Integrating Geophysical and Geospatial Technology for Groundwater Exploration: A Case Study of Villages in Bumbaire Sub-County, Uganda

Integrating Geophysical and Geospatial Technology for Groundwater Exploration: A Case Study of Villages in Bumbaire Sub-County, Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Geophysical and Geospatial Technology for Groundwater Exploration: A Case Study of Villages in Bumbaire Sub-County, Uganda Ahmed Abdirizak Dirie, Abdulkadir Ahmed Mohamed, Farhan Nur Adan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9238098/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Groundwater is a critical resource for rural communities in Uganda, yet its sustainable development is often hindered by a lack of detailed subsurface characterization. This study addresses this gap by integrating geophysical and geospatial techniques to map groundwater potential zones in four villages (Kihunda, Bumbaire, Rwencence, and Kamutazya) of Bumbaire Sub-County, Uganda. Vertical Electrical Sounding (VES) surveys were conducted at 20 locations using a Schlumberger array to obtain subsurface resistivity profiles. The acquired apparent resistivity data were inverted using IPI2win software to derive true resistivity values at target depths of 15, 25, and 35 meters. These point data were then spatially interpolated using the Inverse Distance Weighting (IDW) method in a GIS environment to generate continuous groundwater potential distribution maps. The results reveal distinct spatial and vertical variability in groundwater potential across the study area. Kihunda and Rwencence villages exhibited good to very good groundwater potential, particularly at greater depths (25 m and 35 m), indicating favorable aquifer conditions within weathered and fractured basement rocks. In contrast, Bumbaire village showed predominantly poor to moderate potential, while Kamutazya displayed a more heterogeneous pattern with localized promising zones. The generated maps effectively delineate zones suitable for borehole development, thereby reducing drilling risks and supporting targeted groundwater exploitation. This research demonstrates that the integration of VES and GIS provides a cost-effective, non-invasive, and reproducible framework for groundwater exploration in data-scarce basement terrains. The findings offer a practical decision-support tool for local water resource planners and contribute to sustainable groundwater management in rural Uganda. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Solid earth sciences Groundwater distribution Vertical Electrical Sounding (VES) Geographic Information System (GIS) Electrical resistivity Inverse Distance Weighting (IDW) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 INTRODUCTION Groundwater serves as a vital freshwater source globally, playing a crucial role in sustaining domestic, agricultural, and ecological needs (Bienibuor et al., 2025). It accounts for a small fraction of Earth’s available freshwater yet represents reserves approximately 100 times greater than surface water volumes in rivers and lakes (Edukondal et al., 2019). However, increasing demands from population growth and agricultural intensification threaten groundwater sustainability, particularly in regions with limited hydrogeological data (Riwayat et al., 2018; Shabbir et al., 2020).Accurate mapping the distribution of groundwater is therefore essential for informed water resource management and development(Alemu et al., 2025; Mohammed et al., 2024). In Uganda, this freshwater source is increasingly relied upon to mitigate surface water stresses, yet its distribution remains poorly characterized in many rural areas due to complex geology and insufficient surveying(Okot-Okumu & Otim, 2015) . the residents of Bumbaire Sub-County, Bushenyi District, where villages depend significantly on groundwater but there is lack of detailed subsurface information to guide sustainable extraction and well placement (Miguel et al., 2020) Water access in Bumbaire villages presents a major challenge, adversely impacting the livelihoods of its residents(Adomi Mbina et al., 2020). This is exacerbated by the poor functionality of water points (67%, the district's lowest) and the degradation of alternative surface water sources due to encroachment. Effective mitigation strategies are therefore crucial to resolve the immediate crisis and ensure the long-term sustainability of water resources for these communities(Marks et al., 2020). Geophysical methods, particularly the Electrical Resistivity Method (ERM), provide a non-invasive and efficient means of investigating subsurface conditions and the condition of groundwater(Hussein & Ali, 2023).Specifically vertical Electrical Sounding (VES) is the first method which provided 1D data of subsurface strata and it is a well-established technique for identifying aquifer characteristics by measuring variations in subsurface resistivity(State et al., 2022).When combined with Geographic Information Systems (GIS), VES data can be spatially interpolated and analyzed to produce detailed groundwater distribution maps (Khan et al., 2024). In a study conducted by (Kubingwa et al., 2023) ,researchers investigated and mapped groundwater potential areas (GWPAs) in the semi-arid Bukombe district of Tanzania using an integrated approach that combined geospatial and geophysical methods. The aim was to assess the groundwater potential comprehensively by incorporating both surface and subsurface characteristics. Although this methodology is considered crucial and highly reliable, there exists a research gap in understanding groundwater distribution in the Bumbaire subcounty villages. The findings of our research are expected to fill this geographical gap and provide a detailed groundwater distribution map of the study area. this study applies an integrated VES and GIS approach to delineate groundwater potential zones in selected villages of Bumbaire Sub-County. The research seeks to address the existing gap in localized groundwater assessment and provide a practical, reproducible framework for identifying high-potential zones to support sustainable groundwater development in the region. To achieve the result of this study, we set two specific objectives, which eventually contribute to get a solution for our main objectives of Mapping Groundwater potential zones in these villages . to conduct Vertical Electrical Sounding survey to collect subsurface resistivity data across the study area; to generate groundwater potential distribution maps at 12m, 24m, and 36m depths using GIS-based interpolation of the collected VES data; The research seeks to address the existing gap in localized groundwater assessment and provide a practical, reproducible framework for identifying high-potential zones to support sustainable groundwater development in the region. 2 METHODS AND MATERIAL 2.1Study area 2.1.1 Geographical Scope of the study Area 2.1.2 Geological and Hydrological Condition of the study area The geology of the study area which in Bushenyi District is predominantly characterized by Precambrian basement rocks, which are widely distributed across Bushenyi District and also southwestern Uganda. These rocks mainly consist of granites, and quartzites that have undergone varying degrees of metamorphism. Over time, intense weathering under humid tropical conditions has led to the development of a thick regolith layer composed of lateritic soils and weathered rock materials. This weathered zone is particularly important because it often serves as the main groundwater storage unit, controlling infiltration, percolation, and shallow aquifer formation the geological data of this study is downloaded from USGS (United States Geological Society). Structurally, the basement rocks are commonly fractured and jointed, which significantly enhances secondary porosity and permeability in the area. These fractures, faults, and shear zones act as preferential pathways for groundwater movement and storage, making them critical targets for groundwater exploration. Understanding the local geology is essential for sustainable water resource development, as it helps in identifying productive aquifer zones and minimizing the risk of drilling dry or low-yield boreholes. Climatically, the study area is characterized by relatively high annual rainfall, averaging about 1500 mm, as indicated in the legend. This amount of rainfall suggests favorable conditions for surface water availability, groundwater recharge, and agricultural activities. The uniform rainfall distribution shown across the study area implies limited spatial variability at the local scale, which is important for interpreting hydrological processes and water resource potential. Such climatic conditions play a significant role in influencing soil moisture, runoff patterns, and overall water quality within the study area. The rainfall fall was provided by UNMA (Uganda National Meteorological Agency) 2.1 VES DATA PREPARATION AND INVERSION The data collection process was carefully planned and carried out to ensure that the results were both accurate and meaningful. Vertical Electrical Sounding (VES) surveys were conducted using the Schlumberger array configuration(Nyaberi, 2023 ) . The study area was divided into a grid system with line spacings of 400 meters, ensuring systematic coverage of the entire region. The target points for the surveys were the intersections of these grid lines, which provided a structured and evenly distributed set of locations for data collection(Nyaberi, 2024 ). This grid-based approach ensured that no part of the study area was overlooked and that the data collected was representative of the entire region. Four electrodes were carefully placed in a straight line. The two outer electrodes, called current electrodes, were used to send electrical currents into the ground, while the two inner electrodes, known as potential electrodes, measured the voltage differences caused by those currents(Wang et