Evaluation of Aquifer Vulnerability Using Geo-Electrical Surveys: Case Study of Obbo Payam, South Sudan

Evaluation of Aquifer Vulnerability Using Geo-Electrical Surveys: Case Study of Obbo Payam, South Sudan | 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 comment Evaluation of Aquifer Vulnerability Using Geo-Electrical Surveys: Case Study of Obbo Payam, South Sudan Nelson Okot, Akobundu Amadi, Marino Albino, Ilunga Nyembwe, Gilbert Ndatimana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7411257/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Groundwater resources in data-scarce regions of Sub-Saharan Africa face increasing pressure from overexploitation and contamination. Assessing aquifer vulnerability is therefore critical for sustainable management. This study evaluates aquifer protective capacity of overburden materials in Obbo Payam, South Sudan using Vertical Electrical Sounding (VES) and Total Longitudinal Conductance (TLC) as proxy indicator. A total of 14 VES points were analyzed using the Schlumberger configuration. Interpretation was conducted using IP2Win software, and aquifer protective capacity was inferred from and Total Longitudinal Conductance (TLC) values. The results categorized into five curve types (H, QH, A, K, and HK) with three layers models dominating (79%) and four layer models (21%). The results of TLC reveal significant spatial variability: 14% of the area exhibit excellent protection, 29% are classified as very good to good, 21% as moderate and 36% as weak to poor. The zones with high TLC values correspond to thicker clay-rich layers that enhance natural infiltration potential, whereas areas with low TLC values are more vulnerable to contamination. This study demonstrates that combining VES TLC analysis provides a cost-effective and practical approach for delineating aquifer vulnerability in regions lacking detailed hydrogeological data. The findings support the prioritization of groundwater protection measures and contribute to broader goals such as Sustainable Development Goal 6 on clean water and sanitation. Longitudinal Conductance Geo-electrical survey Resistivity Protective Capacity Rate Groundwater Protection Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Groundwater constitutes approximately 30% of global freshwater resources and serves as a vital source for domestic, agricultural and industrial needs (Shekhar & Pandey, 2014 ). However, its sustainability is increasingly at risk due to over-extraction, contamination and mismanagement (Tejasvi Hora, 2022 ). Aquifers vary in depth, confinement, and geological composition. Shallow aquifers often composed of unconsolidated alluvial deposits or weathered regolith are common in tropical and semi-arid regions, including parts of Sub-Saharan Africa, South Asia and South America (Grönwall et al., 2010 ). These systems are typically recharged by local precipitation but highly susceptible to contamination from anthropogenic sources (Geris et al., 2022 ). However, deep confined aquifers, such as Nubian Sandstone Aquifers and Ogallala formations are located beneath multiple geological layers offering greater protection (Tilahun, 2024 ). These systems often contain fossil water and are generally less vulnerable to surface contamination. Nevertheless, shallow aquifers, in tropical and semi-arid climates remain particularly vulnerable due to their rapid recharge rates and shallow depth (Jasechko et al., 2024; Machiwal et al., 2018 ). This allows infiltration of surface drive contaminants such as agricultural runoff and industrial effluents. As a result, pollutants can infiltrate directly into the groundwater system with minimal attenuation (Denham et al., 2020 ). This can accumulate harmful substances such as nitrates, pathogens, heavy metals that compromise water quality and pose risk to public health. In sub-Saharan Africa, and notably in South Sudan, groundwater plays an indispensable role. Over 70% of the population relies on groundwater for domestic use due to unreliable surface water and the seasonal nature of rivers (Bank, 2018 ; Okot et al., 2025 ). Yet, the groundwater sustainability in the region is compromised by poor data availability and weak governance structures (Owor et al., 2022 ; Schonberger & Wijnen, 2018 ). In addition, the human-induced factors such as agricultural runoff, urban expansion, and inadequate sanitation pose significantly threats to groundwater quality (Burri et al., 2019 ; Yusuf & Abiye, 2019 ). Thus, assessing aquifer vulnerability is crucial for identifying areas at risk, guiding land use, and implementing measures pollution and safeguard public health. Aquifer Vulnerability is influenced by multiple factors, including the geological, hydrogeological and human anthropogenic factors, which controls contaminants infiltration and groundwater quality (Machiwal et al., 2018 ). A key component is the protective capacity of subsurface materials, which determines how effectively they filter pollutants. Thus, materials with high longitudinal conductance, often associated with clay layers, generally offer better protection (Ayua et al., 2024 ). Conversely, areas with fractured or weathered zones lacking clay layers are more vulnerable to contamination. Effective groundwater management relies on aquifer characterization. This typically involves the integration of diverse methods such as pumping test, hydro-chemical analysis, geostatistical tools (kriging) and geophysical methods (Crosbie et al., 2018 ; Muchingami et al., 2021 ; Nyembwe et al., 2024 ; Teixeira et al., 2015 ). Among these, geophysical methods, particularly the Vertical Electrical Sounding (VES) method, has proven particularly valuable across Africa for delineating subsurface layers and assessing aquifer vulnerability in various African countries (e.g, Mengistu et al., 2022 ; Nazih et al., 2022 ; Nyembwe et al., 2024 ). This method, particularly using Schlumberger configuration, measures apparent resistivity of geological formations, provides insights into the nature and saturation of subsurface layers (Bahammou et al., 2021; Yaman et al., 2020 ). Its advantages due to low cost-effectiveness, portability, reasonable depth making it ideal for remote or data-scarce environments (Tripathi, 2025 ). Despite these advantages, most applications of VES in Sub-Saharn Africa have focused on groundwater potential mapping, with limited attention to vulnerability classification. For instance, Ibrahim et al. ( 2023 ) and Yusuf & Abiye ( 2019)applied VES for aquifer delineation, often supported by borehole hydraulic data. In South Sudan, studies such as Nazario et al. ( 2023 ) have used VES to map groundwater potential but not evaluate vulnerability or protective capacity. While Several studies across East Africa have employed geophysical methods to explore groundwater resources, most have focused on aquifer delineations or yield estimation rather than vulnerability assessment. For instance, Nazario et al. ( 2023 ) conducted VES surveys in Kapuri, South Sudan, to map groundwater potential but did not classify protective capacity. Similarly, (MacDonald et al.,2012) presented a continental-scale groundwater resource map that lacked local vulnerability assessment tailored to basement terrains. In Uganda, Owar et al. (2022) focused on groundwater recharge and resilience, relying largely on hydrochemical and borehole data. In Ethiopia, and Kenya, geophysical surveys have been used to delineate aquifers (Mengistu et al., 2022 ; Achieng et al., 2023), but few have applied Dar-Zarrouk parameters such as Total Longitudinal Conductance (TLC) to quantify protective capacity. This study aims address this gap by applying VES and TLC analysis to assess aquifer vulnerability in Obbo Payam. By focusing on protective capacity, this research offers a locally relevant framework for vulnerability classification in basement terrains. The approach provides practical insights for borehole siting and groundwater protection, especially in areas with limited hydrogeological data limited data. It also contributes to broader water security and planning efforts in data-scarce areas of Sub Saharan Africa. 2. Methods and Materials 2. 1 Study area description Obbo Payam is located in Magwi County, Eastern Equatoria State of South Sudan, and spans latitudes 3º 52ʹ 0ʺ to 4º 8ʹ 30ʺ N, and longitudes 32º 7ʹ 0ʺ to 32º 29ʹ 0ʺ E (Fig: 1). The landscape features a mix of hills, valleys, and floodplains, all shaped by tectonic and erosion processes. The Ayi River, along with its tributaries such as the Kimaru stream, serves as the primary perennial watercourse in the area (Okot et al., 2025 ). Additionally, the climate is a tropical savannah with high humidity. The rainfall averages between 900–1200 mm per annum. The peaks during the rainy season from July to September serve as the main sources of groundwater recharge. The vegetation in the area includes patches of forest in the valleys and plains, interspersed with savannah grassland and scattered shrub (Bank, 2018 ). 2.1.1 Hydrological and Geological Setting The hydrogeological setup of Obbo Payam is shaped by its complex lithological foundation, tectonic history and surface water interactions. The subsurface water resources are primarily stored in weathered and fractured zones of the basement complex, which have undergone significant alteration due to tectonics and prolonged weathering processes and tectonic activities (Okot et al., 2025 ). Obbo Payam is underlain by the Precambrian Basement Complex, a component of the East African Orogen (Mozambique Belt) that extends into northern Uganda, western Kenya, and western Ethiopia. The area is characterized by ancient, highly metamorphosed Precambrian rocks such as gneisses, amphibolite, charnockite, amphibolite, and granite, which are often exposed at or near the surface. These lithology are associated with the Neoproterozoic Pan African Orogeny, controlled fractured aquifers that form key groundwater reservoirs in the area (Abdelsalam et al., 2002 ). Furthermore, the tectonic influences from the nearby East African Rift System introduce faults and fractures that enhance secondary porosity and groundwater storage potential. Unlike the Nubian Sandstone and Um Ruwaba formations that dominate northern and central South Sudan, Obbo Payam features exposed basement rocks, allowing direct access to fractured aquifers crucial for water supply development. 