Sensitivity analysis and development of an empirical law based on a groundwater age modelling approach. Journal of Hydrology, 552: 460−473. DOI:10.1016/j.jhydrol.2017.07.013.
DOI Google Scholar

Attard G, Winiarski T, Rossier Y, et al. 2016. Review: Impact of underground structures on the flow of urban groundwater. Hydrogeology Journal, 24: 5−19. DOI:10.1007/s10040-015-1317-3.
DOI Google Scholar

Bobylev N. 2009. Mainstreaming sustainable development into a city’s Master plan: A case of Urban Underground Space use. Land Use Policy, 26: 1128−1137. DOI:10.1016/j.landusepol.2009.02.003.
DOI Google Scholar

Borgia A, Cattaneo L, Marconi D, et al. 2011. Using a MODFLOW grid, generated with GMS, to solve a transport problem with TOUGH2 in complex geological environments: The intertidal deposits of the Venetian Lagoon. Computers & Geosciences, 37: 783−790. DOI:10.1016/j.cageo.2010.11.007.
DOI Google Scholar
Chandrasekharan H, Tadi SG, Sarangi A, et al. 2005. Urbanization effects on the groundwater status of Delhi, India. In: Savic DA, Marino MA, Savenije HG, Bertoni JC, editors. Sustainable Water Management Solutions for Large Cities: 293, 294-298.

Chen Y, Liu G, Huang X, et al. 2020. Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis. Journal of Hydrology: 125726. DOI:10.1016/j.jhydrol.2020.125726.
DOI Google Scholar

Chen ZL, Chen JY, Liu H, et al. 2018. Present status and development trends of underground space in Chinese cities: Evaluation and analysis. Tunnelling and Underground Space Technology, 71: 253−270. (in Chinese) DOI:10.1016/j.tust.2017.08.027.
DOI Google Scholar

Chi G, Su X, Lyu H, et al. 2021. Simulating the shallow groundwater level response to artificial recharge and storage in the Plain area of the Daqing River Basin, China. Sustainability, 13: 5626. DOI:10.3390/su13105626.
DOI Google Scholar

Cui JQ, Broere W, Lin D. 2021. Underground space utilisation for urban renewal. Tunnelling and Underground Space Technology, 108: 103726. DOI:10.1016/j.tust.2020.103726.
DOI Google Scholar

De Caro M, Crosta GB, Previati A. 2020. Modelling the interference of underground structures with groundwater flow and remedial solutions in Milan. Engineering Geology, 272: 105652. DOI:10.1016/j.enggeo.2020.105652.
DOI Google Scholar

Dong L, Zhang J. 2021. Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach. Science of the Total Environment: 799. DOI:10.1016/j.scitotenv.2021.149509.
DOI Google Scholar

Foster S, Garduno H, Evans R, et al. 2004. Quaternary aquifer of the North China Plain — assessing and achieving groundwater resource sustainability. Hydrogeology Journal, 12: 81−93. DOI:10.1007/s10040-003-0300-6.
DOI Google Scholar

Han JC, Huang Y, Li Z, et al. 2016. Groundwater level prediction using a SOM-aided stepwise cluster inference model. Journal of Environmental Management, 182: 308−321. DOI:10.1016/j.jenvman.2016.07.069.
DOI Google Scholar

He DF, Shan SQ, Zhang YY, et al. 2018. 3-D geologic architecture of Xiong’an New Area: Constraints from seismic reflection data. Science China Earth Sciences, 48(9): 1207−1222.
Google Scholar

Hu ZH, Li XZ, Zhao XB, et al. 2008. Numerical analysis of factors affecting the range of heat transfer in earth surrounding three subways. Journal of China University of Mining and Technology, 18: 67−71. DOI:10.1016/S1006-1266(08)60015-2.
DOI Google Scholar

Li HP, Wickham JD, Bushley K, et al. 2020. New approaches in urban forestry to minimize invasive species impacts: The case of Xiongan New Area in China. Insects: 11. DOI:10.3390/insects11050300.
DOI Google Scholar

