Groundwater Potential Mapping in Part of Minjibir, Kano State, Northwestern Nigeria Using Integrated Remote Sensing, GIS, and Geophysical Methods

Abstract

This study integrated Remote sensing, GIS and Electrical resistivity techniques to investigate groundwater potential zones in part of Minjibir Local Government Area, Kano State, Nigeria, located between 12°10?00?N to 12°13?30?N and 8°32?30?E to 8°36?00?E. A total of 45 Vertical Electrical Sounding (VES) stations were surveyed using the Schlumberger array configuration with data acquired using an ABEM Terrameter (SAS 1000). The coordinates of all VES points were recorded using a Global Positioning System (GPS). Remote sensing data were obtained from ASTER Digital Elevation Model (DEM) and processed to generate groundwater controlling factors such as slope, lineament distribution, lineament density, and drainage density. VES data were processed and interpreted using IP12Win, while spatial analysis and groundwater potential zoning were carried out using ArcGIS and Surfer 16 software. The geoelectric interpretation revealed variations in groundwater prospects across the study area. Several VES points indicated shallow aquifer units of limited thickness, suggesting low groundwater yield. Moderate groundwater potential was observed in areas where thicker aquifer layers occurred at depths between 34m and 40m.In some locations, such as VES 4, productive aquifers were inferred to occur at depths exceeding 450 m, indicating that deep drilling may be required and could be uneconomical. The groundwater potential map classified the area into very low, low, moderate, high, and very high potential zones. Validation was achieved by overlaying borehole yield data, hand pump records, and VES results. High potential zones correspond to areas with high lineament density, gentle slope, and low drainage density, while low potential zones were associated with clay/shale soils, high drainage density, and low lineament density. The study provides a reliable framework for selecting suitable borehole locations and reducing borehole failure.

Introduction

This study employed an integrated approach combining electrical resistivity (VES) and remote sensing/GIS techniques to delineate groundwater potential zones in Minjibir Local Government Area, Kano State, Nigeria.

Key Findings

VES Survey Results

• Number of Stations: 45 VES stations analyzed.

• Subsurface Lithology: Varied across the study area, revealing differences in aquifer depth and thickness.

• Groundwater Prospects:

  • Limited Potential: Most aquifers were shallow with small thickness, likely seasonal and unreliable (e.g., VES 1, VES 5, VES 8).

  • Moderate Potential: Thicker aquifer layers at depths of 34–40 m (e.g., VES 2, VES 6, VES 7).

  • Low Potential/Deep Aquifer: Productive aquifers may occur only at very deep levels (~450 m at VES 4), making development economically challenging.

Remote Sensing & GIS Analysis

• Groundwater Potential Zoning: Areas classified as very low, low, moderate, high, and very high potential zones.

• Key Factors Influencing Potential:

  • High/Very High Potential: Areas with high lineament density, gentle slopes, and low drainage density, which favor infiltration and recharge.

  • Low Potential: Areas with clay/shale soils, high drainage density, and low lineament density, promoting surface runoff and limiting groundwater accumulation.

Validation

• Cross-checked with borehole yield data, hand pump records, and VES interpretations, confirming reliability of the groundwater potential model.

Conclusion

This study successfully applied an integrated approach involving electrical resistivity (VES and remote sensing/GIS techniques to delineate groundwater potential zones in part of Minjibir Local Government Area, Kano State, Nigeria. The results from the 45 VES stations revealed variations in subsurface lithology and groundwater occurrence across the study area. Interpretation of the geoelectric layers indicated that groundwater prospects are generally limited, as most of the identified aquiferous units occur at shallow depths with relatively small thicknesses. Locations such as VES 1, VES 5, and VES 8 revealed shallow aquifers of limited thickness, suggesting that groundwater in these zones may be seasonal and unreliable. Moderate groundwater potential was observed in areas such as VES 2, VES 6, and VES 7, where aquifer layers were thicker and occurred at depths between approximately 34 m and 40 m. However, the result from VES 4 indicated that a productive aquifer may only be encountered at depths exceeding 450 m, suggesting that groundwater development in such areas may require deep drilling and could be economically challenging. The groundwater potential zoning map generated from remote sensing and GIS analysis classified the area into very low, low, moderate, high, and very high groundwater potential zones. Validation using borehole yield information, hand pump records, and VES interpretation confirmed the reliability of the groundwater potential model.

