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Article Navigation >  CARSOLOGICA SINICA  > 2026  > Accepted Manuscript
WU Chengye, WANG Xiaoming, YANG Dongsheng, HUANG Junlong. Vulnerability assessment of karst water in cloud floating city based on PLEIK-V model[J]. CARSOLOGICA SINICA. doi: 10.11932/karst2026y019
Citation: WU Chengye, WANG Xiaoming, YANG Dongsheng, HUANG Junlong. Vulnerability assessment of karst water in cloud floating city based on PLEIK-V model[J]. CARSOLOGICA SINICA. doi: 10.11932/karst2026y019
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Vulnerability assessment of karst water in cloud floating city based on PLEIK-V model

doi: 10.11932/karst2026y019

  • Guangdong Geological Prospecting Engineering Survey Institute, Guangzhou , Guangdong 510800, China

  • Received Date: 2026-01-09
  • Accepted Date: 2026-05-15
  • Rev Recd Date: 2026-05-08
    • Available Online: 2026-06-18
    Abstract
  • This study addresses the critical issue of the dynamic nature deficiency inherent in existing models for assessing the vulnerability of karst groundwater systems. Karst aquifers are particularly susceptible to contamination due to their unique hydrogeological characteristics, such as rapid recharge, high flow velocities, and limited natural attenuation capacity. Traditional assessment methods often fail to capture the temporal and spatial variability of these systems, leading to inaccurate vulnerability evaluations. To bridge this research gap, the city of Yunfu in Guangdong Province, China, was selected as a representative study area due to its typical karst hydrogeological features and the widespread development of karst landforms. Yunfu's geological setting, characterized by soluble carbonate rocks and complex aquifer systems, provides an ideal natural laboratory for testing and refining vulnerability assessment methodologies.Based on an in-depth analysis of the regional geological background, hydrogeological conditions, and anthropogenic influences, this study employs Geographic Information System (GIS) technology to develop a novel assessment framework—the PLEIK-V system. This model integrates six key indicators to systematically evaluate the vulnerability of karst groundwater: the protective cap (P), which refers to the overlying layers that shield the aquifer from surface contaminants; land type (L), encompassing land use and land cover patterns that influence contaminant loading; the intensity of surface karst zone development (E), which affects direct recharge and pollutant transport; recharge intensity (I), representing the amount and rate of water infiltrating the aquifer; the degree of karst network development (K), reflecting the internal drainage and storage characteristics; and the average annual rate of decline in groundwater level (V), a dynamic indicator that captures the stress and recovery potential of the groundwater system. By incorporating this dynamic component, the PLEIK-V model addresses a significant limitation of conventional static assessment tools.The study demonstrates that the PLEIK-V model exhibits robust applicability in the field of karst water vulnerability assessment. The empirical analysis reveals that the vulnerability of karst groundwater in Yunfu presents a distinct "three-class" spatial pattern. Specifically, approximately 24.1% of the study area is classified as low vulnerability, 51.5% as medium vulnerability, and 24.4% as high vulnerability. This zoning reflects the heterogeneous nature of the karst aquifer system and the varying degrees of exposure to contamination risks. High vulnerability areas are typically associated with thin protective covers, intense karst development, and significant groundwater level fluctuations, making them particularly prone to pollution from surface sources.To validate the scientificity and reliability of the PLEIK-V assessment model, a correlation analysis was conducted between the vulnerability index and two key groundwater quality parameters: overall groundwater quality types and nitrate concentrations. Nitrate is a common indicator of anthropogenic contamination from agricultural and urban activities. The results show a significant positive correlation, with a correlation coefficient of 0.892 and a coefficient of determination (R2) of 0.797. This strong statistical relationship confirms that areas identified as highly vulnerable indeed exhibit poorer water quality and higher nitrate levels, thereby substantiating the predictive capability of the model.Based on the vulnerability assessment results and considering the distinctive characteristics of special geological units within the karst system, the vulnerability zoning of karst groundwater in Yunfu has been further optimized. This refined zoning provides a more nuanced understanding of the spatial distribution of contamination risks, accounting for local geological anomalies and hydrogeological complexities. The outcomes of this study offer a valuable environmental hydrogeological basis for the prevention and control of urban karst groundwater pollution. By identifying priority areas for protection and guiding land use planning, the PLEIK-V model can support sustainable groundwater management and inform policy decisions aimed at safeguarding water resources in karst regions. Future research could focus on integrating additional dynamic factors, such as climate change impacts and long-term monitoring data, to further enhance the model's predictive accuracy and applicability in diverse karst environments worldwide.

     

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    • References(27)
  • [1]
    郑小战. 广花盆地岩溶地面塌陷灾害形成机理及风险评估研究[D]. 长沙: 中南大学, 2010.