Li R, Zhang J, Krebs P. 2021. Consumption- and income-based sectoral emissions of Polycyclic Aromatic Hydrocarbons in China from 2002 to 2017. Environmental Science & Technology, 55: 3582−3592. DOI:10.1021/acs.est.0c08119.
DOI Google Scholar

Li X, Ye SY, Wei AH, et al. 2017. Modelling the response of shallow groundwater levels to combined climate and water-diversion scenarios in Beijing-Tianjin-Hebei Plain, China. Hydrogeology Journal, 25: 1733−1744. DOI:10.1007/s10040-017-1574-4.
DOI Google Scholar

Li XG, Yuan DJ. 2012. Response of a double-decked metro tunnel to shield driving of twin closely under-crossing tunnels. Tunnelling and Underground Space Technology, 28: 18−30. DOI:10.1016/j.tust.2011.08.005.
DOI Google Scholar

Liu FY, Cui JH, Chen LJ, et al. 2009. A view on geomorphologic zonalization of north china plain. Geography and Geo-Information Science, 25(4): 100−103.
Google Scholar

Ma Y, Li HQ, Zhang J, et al. 2020. Geophysical technology for underground space exploration in Xiongan New Area. Acta Geoscientica Sinica, 41(4): 535−542.
Google Scholar

Ma Z, Xia YB, Wang XD, et al. 2019. Integration of Engineering Geological Investigation Data and Construction of a 3D Geological Structure Model in the Xiong’an New Area. Geology in China, 46: 123−138.
Google Scholar

Makungo R, Odiyo JO. 2017. Estimating groundwater levels using system identification models in Nzhelele and Luvuvhu areas, Limpopo Province, South Africa. Physics and Chemistry of the Earth, Parts A/B/C, 100: 44−50. DOI:10.1016/j.pce.2017.01.019.
DOI Google Scholar

Melaku ND, Wang J. 2019. A modified SWAT module for estimating groundwater table at Lethbridge and Barons, Alberta, Canada. Journal of Hydrology, 575: 420−431. DOI:10.1016/j.jhydrol.2019.05.052.
DOI Google Scholar

Mengistu HA, Demlie MB, Abiye TA, et al. 2019. Conceptual hydrogeological and numerical groundwater flow modelling around the Moab Khutsong deep gold mine, South Africa. Groundwater for Sustainable Development, 9: 100266. DOI:10.1016/j.gsd.2019.100266.
DOI Google Scholar

Paris A, Teatini P, Venturini S, et al. 2010. Hydrological effects of bounding the Venice (Italy) industrial Harbor by a protection cutoff wall: Modeling study. Journal of Hydrologic Engineering, 15: 882−891. DOI:10.1061/(ASCE)HE.1943-5584.0000258.
DOI Google Scholar

Qiao YK, Peng FL. 2016. Master planning for underground space in Luoyang: A case of a representative historic city in China. Procedia Engineering, 165: 119−125. DOI:10.1016/j.proeng.2016.11.743.
DOI Google Scholar

Qin H, Cao G, Kristensen M, et al. 2013. Integrated hydrological modeling of the North China Plain and implications for sustainable water management. Hydrology and Earth System Sciences, 17: 3759−3778. DOI:10.5194/hess-17-3759-2013.
DOI Google Scholar
Ricci G, Enrione R, Eusebio A, et al. 2007. Numerical modelling of the interference between underground structures and aquifers in urban environment. The Turin subway — Line 1. Available on https://www.researchgate.net/publication/283166768.

