Institute of Hydrogeology and Environmental Geology,
Chinese Academy of Geological Sciences		Host
Groundwater Science and Engineering LimitedPublish
Advanced Search
Previous Article Next Article
2023 Vol. 11, No. 4
Article Contents
Article navigation > Journal of Groundwater Science and Engineering > 2023 > 11(4): 317-332
Next Article Previous Article

Siziba Nyasha Ashleigh, Chifamba Pepukai. 2023. Using geospatial technologies to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane District, Zimbabwe. Journal of Groundwater Science and Engineering, 11(4): 317-332. doi: 10.26599/JGSE.2023.9280026
Citation: Siziba Nyasha Ashleigh, Chifamba Pepukai. 2023. Using geospatial technologies to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane District, Zimbabwe. Journal of Groundwater Science and Engineering, 11(4): 317-332. doi: 10.26599/JGSE.2023.9280026
shu

Using geospatial technologies to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane District, Zimbabwe

  • 1.

    Department of Geospatial Science and Earth Observation, Zimbabwe National Geospatial and Space Agency (ZINGSA), 630 Churchill Ave, Mount Pleasant, Harare Zimbabwe

More Information
Received Date:  10 December 2022
Accepted Date:  24 October 2023
Available Online:  10 December 2023
Publish Date:  31 December 2023
    Abstract

  • The main objective of the study was to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane districts, Zimbabwe, utilizing geospatial technologies and thematic mapping. Various factors, including geology, soil, rainfall, land use/land cover, drainage density, lineament density, slope, Terrain Ruggedness Index (TRI), and Terrain Wetness Index (TWI), were incorporated as thematic layers. The Multi Influencing Factor (MIF) and Analytical Hierarchical Process (AHP) techniques were employed to assign appropriate weights to these layers based on their relative significance, prioritizing GWPZ mapping. The integration of these weighted layers resulted in the generation of five GWPZ classes: Very high, high, moderate, low, and very low. The MIF method identified 3% of the area as having very high GWPZ, 19% as having high GWPZ, 40% as having moderate GWPZ, 24% as having low GWPZ, and 14% as having very low GWPZ. The AHP method yielded 2% for very high GWPZ, 14% for high GWPZ, 37% for moderate GWPZ, 37% for low GWPZ, and 10% for very low GWPZ. A strong correlation (ρ of 0.91) was observed between the MIF results and groundwater yield. The study successfully identified regions with abundant groundwater, providing valuable target areas for groundwater exploitation and high-volume water harvesting initiatives. Accurate identification of these crucial regions is essential for effective decision-making, planning, and management of groundwater resources to alleviate water shortages.

    • FullText(HTML)
  • 加载中
    • References
  • Adeyeye OA, Ikpokonte EA, Arabi SA. 2019. GIS-based groundwater potential mapping within Dengi area, North Central Nigeria. The Egyptian Journal of Remote Sensing and Space Science, 22(2): 175-181.

    Google Scholar

    Ahmadi H, Pekkan E. 2021. Fault-based geological lineaments extraction using Remote Sensing and GIS—A review. Geosciences, 11(5): 183. DOI:10.3390/geosciences11050183.

    CrossRef Google Scholar

    Al-Nahmi F, Alami OB, Baidder L, et al. 2016. Using remote sensing for lineament extraction in Al MaghrAbah area–Hajjah, Yemen. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W1: 137–142.

    Google Scholar

    Akbari M, Meshram SG, Krishna RS, et al. 2021. Identification of the groundwater potential recharge zones using MCDM Models: Full Consistency Method (FUCOM), Best Worst Method (BWM) and Analytic Hierarchy Process (AHP). Water Resources Management, 35(14): 4727–4745.

    Google Scholar

    Chaudhary BS, Kumar S. 2018. Identification of groundwater potential zones using Remote Sensing and GIS of K-J Watershed, India. Journal of the Geological Society of India, 91(6): 717−721. DOI:10.1007/s12594-018-0929-3.