    Zheng Xiaozhan. Research on Genetic Mechanism and Risk Evaluationof the Karst Collapse in Guanghua Basin[D]. Changsha: Central South University, 2010.
    [2]
    程文汉. 广东韶关市岩溶发育规律与富水性关系探讨[J]. 矿产勘查, 2013, 4(2): 224-228. doi: 10.3969/j.issn.1674-7801.2013.02.016

    Cheng Wenhan. Study on karst development mechanism and its relationship with water yield in Shaoguan City, Guangdong[J]. Mineral Exploration, 2013, 4(2): 224-228. doi: 10.3969/j.issn.1674-7801.2013.02.016
    [3]
    Dávila Pórcel R A, Schüth C, De León-Gómez H, Hoppe A, Lehné R. Land-use impact and nitrate analysis to validate DRASTIC vulnerability maps using a GIS platform of Pablillo River Basin, Linares, N. L. , Mexico[J]. International Journal of Geosciences, 2014, 5(12): 1468-1489.
    [4]
    Taghavi N, Niven R K, Paull D J, Kramer M. Groundwater vulnerability assessment: a review including new statistical and hybrid methods[J]. Science of the Total Environment, 2022, 822: 153486. doi: 10.1016/j.scitotenv.2022.153486
    [5]
    Jakada H, Chen Zhihua, Luo Zhaohui, Luo Mingming, Ibrahim A, Tanko N. Coupling intrinsic vulnerability mapping and tracer test for source vulnerability and risk assessment in a karst catchment based on EPIK method: A case study for the Xingshan County, Southern China[J]. Arabian Journal for Science and Engineering, 2019, 44: 377-389. doi: 10.1007/s13369-018-3392-y
    [6]
    Kamenan Y M, Mangoua O M J, Dibi B, Georges S E, Kouassi K L, Kouassi K A. Assessmentof vulnerability to groundwater pollution in the Lobo Watershed atNibéhibé ( Central-West, Côte d’ Ivoire)[J]. Journal of Water Resource and Protection, 2020, 12(8): 657-671.
    [7]
    Shrestha S, Kafle R, Pandey V P. Evaluation of index-overlay methods for groundwater vulnerability and risk assessment in Kathmandu Valley, Nepal[J]. Science of the Total Environment, 2017, 575: 779-790. doi: 10.1016/j.scitotenv.2016.09.141
    [8]
    Gutiérrez F, Parise M, De Waele J, Jourde H. A review on natural and human-induced geohazards and impacts in karst[J]. Earth-Science Reviews, 2014, 138: 61-88. doi: 10.1016/j.earscirev.2014.08.002
    [9]
    Goyal D, Haritash A K, Singh S K. A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods[J]. Journal of Environmental Management, 2021, 296: 113161. doi: 10.1016/j.jenvman.2021.113161
    [10]
    Hasan M, Islam M A, Hasan M A, Alam M J, Peas M H. Groundwater vulnerability assessment in Savar upazila of Dhaka District, Bangladesh: A GIS-based DRASTIC modeling[J]. Groundwater for Sustainable Development, 2019, 9: 100220. doi: 10.1016/j.gsd.2019.100220
    [11]
    王瑞青. 济南趵突泉泉域岩溶地下水污染风险识别及防控区划研究[D]. 长春: 吉林大学, 2021.

    Wang Ruiqing. Study on Risk Identification and Zoning Prevention and Control of Karst Groundwater Pollution in Baotu Spring Area of Jinan [D]. Changchun: Jilin University, 2021.
    [12]
    刘双, 吉勤克补子, 李强, 周亚男, 江峰, 梁峰, 刘皓雯. 基于PLEIK模型的西南岩溶区地下水污染风险评价 [J]. 人民黄河, 2025, 47 (12): 116-122.

    Liu Shuang, Jiqin Kebuzi, Li Qiang, Zhou Yanan, Jiang Feng, Liang Feng, Liu Haowen. Groundwater Contamination Risk Assessment for Karst Area in Southwest China Based on the PLEIK Model[J]. Yellow River, 2025, 47 (12): 116-122.
    [13]
    Majandang J, Sarapirome S. Groundwater vulnerability assessment and sensitivity analysis in Nong Rua, Khon Kaen, Thailand, using a GIS-based SINTACS model[J]. Environmental Earth Sciences, 2013, 68: 2025-2039. doi: 10.1007/s12665-012-1890-x
    [14]
    Zghibi A, Merzougui A, Chenini I, Ergaieg K, Zouhri L, Tarhouni J. Groundwater vulnerability analysis of Tunisian coastal aquifer: An application of DRASTIC index method in GIS environment[J]. Groundwater for Sustainable Development, 2016, 2-3: 169-181.
    [15]
    杨继翔, 张志才, 陈喜, 刘秀强, 谢永玉, 彭韬, 陈波. 喀斯特关键带裂隙土壤水分时空变化特征及其水流路径[J]. 中国岩溶, 2025, 44(5): 1063-1073. doi: 10.11932/karst20250510

    Yang Jixiang, Zhang Zhicai, Chen Xi, Liu Xiuqiang, Xie Yongyu, Peng Tao, Chen Bo. Spatiotemporal variation characteristics and water flow path of soil moisture of the critical zones fissures in karst area[J]. Carsologica Sinica, 2025, 44(5): 1063-1073. doi: 10.11932/karst20250510
    [16]
    孙才志, 林山杉. 地下水脆弱性概念的发展过程与评价现状及研究前景[J]. 吉林地质, 2000(1): 30-36.