al., 2016 ). By gradually increasing the distance between the current electrodes, it was easy to measure resistivity at different depths. This helped us to create a detailed map of the subsurface, revealing layers of soil and rock and identifying areas where groundwater was likely to be found (Chinyem & Ovwamuedo, 2024 ). At each survey point, as soon as the first measurement was taken, the exact coordinates were recorded using a GPS device to ensure precise location data. Simultaneously, the resistivity readings were noted. Knowing the type of array used (Schlumberger), the current (I) injected into the ground, and the voltage (V) measured by the potential electrodes, the geometric factor (K) was manually calculated for each measurement. The geometric factor is a crucial parameter that depends on the spacing of the electrodes and is used to convert the measured voltage and current into apparent resistivity. For the Schlumberger array, the geometric factor (K) is calculated using the formula: $$\:K\:=\:\pi\:\:\frac{(A{B}^{2}-\:M{N}^{2})}{\:4MN}$$ where: (K) geometric factor AB is the distance between the current electrodes, MN is the distance between the potential electrodes. After acquisition of raw data of the apparent resistivity, the data was imported into IPI2win software which easy identifying the depth of each layer and the true resistivity 2.2 Inverse Distance Weighted (IDW) interpolation Inverse Distance Weighting (IDW) is a deterministic geostatistical interpolation technique widely used for mapping spatial variables. It is particularly valued for its simplicity and ease of implementation in Geographic Information Systems (GIS) software, such as within the ArcGIS Pro Geostatistical Analyst toolbar(Talukdar et al., 2023 ). Originating in mining and geological engineering, IDW operates on a fundamental principle: the value at an unknown location is estimated as a weighted average of values from known sample points, where the weights are inversely proportional to the distance between the sampled points and the prediction location(Gundala & Tanikonda, 2023 ) .Essentially, sampled points closer to the prediction location exert a stronger influence than those farther away(Dheeraj et al., 2025 ). The method relies on both statistical and mathematical concepts to create continuous surfaces from point data and predict values at unmeasured locations. The core of IDW is a weighted linear combination of sample points. Z ( s 0​) = \(\:\frac{{\sum\:}_{\varvec{i}=1}^{\varvec{n}}\mathbf{}\varvec{w}\varvec{i}\mathbf{}\varvec{Z}\left(\varvec{s}\varvec{i}\mathbf{}\right)}{{\sum\:}_{\varvec{i}=1}^{\varvec{n}}\mathbf{}\varvec{w}\varvec{i}\mathbf{}}\) where the weight wi ​ is defined as: wi = \(\:\frac{1}{\varvec{d}(\varvec{s}0\mathbf{},\varvec{s}\varvec{i}\mathbf{})\varvec{p}}\) 3 RESULT AND DISCUSSION This study employed a multi-stage geospatial workflow to delineate groundwater potential at 15m, 25m and 35m depth using Vertical Electrical Sounding (VES) data. First, field-collected VES measurements were processed through IPI2win inversion software to derive layered resistivity models, The resistivity value of these depth layers was extracted from each inverted VES point and georeferenced using survey coordinates. These point data were then imported into a GIS environment specially Argis pro for spatial interpolation. For the spatial interpolation of the inverted true resistivity values, the Inverse Distance Weighting (IDW) method was selected as the most appropriate technique for this initial visualization stage. The primary rationale for this choice lies in its combination of computational efficiency and conceptual simplicity, which allowed for the rapid generation of a continuous subsurface model from the irregularly spaced nodal data. Crucially, as an exact interpolator, IDW ensures that the generated surface honors the original data points at their precise locations, preserving the high-resistivity anomalies indicative of key geological features like fractures or sand layers. 3.1 Curve Types The raw Vertical Electrical Sounding (VES) data, consisting of voltage (V) and current (I) measurements, were processed using Microsoft Excel to calculate the apparent resistivity (ρₐ) values. The analysis of the data was achieved using the IPI2win inversion software which was further modelled to curves with associated resistivity values as presented in Fig. 4 . The curve-matching procedures are key in curves interpretation of VES (vertical electrical sounding) as theoretical curves (Orellana & Mooney, 1966) are used in combination with the auxiliary-point method of partial curve matching (Zohdy, 1965). The analyzed curves are noted as representing the curves are founded on a three-layered imagery of a subsurface categorized as K, A and Q type curves. These curve classifications were based on shape which emanated in the plot of correlation between AB/2 and apparent resistivity and thus are given as: A-type; ρ1 < ρ2 ρ2 < ρ3, K-type; ρ1 ρ3, Q-type; ρ1 > ρ2 > ρ3, as standalone curves, whereas combinations of the above helps represent several layers in the subsurface (Nyaberi, 2023 ). The study herein has come up with modelled curves of Q-type; ρ1 > ρ2 > ρ3 (Figs. 1 to 3 ). The type Q curve exhibits a decreasing continuously along with a progressive decrease of resistivity values with increasing depth. As noted, each layer is characterized by a thickness and an associated resistivity, which are determined during modeling. The curve types in this study are 3 in total, which means many VES station shares same curve type. The curve types are presented in Fig. 4 3.2 Resistivity measurements at 15 m, 25 m, and 35 m depths from each VES stations the interpreted true resistivity values obtained from Vertical Electrical Sounding (VES) surveys at targeted subsurface depths of 15 m, 25 m, and 35 m are presented in Table 1 , 2 , 3 . The data were derived through quantitative inversion of field measurements using IPI2Win software, which models the geoelectric layer parameters from apparent resistivity curves. The resulting resistivity profiles reflect the electrical properties at these specific depths and are critical for characterizing or identifying aquifers, detecting anomalous zones, or mapping subsurface structures across the study area. The values listed provide a clear, depth-specific snapshot of the electrical resistivity distribution beneath each VES station. Table 1 Resistivity measurements at 3 depths Depth VES 1 VES2 VES3 VES4 VES5 VES6 VES7 VES8 15 52.9 75 82 287 122.9 153 499.9 247 25 29.7 32.95 50 329 83. 97 319 119 35 25 22.94 39.1 347 63.03 75.6 249.9 80.3 Longitude -0.583544 -0.583507 -0.583532 -0.583618 -0.583536 -0.587319 -0.587321 -0.587407 Latitude 30.1987 30.2024 30.2062 30.21 30.2137 30.1986 30.2024 30.2061 Table 2 Resistivity measurements at 3 depths Depth VES 15 VES 16 VES 17 VES 18 VES 19 VES 20 15 131.7 81 158.9 490.12 18 59 25 78.74 48.9 170.42 538 21.99 53 35 51.31 41.9 165.03 547 23.01 60.9 Longitude -0.594906 -0.591158 -0.587451 -0.587327 -0.591202 -0.594908 Latitude 30.2099 30.2099 30.21 30.2136 30.2137 30.2138 Table 3 Resistivity measurements at 3 depths Depth VES9 VES10 VES11 VES 12 VES 13 VES 14 15 203.5 264.6 342.01 172.01 245.56 201.02 25 202 274 472.4 101.01 249.54 250.94 35 199 249 547.7 78.15 237.79 299 Longitude -0.591072 -0.594904 -0.594987 -0.591112 -0.591068 -0.594943 Latitude 30.2061 30.2061 30.2023 30.2024 30.1985 30.1986 3.3 Groundwater distribution map at all Depth 3.3.1 At 15m Depth The groundwater distribution map which is presented in Fig. 5 at a depth of 15 m was generated based on true resistivity measurements obtained from the vertical electrical sounding (VES) after the inversion of the data. The map shows distinct spatial variation in groundwater potential across the study area, classified into very poor, poor, moderate, good, and very good zones. The very good groundwater potential zones are predominantly concentrated in the central part mainly Rwencence village and Kihunda village, forming relatively continuous belts. These zones reflect subsurface conditions characterized by favorable true resistivity values, indicating enhanced groundwater storage and transmission at the investigated depth. Also, the good groundwater potential zones appear as localized pockets mainly within the Rwencence and some in Kamutazya village, often adjacent to very good potential zones. These zones represent transitional resistivity conditions where groundwater occurrence is significant but spatially variable. The moderate groundwater potential zones are widely distributed, particularly in the northern and eastern parts of the study area, forming broad transition zones between high- and low-potential regions. Based on the true resistivity response, these areas indicate moderate subsurface permeability and groundwater availability, suggesting that successful groundwater abstraction may depend on careful borehole siting. In contrast, the poor and very poor groundwater potential zones are mainly confined to peripheral and isolated parts of the study area. Poor potential zones occur largely in Bumbaire village, while very poor zones appear as small, discrete pockets within the Rwencence and Kamutazya in very tiny sections. These zones are characterized by unfavorable, suggesting subsurface materials with limited groundwater storage capability at 15 m depth. Overall, the interpretation based on true resistivity measurements indicates that groundwater availability is generally favorable in the central and south western area which is Kihunda village, while marginal and isolated zones exhibit reduced groundwater potential. Figure 5 is illustrated the distribution of groundwater across the study area at 15M depth 3.3.2 At 25m Depth At 25 m depth, the Distribution of groundwater map shows clear spatial variation across the villages of Bumbaire, Kihunda, Rwencence, and Kamutazya. The Bumbaire village, located in the northern part of the study area, is largely or at all dominated