DOI: https://doi.org/10.14710/geoplanning.10.2.111-122

Modelling Groundwater Vulnerability to Contamination using DRASTIC Model through Geospatial Techniques over Northern Kwazulu-Natal, South Africa

Nomonde Shantel Tshiwela Mabogo  -  Division of Geomatics, School of Architecture, Planning and Geomatics, University of Cape Town, Cape Town, South Africa, South Africa
*Patroba Achola Odera orcid scopus  -  Division of Geomatics, School of Architecture Planning and Geomatics, University of Cape Town, Cape Town, South Africa, South Africa

Citation Format:
Abstract

This study models groundwater vulnerability to contamination in three northern district municipalities (Amajuba, Zululand and Umkhanyakude) in KwaZulu Natal province in South Africa using GIS-based DRASTIC model. The method considers seven parameters: depth to water table (D), recharge (R), aquifer media (A), soil media (S), topography (T), impact of the vadose zone (I), and hydraulic conductivity (C). DRASTIC parameter maps are generated in ArcGIS environment and relevant weights assigned. A weighted overlay analysis is then employed to generate the groundwater vulnerability map for the study area. Finally, the groundwater vulnerability map is combined with land use/cover to obtain groundwater pollution risk map. Results indicate that 22, 45, 21 and 12% of the total area are under low, moderate, high, and very high groundwater contamination vulnerable zones, respectively. Low, moderate, high, and very high groundwater pollution risk are found in 23, 40, 27 and 10% of the total area, respectively. These results can be used by environmental managers, spatial planers and other policy makers in formulating integrated and sustainable development plans to ensure optimal groundwater exploitation and conservation in the northern KwaZulu Natal region.

Fulltext View|Download
Keywords: DRASTIC Index,; Groundwater contamination; GIS overlay analysis; Groundwater pollution risk

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Comparative Study for Understanding the Spatial Growth Pattern of Pune and Jaipur City from 1990 to 2020 Application of Remote Sensing and Geographic Information System in Identification of Urban Growth nodes: A Case of Surat City, India Modelling Groundwater Vulnerability to Contamination using DRASTIC Model through Geospatial Techniques over Northern Kwazulu-Natal, South Africa Sonic Justice and Tree Equity: Exploring Spatial Correlations between Aviation-Related Noise, Demographics, and Tree Canopy More recent articles
Most cited articles
SUBAK LAND INFORMATION SYSTEM BASED ON REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM IN DENPASAR CITY DETEKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS: STUDI KASUS DI WILAYAH METROPOLITAN BANDUNG, INDONESIA A GIS-BASED TSUNAMI EVACUATION MODEL CONSIDERING LAND COVER AND SPATIAL CONFIGURATION (CASE OF PURWOREJO REGENCY, INDONESIA) LAND USE CHANGE IN SUBURBAN AREA: A CASE OF MALANG CITY, EAST JAVA PROVINCE GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND FACTOR EFFECT ANALYSIS BY CERTAINTY FACTOR IN UPSTREAM OF JENEBERANG RIVER, INDONESIA More cited articles

  1. Aller, L., Bennet, T., Leher, J.H., Petty, R.J., & Hackett, G. (1987). DRASTIC: a standardized system for evaluating groundwater pollution potential using hydro-geological settings. EPA 600/2-87-035, US Environmental Protection Agency, Washington DC, United States.

  2. Adnan, S., Iqbal, J., Maltamo, M., & Valbuena, R. (2018). GIS-based DRASTIC Model for groundwater vulnerability and pollution risk assessment in the Peshawar District, Pakistan. Arabian Journal of Geosciences, 11(458). https://doi.org/10.1007/s12517-018-3795-9">[Crossref]

  3. Amajuba District Municipality – ADM. (2018). Integrated Development Plan for Amajuba District Municipality, 5 Year Development Plan: 2017/2018- 2021/22.

  4. Barber, C., Bates, L.E., Barron, R., & Allison, H. (1993). Assessment of the relative vulnerability of groundwater to pollution: a review and background paper for the conference workshop on vulnerability assessment. AGSO Journal of Australian Geology and Geophysics, 14(2/3),1147–1154.

  5. Bera, A., Mukhopadhyay, B.P., & Das, S. (2022). Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques. Chemosphere, 307 (2), 135831. https://doi.org/10.1016/j.chemosphere.2022.135831">[Crossref]

  6. Duarte, L., Marques, J.E., & Teodoro, A.C. (2019). An open-source GIS-based application for the assessment of groundwater vulnerability to pollution. Environments, 6(86). https://doi.org/10.3390/environments6070086">[Crossref]

  7. Ersoy, A.F., & Gültekin, F. (2013). DRASTIC-based methodology for assessing groundwater vulnerability in the Gümüşhacıköy and Merzifon basin (Amasya, Turkey). Earth Sciences Research Journal, 17(1), 33–40.

  8. Foster, S.S.D., Hirata, R., Gomes, D., D’Elia, M., & Paris, M. (2002). Groundwater quality protection: a guide for water utilities, municipal authorities and environment agencies. World Bank, Washington DC, United States.

