Abstract
Accurate assessment of groundwater vulnerability objectively reflects an area’s potential for groundwater pollution and provides a reference basis for pollution control and prevention. The main objective of this study was to modify the original DRASTIC model to improve the consistency of groundwater vulnerability assessment results with regard to the actual conditions of the study area. To optimize the assessment objectivity, two additional factors that are influenced by human activities (land use and degree of groundwater extraction) were added to form the DRASTICLE model. Then, based on the correlation between all factors and measured nitrate concentrations, the improved three-scale analytic hierarchy process (AHP) and the weights of evidence (WOE) methods were used to reassign the factor weights of the DRASTICLE model. The area under the receiver operating characteristic (ROC) curve, denoted as AUC, was used to quantitatively evaluate the accuracy of all four models (original DRASTIC model AUC: 0.62). By modifying the factors and weights, the three new models showed better performance, AUC values were 0.64, 0.73 and 0.85 for the DRASTICLE, AHP-DRASTICLE, and WOE-DRASTICLE models, respectively. These results indicate that the modified models could more accurately convey groundwater vulnerability in the study area. The WOE-DRASTICLE model, which had the best performance, was then used to assess groundwater vulnerability in 2000 and 2010 and these were compared to 2018. In 2000, 2010, and 2018, the proportion of areas with very high groundwater vulnerability was 5%, 6%, 8%, respectively. Meanwhile, the proportion of areas with very low vulnerability decreased from 73 to 75%, and then rose to 82%, indicating that the spatial distribution of groundwater vulnerability has changed over time. Findings of this study are expected to provide a new theoretical basis for the Baicheng City municipal government in China to better manage and exploit groundwater resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The dataset used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Abu-Bakr HAE-A (2020) Groundwater vulnerability assessment in different types of aquifers. Agric Water Manag. https://doi.org/10.1016/j.agwat.2020.106275
Agterberg FP (1989) Computer programs for mineral exploration. Science 245(4913):76–81. https://doi.org/10.1126/science.245.4913.76
Al-Adamat RAN, Foster IDL, Baban SMJ (2003) Groundwater vulnerability and risk mapping for the Basaltic aquifer of the Azraq basin of Jordan using GIS, Remote Sensing and DRASTIC. Appl Geogr 23(4):303–324. https://doi.org/10.1016/j.apgeog.2003.08.007
Aller L, Bennett T, Lehr J, Petty R, Hackett G (1987) DRASTIC: standardized system for evaluating groundwater pollution potential using hydrogeologic settings. J Geol Soc India 29
Almasri MN (2008) Assessment of intrinsic vulnerability to contamination for Gaza coastal aquifer, Palestine. J Environ Manag 88(4):577–593. https://doi.org/10.1016/j.jenvman.2007.01.022
An Y, Lu W (2018) Assessment of groundwater quality and groundwater vulnerability in the northern Ordos Cretaceous Basin, China. Arab J Geosci. https://doi.org/10.1007/s12517-018-3449-y
Antonakos AK, Lambrakis NJ (2007) Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. J Hydrol 333(2–4):288–304. https://doi.org/10.1016/j.jhydrol.2006.08.014
Arabameri A, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J (2019) GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci Total Environ 658:160–177. https://doi.org/10.1016/j.scitotenv.2018.12.115
Babiker IS, Mohamed MA, Hiyama T, Kato K (2005) A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Sci Total Environ 345(1–3):127–140. https://doi.org/10.1016/j.scitotenv.2004.11.005
Bai L, Wang Y, Meng F (2012) Application of DRASTIC and extension theory in the groundwater vulnerability evaluation. Water Environ J 26(3):381–391. https://doi.org/10.1111/j.1747-6593.2011.00298.x
Barber C, Bates L, Barron R, Allison H (1998) Comparison of standardised and region-specific methods for assessment of the vulnerability of groundwater to pollution; a case study in an agricultural catchment. Environ Sci. https://doi.org/10.1201/9781482287349-8