by poor to moderate groundwater potential zones. These zones are relatively extensive and continuous, indicating subsurface conditions that are less favorable for groundwater storage at this depth. This suggests that groundwater occurrence in Bumbaire is limited and may require deeper investigation or careful borehole siting. In Kihunda and Rwencence, which occupy the central portion of the study area, groundwater potential improves significantly. These areas are characterized by widespread very good groundwater potential zones, with embedded good potential pockets around several VES stations. The spatial continuity and concentration of these zones indicate favorable true resistivity responses at 25 m depth, suggesting well-developed groundwater-bearing formations. These villages therefore represent the most promising areas for groundwater abstraction, where sustainable borehole yields are more likely to be achieved. The Kamutazya area, situated in the eastern and south-eastern parts of the study area, exhibits a mixed groundwater potential pattern. While large portions are classified as poor to moderate groundwater potential, localized good and very poor zones are also evident. The poor zones dominate the outer sections, indicating unfavorable subsurface conditions, whereas the isolated very poor zones reflect localized resistivity anomalies associated with limited groundwater storage. Overall, the true resistivity-based interpretation at 25 m depth indicates that Rwencence and Kihunda have the highest groundwater potential, while Bumbaire and Kamutazya show comparatively lower and more variable groundwater prospects. At 35m Depth The groundwater distribution map at a depth of 35 m shows clear spatial variation across the different villages within the study area. In Bumbaire, groundwater distribution is mainly poor to moderate, with limited zones indicating improved conditions. This suggests that groundwater occurrence at this depth is generally low, and suitable locations are relatively scattered. The overall pattern in Bumbaire points to less favorable groundwater availability compared to other villages. In Kihunda, groundwater distribution is predominantly good, especially toward the center part of the village. These areas indicate good groundwater presence at 35 m depth, making them more suitable for groundwater development. Rwencence displays a more variable pattern, with pockets of moderate to good groundwater distribution in the central parts of the village. This heterogeneity suggests that groundwater availability in Rwencence is localized and depends strongly on site-specific subsurface conditions. By contrast, Kamutazya shows the most favorable groundwater distribution within the study area. Large portions of the village are classified as good, very good and moderate, particularly in the upper areas to north which is in a good situation. This indicates relatively higher groundwater availability at 35 m depth, making Kamutazya a more promising area for groundwater abstraction at 35 and it is likely expected that this is confined aquifer. Overall, the map highlights strong village-level differences in groundwater distribution, which is essential for informed groundwater planning and borehole siting. 4 CONCLUSIONS This study successfully applied an integrated geophysical and geospatial approach to delineate groundwater potential zones across four villages in Bumbaire Sub-County, Uganda. Vertical Electrical Sounding (VES) surveys were conducted at 20 locations to derive subsurface resistivity profiles, which were subsequently inverted and spatially interpolated using the Inverse Distance Weighting (IDW) method in a GIS environment. The resulting groundwater distribution maps at 15 m, 25 m, and 35 m depths revealed significant spatial heterogeneity in groundwater potential across the study area. The findings indicate that groundwater potential is not uniformly distributed and varies both by location and depth. Kihunda and Rwencence villages generally exhibited good to very good groundwater potential, particularly at the all depths, suggesting the presence of weathered/fractured aquifers with favorable hydrogeological conditions. In contrast, Bumbaire village showed predominantly poor to moderate potential across all depths, while Kamutazya displayed variable conditions, with promising zones mainly at greater depths. According to the results it is highly expected that the first aquifer depth of the unconfined lays the depth between 15m to 25m. while the depth of aquifer occurs the depth of 35m is the intail depth of the unconfined aquifer. The integration of VES and GIS proved to be a cost-effective, non-invasive, and reproducible framework for preliminary groundwater assessment in data-scarce regions. This approach provides a critical scientific basis for sustainable groundwater development, enabling targeted borehole siting and optimized resource allocation. This study directly supports SDG 6 (Clean Water and Sanitation) by providing a scientific basis for improving groundwater access in a rural, underserved area. It also contributes to SDG 11 (Sustainable Cities and Communities) by promoting resilient water infrastructure and to SDG 13 (Climate Action) by enhancing adaptive capacity to climate-related water stress. The methodological framework supports SDG 9 (Industry, Innovation, and Infrastructure) through the application of affordable geospatial technology for sustainable resource management. In conclusion, this research fills a critical geographical and hydrogeological knowledge gap in Bumbaire Sub-County and provides a transferable model for groundwater exploration in analogous basement terrains, ultimately supporting water security and socio-economic resilience in rural Uganda. 5 LIMITATIONS The study’s reliance on 1D VES data limits the resolution of complex subsurface geometries, such as dipping layers or localized fracture zones. The study did not incorporate hydrochemical data to assess water quality alongside availability. The relatively coarse survey grid (400 m spacing) may have missed small-scale aquifer features. Future Research Gaps Employing advanced geophysical techniques like 2D Electrical Resistivity Tomography (ERT) would better resolve complex subsurface structures and fracture networks. Furthermore, assessing temporal dynamics through seasonal monitoring is necessary to understand the groundwater variability and aquifer resilience. Finally, interdisciplinary socio-hydrological studies are needed to evaluate how groundwater availability interacts with community access patterns, livelihood sustainability, and projected climate and land-use changes, ensuring findings directly inform adaptive and equitable water governance. Declarations Acknowledgments Firstly, I thank to God for giving us the strength and time to conduct this research and secondly, I deeply appreciate Dr. Deepa and Dr. Ambiga for their moral support and guidance. Uganda National Meteorological Authority and USGS deserves to be appreciated the provision of necessary data. Author contributions Ahmed Abdirizak Dirie was involved in conceptualization, methodology, data analysis, writing-original draft preparation. Farhan Nur Adan, Abdishakur Abshir Mohamed and Abdulkadir Ahmed Mohamed were involved technical support and editing. Compliance with ethical standards: Conflict of interest The author declares that there was no conflict of interest. Ethical Approval This study was conducted in accordance with ethical standards and guidelines. Funding The author did not receive any funding from public or private organization(s) for the submitted work. Data Availability Statement All data generated or analyzed during this study are included in this article. Ethics and Consent to Participate Not applicable. Consent to Publish The authors declare that this manuscript does not contain any personal data or sensitive information that requires consent for publication. Clinical Trial Declaration Not applicable. References Adomi Mbina, S., Wilson, G., Daniel Eze, E., Pius, T., Robinson, S., Moyosore Afodun, A., & Ezekiel, I. (2020). Contaminants of Domestic Rural Spring Water Sources in Bushenyi-Ishaka Municipality, Western Uganda. Journal of Health and Environmental Research , 6 (3), 51. https://doi.org/10.11648/j.jher.20200603.12 Alemu, W. T., Suryabhagavan, K. V., Azagegn, T., Terefe, T. T., Legese, B. B., Tsegaye, A. M., Bojer, A. K., Alam, B. M., & Kumsa, A. (2025). Groundwater Potential Zone Mapping Using GIS and Remote Sensing: A Case of Teji River catchment, Southwest Shewa Zone, Ethiopia. Earth Systems and Environment . https://doi.org/10.1007/s41748-025-00811-y Bienibuor, A. K., Preko, K., Aning, A. A., Menyeh, A., Wemegah, D. D., Appiah, M. K., & Gyilbag, A. (2025). Application of the electrical resistivity tomography (ERT) method in identifying high groundwater potential sites in the Atebubu municipality of Ghana. Discover Geoscience , 3 (1). https://doi.org/10.1007/s44288-025-00120-x Chinyem, F. I., & Ovwamuedo, G. (2024). Evaluation of Aquifer Characteristics and Groundwater Protective Capacity in Abavo, Nigeria. International Journal of Geosciences , 15 (11), 841–860. https://doi.org/10.4236/ijg.2024.1511046 Dheeraj, V. P., Singh, C. S., Alam, A., & Sonkar, A. K. (2025). Hydrogeochemical quality investigation of groundwater resource using multivariate statistical methods, water quality indices (WQIs), and health risk assessment in Korba Coalfield Region, India. Stochastic Environmental Research and Risk Assessment , 39 (3), 937–958. https://doi.org/10.1007/s00477-024-02895-w Gundala, V., & Tanikonda, V. R. (2023). Delineation of Seawater Intrusion into Freshwater Aquifers by Using VES & GIS in the Kakinada Region, East Godavari District, Andhra Pradesh, India. Springer Water , Part F1186 , 333–341. https://doi.org/10.1007/978-3-031-35279-9_16 Hussein, M. A., & Ali, M. Y. (2023). Groundwater Investigation through Electrical Resistivity Tomography in the Galhareri District , Galgaduud Region , Somalia : Insights into Hydrogeological Properties . Khan, A. Y., Ullah, W., Niaz, A., Bibi, T., Imtiaz, M. M., Fiaz, R., Gul, S., Hameed, K., & Islam, F. (2024). Integrated geophysical and geospatial techniques for surface and groundwater modeling. Scientific Reports , 14 (1), 1–19. https://doi.org/10.1038/s41598-024-76262-8 Kubingwa, J. N., Makoba, E. E., & Mussa, K. R. (2023). Integrated Geospatial and Geophysical Approaches for Mapping Groundwater Potential in the Semi-Arid Bukombe District, Tanzania. Earth (Switzerland) , 4 (2), 241–265. https://doi.org/10.3390/earth4020013 Marks, S. J., Clair-Caliot, G., Taing, L., Bamwenda, J. T., Kanyesigye, C., Rwendeire, N. E., Kemerink-Seyoum, J. S., Kansiime, F., Batega, D. W., & Ferrero, G. (2020). Water supply and sanitation services in small towns in rural–urban transition zones: The case of Bushenyi-Ishaka Municipality, Uganda. Npj Clean Water , 3 (1), 1–9. https://doi.org/10.1038/s41545-020-0068-4 Miguel, L., Sanchez, S., & Kemerink-, J. S. (2020). Caught in the middle ? Access to water in the rural to urban transformation of Bushenyi-Ishaka municipality , Uganda . 