    CrossRef Google Scholar

    Das B, Pal SC, Malik S, et al. 2019. Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geology, Ecology, and Landscapes, 3(3): 223-237.

    Google Scholar

    Das S. 2017. Delineation of groundwater potential zone in hard rock terrain in Gangajalghati block, Bankura district, India using remote sensing and GIS techniques. Modeling Earth Systems and Environment, 3(4): 1589–1599.

    Google Scholar

    Doke A. 2019. Delineation of the groundwater potential using remote sensing and GIS: A case study of Ulhas Basin, Maharashtra, India. Archives of Photogrammetry, Cartography and Remote Sensing, 31(1): 49−64. DOI:10.2478/apcrs-2019-0004.

    CrossRef Google Scholar

    Einlo F, Ekhtesasi MR, Ghorbani M, et al. 2023. Determine the most appropriate strategy for groundwater management in arid and semi-arid regions, Abhar Plain, Iran. Journal of Groundwater Science and Engineering, 11(2): 97−115. DOI:10.26599/JGSE.2023.9280010.

    CrossRef Google Scholar

    Emami H, Shahamat A. 2022. Geospatial groundwater potential zone mapping using geological, remote sensing data and multi criteria analysis techniques. Earth Observation and Geomatics Engineering, 6(1): 43−57. DOI:10.22059/EOGE.2022.344474.1116.

    CrossRef Google Scholar

    Gnanachandrasamy G, Zhou Y, Bagyaraj M, et al. 2018. Remote sensing and GIS based groundwater potential zone mapping in Ariyalur District, Tamil Nadu. Journal of the Geological Society of India, 92(4): 484-490.

    Google Scholar

    Goshime DW, Absi R, Haile AT, et al. 2020. Bias-corrected CHIRP satellite rainfall for water level simulation, Lake Ziway, Ethiopia. Journal of Hydrologic Engineering, 25(9): 05020024.

    Google Scholar

    Gumindoga W, Rientjes THM, Haile AT, et al. 2016. Bias correction schemes for CMORPH satellite rainfall estimates in the Zambezi River Basin. Hydrology and Earth System Sciences Discussions, 1−36.

    Google Scholar

    Hanane T, Ahmed A, Abdellah A. 2023. Groundwater potentiality mapping using machine learning algorithms BouSbaa area, Marrakech, Morocco. In Review. Research Square, 1-21.

    Google Scholar

    Hutti B, Nijagunappa R. 2011. Identification of groundwater potential zone using Geoinformatics in Ghataprabha Basin, North Karnataka, India. International Journal of Geomatics and Geosciences, 2(1): 91-109.

    Google Scholar

    Kabeto J, Adeba D, Regasa MS, et al. 2022. Groundwater potential assessment using GIS and Remote Sensing Techniques: Case study of West Arsi Zone, Ethiopia. Water, 14(12): 1838. DOI:10.3390/w14121838.

    CrossRef Google Scholar

    Karimi-Rizvandi S, Goodarzi HV, Afkoueieh JH, et al. 2021. Groundwater-potential mapping using a self-learning bayesian network model: A comparison among metaheuristic algorithms. Water, 13(5): 658. DOI:10.3390/w13050658.

    CrossRef Google Scholar

    Kazerani P, Ziaei AN, Davari K. 2023. Determining safe yield and mapping water level zoning in groundwater resources of the Neishabour Plain. Journal of Groundwater Science and Engineering, 11(1): 47−54. DOI:10.26599/JGSE.2023.9280005.

    CrossRef Google Scholar

    Kumar P, Herath S, Avtar R, et al. 2016. Mapping of groundwater potential zones in Killinochi area, Sri Lanka, using GIS and remote sensing techniques. Sustainable Water Resources Management, 2(4): 419−430. DOI:10.1007/s40899-016-0072-5.