  9. Institute of Natural Resources. (2019). Amajuba District Municipality Environmental Management Framework Summary Report – 2019.

  10. Khan, Q., Liaqat, M.U., & Mohamed, M.M. (2022). A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers. Geocarto International, 37(20), 5832–5850. https://doi.org/10.1080/10106049.2021.1923833">[Crossref]

  11. Khosravi, K., Sartaj, M., Karimi, M., Levison, J., & Lotfi, A. (2021). A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models. Environmental Science and Pollution Research, 28, 50525–50541. https://doi.org/10.1007/s11356-021-13706-y

  12. Koon, A.B., Anornu, G.K., Dekongmen, B.W., Sunkari, E.D., Agyare, A., & Gyamfi, C. (2023). Evaluation of groundwater vulnerability using GIS-based DRASTIC model in Greater Monrovia, Montserrado County, Liberia. Urban Climate, 48, 101427. https://doi.org/10.1016/j.uclim.2023.101427">[Crossref]

  13. Kumar, A., & Krishna, A.P. (2020). Groundwater vulnerability and contamination risk assessment using GIS-based modified DRASTIC-LU model in hard rock aquifer system in India. Geocarto International, 35(11), 1149–1178. https://doi.org/10.1080/10106049.2018.1557259">[Crossref]

  14. Kumar, P., Thakur, P.K., & Debnath, S.K. (2019). Groundwater vulnerability assessment and mapping using DRASTIC Model (1st ed.). CRC Press. https://doi.org/10.1201/9780429287862">[Crossref]

  15. Machdar, I., Zulfikar, T., Rinaldi, W., & Alfiansyah, Y. (2018). Assessment of groundwater vulnerability using DRASTIC Model and GIS: A case study of two sub-districts in Banda Aceh city, Indonesia. IOP Conference Series: Materials Science and Engineering 334 (2018).012032. https://doi.org/10.1088/1757-899X/334/1/012032">[Crossref]

  16. Maherry, A., Tredoux, G., Clarke, S., & Engelbrecht, P. (2010). State of nitrate pollution in groundwater in South Africa. CSIR: Pretoria, South Africa. Available at:       

  17. http://researchspace.csir.co.za/dspace/bitstream/handle/10204/4288/Maherry_2010_P.pdf;jsessionid=F8829078F370776B282B7B9E1E1EBA63?sequence=1">  

  18. Mondal, N.C., Adike, S., Singh, V.S., Ahmed, S., & Jayakumar, K.V. (2017). Determining shallow aquifer vulnerability by the DRASTIC model and hydrochemistry in granitic terrain, Southern India. Journal of Earth System Science, 126(89). https://doi.org/10.1007/s12040-017-0870-7">[Crossref]

  19. Musekiwa, C., & Majola K. (2013). Groundwater Vulnerability Map for South Africa. South African Journal of Geomatics, 2(2), 152 –163.

  20. Oke S.A., & Fourie, F. (2017). Guidelines to groundwater vulnerability mapping for Sub-Saharan Africa. Groundwater for Sustainable Development, 5, 168–177. http://dx.doi.org/10.1016/j.gsd.2017.06.007">[Crossref]

  21. Ouedrago, I., Defourny, P., & Vanclooster, M. (2016). Mapping the groundwater vulnerability for pollution at the Pan African Scale. Science of the Total Environment, 544, 939–953. http://dx.doi.org/10.1016/j.scitotenv.2015.11.135">[Crossref]

  22. Sakala, E., Fourie, F., Gomo, M., & Coetzee, H. (2018). GIS based groundwater vulnerability modelling: A case study of the Witbank, Ermelo and Highveld coalfields in South Africa. Journal of African Earth Sciences, 137, 46–60. https://doi.org/10.1016/j.jafrearsci.2017.09.012">[Crossref] 

  23. Shirazi S.M., Imran M.H., Akib S., Yusop, Z., & Harun, Z.B. (2013). Groundwater vulnerability assessment in Melaka state of Malaysia using DRASTIC and GIS techniques. Environmental Earth Sciences, 70, 2293–2304. https://doi.org/10.1007/s12665-013-2360-9">[Crossref]

  24. Sililo, O.T.N., Saayman, I.C., & Fey, M.V. (2001). Groundwater vulnerability to pollution in urban catchments. Water Research Commission report, (1008/1), WRC, Pretoria, South Africa. 

  25. Statistics South Africa – Stats SA. (2016). Community survey 2016, statistical release P0301. Statistics South Africa, Pretoria, South Africa. Available at: http://cs2016.statssa.gov.za/wp-content/uploads/2016/07/NT-30-06-2016-RELEASE-for-CS-2016-_Statistical-releas_1-July-2016.pdf">         

  26. Umkhanyakude District Municipality – UKDM. (2016). Integrated Development Plan Review for Umkhanyakude District Municipality, final 3rd generation report - 2016/2017.

  27. Zululand District Municipality – ZDM. (2019). Integrated Development Plan Review for Zululand District Municipality report - 2019/2020.


Last update:

No citation recorded.

Last update: 2024-12-26 14:22:20

No citation recorded.