References

[1] Abdel Aal, G., Atekwana, E. A., & Slater, L. D. (2017). Self-potential (SP) method for groundwater exploration: A review. Journal of Hydrology, 15(3), 45–56. [2] Adeyeye, A. A., & Oyedele, A. A. (2019). Use of remote sensing and GIS techniques for groundwater exploration in the basement complex terrain of Ado-Ekiti, southwestern Nigeria. Applied Water Science, 9, 51. [https://doi.org/10.1007/s13201-019-0917-9](https://doi.org/10.1007/s13201-019-0917-9) [3] Baker, V. R., Sweeney, L. J., & Wheatcraft, S. W. (2020). The relationship between drainage density and groundwater recharge in arid regions. Hydrology and Earth System Sciences, 24(5), 2239–2253. [https://doi.org/10.5194/hess-24-2239-2020](https://doi.org/10.5194/hess-24-2239-2020) [4] Esri. (2021). Using the slope tool in ArcGIS. [https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst toolbox/slope.htm](https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/slope.htm) [5] Falebita, D., Olajuyigbe, O., Abeiya, S. S., & Christopher, O. (2020). Interpretation of geophysical and GIS-based remote sensing data for sustainable groundwater resource management in northeastern Osun State, Nigeria. SN Applied Sciences, 2, 1608. [https://doi.org/10.1007/s42452-020-03366-x](https://doi.org/10.1007/s42452-020-03366-x) [6] Kumar, S., Gupta, A., & Bhattacharya, A. (2022). Evaluating the influence of landscape and drainage density on groundwater recharge in semi-arid regions. Groundwater, 60(1), 65–78. [https://doi.org/10.1111/gwat.13166](https://doi.org/10.1111/gwat.13166) [7] Lawal, L., Tijani, M. N., Nuru, N. A., John, S., & Mustapha, A. (2021). Assessment of groundwater recharge potential in a typical geological transition zone in Bauchi, NE-Nigeria using remote sensing/GIS and MCDA approaches. Heliyon, 7(4), e06762. [https://doi.org/10.1016/j.heliyon.2021.e06762](https://doi.org/10.1016/j.heliyon.2021.e06762) [8] Osinowo, O. O., & Arowoogun, K. I. (2020). A multi-criteria decision analysis for groundwater potential evaluation in parts of Ibadan, southwestern Nigeria. Applied Water Science, 10, 228. [https://doi.org/10.1007/s13201-020-01311-2](https://doi.org/10.1007/s13201-020-01311-2) [9] Osumeje, J., & Recto, L. (2017). Water resources and environmental sustainability: A review. (Journal details not fully verified). [10] Revil, A., & Jardani, A. (2013). The self-potential method: Theory and applications in environmental geosciences. Cambridge University Press. [11] Saha, S., Sharma, A., & Gupta, R. (2023). Influence of terrain slope on groundwater recharge in a semi-arid environment: A case study. Water, 15(2), 312. [https://doi.org/10.3390/w15020312](https://doi.org/10.3390/w15020312) [12] Sahoo, A., Das, S., & Kumar, M. (2023). Rainfall variability and its impact on groundwater recharge in the Eastern Indian region. Journal of Hydrology, 609, 128040. [https://doi.org/10.1016/j.jhydrol.2022.128040](https://doi.org/10.1016/j.jhydrol.2022.128040) [13] Sahu, P. K., & Bansal, R. (2021). Impact of drainage density on groundwater recharge: A study of the Gangetic Plain, India. Journal of Water and Climate Change, 12(3), 905–917. [https://doi.org/10.2166/wcc.2021.005](https://doi.org/10.2166/wcc.2021.005) [14] Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill. [15] Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Reidy Liermann, C., & Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. [https://doi.org/10.1038/nature09440](https://doi.org/10.1038/nature09440) [16] Yulianto, F., Prasetyo, Y., & Nugroho, A. (2022). Remote sensing applications for water resources monitoring and planning: A review. Remote Sensing Applications: Society and Environment, 25, 100675. [https://doi.org/10.1016/j.rsase.2021.100675](https://doi.org/10.1016/j.rsase.2021.100675)

Copyright

Copyright © 2026 Muntaka Mansur, Abubakar Magaji, Abdulmalik Usman, Shehu Abdulrashid Edicha, Yakubu Men Daniel, Yahaya Abubakar Sediq, Abdulkadir Danasabe Abdulkadir, Yusuf Suraju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.