Vulnerability assessment of karst water in cloud floating city based on PLEIK-V model
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摘要: 本研究针对现有模型在岩溶地下水脆弱性评估中的动态性缺陷问题，选取广东省云浮市作为典型研究区域，该地区具有典型的岩溶水文地质特征且发育广泛。基于深入的区域地质背景分析，本研究采用地理信息系统技术，构建了一个由保护性盖层P、土地利用类型L、表层岩溶带发育强度E、入渗补给强度I、岩溶网络发育程度K以及地下水位年均下降速率（ V ）六个关键指标构成的PLEIK-V评价体系，用于系统性评估岩溶地下水脆弱性。研究表明，该PLEIK-V模型在岩溶水脆弱性评价领域展现出良好的适用性，实证分析结果显示，云浮市地下水中岩溶水脆弱性呈现"三类分区"格局，其中低、中、高脆弱性区域占比分别为24.1%、51.5%和24.4%，脆弱性指数与地下水水质类型及硝酸盐浓度之间存在显著相关关系，相关系数达到0.892，决定系数为0.797，这一发现充分验证了PLEIK-V评价模型的科学性和可靠性。根据脆弱性评价结果结合岩溶水特殊地质单元特征，进一步优化了该市岩溶地下水脆弱性分区，结果可为城市岩溶地下水环境防治提供环境水文地质依据。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. -
Key words:
- Karst water prevention /
- PLEIK-V model /
- Vulnerability assessment /
- Yunfu City
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图 2 PLEIK-V 模型岩溶水脆弱性评价指标分布图
Figure 2. Distribution map of the vulnerability evaluation index of karst water in the PLEIK-V model
图 4 研究区硝酸盐离子浓度与PLEIK-V模型指数散点图及趋势线分析
Figure 4. Scatter plot and trend line analysis of the nitrate ion concentration in the study area and the PLEIK-V
图 5 云浮市岩溶水脆弱性评价结果优化图
Figure 5. Optimized map of the evaluation results of the vulnerability of karst water in Yunfu City
表 1 层次分析法的平均随机一致性指标值
Table 1. The average random consistency index value of the analytic hierarchy process
矩阵阶数 1 2 3 4 5 6 7 8 9 平均随机一致性指标Ir 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 
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表 2 PLEIK-V模型岩溶水脆弱性评价指标权重
Table 2. Weights of the evaluation indicators for the vulnerability of karst water in the PLEIK-V model
保护性盖层（P） 土地利用类型（L） 表层岩溶带
发育强度（E）入渗补给强度（I） 岩溶网络
发育程度（K）地下水水位年平均下降速率（V） 0.29 0.08 0.22 0.23 0.14 0.04
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表 3 PLEIK-V模型岩溶水脆弱性分级和评分表
Table 3. Vulnerability classification and scoring table of karst water in the PLEIK-V model
评分 保护性盖层P，（CEC
含量/meq∙100 g−1；
土层厚度/cm）土地利用
类型L表层岩溶带
发育强度E入渗补给强度I/
mm∙d−1岩溶网络
发育程度K/
L∙（s·km2）−1地下水水位
年平均下降
速率V/m∙y−110 <10；（0,20] 城镇村及工矿用地 强烈发育的表层岩溶带 >25 >17 >1.0 9 [10,40)；（20,60] 裸土地 [22,25) [15,17) [0.9,1.0) 8 [40,70)；（60,100] 耕地 高度发育的表层岩溶带 [19,22) [13,15) [0.8,0.9) 7 [70,100)；（100,125] [16,19) [11,13) [0.7,0.8) 6 [100,250)；（125,150] 园地 中等发育的表层岩溶带 [13,16) [9,11) [0.6,0.7) 5 [250,300)；（150,175] [10,13) [7,9) [0.5,0.6) 4 [300,350)；（175,200] 轻度发育的表层岩溶带 [7,10) [5,7) [0.4,0.5) 3 [350,400)；（200,225] 草地 [4,7) [3,5) [0.3,0.4) 2 [450,500)；（225,250] 不明显发育的表层岩溶带 [1,4) [1,3) [0.2,0.3) 1 ≥500，>250 林地 发育不清楚的表层岩溶带 <1 <1 <0.2
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表 4 PLEIK-V模型评价结果中地下水超标点个数
Table 4. The number of groundwater over-standard points in the evaluation results of PLEIK-V model
PLEIK-V 水质超标点 水质未超标点 低脆弱区 0 4 中等脆弱区 6 2 高脆弱区 4 1
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表 5 云浮市岩溶地下水岩溶水水样硝酸盐(NO3)含量与脆弱性指数
Table 5. Nitrate content and vulnerability index of rock-soluble groundwater samples in Yunfu City
取样点 硝酸盐离子
浓度/mg∙L−1脆弱性
指数取样点 硝酸盐离子
浓度/mg∙L−1脆弱性
指数取样点 硝酸盐离子
浓度/mg∙L−1脆弱性
指数JC1 1.23 5.41 JC7 1.13 5.41 JC13 1.45 5.53 JC2 0.56 3.73 JC8 0.95 5.30 JC14 1.35 6.25 JC3 0.15 4.23 JC9 1.42 5.79 JC15 0.98 5.56 JC4 0.15 4.13 JC10 1.52 5.79 JC16 1.80 7.26 JC5 1.01 5.73 JC11 1.63 6.26 JC17 0.80 4.50 JC6 0.77 5.25 JC12 1.53 6.15
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