22 , 670–685. https://doi.org/10.2166/wp.2020.024 Mohammed, M. A. A., Szabó, N. P., Alao, J. O., & Szűcs, P. (2024). Geophysical characterization of groundwater aquifers in the Western Debrecen area, Hungary: insights from gravity, magnetotelluric, and electrical resistivity tomography. Sustainable Water Resources Management , 10 (2), 1–15. https://doi.org/10.1007/s40899-024-01062-x Nyaberi, D. M. (2023). Application of Vertical Electrical Sounding in Mapping Lateral and Vertical Changes in the Subsurface Lithologies: A Case Study of Olbanita, Menengai Area, Nakuru, Kenya. Open Journal of Geology , 13 (01), 23–50. https://doi.org/10.4236/ojg.2023.131002 Nyaberi, D. M. (2024). Use of Vertical Electrical Sounding in Mapping Lateral and Vertical Changes in Subsurface Lithologies: A Case Study of Olbanita, Menengai Area, Nakuru, Kenya. In Research Advances in Environment, Geography and Earth Science Vol. 6 . https://doi.org/10.9734/bpi/raeges/v6/8300e Okot-Okumu, J., & Otim, J. (2015). The quality of drinking water used by the communities in some regions of Uganda. International Journal of Biological and Chemical Sciences , 9 (1), 552. https://doi.org/10.4314/ijbcs.v9i1.47 Riwayat, A. I., Ahmad Nazri, M. A., & Zainal Abidin, M. H. (2018). Application of Electrical Resistivity Method (ERM) in Groundwater Exploration. Journal of Physics: Conference Series , 995 (1). https://doi.org/10.1088/1742-6596/995/1/012094 Shabbir, H., Butt, N. A., Zafar, A., & Mir, M. K. (2020). Role of electrical resistivity method to identify fresh water aquifers in Nankana Sahib, Punjab, Pakistan. Journal of Himalayan Earth Sciences , 53 (2), 52–59. State, T., Saleemmiya, S., Reddy, P. I. P., & Kunsoth, S. (2022). Groundwater Exploration by using Electrical Resistivity Meter in Granitic Terrain of Hyderabad Region , . 8 (12), 1316–1322. Talukdar, P., Mokenepally, T., & Kulkarni, V. V. (2023). Lake water quality assessment using spatial interpolation and the development of the WQI on an educational campus, Assam, India. Water Practice and Technology , 18 (10), 2244–2257. https://doi.org/10.2166/wpt.2023.138 Wang, Z., Wang, S., Fang, G., & Zhang, Q. (2016). Investigation on a Novel Capacitive Electrode for Geophysical Surveys. Journal of Sensors , 2016 . https://doi.org/10.1155/2016/4209850 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9238098","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617534619,"identity":"46a6a4a8-5ea9-4021-abb8-a085174517de","order_by":0,"name":"Ahmed Abdirizak Dirie","email":"data:image/png;base64,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","orcid":"","institution":"Kampala International University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"Abdirizak","lastName":"Dirie","suffix":""},{"id":617534620,"identity":"3ac89f97-631d-4bdc-a8bd-caed652695ab","order_by":1,"name":"Abdulkadir Ahmed Mohamed","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"Ahmed","lastName":"Mohamed","suffix":""},{"id":617534627,"identity":"48d96717-4b68-4756-821b-66c1eb1c2328","order_by":2,"name":"Farhan Nur Adan","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Farhan","middleName":"Nur","lastName":"Adan","suffix":""},{"id":617534629,"identity":"fb01eb66-b979-4f53-8761-02b3a05b7ea5","order_by":3,"name":"Abdishakur Abshir Mohamed","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Abdishakur","middleName":"Abshir","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2026-03-26 21:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9238098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9238098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106214792,"identity":"bbd22aa5-e9b1-4b4b-9f53-e1e7457204d8","added_by":"auto","created_at":"2026-04-06 08:17:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/d67b47ea42957a9078721594.jpg"},{"id":106403012,"identity":"75569fc0-3b2d-4ad4-9c0c-cc57261f34b3","added_by":"auto","created_at":"2026-04-08 09:13:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeological map of the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/79a19a954c6766e81ad19400.jpg"},{"id":106402328,"identity":"d4d6a37a-6023-4fcf-8556-7af5095e66c9","added_by":"auto","created_at":"2026-04-08 09:11:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRainfall data of the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/c0a8d90e343905556711668e.jpg"},{"id":106214795,"identity":"89797fcc-47fa-472a-a8fd-5ba877b95d60","added_by":"auto","created_at":"2026-04-06 08:17:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCurve Types\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/b66f546a67d9500178d38a6c.jpg"},{"id":106214796,"identity":"07e6854d-a141-4bb8-942e-c661bd0d80d4","added_by":"auto","created_at":"2026-04-06 08:17:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116064,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater Distribution at 15m Depth\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/fdef4ad0d6c49f632a2c56ed.jpg"},{"id":106403006,"identity":"f1abc9b3-ee08-46f3-9e68-dac75846dcc9","added_by":"auto","created_at":"2026-04-08 09:13:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroundwater Distribution at 25m Depth\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/3770e2519d2536468f5b4707.jpg"},{"id":106403754,"identity":"8d810544-8011-4f83-aa1e-f97946af8894","added_by":"auto","created_at":"2026-04-08 09:14:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":115066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroundwater Distribution at 35m Depth\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/9d57fb47aca4ca9243dabfac.jpg"},{"id":106959483,"identity":"365a52c6-18d7-4a77-973b-658b9983a54b","added_by":"auto","created_at":"2026-04-15 09:10:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1733970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9238098/v1/d7763c76-42eb-41c5-b680-0fe0c776aebc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegrating Geophysical and Geospatial Technology for Groundwater Exploration: A Case Study of Villages in Bumbaire Sub-County, Uganda\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eGroundwater serves as a vital freshwater source globally, playing a crucial role in sustaining domestic, agricultural, and ecological needs (Bienibuor et al., 2025). It accounts for a small fraction of Earth\u0026rsquo;s available freshwater yet represents reserves approximately 100 times greater than surface water volumes in rivers and lakes (Edukondal et al., 2019). However, increasing demands from population growth and agricultural intensification threaten groundwater sustainability, particularly in regions with limited hydrogeological data (Riwayat et al., 2018; Shabbir et al., 2020).Accurate mapping the distribution of groundwater is therefore essential for informed water resource management and development(Alemu et al., 2025; Mohammed et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn Uganda, this freshwater source is increasingly relied upon to mitigate surface water stresses, yet its distribution remains poorly characterized in many rural areas due to complex geology and insufficient surveying(Okot-Okumu \u0026amp; Otim, 2015) . the residents of Bumbaire Sub-County, Bushenyi District, where villages depend significantly on groundwater but there is lack of detailed subsurface information to guide sustainable extraction and well placement (Miguel et al., 2020)\u003c/p\u003e\n\u003cp\u003eWater access in Bumbaire villages presents a major challenge, adversely impacting the livelihoods of its residents(Adomi Mbina et al., 2020). This is exacerbated by the poor functionality of water points (67%, the district\u0026apos;s lowest) and the degradation of alternative surface water sources due to encroachment. Effective mitigation strategies are therefore crucial to resolve the immediate crisis and ensure the long-term sustainability of water resources for these communities(Marks et al., 2020).\u003c/p\u003e\n\u003cp\u003eGeophysical methods, particularly the Electrical Resistivity Method (ERM), provide a non-invasive and efficient means of investigating subsurface conditions and the condition of groundwater(Hussein \u0026amp; Ali, 2023).Specifically vertical Electrical Sounding (VES) is the first method which provided 1D data of subsurface strata and it is \u0026nbsp;a well-established technique for identifying aquifer characteristics by measuring variations in subsurface resistivity(State et al., 2022).When combined with Geographic Information Systems (GIS), VES data can be spatially interpolated and analyzed to produce detailed groundwater distribution maps (Khan et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn a study conducted\u0026nbsp;by (Kubingwa et al., 2023) ,researchers investigated and mapped groundwater potential areas (GWPAs) in the semi-arid Bukombe district of Tanzania using an integrated approach that combined geospatial and geophysical methods. The aim was to assess the groundwater potential comprehensively by incorporating both surface and subsurface characteristics. Although this methodology is considered crucial and highly reliable, there exists a research gap in understanding groundwater distribution in the Bumbaire subcounty villages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings of our research are expected to fill this geographical gap and provide a detailed groundwater distribution map of the study area.