Barzegar R, Moghaddam AA, Baghban H (2015) A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran. Stoch Environ Res Risk Assess 30(3):883–899. https://doi.org/10.1007/s00477-015-1088-3
Barzegar R, Moghaddam AA, Deo R, Fijani E, Tziritis E (2018) Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. Sci Total Environ 621:697–712. https://doi.org/10.1016/j.scitotenv.2017.11.185
Bojórquez-Tapia LA, Cruz-Bello GM, Luna-González L, Juárez L, Ortiz-Pérez MA (2009) V-DRASTIC: Using visualization to engage policymakers in groundwater vulnerability assessment. J Hydrol 373(1–2):242–255. https://doi.org/10.1016/j.jhydrol.2009.05.005
Bonfanti M, Ducci D, Masetti M, Sellerino M, Stevenazzi S (2016) Using statistical analyses for improving rating methods for groundwater vulnerability in contamination maps. Environ Earth Sci. https://doi.org/10.1007/s12665-016-5793-0
Brindha K, Elango L (2015) Cross comparison of five popular groundwater pollution vulnerability index approaches. J Hydrol 524:597–613. https://doi.org/10.1016/j.jhydrol.2015.03.003
Feng X (2019) Study on the protection scheme of groundwater resources in Baicheng City (master), Jilin University, Available from Cnki
Ferreira JPL, Oliveira MM (2004) Groundwater vulnerability assessment in Portugal. Geofísica Internacional 43(4):541–550
Ghazavi R, Ebrahimi Z (2015) Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models. Int J Environ Sci Technol 12(9):2909–2918. https://doi.org/10.1007/s13762-015-0813-2
Gogu RC, Dassargues A (2000) Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ Geol 39(6):549–559. https://doi.org/10.1007/s002540050466
Hong HY, Ilia I, Tsangaratos P, Chen W, Xu C (2017) A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 290:1–16. https://doi.org/10.1016/j.geomorph.2017.04.002
Huan H, Wang J, Teng Y (2012) Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin City of northeast China. Sci Total Environ 440:14–23. https://doi.org/10.1016/j.scitotenv.2012.08.037
Huan H, Wang JS, Zhai YZ, Xi BD, Li J, Li MX (2016) Quantitative evaluation of specific vulnerability to nitrate for groundwater resource protection based on process-based simulation model. Sci Total Environ 550:768–784. https://doi.org/10.1016/j.scitotenv.2016.01.144
Jhariya DC (2019) Assessment of groundwater pollution vulnerability using GIS-based DRASTIC model and its validation using nitrate concentration in Tandula Watershed, Chhattisgarh. J Geol Soc India 93(5):567–573. https://doi.org/10.1007/s12594-019-1218-5
Kazakis N, Voudouris KS (2015) Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: Modifying the DRASTIC method using quantitative parameters. J Hydrol 525:13–25. https://doi.org/10.1016/j.jhydrol.2015.03.035
Khan R, Jhariya DC (2019) Assessment of groundwater pollution vulnerability using GIS based modified DRASTIC model in Raipur city, Chhattisgarh. J Geol Soc India 93(3):293–304. https://doi.org/10.1007/s12594-019-1177-x
Khosravi K, Sartaj M, Tsai FT, Singh VP, Kazakis N, Melesse AM, Prakash I, Tien Bui D, Pham BT (2018) A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 642:1032–1049. https://doi.org/10.1016/j.scitotenv.2018.06.130
Mukherjee I, Singh UK (2020) Delineation of groundwater potential zones in a drought-prone semi-arid region of east India using GIS and analytical hierarchical process techniques. CATENA. https://doi.org/10.1016/j.catena.2020.104681
O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690. https://doi.org/10.1007/s11135-006-9018-6
Omotola OO, Oladapo MI, Akintorinwa OJ (2020) Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain case of vulnerability techniques application comparison study. Model Earth Syst Environ 6(3):1253–1280. https://doi.org/10.1007/s40808-020-00720-1
Pacheco FA, Pires LM, Santos RM, Sanches Fernandes LF (2015) Factor weighting in DRASTIC modeling. Sci Total Environ 505:474–486. https://doi.org/10.1016/j.scitotenv.2014.09.092
Perrin J, Cartannaz C, Noury G, Vanoudheusden E (2015) A multicriteria approach to karst subsidence hazard mapping supported by weights-of-evidence analysis. Eng Geol 197:296–305. https://doi.org/10.1016/j.enggeo.2015.09.001
Rezaei F, Safavi HR, Ahmadi A (2013) Groundwater vulnerability assessment using fuzzy logic: a case study in the zayandehrood aquifers, Iran. Environ Manag 51(1):267–277. https://doi.org/10.1007/s00267-012-9960-0