    CrossRef Google Scholar

    Kumar YY, Moorthy DVS, Srinivas GS. 2017. Identification of groundwater potential zones using Remote Sensing and Geographical information System. International Journal of Civil Engineering and Technology, 8(3): 01−10.

    Google Scholar

    Magesh NS, Chandrasekar N, Soundranayagam JP. 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geoscience Frontiers, 3(2): 189-196.

    Google Scholar

    Mallick J, Singh CK, AlMesfer MK, et al. 2021. Groundwater quality studies in the Kingdom of Saudi Arabia: Prevalent research and management dimensions. Water, 13(9): 1266. DOI:10.3390/w13091266.

    CrossRef Google Scholar

    Manap MA, Sulaiman WNA, Ramli MF, et al. 2013. A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arabian Journal of Geosciences, 6(5): 1621−1637. DOI:10.1007/s12517-011-0469-2.

    CrossRef Google Scholar

    Masroor M, Sajjad H, Kumar P, et al. 2023. Novel ensemble machine learning modeling approach for groundwater potential mapping in Parbhani District of Maharashtra, India. Water, 15(3): 419. DOI:10.3390/w15030419.

    CrossRef Google Scholar

    Mathew TG, Ariffin KS. 2018. Remote sensing technique for lineament extraction in association with mineralization pattern in Central Belt Peninsular Malaysia. Journal of Physics: Conference Series, 1082: 012092. DOI:10.1088/1742-6596/1082/1/012092.

    CrossRef Google Scholar

    Melese T, Belay T. 2022. Groundwater potential zone mapping using Analytical Hierarchy Process and GIS in Muga Watershed, Abay Basin, Ethiopia. Global Challenges, 6(1): 2100068. DOI:10.1002/gch2.202100068.

    CrossRef Google Scholar

    Moore ID, Gessler PE, Neilsen GA, et al. 1993. Soil attribute predicted using terrain analysis. Soil Science Society of America Journal, 57: 443-452.

    Google Scholar

    Mukherjee S, Joshi PK, Mukherjee S, et al. 2013. Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21: 205−217. DOI:10.1016/j.jag.2012.09.004.

    CrossRef Google Scholar

    Murugesan V, Krishnaraj S, Kannusamy V, et al. 2011. Groundwater potential zoning in Thirumanimuttar sub-basin Tamilnadu, India-A GIS and remote sensing approach. Geo-Spatial Information Science, 14(1): 17−26. DOI:10.1007/s11806-011-0422-2.

    CrossRef Google Scholar

    Muthamilselvan A, Anamika S, Emmanuel I. 2022. Identification of groundwater potential in hard rock aquifer systems using Remote Sensing, GIS and Magnetic Survey in Veppanthattai, Peram-balur, Tamilnadu. Journal of Groundwater Science and Engineering, 10(4): 367−380. DOI:10.19637/j.cnki.2305-7068.2022.04.005.

    CrossRef Google Scholar

    Osinowo OO, Arowoogun KI. 2020. A multi-criteria decision analysis for groundwater potential evaluation in parts of Ibadan, southwestern Nigeria. Applied Water Science, 10(11): 228. DOI:10.1007/s13201-020-01311-2.

    CrossRef Google Scholar

    Oyedele AA. 2019. Use of remote sensing and GIS techniques for groundwater exploration in the basement complex terrain of Ado-Ekiti, SW Nigeria. Applied Water Science, 9(3): 1-13.

    Google Scholar

    Prasad P, Loveson VJ, Kotha M, et al. 2020. Application of machine learning techniques in groundwater potential mapping along the west coast of India. GIScience & Remote Sensing, 57(6): 735−752. DOI:10.1080/15481603.2020.1794104.

    CrossRef Google Scholar

    Raju BA, Rao PV, Subrahmanyam M. 2023. Estimating aquifer transmissivity using Dar-Zarrouk parameters to delineate groundwater potential zones in Alluri Seetharama Raju District, Andhra Pradesh, India. Journal of Groundwater Science and Engineering, 11(2): 116−132. DOI:10.26599/JGSE.2023.9280011.