\u0026nbsp;this study applies an integrated VES and GIS approach to delineate groundwater potential zones in selected villages of Bumbaire Sub-County. The research seeks to address the existing gap in localized groundwater assessment and provide a practical, reproducible framework for identifying high-potential zones to support sustainable groundwater development in the region.\u003c/p\u003e\n\u003cp\u003eTo achieve the result of this study, we set two specific objectives, which eventually contribute to get a solution for our main objectives of Mapping Groundwater potential zones in these villages .\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eto conduct Vertical Electrical Sounding survey to collect subsurface resistivity data across the study area;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;to generate groundwater potential distribution maps at 12m, 24m, and 36m depths using GIS-based interpolation of the collected VES data;\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe research seeks to address the existing gap in localized groundwater assessment and provide a practical, reproducible framework for identifying high-potential zones to support sustainable groundwater development in the region.\u003c/p\u003e"},{"header":"2 METHODS AND MATERIAL","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Study area\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Geographical Scope of the study Area\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Geological and Hydrological Condition of the study area\u003c/h2\u003e \u003cp\u003eThe geology of the study area which in Bushenyi District is predominantly characterized by Precambrian basement rocks, which are widely distributed across Bushenyi District and also southwestern Uganda. These rocks mainly consist of granites, and quartzites that have undergone varying degrees of metamorphism. Over time, intense weathering under humid tropical conditions has led to the development of a thick regolith layer composed of lateritic soils and weathered rock materials. This weathered zone is particularly important because it often serves as the main groundwater storage unit, controlling infiltration, percolation, and shallow aquifer formation the geological data of this study is downloaded from USGS (United States Geological Society). Structurally, the basement rocks are commonly fractured and jointed, which significantly enhances secondary porosity and permeability in the area. These fractures, faults, and shear zones act as preferential pathways for groundwater movement and storage, making them critical targets for groundwater exploration. Understanding the local geology is essential for sustainable water resource development, as it helps in identifying productive aquifer zones and minimizing the risk of drilling dry or low-yield boreholes.\u003c/p\u003e \u003cp\u003eClimatically, the study area is characterized by relatively high annual rainfall, averaging about 1500 mm, as indicated in the legend. This amount of rainfall suggests favorable conditions for surface water availability, groundwater recharge, and agricultural activities. The uniform rainfall distribution shown across the study area implies limited spatial variability at the local scale, which is important for interpreting hydrological processes and water resource potential. Such climatic conditions play a significant role in influencing soil moisture, runoff patterns, and overall water quality within the study area. The rainfall fall was provided by UNMA (Uganda National Meteorological Agency)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 VES DATA PREPARATION AND INVERSION\u003c/h2\u003e \u003cp\u003eThe data collection process was carefully planned and carried out to ensure that the results were both accurate and meaningful. Vertical Electrical Sounding (VES) surveys were conducted using the Schlumberger array configuration(Nyaberi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eThe study area was divided into a grid system with line spacings of 400 meters, ensuring systematic coverage of the entire region. The target points for the surveys were the intersections of these grid lines, which provided a structured and evenly distributed set of locations for data collection(Nyaberi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This grid-based approach ensured that no part of the study area was overlooked and that the data collected was representative of the entire region.\u003c/p\u003e \u003cp\u003eFour electrodes were carefully placed in a straight line. The two outer electrodes, called current electrodes, were used to send electrical currents into the ground, while the two inner electrodes, known as potential electrodes, measured the voltage differences caused by those currents(Wang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). By gradually increasing the distance between the current electrodes, it was easy to measure resistivity at different depths. This helped us to create a detailed map of the subsurface, revealing layers of soil and rock and identifying areas where groundwater was likely to be found (Chinyem \u0026amp; Ovwamuedo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt each survey point, as soon as the first measurement was taken, the exact coordinates were recorded using a GPS device to ensure precise location data. Simultaneously, the resistivity readings were noted. Knowing the type of array used (Schlumberger), the current (I) injected into the ground, and the voltage (V) measured by the potential electrodes, the geometric factor (K) was manually calculated for each measurement. The geometric factor is a crucial parameter that depends on the spacing of the electrodes and is used to convert the measured voltage and current into apparent resistivity. For the Schlumberger array, the geometric factor (K) is calculated using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:K\\:=\\:\\pi\\:\\:\\frac{(A{B}^{2}-\\:M{N}^{2})}{\\:4MN}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e(K) geometric factor\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAB is the distance between the current electrodes,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMN is the distance between the potential electrodes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAfter acquisition of raw data of the apparent resistivity, the data was imported into IPI2win software which easy identifying the depth of each layer and the true resistivity\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inverse Distance Weighted (IDW) interpolation\u003c/h2\u003e \u003cp\u003eInverse Distance Weighting (IDW) is a deterministic geostatistical interpolation technique widely used for mapping spatial variables. It is particularly valued for its simplicity and ease of implementation in Geographic Information Systems (GIS) software, such as within the ArcGIS Pro Geostatistical Analyst toolbar(Talukdar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Originating in mining and geological engineering, IDW operates on a fundamental principle: the value at an unknown location is estimated as a weighted average of values from known sample points, where the weights are inversely proportional to the distance between the sampled points and the prediction location(Gundala \u0026amp; Tanikonda, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) .Essentially, sampled points closer to the prediction location exert a stronger influence than those farther away(Dheeraj et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The method relies on both statistical and mathematical concepts to create continuous surfaces from point data and predict values at unmeasured locations. The core of IDW is a weighted linear combination of sample points.\u003c/p\u003e \u003cp\u003e \u003cb\u003eZ\u003c/b\u003e \u003cb\u003e(\u003c/b\u003e \u003cb\u003es\u003c/b\u003e \u003cb\u003e0​) =\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\mathbf{}\\varvec{w}\\varvec{i}\\mathbf{}\\varvec{Z}\\left(\\varvec{s}\\varvec{i}\\mathbf{}\\right)}{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\mathbf{}\\varvec{w}\\varvec{i}\\mathbf{}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere the weight \u003cem\u003ewi\u003c/em\u003e​ is defined as:\u003c/p\u003e \u003cp\u003e \u003cb\u003ewi\u003c/b\u003e \u003cb\u003e=\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\varvec{d}(\\varvec{s}0\\mathbf{},\\varvec{s}\\varvec{i}\\mathbf{})\\varvec{p}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULT AND DISCUSSION","content":"\u003cp\u003eThis study employed a multi-stage geospatial workflow to delineate groundwater potential at 15m, 25m and 35m depth using Vertical Electrical Sounding (VES) data. First, field-collected VES measurements were processed through IPI2win inversion software to derive layered resistivity models, The resistivity value of these depth layers was extracted from each inverted VES point and georeferenced using survey coordinates. These point data were then imported into a GIS environment specially Argis pro for spatial interpolation.\u003c/p\u003e \u003cp\u003eFor the spatial interpolation of the inverted true resistivity values, the Inverse Distance Weighting (IDW) method was selected as the most appropriate technique for this initial visualization stage. The primary rationale for this choice lies in its combination of computational efficiency and conceptual simplicity, which allowed for the rapid generation of a continuous subsurface model from the irregularly spaced nodal data. Crucially, as an exact interpolator, IDW ensures that the generated surface honors the original data points at their precise locations, preserving the high-resistivity anomalies indicative of key geological features like fractures or sand layers.