2.2. Study Design This study employed the VES method using the Schlumberger array to investigate the subsurface resistivity and delineate the potential aquifer zones. The methodological framework consists of four stages: data acquisition, processing, interpretation, and evaluation of geo-electrical parameters (Fig. 2 ). 2.2.1 Data Acquisition and Processing A total of fourteen (14) Vertical Electrical Sounding (VES) points were established across the study area using a Schlumberger configuration to investigate subsurface geological and hydrogeological structures. In this method, the current electrodes (AB/2) and potential electrode (MN/2) were systematically expanded to explore greater depth, reaching a maximum AB/2 spread of 200 m (Nugraha et al., 2023 ). Apparent resistivity measurements were systematically recorded at each location, and survey points were accurately geo-referenced using GPS to enable spatial analysis and mapping (Omale et al., 2022 ). Furthermore, the collected field data were processed using IP2WIN software. Which performs one-dimensional (1D) inversion modelling. Apparent resistivity values were iteratively adjusted by modifying layer resistivity and thickness until the modelled curves satisfactorily matched the observed data (Ibrahim et al., 2023 ). The software generated both resistivity depth profiles and pseudo-cross-sections, providing visual and quantitative interpretations of subsurface stratigraphy. 2.2.2 Data Interpretation and Applications The IP2WIN software was used for data interpretation, which involves three key steps. First, depth and resistivity modelling were carried out by extracting resistivity values and corresponding depth from each VES point to delineate subsurface layers and identify aquifer-bearing formations. Second, curve typing and classification grouped the sounding curves into standard types such as A, H, K, Q, QH, and HK based on their geometric patterns (Ibrahim et al., 2023 ; Nugraha et al., 2023 ). This provides insights into the stratigraphy and hydrogeological relevance of the different zones. Third, results were tabulated, and Dar-Zarouk parameters such as longitudinal conductance (S) and transverse resistance (T) were derived to evaluate aquifer protective capacity and transmissivity (Nugraha et al., 2023 ; Nyembwe et al., 2024 ; Shehu et al., 2018 ). Additionally, both parameters, longitudinal conductance and transverse resistance of Dar-Zarrouk parameters, were selected as summarized in Table 1 , which classifies aquifer protective capacity based on longitudinal conductance. The Dar Zarrouk parameters (Yusuf et al. 2021; Egbai and Iserhien-Emekeme, 2015) are calculated by combining the resistivity and thicknesses of each geo-electric layer. These parameters include longitudinal conductance, S (Ω-1) and transverse resistance, Tr (Ωm2). For a series of n layers with resistivity ρi and thickness hi, the longitudinal unit conductance (S) (Eq. 1) and transverse unit resistance (T) (Eq. 2) are defined as: \(\:S=\sum\:_{i=1}^{n}\frac{{h}_{i}}{{p}_{i}}\) Eq. (1) \(\:T=\sum\:_{i=1}^{n}{h}_{i}{p}_{i}\) Eq. (2) Where: hi -= thickness of the i th layer (m) pi = resistivity of the i th layer (Ω-m) S is the longitudinal conductance (Ω-1); Tr is the transverse resistance (Ωm2) Protective capacity and longitudinal unit conductance are thought to have a proportionate connection. Longitudinal unit conductance, S , can be employed directly in the protective capacity evaluation of aquifers to represent the restriction of pollutants' percolation into the aquifer (Yusuf & Abiye, 2019 ). Although various indices like DRASTIC and GOD have been widely applied in aquifer vulnerability mapping, this study prioritized the use of Total Longitudinal Conductance (TLC) due to its strong sensitivity to clay content and effectiveness in hydrogeologically data-scarce settings. In contrast to DRASTIC, which requires parameters such as recharge rate, land use, and depth to water table, TLC is derived directly from resistivity data and has been shown to correlate well with protective capacity in crystalline basement terrains (Busato et al., 2019 ; Olawuyi, 2021 ). This makes the method suitable for Obbo Payam, where detailed hydrogeological and land use datasets are inadequate. Table 1 Classification of Aquifer Protective Capacity Rate based on longitudinal conductance (adopted from Nyembwe et al. ( 2024 ) Longitudinal Conductance (Ω −1 ) Protective Capacity Rate > 10 Excellent 5–10 Very good 0.7–4.9 Good 0.2–0.69 Moderate 0.1–0.19 Weak < 0.1 Poor 2.2.3 Potential Biases and Validation Strategies Despite the VES method and Dar-Zarrouk parameters being widely applied in groundwater studies, they have inherent limitations. A key issue is reduced sensitivity at greater depths, which can obscure deeper clay layers or fractured zones. The method also assumes horizontally layered, homogeneous subsurface conditions, an oversimplification that may not reflect complex geological settings (Yusuf & Abiye, 2019 ). Moreover, resistivity measurements are vulnerable to external influences such as anthropogenic noise, environmental variability, and electrode placement errors. Since VES measure electrical rather than hydraulic properties, critical factors like porosity and recharge must be referred to indirectly. Although this study recognizes the advantages of the VES method, it also acknowledge its inherent limitation in resolving complex subsurface structures. The technique assumes lateral homogeneity within layers, which may not capture abrupt changes in lithology, structural discontinuities, or heterogeneities common in basement terrains. Moreover, in the absence of boreholes lithology or pumping test data, the protective capacity inferred from longitudinal conductance remains an indirect proxy. To improve reliability, future research should integrate confirmatory datasets such as boreholes los, hydraulic conductivity measurement or hydrochemical sampling to ground-truth resistivity-derive interpretations. Additionally, where drilling is not feasible, proxy validation using nearby boreholes or integrating satellite-based recharge estimates may enhances interpretive confidence. Furthermore, applying 2D or 3D geophysical methods and geostatistical techniques such as kriging could help account for lateral variations and reduce classification uncertainty. 3. Results and Discussion 3.1 Subsurface Resistivity Interpretation and limitation The Vertical Electrical Sounding (VES) survey conducted in Obbo Payam provides insight into subsurface geological structure. The results show that 79% of the surveyed points exhibit three distinct geo-electrical layers, while the remaining 21% display four-layer configurations. The predominance of three layers suggests a relatively simple geological setting, possibly due to uniform deposition and limited tectonic activity. The four-layer profiles, in contrast, indicates a complex subsurface conditions caused by lithological heterogeneity, faulting, or deep weathering (Adeyeye et al., 2019 ; Olawuyi, 2021 ; Nag & Kundu, 2016 ). Such complexity often supports the development of multiple or deeper aquifer systems, potentially enhancing groundwater storage and yield capacity under favourable hydrogeological conditions. Building on the stratigraphic analysis, five distinct geo-electrical curve types were identified: H, QH, A, K, and HK (Fig. 3 ). The H-type curves, observed at 43% of the stations (VES1, VES4, VES5, VES6, VES8, and VES14). These curves exhibit a resistive-conductive-resistive sequence (Fig. 3 : “A”), indicating a weathered or fractured aquifer between resistive layers. Moreover, the A-type curves, comprising 29% of the stations (VES7, VES11, VES12, and VES13), display a gradual increase in resistivity with depth (Fig. 3 : “D”). Such pattern typically associated with lateritic soils overlying crystalline bedrock, and generally low recharge potential. Additionally, the QH-type curves, found at 14% of the stations (VES3, VES9), show low-resistivity zones likely composed of saturated clay-rich materials (Fig. 3 : “C”), implying confined aquifers with limited permeability. Less common configuration were the HK-type curve, (7%; VES10), suggests interbedded sand and clay (Fig. 3 : “E”). This is a characteristic of fluvial deposit, potentially hosting varied aquifers with heterogeneous characteristics. Lastly, the K-type curve, also found at one station (7%; VES2), consists of a classic clay-sand-clay sequence (Fig. 3 : “B”). In such cases, sand layer act as aquifer confined by less permeable clay (Ibrahim et al., 2023 ; Olawuyi, 2021 ). These curve types reflect variation in subsurface composition that influence the distribution of aquifers. Further analysis of resistivity values revealed notable spatial variability in subsurface properties and aquifer potential. Very low first-layer resistivity (< 1 𝝮m) at VES5, VES6, VES7, VES12, and VES13, suggesting water-saturated or topsoil. Although these layers may offer protection from contamination, they typically exhibit low permeability and limited aquifer yields. Conversely, extremely high resistivity values in deeper layers at VES3 (22623 𝝮m) and VES9 (27022 𝝮m) suggest to intact crystalline basement rock. This offers minimal groundwater storage potential due to its low porosity and hence low groundwater storage capacity. Additionally, moderate resistivity observed at VES1 (582 𝝮m) and VES4 (251 𝝮m) indicates weathered or fractured basement zones, generally offer better groundwater storage due to enhanced secondary porosity. These interpretations align with findings from similar studies conducted in crystalline basement