Saaty T, Kearns K (1985) The analytic hierarchy process. Anal Plan. https://doi.org/10.1016/B978-0-08-032599-6.50008-8
Sahoo M, Sahoo S, Dhar A, Pradhan B (2016) Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context. J Hydrol 541:1303–1315. https://doi.org/10.1016/j.jhydrol.2016.08.035
Sener E, Davraz A (2012) Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: the case of Egirdir Lake basin (Isparta, Turkey). Hydrogeol J 21(3):701–714. https://doi.org/10.1007/s10040-012-0947-y
Shrestha S, Semkuyu DJ, Pandey VP (2016) Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley, Nepal. Sci Total Environ 556:23–35. https://doi.org/10.1016/j.scitotenv.2016.03.021
Thirumalaivasan D, Karmegam M, Venugopal K (2003) AHP-DRASTIC: software for specific aquifer vulnerability assessment using DRASTIC model and GIS. Environ Model Softw 18(7):645–656. https://doi.org/10.1016/s1364-8152(03)00051-3
Victorine Neh A, Ako Ako A, Richard Ayuk A, Hosono T (2015) DRASTIC-GIS model for assessing vulnerability to pollution of the phreatic aquiferous formations in Douala-Cameroon. J Afr Earth Sc 102:180–190. https://doi.org/10.1016/j.jafrearsci.2014.11.001
Voutchkova DD, Schullehner J, Rasmussen P, Hansen B (2021) A high-resolution nitrate vulnerability assessment of sandy aquifers (DRASTIC-N). J Environ Manag 277:111330. https://doi.org/10.1016/j.jenvman.2020.111330
Wang JL, Yang YS (2008) An approach to catchment-scale groundwater nitrate risk assessment from diffuse agricultural sources: a case study in the Upper Bann, Northern Ireland. Hydrol Process 22(21):4274–4286. https://doi.org/10.1002/hyp.7036
Wu X, Li B, Ma C (2018) Assessment of groundwater vulnerability by applying the modified DRASTIC model in Beihai City, China. Environ Sci Pollut Res Int 25(13):12713–12727. https://doi.org/10.1007/s11356-018-1449-9
Zhang ZJ, Zuo RG, Xiong YH (2016) A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Sci China Earth Sci 59(3):556–572. https://doi.org/10.1007/s11430-015-5178-3
Zuo J (1988) The indirect method of judgment matrix in analytic hierarchy process. Syst Eng (06): 56–63. CNKI:SUN:GCXT.0.1988-06-013
Acknowledgements
This study was supported by the National Natural Science Foundation of China, Research on the Impact of In-situ Oil Shale Exploitation on Groundwater Environment [project number 41572216], the China Geological Survey project, regional water resources survey methods, and groundwater ecological threshold survey research [project number DD20190340], Geological Exploration Fund of Jilin Province, Geothermal Resources Survey in the Middle and West of Jilin Province [project number 2018-13]; Special Project of the Provincial University Co-Construction Program-Frontier Science and Technology Guidance Category, Research on the Interdependent Ecosystem and Sustainable Utilization of Natural Mineral Water in Changbai Mountain [project number SXGJQY2017-6]; Key research and development program of Shaanxi Province, Construction of Big Data Platform for Geotechnical Engineering [project number 2017ZDCXL-SF-03-01-01]. The authors would like to thank Jilin Jingyu field scientific observation and research base of volcanoes and mineral springs, which provided the necessary data to conduct this study. We thank the anonymous reviewers and editors who contributed valuable comments, which were useful in improving the quality of the manuscript.
Funding
This study was funded by National Natural Science Foundation of China (41572216); the China Geological Survey project (DD20190340); Geological Exploration Fund of Jilin Province (2018-13); Special Project of the Provincial University Co-Construction Program-Frontier Science and Technology Guidance Category (SXGJQY2017-6); Key research and development program of Shaanxi Province (2017ZDCXL-SF-03-01-01)
Author information
Authors and Affiliations
Contributions
ML: Writing—original draft, data curation, formal analysis. CX: Conceptualization, writing—review, supervision, funding acquisition. XL: Methodology and editing, formal analysis.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Liu, M., Xiao, C. & Liang, X. Assessment of groundwater vulnerability based on the modified DRASTIC model: a case study in Baicheng City, China. Environ Earth Sci 81, 230 (2022). https://doi.org/10.1007/s12665-022-10350-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-022-10350-8