    CrossRef Google Scholar

    Różycka M, Migoń P, Michniewicz A. 2017. Topographic wetness index and terrain ruggedness index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Zeitschrift Für Geomorphologie, Supplementary Issues, 61(2): 61-80.

    Google Scholar

    Salimi AH, Masoompour Samakosh J, Sharifi E, et al. 2019. Optimized artificial neural networks-based methods for statistical downscaling of gridded precipitation data. Water, 11(8): 1653. DOI:10.3390/w11081653.

    CrossRef Google Scholar

    Sarkar SK, Talukdar S, Rahman AS, et al. 2022. Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management. Frontiers in Engineering and Built Environment, 2(1): 43−54. DOI:10.1108/FEBE-09-2021-0044.

    CrossRef Google Scholar

    Senapati U, Das TK. 2022. GIS-based comparative assessment of groundwater potential zone using MIF and AHP techniques in Cooch Behar district, West Bengal. Applied Water Science, 12(3): 43. DOI:10.1007/s13201-021-01509-y.

    CrossRef Google Scholar

    Sohail AT, Singh SK, Kanga S. 2019. A geospatial approach for groundwater potential assessment using Multi Influence Factor (MIF) technique. International Journal on Emerging Technologies, 10(1): 183−196.

    Google Scholar

    Sutradhar S, Mondal P, Das N. 2021. Delineation of groundwater potential zones using MIF and AHP models: A micro-level study on Suri Sadar Sub-Division, Birbhum District, West Bengal, India. Groundwater for Sustainable Development, 12: 100547. DOI:10.1016/j.gsd.2021.100547.

    CrossRef Google Scholar

    Thapa R, Gupta S, Guin S, et al. 2017. Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: A case study from Birbhum district, West Bengal. Applied Water Science, 7(7): 4117−4131. DOI:10.1007/s13201-017-0571-z.

    CrossRef Google Scholar

    Yadav S, Salui CL. 2021. Artificial neural network for identification of groundwater potential zones in part of Hugli District, West Bengal, India. In Shit PK, Bhunia GS, Adhikary PP, (Eds. ), Groundwater and Society, 247–257. Springer International Publishing.

    Google Scholar

    Yang L, Zhang YP, Wen XR, et al. 2020. Characteristics of groundwater and urban emergency water sources optimazation in Luoyang, China. Journal of Groundwater Science and Engineering, 8(3): 298−304. DOI:10.19637/j.cnki.2305-7068.2020.03.010.

    CrossRef Google Scholar

    Yeh HF, Cheng YS, Lin HI, et al. 2016. Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustainable Environment Research, 26(1): 33−43. DOI:10.1016/j.serj.2015.09.005.

    CrossRef Google Scholar

    Zghibi A, Mirchi A, Msaddek MH, et al. 2020. Using analytical hierarchy process and multi-influencing factors to map groundwater recharge zones in a semi-arid Mediterranean Coastal Aquifer. Water, 12(9): 2525. DOI:10.3390/w12092525.

    CrossRef Google Scholar

    Zhang K, Gann D, Ross M, et al. 2019. Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment, 225: 290−306. DOI:10.1016/j.rse.2019.02.028.

    CrossRef Google Scholar

    ZIMSTAT. 2022. Population and Housing Census. https://www.zimstat.co.zw/wp-content/uploads/2022/07/Census2022_Preliminary_Report.pdf

    Google Scholar

    Zeinolabedini M, Esmaeily A. 2015. Groundwater potential assessment using Geographic Information Systems and AHP method (Case study: Baft City, Kerman, Iran). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W5: 769–774.

    Google Scholar

    • Relative Articles
  • Cited by
  • Supplements
    • Proportional views
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(10)

Tables(4)

Export Citation
PDF
XML

Article Metrics

Article views(967) PDF downloads(35) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    版权所有：中国地质调查局发展研究中心 copyright @2020