terrain in Nigeria and Ethiopia, where moderate resistivity zones correlate with higher groundwater yields (Nyembwe et al., 2024 ; Ibrahim et al., 2023 ; Olawuyi, 202; mengistu et al., 2022 ). For instance, Ibrahim et al. ( 2023 ) found that moderate resistivity in north-central Nigeria had greater groundwater potentials. Similarly, Nyembwe et al. ( 2024 ) observed strong alignment between TLC-based protective zones and borehole yields in Gwagwalada, Nigeria. These comparison highlights both the utility and limitations of resistivity-based interpretations in basement environment, particularly in the absence of direct hydrogeological measurement such as boreholes log or pumping test results. However, While similar TLC-based methods have been applied in Nigeria and Ethiopia, the Obbo Payam setting present unique hydrogeological conditions. Unlike the relatively more studied terrains in those regions, Obbo lies within a less mapped crystalline basement with limited data availability. The study area may also experience different clay mineral distributions and weathering profiles, affecting both resistivity and protective capacity. Furthermore, the combinations of tectonic stability and limited infrastructure development increase groundwater reliance. These factors highlights the need for site-specific assessments in South Sudan. The classification of aquifer zones based on curve types and resistivity depth profiles provides a practical framework for guiding groundwater exploration. For instance, zones with H and QH-type curves can be prioritized for drilling due to their potential aquifer characteristics. However, sites with A-type or K-type curves require caution due to limited recharge capacity. Furthermore, the variation in depth to basement and overburden thickness across the area influences groundwater potential. The thickest weathered profiles were observed at VES3 (46 m) and VES9 (20.4 m), suggesting substantial groundwater accumulation potential in these locations. Conversely, shallow overburden thickness of less than 10 meters were recorded at VES2, VES6, VES7, VES10, VES12, and VES13, which display shallow overburden (< 10 m), may support only seasonal or hand-dug wells. The reliability of the inversion models was evaluated using the Root Mean Square (RMS) error values, which range from 0.664% (VES2) and 2.94% (VES14). All values fall within acceptable limits (< 5%), indicating high confidence in the inversion variability. However, further evaluation of layer statistics reveals substantial spatial variability. In the first layer, the mean resistivity value is 359.86 Ωm, with a high standard deviation of 732.86 Ωm, reflecting a diverse mix of conductive and resistive materials. This heterogeneity can undermine the precision of vulnerability classification by skewing interpretations. In the second layer, a lower mean resistivity of 164.42 Ωm, with a standard deviation of 398.11 Ω. m, further emphasis the present of variable subsurface conditions. These results suggest that even similar average resistivity may represent drastically different aquifer vulnerability levels depending on localized lithology. This spatial variability introduces interpretative challenges. For instance, areas with extremely high resistivity may correspond to dry, resistive layers like gravel or sand that allow rapid contaminants infiltration despite being interpreted as “protective”. Conversely, conductive zones could be misinterpreted as thick protective clay layers, even when they are thin and ineffective. These interpretative limitations reflects the findings of Busato et al. ( 2019 ) and Ibrahim et al. ( 2023 ) highlight the need for validation methods to enhance the accuracy of vulnerability assessment. Therefore, correlating resistivity-derived classification with ground truth data, such as borehole logs or water quality measurements, can provide critical validation and ensure the reliability of the results. Additionally, incorporating geostatistical tools such as kriging (Nugraha et al., 2023 ) and complementary geophysical techniques, such as Ground Penetrating Radar (GPR), could further refine subsurface characterization. Such an integrative approach thereby reducing classification uncertainty and enhancing the reliability of vulnerability assessments, particularly in a data-scarce environment like Obbo Payam, including unsampled locations. Despite demonstrating the utility of VES and TLC in delineating aquifer vulnerability, this study acknowledges several limitations. Most notably, the lack of borehole lithology, pumping test data, and hydrochemical analysis prevents direct validation of the interpreted resistivity layers and protective capacity ratings. As a result, the correspondence between calculated TLC values and actual clay content or aquifer yields remains inferred rather than confirmed. Additionally, the VES method, being a 1D sounding technique, assumes horizontally layered and laterally homogeneous subsurface conditions, which may not capture local heterogeneities, fault zones, or complex weathered profiles typical of basement terrains. Additionally, resistivity data are sensitive to near-surface conditions, electrode contact quality, and anthropogenic noise, which may introduce uncertainty particularly for extreme TLC values like that observed at VES7. Future work should incorporate borehole logging, and water quality sampling as well as land use and recharge data to support multi-criteria evaluation. Thereby, establishing a more robust framework for sustainable groundwater management in Obbo Payam and beyond. 3.2 Spatial Analysis of Aquifer Protective Capacity based on Total Longitudinal Conductance The protective capacity of an overburden plays a key role in limiting contaminations of the underlying aquifer. It controls how vulnerable the aquifer is to pollutants such as industrial waste, agricultural runoff, and chemical spills (Olatinsu et al., 2025 ). In this study, Total Longitudinal Conductance (TLC) was used to assess natural protective capacity analysis. The TLC values were derived from Vertical Electrical Sounding (VES) data collected in Obbo Payam (Table 2 ). Through resistivity data, reflects the cumulative ability of overburden materials specifically clay rich layers to attenuate surface contaminants before they reach the aquifer (Fig. 4). It serves as a useful proxy for evaluating the natural protection in data-scarce environments. The results show 14% of sites (VES5 and VES7) “exhibit excellent” protection, likely due to thick, highly conductive clay layers. Another 29% of sites (VES1, VES6, VES12, and VES13) fall within the “very good” to “good” protection category, indicating moderate clay content that can reasonably limit pollutant infiltration. By contrast, 21% of the sites (VES8, VES9, and VES14) are classified as “moderately” protected, with thinner or less conductive clay horizons offering only partial attenuation. The remaining 36% of the sites (VES2, VES3, VES4, VES10, and VES11) show “poor” to “weak” protections, reflecting minimal or absent clay barriers and thus greater vulnerability to surface contamination. These results highlights strong spatial heterogeneity in the protective capacity of the overburden. Tis variations supports the need for site-specific groundwater protections measures. The most suitable zones for borehole development are those with high TLC values particularly VES5 and VES7. Moreover, the sites rated “good” and “very good” may also be suitable, though land use regulations should be applied to minimize pollution risk. Additionally, areas with moderated ratings requires cautions. Whereas poorly protected sites should be avoided unless popper engineering measures (e.g. sealed casing or sanitary protection) are implemented. These classification and associated Dar-Zarrouk values are summarized in Table 2 . The spatial pattern in Obbo Payam are consistent with those found in other Precambrian Basement regions. Studies in Niger and Ethiopia also reported that TLC values below 1 Ω −1 typically indicates zones with little or no clay protection (Olatinsu et al., 2025 ; Yusuf & Abiye, 2019 ). However, the TLC values at VES7 (387.6 Ω −1 ), is exceptionally high and exceed typical values found in crystalline terrain in Sub-Saharan Africa, which usually range between below 10–50 Ω −1 (Ibrahim et al., 2023 ; Nyembwe et al., 2024 ). Such an extreme value may indicate the present of unusually thick or highly conductive clay layer that strongly attenuates surface contaminants. However, it could also reflect limitations in data inversion, local electrode coupling effects, or lateral heterogeneity not captured by 1D VES model. Given the absence of borehole lithology or hydrochemical validation data, this result should be interpreted cautiously. Ground-truth through drilling, soil sampling, or additional geophysical surveys (e.g., electromagnetic profiling) is recommended to confirm whether this TLC truly corresponds to a protective clay layer or arises from measurement or modeling artifacts (Fig. 4). Comparing studies in Lokoja and Gwagwalada (Ayua et al., 2024 ; Nyembwe et al., 2024 ) demonstrated that zones with TLC above 10 Ω consistently coincided with a thicker clay layer and higher natural protection. In these areas, engineered protections such as sealed well casing, sanitary grouting, and designated exclusion zones may be necessary to mitigate contamination. Although no borehole lithology was not available for direct correlation, the TLC and resistivity trends match expected groundwater- bearing conditions. For instance, high TLC values at VES5 and VES7 likely indicate saturated weathered zones. These areas may also align with elevated recharge zones as suggested by recent satellite-based studies (Okot et al., 2025 ). Further research should explore this connection to validate aquifer production and refine vulnerability zoning. Table 2 Dar Zarrouk parameters and Protective capacity rating of VESs in Obbo No of VES Longitude E Latitude N TLC (Ω) PCR VES1 32.42643 