Rwanga S, Ndambuki J. 2020. Solving groundwater problems fraught with uncertain recharge: An application to Central Limpopo, South Africa. Groundwater for Sustainable Development, 10: 100305. DOI:10.1016/j.gsd.2019.100305.
DOI Google Scholar

Sathe SS, Mahanta C. 2019. Groundwater flow and arsenic contamination transport modeling for a multi aquifer terrain: Assessment and mitigation strategies. Journal of Environmental Management, 231: 166−181. DOI:10.1016/j.jenvman.2018.08.057.
DOI Google Scholar

Serrano-Juan A, Pujades E, Vázquez-Suñè E, et al. 2018. Integration of groundwater by-pass facilities in the bottom slab design for large underground structures. Tunnelling and Underground Space Technology, 71: 231−243. DOI:10.1016/j.tust.2017.07.020.
DOI Google Scholar

Singha S, Pasupuleti S, Singha SS, et al. 2020. Effectiveness of groundwater heavy metal pollution indices studies by deep-learning. Journal of Contaminant Hydrology, 235: 103718. DOI:10.1016/j.gsd.2019.100245.
DOI Google Scholar

Uddin MG, Moniruzzaman M, Quader MA, et al. 2018. Spatial variability in the distribution of trace metals in groundwater around the Rooppur nuclear power plant in Ishwardi, Bangladesh. Groundwater for Sustainable Development, 7: 220−231. DOI:10.1016/j.gsd.2018.06.002.
DOI Google Scholar

Wang C, Masoudi A, Wang M, et al. 2021. Land-use types shape soil microbial compositions under rapid urbanization in the Xiong’an New Area, China. Science of the Total Environment, 777: 145976. DOI:10.1016/j.scitotenv.2021.145976.
DOI Google Scholar
Wang GL, Liu ZM, Chen H, et al. 2010. Compilation of technical methods for investigation and evaluation of groundwater resources. Beijing, Geological Publishing House.

Wu Y. 2003. Mechanism analysis of hazards caused by the interaction between groundwater and geo-environment. Environmental Geology, 44: 811−819. DOI:10.1007/s00254-003-0819-9.
DOI Google Scholar

Xie SL, Su YB, Xu WH, et al. 2019. The effect of habitat changes along the urbanization gradient for breeding birds: An example from the Xiong’an New Area. PeerJ, 7(10): 7961. DOI:10.7717/peerj.7961.
DOI Google Scholar

Xu HQ, Wang MY, Shi TT, et al. 2018. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93: 730−740. DOI:10.1016/j.ecolind.2018.05.055.
DOI Google Scholar
Xu YS, Shen SL, Du YJ, et al. 2013. Modelling the cutoff behavior of underground structure in multi-aquifer-aquitard groundwater system. Natural Hazards, 66: 731-748. Available on https://link.springer.com/article/10.1007/s11069-012-0512-y

Xu YS, Ma L, Shen SL, et al. 2012. Evaluation of land subsidence by considering underground structures that penetrate the aquifers of Shanghai, China. Hydrogeology Journal, 20: 1623−1634. DOI:10.1007/s10040-012-0892-9.
DOI Google Scholar

Yang W, Wang Z, Hua P, et al. 2021. Impact of green infrastructure on the mitigation of road-deposited sediment induced stormwater pollution. Science of the Total Environment: 770.
Google Scholar

Yang W, Zhang J. 2021. Assessing the performance of gray and green strategies for sustainable urban drainage system development: A multi-criteria decision-making analysis. Journal of Cleaner Production: 293. DOI:10.1016/j.jclepro.2021.126191.
DOI Google Scholar

Zhu X, Wang G, Wang X, et al. 2022. Hydrogeochemical and isotopic analyses of deep geothermal fluids in the Wumishan formation in Xiong’an New Area, China. Lithosphere: 2021.
Google Scholar

Zou WY, Yin SQ, Wang WT. 2021. Spatial interpolation of the extreme hourly precipitation at different return levels in the Haihe River basin. Journal of Hydrology, 598: 126273. DOI:10.1016/j.jhydrol.2021.126273.
DOI Google Scholar
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Publication history

Received: 16 June 2022
Accepted: 25 November 2022
Published: 20 March 2023
Issue date: March 2023

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© 2023 Journal of Groundwater Science and Engineering Editorial Office

Acknowledgements

Acknowledgements

This work was supported by the Evaluation of soil and water quality and engineering geological survey in Xiong’an New Area Program of China (Grant No. DD20189122), National Natural Science Foundation of China (Grant No. 42102294).

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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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