    Sun Caizhi, Lin Shanshan. Review of ground water vulnerability concept and assessment[J]. Jilin Geology, 2000(1): 30-36.
    [17]
    汪莹, 罗朝晖, 吴亚, 李洁, 顾栩. 岩溶地下水脆弱性评价的城镇化因子: 以水城盆地为例[J]. 地球科学, 2019, 44(9): 2909-2919.

    Wang Ying, Luo Zhaohui, Wu Ya, Li Jie, Gu Yu. Urbanization Factors of Groundwater Vulnerability Assessment in Karst Area: A Case Study of Shuicheng Basin[J]. Earth Science, 2019, 44(9): 2909-2919.
    [18]
    万利勤. 济南泉域岩溶地下水的示踪研究[D]. 北京: 中国地质大学(北京), 2008.

    Wan Liqin. Trace Study on Karst Groundwater in Jinan Spring Area [D]. Beijing: China University of Geosciences (Beijing), 2008.
    [19]
    窦舒畅. 济南泉域岩溶水抗生素污染特征及源解析[D]. 济南: 济南大学, 2023.

    Dou Shuchang. Characteristics and source analysis of antibiotic pollution in karst water of Jinan spring area [D]. Jinan: University of Jinan, 2023.
    [20]
    郭晓东, 赵海卿, 马诗敏. 基于DTIV的珲春盆地地下水固有脆弱性评价[J]. 节水灌溉, 2014(10): 54-57.

    Guo Xiaodong, Zhao Haiqing, Ma Shimin. Groundwater Intrinsic Vulnerability Assessment in Hunchun Basin Based on DTIV[J]. Water-Saving Irrigation, 2014(10): 54-57.
    [21]
    雷敏. 基于过程模拟法的地下水脆弱性评价: 以河南省鹿邑县为例[D]. 廊坊: 防灾科技学院, 2021.

    Lei Min. Groundwater vulnerability assessment based on processsimulation- A case study of Luyi County, Henan Province [D]. Langfang: Institute of Disaster Prevention , 2021.
    [22]
    Shakeri R, Alijani F, Nassery H R. Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj Aquifer, Central Iran, using MCDM, SWARA, and BWM methods[J]. Environmental Earth Sciences, 2023, 82(4): 97. doi: 10.1007/s12665-023-10773-x
    [23]
    胡兆鑫, 罗为群, 蒋忠诚, 吴泽燕, 汤庆佳. 基于生态系统脆弱性评价的典型岩溶区生态治理分区[J]. 中国岩溶, 2024, 43(3): 661-671, 703.

    Hu Zhaoxin, Luo Weiqun, Jiang Zhongcheng, Wu Zeyan, Tang Qingjia. Zoning of ecological treatment in typical karst areas based on ecosystem vulnerability assessment[J]. Carsologica Sinica, 2024, 43(3): 661-671, 703.
    [24]
    Boufekane A, Saighi O. Application of groundwater vulnerability overlay and index methods to the Jijel Plain Area (Algeria)[J]. Groundwater, 2018, 56(1): 143-156. doi: 10.1111/gwat.12582
    [25]
    张佳文, 张伟红, 陈震, 陈震, 徐斌, 赵勇胜. 北京密怀顺地区地下水污染风险评价方法探究[J]. 环境科学学报, 2018, 38(7): 2876-2883. doi: 10.13671/j.hjkxxb.2018.0114

    Zhang Jiawen, Zhang Weihong, Chen Zhen, Chen Xia, Xu Bin, Zhao Yongsheng. Study on risk assessment method of groundwater pollution in Beijing Mi-Huai-Shun area[J]. Acta Scientiae Circumstantiae, 2018, 38(7): 2876-2883. doi: 10.13671/j.hjkxxb.2018.0114
    [26]
    高爽. 通辽市平原区地下水脆弱性评价[D]. 北京: 中国地质大学(北京), 2015.

    Gao Shuang. Groundwater Vulnerability Assessment in Tongliao Plain [D]. Beijing: China University of Geosciences (Beijing), 2015.
    [27]
    王泱泱, 黄胜东, 潘东, 黄贵任, 王宇, 常河, 蒲悦. 滇西腾冲火山群典型地下水系统特征及地下水脆弱性评价[J]. 中国岩溶, 2024, 43(6): 1327-1340. doi: 10.11932/karst20240610

    Wang Yong, Huang Shengdong, Pan Dong, Huang Guiren, Wang Yu, Chang He, Pu Yue. Characteristics of groundwater system and assessment of groundwater vulnerability of the Tengchong volcano group in western Yunnan[J]. Carsologica Sinica, 2024, 43(6): 1327-1340. doi: 10.11932/karst20240610
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