4.04398 5.530 Very Good VES2 32.42616 4.0436 0.001 Poor VES3 32.42592 4.04321 0.600 Weak VES4 32.42571 4.04285 0.054 Poor VES5 32.42532 4.04308 53.77 Excellent VES6 32.42554 4.04350 2.986 Good VES7 32.42576 4.04389 387.6 Excellent VES8 32.42597 4.04431 0.331 Moderate VES9 32.42556 4.04449 0.560 Moderate VES10 32.42537 4.04408 0.035 Poor VES11 32.42518 4.04368 1.304 Weak VES12 32.425 4.04326 2.399 Good VES13 32.42458 4.04345 6.814 Very Good VES14 32.42474 4.04386 0.310 Moderate Note: Total transverse resistance (TTR) (Ω); Total longitudinal conductance (TLC) (Ω); Protective Capacity rating (PCR) 4. Conclusion This study applied geo-electrical surveys and Total Longitudinal Conductance (TLC) to assess aquifer vulnerability in Obbo Payam, South Sudan. The analysis identified significant variability in aquifer protective, with 14% of the site showing excellent protection, 29% are classified as good to very good, 21% as moderate and 36% as poor to weak. Zones with high TLC values, such as VES5 and VES7, were interpreted as better protected due to the presence of thick clay-rich layer, whereas lower TLC values are more susceptible to surface contamination. The research demonstrates that TLC-based assessment alone can provide actionable groundwater vulnerability zoning in crystalline basement terrains. However, the absence of borehole lithology and hydrochemical data limits direct validation of these interpretations. Future work should incorporate borehole data, pumping tests, and water quality analysis to enhance model reliability. By introducing a locally adopted vulnerability assessment framework, this study offers a practical tool to guide sustainable groundwater development and protection strategies in Obbo Payam and similar areas, under-resourced across Sub-Saharan Africa. Declarations Ethical approval and consent to participate Note applicable Consent for publication This study does not contain any individual person’s data in any form (including images, videos, or personal identifiers) requiring consent for publication. Availability of Data and Materials Geo-electrical resistivity dataset and models’ outputs used in the aquifer vulnerability analysis are available upon reasonable request from the corresponding author. Conflict of Interest The authors have no conflict of interest. Funding The authors wish to acknowledge the African Water Resources Mobility Network (AWaRMN) for supporting this research through the Intra-African Academic Mobility Programme No. 2019- 1973/004-001, which was funded by the European Union. Author’s contributions , All authors contributed to the study conception and design. Data collection and analysis were performed by [N.O], [M.A]. The first draft of the manuscript was written by [N.O]. [A.A.N], [I.N] and [G.N] wrote and reviewed the manuscript. All authors read and approved the final manuscript. References Abdelsalam, M. G., Liégeois, J. P., & Stern, R. J. (2002). The Saharan Metacraton. Journal of African Earth Sciences , 34 (3–4), 119–136. https://doi.org/10.1016/S0899-5362(02)00013-1 Adeyeye, O. A., Ikpokonte, E. A., & Arabi, S. A. (2019). GIS-based groundwater potential mapping within Dengi area, North Central Nigeria. 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Environmental Management , 66 (6), 1142–1161. https://doi.org/10.1007/s00267-020-01376-4 Geris, J., Comte, J. C., Franchi, F., Petros, A. K., Tirivarombo, S., Selepeng, A. T., & Villholth, K. G. (2022). Surface water-groundwater interactions and local land use control water quality impacts of extreme rainfall and flooding in a vulnerable semi-arid region of Sub-Saharan Africa. Journal of Hydrology , 609 (April), 127834. https://doi.org/10.1016/j.jhydrol.2022.127834 Grönwall, J. T., Mulenga, M., & Mcgranahan, G. (2010). Groundwater, self-supply and poor urban dwellers A review with case studies of Bangalore and Lusaka Human Settlements Working Paper Series Water and Sanitation -26 (Issue November). Ibrahim, A., Omeneke, A. L., Aminu, M. B., Dung, P. D., Salisu, S. M., Odinaka, A. C., Akagbue, B. O., Ibrahim, I. O., & Ayoola, A. H. (2023). Application of Vertical Electrical Sounding (VES) for the Determination of Water-Bearing Zone in Karaworo, Lokoja Kogi State, Nigeria. Journal of Geography, Environment and Earth Science International , 27 (11), 47–73. https://doi.org/10.9734/jgeesi/2023/v27i11726 MacDonald, A.M, Bonsor, H C, O Dochartaigh, B E, and Taylor, R. G. (2012). Quantitative map of groundwater resources in Africa. Enironmental Research Letter7(2) , doi:10.108 . Machiwal, D., Jha, M. K., Singh, V. P., & Mohan, C. (2018). Assessment and mapping of groundwater vulnerability to pollution: Current status and challenges. Earth-Science Reviews , 185 , 901–927. https://doi.org/10.1016/j.earscirev.2018.08.009 Mengistu, T. D., Chang, S. W., Kim, I. H., Kim, M. G., & Chung, I. M. (2022). Determination of Potential Aquifer Recharge Zones Using Geospatial Techniques for Proxy Data of Gilgel Gibe Catchment, Ethiopia. Water (Switzerland) , 14 (9), 1–19. https://doi.org/10.3390/w14091362 Muchingami, I., Mkali, A., Vinqi, L., Pietersen, K., Xu, Y., Whitehead, R., Karsten, J., Villholth, K., & Kanyerere, T. (2021). Integration of hydrogeophysical and geological investigations in enhancing groundwater potential assessment in Houtriver gneiss crystalline basement formation of South Africa. Physics and Chemistry of the Earth , 123 (March), 103009. https://doi.org/10.1016/j.pce.2021.103009 MWEGA, B. W. (2016). GEO-ELECTRIC INVESTIGATION OF THE AQUIFER CHARACTERISTICS AND GROUND WATER POTENTIAL OF THE LAKE CHALA WATERSHED, TAITA TAVETA COUNTY. A Thesis Submitted in Partial Fulfillment for the Degree of Master of Science in Environmental Engineering and Management in the Jomo Kenyatta University of Agriculture and Technology , Unpublishe . Nag, S. K., & Kundu, A. (2016). Delineation of Groundwater Potential Zones in Hard Rock Terrain in Kashipur International Journal of Waste Delineation of Groundwater Potential Zones in Hard Rock Terrain in. International Journal of Waste Resources , 6 (1). https://doi.org/10.4172/2252-5211.1000201 Nazario, M. G., Kuria, Z. N., & Gichaba, C. M. (2023). Groundwater Potential Assessment Using Remote Sensing, Geographical Information System and Electrical Resistivity Methods in Crystalline Terrain in Kapuri Area, Jubek State, South Sudan. Journal of Applied Geology and Geophysics , 11 (2), 49–62. https://doi.org/10.9790/0990-1102014962 Nazih, M., Gobashy, M., Araffa, S., Soliman, K. S., & Abdelhalim, A. (2022). Geophysical studies to delineate groundwater aquifer in arid regions: A case study, Gara Oasis, Egypt. Contributions to Geophysics and Geodesy , 52 (4), 517–564. https://doi.org/10.31577/congeo.2022.52.4.2 Nugraha, G. U., Nur, A. A., Pranantya, P. A., Lubis, R. F., & Bakti, H. (2023). Analysis of groundwater potential zones using Dar-Zarrouk parameters in Pangkalpinang city, Indonesia. Environment, Development and Sustainability , 25 (2), 1876–1898. https://doi.org/10.1007/s10668-021-02103-7 Nyembwe, I., Nwanosike, A. A., Ndatimana, G., Okot, N., & Muamba, T. (2024). Evaluation of aquifer hydraulic properties from resistivity and pumping test data in parts of Gwagwalada , Northcentral Nigeria . 12 , 309–320. https://doi.org/10.26599/JGSE.2024.9280023 Okot, N., Nwanosike, A., Pitia, C., & Ndatimana, G. (2025). Journal of African Earth Sciences Deciphering prolific zones of groundwater using geospatial techniques and Analytical Hierarchy Process ( AHP ) in Obbo and Magwi Payams , South Sudan. Journal of African Earth Sciences , 230 (September 2024), 105737. https://doi.org/10.1016/j.jafrearsci.2025.105737 Olatinsu, O. B. ., Ogieva, M. O., Ige-Adeyeye, A. A. (2025). EVALUATION OF THE OVERBURDEN PROTECTIVE CAPACITY OF AQUIFER- AQUITARD SYSTEMS IN AGBARA TOWN, SOUTHWESTERN NIGERIA USING ELECTRICAL RESISTIVITY TECHNIQUES. Journal of Science , 27 (1), 165–184. Olawuyi, A. K. (2021). Hydrogeophysical investigation for the aquifers in part of Ilorin, Central Nigeria: Implication on groundwater prospect. Tanzania Journal of Science , 47 (2), 520–534. https://doi.org/10.4314/tjs.v47i2.10 Omale, M. E., Udensi, E. E., & Rafiu, A. A. (2022). Geo-electrical investigation of groundwater potential and subsurface structures in part of Pompo Village , . 7 (2), 32–41. Owor, M., Okullo, J., Fallas, H., MacDonald, A. M., Taylor, R., & MacAllister, D. J. (2022). Permeability of the weathered bedrock aquifers in Uganda: evidence from a large pumping-test dataset and its implications for rural water supply. Hydrogeology Journal , 30 (7), 2223–2235. https://doi.org/10.1007/s10040-022-02534-0 Schonberger, S., & Wijnen, M. (2018). Assessment-of-groundwater-challenges-and-opportunities-in-Sub-Saharan-Africa . Shehu, J., Alhassan, U. D., Salako, K. A., Rafiu, A. A., & Adetona, A. A. (2018). Geoelectrical Investigation For Groundwater Potential and Aquifer Protective Capacity of Overburden Units at Unions Site , Gidankwano Campus , Federal University of Technology , Minna , North Central , Nigeria. Lapai Journal of Aplied and Natural Sciences , 3 (1). Shekhar, S., & Pandey, A. C. (2014). Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto International , 30 (4), 402–421. https://doi.org/10.1080/10106049.2014.894584 Teixeira, J., Chaminé, H. I., Espinha Marques, J., Carvalho, J. M., Pereira, A. J. S. C., Carvalho, M. R., Fonseca, P. E., Pérez-Alberti, A., & Rocha, F. (2015). A comprehensive analysis of groundwater resources using GIS and multicriteria tools (Caldas da Cavaca, Central Portugal): environmental issues. Environmental Earth Sciences , 73 (6), 2699–2715. https://doi.org/10.1007/s12665-014-3602-1 Tejasvi Hora. (2022). Addressing groundwater over-extraction in India: assessments, monitoring methods and interventions. UW Space . Tilahun, T. A. (2024). Groundwater Modeling of the Ogallala Aquifer : Use of Machine Learning for Model Parameterization and Sustainability Assessment. Dissertations and Doctoral Documents from University of Nebraska-Lincoln , 157. Tripathi, R. (2025). Determining Subsurface Bedrock Depth Using Vertical Electrical Sounding : Principles , Applications , And Case Studies . 