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Curve Types\u003c/h2\u003e \u003cp\u003eThe raw Vertical Electrical Sounding (VES) data, consisting of voltage (V) and current (I) measurements, were processed using Microsoft Excel to calculate the apparent resistivity (ρₐ) values. The analysis of the data was achieved using the IPI2win inversion software which was further modelled to curves with associated resistivity values as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The curve-matching procedures are key in curves interpretation of VES (vertical electrical sounding) as theoretical curves (Orellana \u0026amp; Mooney, 1966) are used in combination with the auxiliary-point method of partial curve matching (Zohdy, 1965). The analyzed curves are noted as representing the curves are founded on a three-layered imagery of a subsurface categorized as K, A and Q type curves.\u003c/p\u003e \u003cp\u003eThese curve classifications were based on shape which emanated in the plot of correlation between AB/2 and apparent resistivity and thus are given as: A-type; ρ1\u0026thinsp;\u0026lt;\u0026thinsp;ρ2\u0026thinsp;\u0026lt;\u0026thinsp;ρ3, H-type; ρ1\u0026thinsp;\u0026gt;\u0026thinsp;ρ2\u0026thinsp;\u0026lt;\u0026thinsp;ρ3, K-type; ρ1\u0026thinsp;\u0026lt;\u0026thinsp;ρ2\u0026thinsp;\u0026gt;\u0026thinsp;ρ3, Q-type; ρ1\u0026thinsp;\u0026gt;\u0026thinsp;ρ2\u0026thinsp;\u0026gt;\u0026thinsp;ρ3, as standalone curves, whereas combinations of the above helps represent several layers in the subsurface (Nyaberi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study herein has come up with modelled curves of Q-type; ρ1\u0026thinsp;\u0026gt;\u0026thinsp;ρ2\u0026thinsp;\u0026gt;\u0026thinsp;ρ3 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The type Q curve exhibits a decreasing continuously along with a progressive decrease of resistivity values with increasing depth. As noted, each layer is characterized by a thickness and an associated resistivity, which are determined during modeling.\u003c/p\u003e \u003cp\u003eThe curve types in this study are 3 in total, which means many VES station shares same curve type. The curve types are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Resistivity measurements at 15 m, 25 m, and 35 m depths from each VES stations\u003c/h2\u003e \u003cp\u003ethe interpreted true resistivity values obtained from Vertical Electrical Sounding (VES) surveys at targeted subsurface depths of 15 m, 25 m, and 35 m are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data were derived through quantitative inversion of field measurements using IPI2Win software, which models the geoelectric layer parameters from apparent resistivity curves. The resulting resistivity profiles reflect the electrical properties at these specific depths and are critical for characterizing or identifying aquifers, detecting anomalous zones, or mapping subsurface structures across the study area. The values listed provide a clear, depth-specific snapshot of the electrical resistivity distribution beneath each VES station.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResistivity measurements at 3 depths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVES 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVES2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVES3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVES4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVES5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVES6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVES7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVES8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e499.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e249.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.583544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.583507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.583532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.583618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.583536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.587319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.587321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.587407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.2062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.2137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.2061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResistivity measurements at 3 depths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVES\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVES 16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVES 17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVES 18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVES 19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVES 20\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e490.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.594906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.591158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.587451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.587327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.591202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.594908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.2099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.2136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.2137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.2138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResistivity measurements at 3 depths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVES9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVES10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVES11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVES 12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVES 13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVES 14\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e342.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e201.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e472.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e249.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e237.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.591072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.594904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.594987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.591112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.591068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.594943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.2061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.1986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Groundwater distribution map at all Depth\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 At 15m Depth\u003c/h2\u003e \u003cp\u003eThe groundwater distribution map which is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e at a depth of 15 m was generated based on true resistivity measurements obtained from the vertical electrical sounding (VES) after the inversion of the data. The map shows distinct spatial variation in groundwater potential across the study area, classified into very poor, poor, moderate, good, and very good zones. The very good groundwater potential zones are predominantly concentrated in the central part mainly Rwencence village and Kihunda village, forming relatively continuous belts. These zones reflect subsurface conditions characterized by favorable true resistivity values, indicating enhanced groundwater storage and transmission at the investigated depth.\u003c/p\u003e \u003cp\u003eAlso, the good groundwater potential zones appear as localized pockets mainly within the Rwencence and some in Kamutazya village, often adjacent to very good potential zones. These zones represent transitional resistivity conditions where groundwater occurrence is significant but spatially variable. The moderate groundwater potential zones are widely distributed, particularly in the northern and eastern parts of the study area, forming broad transition zones between high- and low-potential regions. Based on the true resistivity response, these areas indicate moderate subsurface permeability and groundwater availability, suggesting that successful groundwater abstraction may depend on careful borehole siting.\u003c/p\u003e \u003cp\u003eIn contrast, the poor and very poor groundwater potential zones are mainly confined to peripheral and isolated parts of the study area. Poor potential zones occur largely in Bumbaire village, while very poor zones appear as small, discrete pockets within the Rwencence and Kamutazya in very tiny sections. These zones are characterized by unfavorable, suggesting subsurface materials with limited groundwater storage capability at 15 m depth. Overall, the interpretation based on true resistivity measurements indicates that groundwater availability is generally favorable in the central and south western area which is Kihunda village, while marginal and isolated zones exhibit reduced groundwater potential. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e is illustrated the distribution of groundwater across the study area at 15M depth\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 At 25m Depth\u003c/h2\u003e \u003cp\u003eAt 25 m depth, the Distribution of groundwater map shows clear spatial variation across the villages of Bumbaire, Kihunda, Rwencence, and Kamutazya. The Bumbaire village, located in the northern part of the study area, is largely or at all dominated by poor to moderate groundwater potential zones. These zones are relatively extensive and continuous, indicating subsurface conditions that are less favorable for groundwater storage at this depth. This suggests that groundwater occurrence in Bumbaire is limited and may require deeper investigation or careful borehole siting.