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Journal of Groundwater Science and Engineering 2023, 11(4): 317-332 https://doi.org/10.26599/JGSE.2023.9280026
Research paper | Open Access | Issue | Published: 10 December 2023

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

Show Author's Information Nyasha Ashleigh Siziba1( ),  Pepukai Chifamba1
Department of Geospatial Science and Earth Observation, Zimbabwe National Geospatial and Space Agency (ZINGSA), 630 Churchill Ave, Mount Pleasant, Harare Zimbabwe
Keywords:
Groundwater resources, Analytical Hierarchical Process, Multi Influence Factor, Lineaments density, Terrain Wetness Index, Ground Water Potential Zone
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Siziba NA, Chifamba P. Using geospatial technologies to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane District, Zimbabwe. Journal of Groundwater Science and Engineering, 2023, 11(4): 317-332. https://doi.org/10.26599/JGSE.2023.9280026
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Nyasha Ashleigh Siziba1( ), Pepukai Chifamba1
Department of Geospatial Science and Earth Observation, Zimbabwe National Geospatial and Space Agency (ZINGSA), 630 Churchill Ave, Mount Pleasant, Harare Zimbabwe

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.

Keywords: Groundwater resources, Analytical Hierarchical Process, Multi Influence Factor, Lineaments density, Terrain Wetness Index, Ground Water Potential Zone

References(48)

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.
DOI

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.
DOI 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.
DOI
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.
DOI

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.
DOI 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.
DOI
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.
DOI

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

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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI

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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI

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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI

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.
DOI 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.
DOI 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.
DOI

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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI

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.
DOI 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.
DOI 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.
DOI 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.
DOI Google Scholar
ZIMSTAT. 2022. Population and Housing Census. https://www.zimstat.co.zw/wp-content/uploads/2022/07/Census2022_Preliminary_Report.pdf
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.
DOI
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Received: 10 December 2022
Accepted: 24 October 2023
Published: 10 December 2023
Issue date: December 2023

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2305-7068/© 2023 Journal of Groundwater Science and Engineering Editorial Office

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