02 (01), 1–7. Yaman, A., Idris-Nda, A., Goro, A. I., & Ejepu, J. S. (2020). The Geology and Hydrogeology of Parts of Minna Sheet 164 NE. Minna Journal of Geoscience , 4 (2), 96–107. http://repository.futminna.edu.ng:8080/jspui/handle/123456789/5429 Yusuf, M. A., & Abiye, T. A. (2019). Groundwater for Sustainable Development Risks of groundwater pollution in the coastal areas of Lagos , southwestern Nigeria. Groundwater for Sustainable Development , 9 (November 2017), 100222. https://doi.org/10.1016/j.gsd.2019.100222 Additional Declarations No competing interests reported. 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(B), K-curve type for VES 2; (C), QH-curve type for VES 3 ; (D), A-curve type for VES 7; and (E), HK-curve type for VES 10.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7411257/v1/6c48526a953897d76b849077.png"},{"id":90320608,"identity":"0d31aa48-7154-433a-8d71-e936ab0ac02c","added_by":"auto","created_at":"2025-09-01 10:48:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97097,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of aquifer protective capacity across Obbo Payam derived from Total Longitudinal Conductance\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7411257/v1/a0f32b5f1fa751df2bb0d03e.png"},{"id":93761242,"identity":"44a43a8e-d6a9-4989-b16d-cb4db5f9194c","added_by":"auto","created_at":"2025-10-17 09:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1152996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7411257/v1/71d9a1c6-743c-4352-b47f-26dd28df6bf1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of Aquifer Vulnerability Using Geo-Electrical Surveys: Case Study of Obbo Payam, South Sudan\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundwater constitutes approximately 30% of global freshwater resources and serves as a vital source for domestic, agricultural and industrial needs (Shekhar \u0026amp; Pandey, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, its sustainability is increasingly at risk due to over-extraction, contamination and mismanagement (Tejasvi Hora, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Aquifers vary in depth, confinement, and geological composition. Shallow aquifers often composed of unconsolidated alluvial deposits or weathered regolith are common in tropical and semi-arid regions, including parts of Sub-Saharan Africa, South Asia and South America (Gr\u0026ouml;nwall et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These systems are typically recharged by local precipitation but highly susceptible to contamination from anthropogenic sources (Geris et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, deep confined aquifers, such as Nubian Sandstone Aquifers and Ogallala formations are located beneath multiple geological layers offering greater protection (Tilahun, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These systems often contain fossil water and are generally less vulnerable to surface contamination. Nevertheless, shallow aquifers, in tropical and semi-arid climates remain particularly vulnerable due to their rapid recharge rates and shallow depth (Jasechko et al., 2024; Machiwal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This allows infiltration of surface drive contaminants such as agricultural runoff and industrial effluents. As a result, pollutants can infiltrate directly into the groundwater system with minimal attenuation (Denham et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This can accumulate harmful substances such as nitrates, pathogens, heavy metals that compromise water quality and pose risk to public health.\u003c/p\u003e\u003cp\u003eIn sub-Saharan Africa, and notably in South Sudan, groundwater plays an indispensable role. Over 70% of the population relies on groundwater for domestic use due to unreliable surface water and the seasonal nature of rivers (Bank, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Okot et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, the groundwater sustainability in the region is compromised by poor data availability and weak governance structures (Owor et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schonberger \u0026amp; Wijnen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, the human-induced factors such as agricultural runoff, urban expansion, and inadequate sanitation pose significantly threats to groundwater quality (Burri et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yusuf \u0026amp; Abiye, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, assessing aquifer vulnerability is crucial for identifying areas at risk, guiding land use, and implementing measures pollution and safeguard public health.\u003c/p\u003e\u003cp\u003eAquifer Vulnerability is influenced by multiple factors, including the geological, hydrogeological and human anthropogenic factors, which controls contaminants infiltration and groundwater quality (Machiwal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A key component is the protective capacity of subsurface materials, which determines how effectively they filter pollutants. Thus, materials with high longitudinal conductance, often associated with clay layers, generally offer better protection (Ayua et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, areas with fractured or weathered zones lacking clay layers are more vulnerable to contamination.\u003c/p\u003e\u003cp\u003eEffective groundwater management relies on aquifer characterization. This typically involves the integration of diverse methods such as pumping test, hydro-chemical analysis, geostatistical tools (kriging) and geophysical methods (Crosbie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muchingami et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teixeira et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among these, geophysical methods, particularly the Vertical Electrical Sounding (VES) method, has proven particularly valuable across Africa for delineating subsurface layers and assessing aquifer vulnerability in various African countries (e.g, Mengistu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nazih et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method, particularly using Schlumberger configuration, measures apparent resistivity of geological formations, provides insights into the nature and saturation of subsurface layers (Bahammou et al., 2021; Yaman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its advantages due to low cost-effectiveness, portability, reasonable depth making it ideal for remote or data-scarce environments (Tripathi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite these advantages, most applications of VES in Sub-Saharn Africa have focused on groundwater potential mapping, with limited attention to vulnerability classification. For instance, Ibrahim et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Yusuf \u0026amp; Abiye ( 2019)applied VES for aquifer delineation, often supported by borehole hydraulic data. In South Sudan, studies such as Nazario et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have used VES to map groundwater potential but not evaluate vulnerability or protective capacity.\u003c/p\u003e\u003cp\u003eWhile Several studies across East Africa have employed geophysical methods to explore groundwater resources, most have focused on aquifer delineations or yield estimation rather than vulnerability assessment. For instance, Nazario et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted VES surveys in Kapuri, South Sudan, to map groundwater potential but did not classify protective capacity. Similarly, (MacDonald et al.,2012) presented a continental-scale groundwater resource map that lacked local vulnerability assessment tailored to basement terrains. In Uganda, Owar et al. (2022) focused on groundwater recharge and resilience, relying largely on hydrochemical and borehole data. In Ethiopia, and Kenya, geophysical surveys have been used to delineate aquifers (Mengistu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Achieng et al., 2023), but few have applied Dar-Zarrouk parameters such as Total Longitudinal Conductance (TLC) to quantify protective capacity.\u003c/p\u003e\u003cp\u003eThis study aims address this gap by applying VES and TLC analysis to assess aquifer vulnerability in Obbo Payam. By focusing on protective capacity, this research offers a locally relevant framework for vulnerability classification in basement terrains. The approach provides practical insights for borehole siting and groundwater protection, especially in areas with limited hydrogeological data limited data. It also contributes to broader water security and planning efforts in data-scarce areas of Sub Saharan Africa.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cp\u003e\u003cb\u003e2. 