\u003c/p\u003e \u003cp\u003eIn Kihunda and Rwencence, which occupy the central portion of the study area, groundwater potential improves significantly. These areas are characterized by widespread very good groundwater potential zones, with embedded good potential pockets around several VES stations. The spatial continuity and concentration of these zones indicate favorable true resistivity responses at 25 m depth, suggesting well-developed groundwater-bearing formations. These villages therefore represent the most promising areas for groundwater abstraction, where sustainable borehole yields are more likely to be achieved.\u003c/p\u003e \u003cp\u003eThe Kamutazya area, situated in the eastern and south-eastern parts of the study area, exhibits a mixed groundwater potential pattern. While large portions are classified as poor to moderate groundwater potential, localized good and very poor zones are also evident. The poor zones dominate the outer sections, indicating unfavorable subsurface conditions, whereas the isolated very poor zones reflect localized resistivity anomalies associated with limited groundwater storage. Overall, the true resistivity-based interpretation at 25 m depth indicates that Rwencence and Kihunda have the highest groundwater potential, while Bumbaire and Kamutazya show comparatively lower and more variable groundwater prospects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAt 35m Depth\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe groundwater distribution map at a depth of 35 m shows clear spatial variation across the different villages within the study area. In Bumbaire, groundwater distribution is mainly poor to moderate, with limited zones indicating improved conditions. This suggests that groundwater occurrence at this depth is generally low, and suitable locations are relatively scattered. The overall pattern in Bumbaire points to less favorable groundwater availability compared to other villages.\u003c/p\u003e \u003cp\u003eIn Kihunda, groundwater distribution is predominantly good, especially toward the center part of the village. These areas indicate good groundwater presence at 35 m depth, making them more suitable for groundwater development. Rwencence displays a more variable pattern, with pockets of moderate to good groundwater distribution in the central parts of the village. This heterogeneity suggests that groundwater availability in Rwencence is localized and depends strongly on site-specific subsurface conditions.\u003c/p\u003e \u003cp\u003eBy contrast, Kamutazya shows the most favorable groundwater distribution within the study area. Large portions of the village are classified as good, very good and moderate, particularly in the upper areas to north which is in a good situation. This indicates relatively higher groundwater availability at 35 m depth, making Kamutazya a more promising area for groundwater abstraction at 35 and it is likely expected that this is confined aquifer. Overall, the map highlights strong village-level differences in groundwater distribution, which is essential for informed groundwater planning and borehole siting.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 CONCLUSIONS","content":"\u003cp\u003eThis study successfully applied an integrated geophysical and geospatial approach to delineate groundwater potential zones across four villages in Bumbaire Sub-County, Uganda. Vertical Electrical Sounding (VES) surveys were conducted at 20 locations to derive subsurface resistivity profiles, which were subsequently inverted and spatially interpolated using the Inverse Distance Weighting (IDW) method in a GIS environment. The resulting groundwater distribution maps at 15 m, 25 m, and 35 m depths revealed significant spatial heterogeneity in groundwater potential across the study area.\u003c/p\u003e\n\u003cp\u003eThe findings indicate that groundwater potential is not uniformly distributed and varies both by location and depth.\u0026nbsp;Kihunda\u0026nbsp;and\u0026nbsp;Rwencence\u0026nbsp;villages generally exhibited good to very good groundwater potential, particularly at the all depths, suggesting the presence of weathered/fractured aquifers with favorable hydrogeological conditions. In contrast,\u0026nbsp;Bumbaire\u0026nbsp;village showed predominantly poor to moderate potential across all depths, while\u0026nbsp;Kamutazya\u0026nbsp;displayed variable conditions, with promising zones mainly at greater depths.\u003c/p\u003e\n\u003cp\u003eAccording to the results it is highly expected that the first aquifer depth of the unconfined lays the depth between 15m to 25m. while the depth of aquifer occurs the depth of 35m is the intail depth of the unconfined aquifer. The integration of VES and GIS proved to be a cost-effective, non-invasive, and reproducible framework for preliminary groundwater assessment in data-scarce regions. This approach provides a critical scientific basis for sustainable groundwater development, enabling targeted borehole siting and optimized resource allocation.\u003c/p\u003e\n\u003cp\u003eThis study directly supports\u0026nbsp;SDG 6 (Clean Water and Sanitation)\u0026nbsp;by providing a scientific basis for improving groundwater access in a rural, underserved area. It also contributes to\u0026nbsp;SDG 11 (Sustainable Cities and Communities)\u0026nbsp;by promoting resilient water infrastructure and to\u0026nbsp;SDG 13 (Climate Action)\u0026nbsp;by enhancing adaptive capacity to climate-related water stress. The methodological framework supports\u0026nbsp;SDG 9 (Industry, Innovation, and Infrastructure)\u0026nbsp;through the application of affordable geospatial technology for sustainable resource management.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this research fills a critical geographical and hydrogeological knowledge gap in Bumbaire Sub-County and provides a transferable model for groundwater exploration in analogous basement terrains, ultimately supporting water security and socio-economic resilience in rural Uganda.\u003c/p\u003e"},{"header":"5 LIMITATIONS","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eThe study\u0026rsquo;s reliance on 1D VES data limits the resolution of complex subsurface geometries, such as dipping layers or localized fracture zones.\u003c/li\u003e\n \u003cli\u003eThe study did not incorporate hydrochemical data to assess water quality alongside availability.\u003c/li\u003e\n \u003cli\u003eThe relatively coarse survey grid (400 m spacing) may have missed small-scale aquifer features.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Gaps\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmploying advanced geophysical techniques like 2D Electrical Resistivity Tomography (ERT) would better resolve complex subsurface structures and fracture networks. Furthermore, assessing temporal dynamics through seasonal monitoring is necessary to understand the groundwater variability and aquifer resilience. Finally, interdisciplinary socio-hydrological studies are needed to evaluate how groundwater availability interacts with community access patterns, livelihood sustainability, and projected climate and land-use changes, ensuring findings directly inform adaptive and equitable water governance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, I thank to God for giving us the strength and time to conduct this research and secondly, I deeply appreciate Dr. Deepa and Dr. Ambiga for their moral support and guidance. Uganda National Meteorological Authority and USGS deserves to be appreciated the provision of necessary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAhmed Abdirizak Dirie\u0026nbsp;was involved in conceptualization, methodology, data analysis, writing-original draft preparation.\u0026nbsp;Farhan Nur Adan, Abdishakur Abshir Mohamed\u0026nbsp;and Abdulkadir Ahmed Mohamed were involved\u0026nbsp;technical support and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there was no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical standards and guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author did not receive any funding from public or private organization(s) for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in\u0026nbsp;this\u0026nbsp;article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this manuscript does not contain any personal data or sensitive information that requires consent for\u0026nbsp;publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdomi Mbina, S., Wilson, G., Daniel Eze, E., Pius, T., Robinson, S., Moyosore Afodun, A., \u0026amp; Ezekiel, I. (2020). Contaminants of Domestic Rural Spring Water Sources in Bushenyi-Ishaka Municipality, Western Uganda. \u003cem\u003eJournal of Health and Environmental Research\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 51. https://doi.org/10.11648/j.jher.20200603.12\u003c/li\u003e\n\u003cli\u003eAlemu, W. T., Suryabhagavan, K. V., Azagegn, T., Terefe, T. T., Legese, B. B., Tsegaye, A. M., Bojer, A. K., Alam, B. M., \u0026amp; Kumsa, A. (2025). Groundwater Potential Zone Mapping Using GIS and Remote Sensing: A Case of Teji River catchment, Southwest Shewa Zone, Ethiopia. \u003cem\u003eEarth Systems and Environment\u003c/em\u003e. https://doi.org/10.1007/s41748-025-00811-y\u003c/li\u003e\n\u003cli\u003eBienibuor, A. K., Preko, K., Aning, A. A., Menyeh, A., Wemegah, D. D., Appiah, M. K., \u0026amp; Gyilbag, A. (2025). Application of the electrical resistivity tomography (ERT) method in identifying high groundwater potential sites in the Atebubu municipality of Ghana. \u003cem\u003eDiscover Geoscience\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1). https://doi.org/10.1007/s44288-025-00120-x\u003c/li\u003e\n\u003cli\u003eChinyem, F. I., \u0026amp; Ovwamuedo, G. (2024). Evaluation of Aquifer Characteristics and Groundwater Protective Capacity in Abavo, Nigeria. \u003cem\u003eInternational Journal of Geosciences\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(11), 841\u0026ndash;860. https://doi.org/10.4236/ijg.2024.1511046\u003c/li\u003e\n\u003cli\u003eDheeraj, V. P., Singh, C. S., Alam, A., \u0026amp; Sonkar, A. K. (2025). Hydrogeochemical quality investigation of groundwater resource using multivariate statistical methods, water quality indices (WQIs), and health risk assessment in Korba Coalfield Region, India. \u003cem\u003eStochastic Environmental Research and Risk Assessment\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(3), 937\u0026ndash;958. https://doi.org/10.1007/s00477-024-02895-w\u003c/li\u003e\n\u003cli\u003eGundala, V., \u0026amp; Tanikonda, V. R. (2023). Delineation of Seawater Intrusion into Freshwater Aquifers by Using VES \u0026amp; GIS in the Kakinada Region, East Godavari District, Andhra Pradesh, India. \u003cem\u003eSpringer Water\u003c/em\u003e, \u003cem\u003ePart F1186\u003c/em\u003e, 333\u0026ndash;341. https://doi.org/10.1007/978-3-031-35279-9_16\u003c/li\u003e\n\u003cli\u003eHussein, M. A., \u0026amp; Ali, M. Y. (2023). \u003cem\u003eGroundwater Investigation through Electrical Resistivity Tomography in the Galhareri District , Galgaduud Region , Somalia : Insights into Hydrogeological Properties\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eKhan, A. Y., Ullah, W., Niaz, A., Bibi, T., Imtiaz, M. M., Fiaz, R., Gul, S., Hameed, K., \u0026amp; Islam, F. (2024). Integrated geophysical and geospatial techniques for surface and groundwater modeling. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 1\u0026ndash;19. https://doi.org/10.1038/s41598-024-76262-8\u003c/li\u003e\n\u003cli\u003eKubingwa, J. N., Makoba, E. E., \u0026amp; Mussa, K. R. (2023). Integrated Geospatial and Geophysical Approaches for Mapping Groundwater Potential in the Semi-Arid Bukombe District, Tanzania. \u003cem\u003eEarth (Switzerland)\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 241\u0026ndash;265. https://doi.org/10.3390/earth4020013\u003c/li\u003e\n\u003cli\u003eMarks, S. J., Clair-Caliot, G., Taing, L., Bamwenda, J. T., Kanyesigye, C., Rwendeire, N. E., Kemerink-Seyoum, J. S., Kansiime, F., Batega, D. W., \u0026amp; Ferrero, G. (2020). Water supply and sanitation services in small towns in rural\u0026ndash;urban transition zones: The case of Bushenyi-Ishaka Municipality, Uganda. \u003cem\u003eNpj Clean Water\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 1\u0026ndash;9. https://doi.org/10.1038/s41545-020-0068-4\u003c/li\u003e\n\u003cli\u003eMiguel, L., Sanchez, S., \u0026amp; Kemerink-, J. S. (2020). \u003cem\u003eCaught in the middle ? Access to water in the rural to urban transformation of Bushenyi-Ishaka municipality , Uganda\u003c/em\u003e. \u003cem\u003e22\u003c/em\u003e, 670\u0026ndash;685. https://doi.org/10.2166/wp.2020.024\u003c/li\u003e\n\u003cli\u003eMohammed, M. A. A., Szab\u0026oacute;, N. P., Alao, J. O., \u0026amp; Szűcs, P. (2024). Geophysical characterization of groundwater aquifers in the Western Debrecen area, Hungary: insights from gravity, magnetotelluric, and electrical resistivity tomography. \u003cem\u003eSustainable Water Resources Management\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 1\u0026ndash;15. https://doi.org/10.1007/s40899-024-01062-x\u003c/li\u003e\n\u003cli\u003eNyaberi, D. M. (2023). Application of Vertical Electrical Sounding in Mapping Lateral and Vertical Changes in the Subsurface Lithologies: A Case Study of Olbanita, Menengai Area, Nakuru, Kenya. \u003cem\u003eOpen Journal of Geology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(01), 23\u0026ndash;50. https://doi.org/10.4236/ojg.2023.131002\u003c/li\u003e\n\u003cli\u003eNyaberi, D. M. (2024). Use of Vertical Electrical Sounding in Mapping Lateral and Vertical Changes in Subsurface Lithologies: A Case Study of Olbanita, Menengai Area, Nakuru, Kenya. In \u003cem\u003eResearch Advances in Environment, Geography and Earth Science Vol. 6\u003c/em\u003e. https://doi.org/10.9734/bpi/raeges/v6/8300e\u003c/li\u003e\n\u003cli\u003eOkot-Okumu, J., \u0026amp; Otim, J. (2015). The quality of drinking water used by the communities in some regions of Uganda. \u003cem\u003eInternational Journal of Biological and Chemical Sciences\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 552. https://doi.org/10.4314/ijbcs.v9i1.47\u003c/li\u003e\n\u003cli\u003eRiwayat, A. I., Ahmad Nazri, M. A., \u0026amp; Zainal Abidin, M. H. (2018). Application of Electrical Resistivity Method (ERM) in Groundwater Exploration. \u003cem\u003eJournal of Physics: Conference Series\u003c/em\u003e, \u003cem\u003e995\u003c/em\u003e(1). https://doi.org/10.1088/1742-6596/995/1/012094\u003c/li\u003e\n\u003cli\u003eShabbir, H., Butt, N. A., Zafar, A., \u0026amp; Mir, M. K. (2020). Role of electrical resistivity method to identify fresh water aquifers in Nankana Sahib, Punjab, Pakistan. \u003cem\u003eJournal of Himalayan Earth Sciences\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(2), 52\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eState, T., Saleemmiya, S., Reddy, P. I. P., \u0026amp; Kunsoth, S. (2022). \u003cem\u003eGroundwater Exploration by using Electrical Resistivity Meter in Granitic Terrain of Hyderabad Region ,\u003c/em\u003e. \u003cem\u003e8\u003c/em\u003e(12), 1316\u0026ndash;1322.\u003c/li\u003e\n\u003cli\u003eTalukdar, P., Mokenepally, T., \u0026amp; Kulkarni, V. V. (2023). Lake water quality assessment using spatial interpolation and the development of the WQI on an educational campus, Assam, India. \u003cem\u003eWater Practice and Technology\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(10), 2244\u0026ndash;2257. https://doi.org/10.2166/wpt.2023.138\u003c/li\u003e\n\u003cli\u003eWang, Z., Wang, S., Fang, G., \u0026amp; Zhang, Q. (2016). Investigation on a Novel Capacitive Electrode for Geophysical Surveys. \u003cem\u003eJournal of Sensors\u003c/em\u003e, \u003cem\u003e2016\u003c/em\u003e. https://doi.org/10.1155/2016/4209850\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Groundwater distribution, Vertical Electrical Sounding (VES), Geographic Information System (GIS), Electrical resistivity, Inverse Distance Weighting (IDW)","lastPublishedDoi":"10.21203/rs.3.rs-9238098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9238098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater is a critical resource for rural communities in Uganda, yet its sustainable development is often hindered by a lack of detailed subsurface characterization. This study addresses this gap by integrating geophysical and geospatial techniques to map groundwater potential zones in four villages (Kihunda, Bumbaire, Rwencence, and Kamutazya) of Bumbaire Sub-County, Uganda. Vertical Electrical Sounding (VES) surveys were conducted at 20 locations using a Schlumberger array to obtain subsurface resistivity profiles. The acquired apparent resistivity data were inverted using IPI2win software to derive true resistivity values at target depths of 15, 25, and 35 meters. These point data were then spatially interpolated using the Inverse Distance Weighting (IDW) method in a GIS environment to generate continuous groundwater potential distribution maps.\u003c/p\u003e \u003cp\u003eThe results reveal distinct spatial and vertical variability in groundwater potential across the study area. Kihunda and Rwencence villages exhibited good to very good groundwater potential, particularly at greater depths (25 m and 35 m), indicating favorable aquifer conditions within weathered and fractured basement rocks. In contrast, Bumbaire village showed predominantly poor to moderate potential, while Kamutazya displayed a more heterogeneous pattern with localized promising zones. The generated maps effectively delineate zones suitable for borehole development, thereby reducing drilling risks and supporting targeted groundwater exploitation.\u003c/p\u003e \u003cp\u003eThis research demonstrates that the integration of VES and GIS provides a cost-effective, non-invasive, and reproducible framework for groundwater exploration in data-scarce basement terrains. The findings offer a practical decision-support tool for local water resource planners and contribute to sustainable groundwater management in rural Uganda.\u003c/p\u003e","manuscriptTitle":"Integrating Geophysical and Geospatial Technology for Groundwater Exploration: A Case Study of Villages in Bumbaire Sub-County, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 08:16:56","doi":"10.21203/rs.3.rs-9238098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-30T09:05:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T21:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98816174774041627919560210055938552576","date":"2026-04-22T06:58:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T12:40:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275750134006709577795209939463320954063","date":"2026-04-01T08:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T08:38:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T20:47:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T12:34:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T12:34:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-26T21:17:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23a39cc5-4800-4a3b-ae08-a5de8c326ddd","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-30T09:05:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T21:48:20+00:00","index":28,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65768597,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65768598,"name":"Earth and environmental sciences/Hydrology"},{"id":65768599,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-04-30T09:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 08:16:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9238098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9238098","identity":"rs-9238098","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}