1 Study area description\u003c/b\u003e\u003c/p\u003e\u003cp\u003eObbo Payam is located in Magwi County, Eastern Equatoria State of South Sudan, and spans latitudes 3\u0026ordm; 52ʹ 0ʺ to 4\u0026ordm; 8ʹ 30ʺ N, and longitudes 32\u0026ordm; 7ʹ 0ʺ to 32\u0026ordm; 29ʹ 0ʺ E (Fig: 1). The landscape features a mix of hills, valleys, and floodplains, all shaped by tectonic and erosion processes. The Ayi River, along with its tributaries such as the Kimaru stream, serves as the primary perennial watercourse in the area (Okot et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, the climate is a tropical savannah with high humidity. The rainfall averages between 900\u0026ndash;1200 mm per annum. The peaks during the rainy season from July to September serve as the main sources of groundwater recharge. The vegetation in the area includes patches of forest in the valleys and plains, interspersed with savannah grassland and scattered shrub (Bank, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e2.1.1 Hydrological and Geological Setting\u003c/div\u003e\u003cp\u003eThe hydrogeological setup of Obbo Payam is shaped by its complex lithological foundation, tectonic history and surface water interactions. The subsurface water resources are primarily stored in weathered and fractured zones of the basement complex, which have undergone significant alteration due to tectonics and prolonged weathering processes and tectonic activities (Okot et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Obbo Payam is underlain by the Precambrian Basement Complex, a component of the East African Orogen (Mozambique Belt) that extends into northern Uganda, western Kenya, and western Ethiopia. The area is characterized by ancient, highly metamorphosed Precambrian rocks such as gneisses, amphibolite, charnockite, amphibolite, and granite, which are often exposed at or near the surface. These lithology are associated with the Neoproterozoic Pan African Orogeny, controlled fractured aquifers that form key groundwater reservoirs in the area (Abdelsalam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Furthermore, the tectonic influences from the nearby East African Rift System introduce faults and fractures that enhance secondary porosity and groundwater storage potential. Unlike the Nubian Sandstone and Um Ruwaba formations that dominate northern and central South Sudan, Obbo Payam features exposed basement rocks, allowing direct access to fractured aquifers crucial for water supply development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study Design\u003c/h2\u003e\u003cp\u003eThis study employed the VES method using the Schlumberger array to investigate the subsurface resistivity and delineate the potential aquifer zones. The methodological framework consists of four stages: data acquisition, processing, interpretation, and evaluation of geo-electrical parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Data Acquisition and Processing\u003c/h2\u003e\u003cp\u003eA total of fourteen (14) Vertical Electrical Sounding (VES) points were established across the study area using a Schlumberger configuration to investigate subsurface geological and hydrogeological structures. In this method, the current electrodes (AB/2) and potential electrode (MN/2) were systematically expanded to explore greater depth, reaching a maximum AB/2 spread of 200 m (Nugraha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Apparent resistivity measurements were systematically recorded at each location, and survey points were accurately geo-referenced using GPS to enable spatial analysis and mapping (Omale et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the collected field data were processed using IP2WIN software. Which performs one-dimensional (1D) inversion modelling. Apparent resistivity values were iteratively adjusted by modifying layer resistivity and thickness until the modelled curves satisfactorily matched the observed data (Ibrahim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The software generated both resistivity depth profiles and pseudo-cross-sections, providing visual and quantitative interpretations of subsurface stratigraphy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Data Interpretation and Applications\u003c/h2\u003e\u003cp\u003eThe IP2WIN software was used for data interpretation, which involves three key steps. First, depth and resistivity modelling were carried out by extracting resistivity values and corresponding depth from each VES point to delineate subsurface layers and identify aquifer-bearing formations. Second, curve typing and classification grouped the sounding curves into standard types such as A, H, K, Q, QH, and HK based on their geometric patterns (Ibrahim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nugraha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This provides insights into the stratigraphy and hydrogeological relevance of the different zones. Third, results were tabulated, and Dar-Zarouk parameters such as longitudinal conductance (S) and transverse resistance (T) were derived to evaluate aquifer protective capacity and transmissivity (Nugraha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shehu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, both parameters, longitudinal conductance and transverse resistance of Dar-Zarrouk parameters, were selected as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which classifies aquifer protective capacity based on longitudinal conductance. The Dar Zarrouk parameters (Yusuf et al. 2021; Egbai and Iserhien-Emekeme, 2015) are calculated by combining the resistivity and thicknesses of each geo-electric layer. These parameters include longitudinal conductance, S (Ω-1) and transverse resistance, Tr (Ωm2). For a series of n layers with resistivity ρi and thickness hi, the longitudinal unit conductance (S) (Eq.\u0026nbsp;1) and transverse unit resistance (T) (Eq.\u0026nbsp;2) are defined as:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S=\\sum\\:_{i=1}^{n}\\frac{{h}_{i}}{{p}_{i}}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;(1)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T=\\sum\\:_{i=1}^{n}{h}_{i}{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;(2)\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003ehi -= thickness of the i\u003csup\u003eth\u003c/sup\u003e layer (m)\u003c/p\u003e\u003cp\u003epi\u0026thinsp;=\u0026thinsp;resistivity of the i\u003csup\u003eth\u003c/sup\u003e layer (Ω-m)\u003c/p\u003e\u003cp\u003e\u003cem\u003eS\u003c/em\u003e is the longitudinal conductance (Ω-1); \u003cem\u003eTr\u003c/em\u003e is the transverse resistance (Ωm2)\u003c/p\u003e\u003cp\u003eProtective capacity and longitudinal unit conductance are thought to have a proportionate connection. Longitudinal unit conductance, \u003cem\u003eS\u003c/em\u003e, can be employed directly in the protective capacity evaluation of aquifers to represent the restriction of pollutants' percolation into the aquifer (Yusuf \u0026amp; Abiye, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough various indices like DRASTIC and GOD have been widely applied in aquifer vulnerability mapping, this study prioritized the use of Total Longitudinal Conductance (TLC) due to its strong sensitivity to clay content and effectiveness in hydrogeologically data-scarce settings. In contrast to DRASTIC, which requires parameters such as recharge rate, land use, and depth to water table, TLC is derived directly from resistivity data and has been shown to correlate well with protective capacity in crystalline basement terrains (Busato et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olawuyi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This makes the method suitable for Obbo Payam, where detailed hydrogeological and land use datasets are inadequate.\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\u003eClassification of Aquifer Protective Capacity Rate based on longitudinal conductance (adopted from Nyembwe et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal Conductance (Ω\u003csup\u003e\u0026minus;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtective Capacity Rate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery good\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.7\u0026ndash;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.2\u0026ndash;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.1\u0026ndash;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\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=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Potential Biases and Validation Strategies\u003c/h2\u003e\u003cp\u003eDespite the VES method and Dar-Zarrouk parameters being widely applied in groundwater studies, they have inherent limitations. A key issue is reduced sensitivity at greater depths, which can obscure deeper clay layers or fractured zones. The method also assumes horizontally layered, homogeneous subsurface conditions, an oversimplification that may not reflect complex geological settings (Yusuf \u0026amp; Abiye, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, resistivity measurements are vulnerable to external influences such as anthropogenic noise, environmental variability, and electrode placement errors. Since VES measure electrical rather than hydraulic properties, critical factors like porosity and recharge must be referred to indirectly. Although this study recognizes the advantages of the VES method, it also acknowledge its inherent limitation in resolving complex subsurface structures. The technique assumes lateral homogeneity within layers, which may not capture abrupt changes in lithology, structural discontinuities, or heterogeneities common in basement terrains. Moreover, in the absence of boreholes lithology or pumping test data, the protective capacity inferred from longitudinal conductance remains an indirect proxy. To improve reliability, future research should integrate confirmatory datasets such as boreholes los, hydraulic conductivity measurement or hydrochemical sampling to ground-truth resistivity-derive interpretations. Additionally, where drilling is not feasible, proxy validation using nearby boreholes or integrating satellite-based recharge estimates may enhances interpretive confidence. Furthermore, applying 2D or 3D geophysical methods and geostatistical techniques such as kriging could help account for lateral variations and reduce classification uncertainty.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Subsurface Resistivity Interpretation and limitation\u003c/h2\u003e\u003cp\u003eThe Vertical Electrical Sounding (VES) survey conducted in Obbo Payam provides insight into subsurface geological structure. The results show that 79% of the surveyed points exhibit three distinct geo-electrical layers, while the remaining 21% display four-layer configurations. The predominance of three layers suggests a relatively simple geological setting, possibly due to uniform deposition and limited tectonic activity. The four-layer profiles, in contrast, indicates a complex subsurface conditions caused by lithological heterogeneity, faulting, or deep weathering (Adeyeye et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olawuyi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nag \u0026amp; Kundu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such complexity often supports the development of multiple or deeper aquifer systems, potentially enhancing groundwater storage and yield capacity under favourable hydrogeological conditions.\u003c/p\u003e\u003cp\u003eBuilding on the stratigraphic analysis, five distinct geo-electrical curve types were identified: H, QH, A, K, and HK (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The H-type curves, observed at 43% of the stations (VES1, VES4, VES5, VES6, VES8, and VES14). These curves exhibit a resistive-conductive-resistive sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u0026ldquo;A\u0026rdquo;), indicating a weathered or fractured aquifer between resistive layers. Moreover, the A-type curves, comprising 29% of the stations (VES7, VES11, VES12, and VES13), display a gradual increase in resistivity with depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u0026ldquo;D\u0026rdquo;). Such pattern typically associated with lateritic soils overlying crystalline bedrock, and generally low recharge potential. Additionally, the QH-type curves, found at 14% of the stations (VES3, VES9), show low-resistivity zones likely composed of saturated clay-rich materials (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u0026ldquo;C\u0026rdquo;), implying confined aquifers with limited permeability. Less common configuration were the HK-type curve, (7%; VES10), suggests interbedded sand and clay (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u0026ldquo;E\u0026rdquo;). This is a characteristic of fluvial deposit, potentially hosting varied aquifers with heterogeneous characteristics. Lastly, the K-type curve, also found at one station (7%; VES2), consists of a classic clay-sand-clay sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u0026ldquo;B\u0026rdquo;). In such cases, sand layer act as aquifer confined by less permeable clay (Ibrahim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Olawuyi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These curve types reflect variation in subsurface composition that influence the distribution of aquifers.\u003c/p\u003e\u003cp\u003eFurther analysis of resistivity values revealed notable spatial variability in subsurface properties and aquifer potential. Very low first-layer resistivity (\u0026lt;\u0026thinsp;1 \u0026#120686;m) at VES5, VES6, VES7, VES12, and VES13, suggesting water-saturated or topsoil. Although these layers may offer protection from contamination, they typically exhibit low permeability and limited aquifer yields. Conversely, extremely high resistivity values in deeper layers at VES3 (22623 \u0026#120686;m) and VES9 (27022 \u0026#120686;m) suggest to intact crystalline basement rock. This offers minimal groundwater storage potential due to its low porosity and hence low groundwater storage capacity. Additionally, moderate resistivity observed at VES1 (582 \u0026#120686;m) and VES4 (251 \u0026#120686;m) indicates weathered or fractured basement zones, generally offer better groundwater storage due to enhanced secondary porosity. These interpretations align with findings from similar studies conducted in crystalline basement terrain in Nigeria and Ethiopia, where moderate resistivity zones correlate with higher groundwater yields (Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ibrahim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Olawuyi, 202; mengistu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Ibrahim et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that moderate resistivity in north-central Nigeria had greater groundwater potentials. Similarly, Nyembwe et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed strong alignment between TLC-based protective zones and borehole yields in Gwagwalada, Nigeria. These comparison highlights both the utility and limitations of resistivity-based interpretations in basement environment, particularly in the absence of direct hydrogeological measurement such as boreholes log or pumping test results. However, While similar TLC-based methods have been applied in Nigeria and Ethiopia, the Obbo Payam setting present unique hydrogeological conditions. Unlike the relatively more studied terrains in those regions, Obbo lies within a less mapped crystalline basement with limited data availability. The study area may also experience different clay mineral distributions and weathering profiles, affecting both resistivity and protective capacity. Furthermore, the combinations of tectonic stability and limited infrastructure development increase groundwater reliance. These factors highlights the need for site-specific assessments in South Sudan.\u003c/p\u003e\u003cp\u003eThe classification of aquifer zones based on curve types and resistivity depth profiles provides a practical framework for guiding groundwater exploration. For instance, zones with H and QH-type curves can be prioritized for drilling due to their potential aquifer characteristics. However, sites with A-type or K-type curves require caution due to limited recharge capacity. Furthermore, the variation in depth to basement and overburden thickness across the area influences groundwater potential. The thickest weathered profiles were observed at VES3 (46 m) and VES9 (20.4 m), suggesting substantial groundwater accumulation potential in these locations. Conversely, shallow overburden thickness of less than 10 meters were recorded at VES2, VES6, VES7, VES10, VES12, and VES13, which display shallow overburden (\u0026lt;\u0026thinsp;10 m), may support only seasonal or hand-dug wells. The reliability of the inversion models was evaluated using the Root Mean Square (RMS) error values, which range from 0.664% (VES2) and 2.94% (VES14). All values fall within acceptable limits (\u0026lt;\u0026thinsp;5%), indicating high confidence in the inversion variability. However, further evaluation of layer statistics reveals substantial spatial variability. In the first layer, the mean resistivity value is 359.86 Ωm, with a high standard deviation of 732.86 Ωm, reflecting a diverse mix of conductive and resistive materials. This heterogeneity can undermine the precision of vulnerability classification by skewing interpretations. In the second layer, a lower mean resistivity of 164.42 Ωm, with a standard deviation of 398.11 Ω. m, further emphasis the present of variable subsurface conditions. These results suggest that even similar average resistivity may represent drastically different aquifer vulnerability levels depending on localized lithology. This spatial variability introduces interpretative challenges. For instance, areas with extremely high resistivity may correspond to dry, resistive layers like gravel or sand that allow rapid contaminants infiltration despite being interpreted as \u0026ldquo;protective\u0026rdquo;. Conversely, conductive zones could be misinterpreted as thick protective clay layers, even when they are thin and ineffective. These interpretative limitations reflects the findings of Busato et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Ibrahim et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight the need for validation methods to enhance the accuracy of vulnerability assessment. Therefore, correlating resistivity-derived classification with ground truth data, such as borehole logs or water quality measurements, can provide critical validation and ensure the reliability of the results. Additionally, incorporating geostatistical tools such as kriging (Nugraha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and complementary geophysical techniques, such as Ground Penetrating Radar (GPR), could further refine subsurface characterization. Such an integrative approach thereby reducing classification uncertainty and enhancing the reliability of vulnerability assessments, particularly in a data-scarce environment like Obbo Payam, including unsampled locations.\u003c/p\u003e\u003cp\u003eDespite demonstrating the utility of VES and TLC in delineating aquifer vulnerability, this study acknowledges several limitations. Most notably, the lack of borehole lithology, pumping test data, and hydrochemical analysis prevents direct validation of the interpreted resistivity layers and protective capacity ratings. As a result, the correspondence between calculated TLC values and actual clay content or aquifer yields remains inferred rather than confirmed. Additionally, the VES method, being a 1D sounding technique, assumes horizontally layered and laterally homogeneous subsurface conditions, which may not capture local heterogeneities, fault zones, or complex weathered profiles typical of basement terrains. Additionally, resistivity data are sensitive to near-surface conditions, electrode contact quality, and anthropogenic noise, which may introduce uncertainty particularly for extreme TLC values like that observed at VES7. Future work should incorporate borehole logging, and water quality sampling as well as land use and recharge data to support multi-criteria evaluation. Thereby, establishing a more robust framework for sustainable groundwater management in Obbo Payam and beyond.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spatial Analysis of Aquifer Protective Capacity based on Total Longitudinal Conductance\u003c/h2\u003e\u003cp\u003eThe protective capacity of an overburden plays a key role in limiting contaminations of the underlying aquifer. It controls how vulnerable the aquifer is to pollutants such as industrial waste, agricultural runoff, and chemical spills (Olatinsu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this study, Total Longitudinal Conductance (TLC) was used to assess natural protective capacity analysis. The TLC values were derived from Vertical Electrical Sounding (VES) data collected in Obbo Payam (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Through resistivity data, reflects the cumulative ability of overburden materials specifically clay rich layers to attenuate surface contaminants before they reach the aquifer (Fig.\u0026nbsp;4). It serves as a useful proxy for evaluating the natural protection in data-scarce environments.\u003c/p\u003e\u003cp\u003eThe results show 14% of sites (VES5 and VES7) \u0026ldquo;exhibit excellent\u0026rdquo; protection, likely due to thick, highly conductive clay layers. Another 29% of sites (VES1, VES6, VES12, and VES13) fall within the \u0026ldquo;very good\u0026rdquo; to \u0026ldquo;good\u0026rdquo; protection category, indicating moderate clay content that can reasonably limit pollutant infiltration. By contrast, 21% of the sites (VES8, VES9, and VES14) are classified as \u0026ldquo;moderately\u0026rdquo; protected, with thinner or less conductive clay horizons offering only partial attenuation. The remaining 36% of the sites (VES2, VES3, VES4, VES10, and VES11) show \u0026ldquo;poor\u0026rdquo; to \u0026ldquo;weak\u0026rdquo; protections, reflecting minimal or absent clay barriers and thus greater vulnerability to surface contamination. These results highlights strong spatial heterogeneity in the protective capacity of the overburden. Tis variations supports the need for site-specific groundwater protections measures. The most suitable zones for borehole development are those with high TLC values particularly VES5 and VES7. Moreover, the sites rated \u0026ldquo;good\u0026rdquo; and \u0026ldquo;very good\u0026rdquo; may also be suitable, though land use regulations should be applied to minimize pollution risk. Additionally, areas with moderated ratings requires cautions. Whereas poorly protected sites should be avoided unless popper engineering measures (e.g. sealed casing or sanitary protection) are implemented. These classification and associated Dar-Zarrouk values are summarized in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe spatial pattern in Obbo Payam are consistent with those found in other Precambrian Basement regions. Studies in Niger and Ethiopia also reported that TLC values below 1 Ω\u003csup\u003e\u0026minus;1\u003c/sup\u003e typically indicates zones with little or no clay protection (Olatinsu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yusuf \u0026amp; Abiye, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the TLC values at VES7 (387.6 Ω\u003csup\u003e\u0026minus;1\u003c/sup\u003e), is exceptionally high and exceed typical values found in crystalline terrain in Sub-Saharan Africa, which usually range between below 10\u0026ndash;50 Ω\u003csup\u003e\u0026minus;1\u003c/sup\u003e (Ibrahim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such an extreme value may indicate the present of unusually thick or highly conductive clay layer that strongly attenuates surface contaminants. However, it could also reflect limitations in data inversion, local electrode coupling effects, or lateral heterogeneity not captured by 1D VES model. Given the absence of borehole lithology or hydrochemical validation data, this result should be interpreted cautiously. Ground-truth through drilling, soil sampling, or additional geophysical surveys (e.g., electromagnetic profiling) is recommended to confirm whether this TLC truly corresponds to a protective clay layer or arises from measurement or modeling artifacts (Fig.\u0026nbsp;4). Comparing studies in Lokoja and Gwagwalada (Ayua et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nyembwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated that zones with TLC above 10 Ω consistently coincided with a thicker clay layer and higher natural protection. In these areas, engineered protections such as sealed well casing, sanitary grouting, and designated exclusion zones may be necessary to mitigate contamination. Although no borehole lithology was not available for direct correlation, the TLC and resistivity trends match expected groundwater- bearing conditions. For instance, high TLC values at VES5 and VES7 likely indicate saturated weathered zones. These areas may also align with elevated recharge zones as suggested by recent satellite-based studies (Okot et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Further research should explore this connection to validate aquifer production and refine vulnerability zoning.\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\u003eDar Zarrouk parameters and Protective capacity rating of VESs in Obbo\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=\"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=\"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\u003eNo of VES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLongitude E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLatitude N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTLC (Ω)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVery Good\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWeak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e387.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e32.42597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.04431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.42556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.42537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.42518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWeak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.42458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVery Good\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVES14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.42474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote: Total transverse resistance (TTR) (Ω); Total longitudinal conductance (TLC) (Ω); Protective Capacity rating (PCR)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study applied geo-electrical surveys and Total Longitudinal Conductance (TLC) to assess aquifer vulnerability in Obbo Payam, South Sudan. The analysis identified significant variability in aquifer protective, with 14% of the site showing excellent protection, 29% are classified as good to very good, 21% as moderate and 36% as poor to weak. Zones with high TLC values, such as VES5 and VES7, were interpreted as better protected due to the presence of thick clay-rich layer, whereas lower TLC values are more susceptible to surface contamination. The research demonstrates that TLC-based assessment alone can provide actionable groundwater vulnerability zoning in crystalline basement terrains. However, the absence of borehole lithology and hydrochemical data limits direct validation of these interpretations. Future work should incorporate borehole data, pumping tests, and water quality analysis to enhance model reliability. By introducing a locally adopted vulnerability assessment framework, this study offers a practical tool to guide sustainable groundwater development and protection strategies in Obbo Payam and similar areas, under-resourced across Sub-Saharan Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any individual person\u0026rsquo;s data in any form (including images, videos, or personal identifiers) requiring consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeo-electrical resistivity dataset and models\u0026rsquo; outputs used in the aquifer vulnerability analysis are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the African Water Resources Mobility Network (AWaRMN) for supporting this research through the Intra-African Academic Mobility Programme No. 2019- 1973/004-001, which was funded by the European Union.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e,\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Data collection and analysis were performed by [N.O], [M.A]. The first draft of the manuscript was written by [N.O]. [A.A.N], [I.N] and [G.N] wrote and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelsalam, M. G., Li\u0026eacute;geois, J. P., \u0026amp; Stern, R. J. (2002). The Saharan Metacraton. \u003cem\u003eJournal of African Earth Sciences\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(3\u0026ndash;4), 119\u0026ndash;136. https://doi.org/10.1016/S0899-5362(02)00013-1\u003c/li\u003e\n\u003cli\u003eAdeyeye, O. A., Ikpokonte, E. A., \u0026amp; Arabi, S. A. (2019). 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Groundwater for Sustainable Development Risks of groundwater pollution in the coastal areas of Lagos , southwestern Nigeria. \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(November 2017), 100222. https://doi.org/10.1016/j.gsd.2019.100222\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Longitudinal Conductance, Geo-electrical survey, Resistivity, Protective Capacity Rate, Groundwater Protection","lastPublishedDoi":"10.21203/rs.3.rs-7411257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7411257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater resources in data-scarce regions of Sub-Saharan Africa face increasing pressure from overexploitation and contamination. Assessing aquifer vulnerability is therefore critical for sustainable management. This study evaluates aquifer protective capacity of overburden materials in Obbo Payam, South Sudan using Vertical Electrical Sounding (VES) and Total Longitudinal Conductance (TLC) as proxy indicator. A total of 14 VES points were analyzed using the Schlumberger configuration. Interpretation was conducted using IP2Win software, and aquifer protective capacity was inferred from and Total Longitudinal Conductance (TLC) values. The results categorized into five curve types (H, QH, A, K, and HK) with three layers models dominating (79%) and four layer models (21%). The results of TLC reveal significant spatial variability: 14% of the area exhibit excellent protection, 29% are classified as very good to good, 21% as moderate and 36% as weak to poor. The zones with high TLC values correspond to thicker clay-rich layers that enhance natural infiltration potential, whereas areas with low TLC values are more vulnerable to contamination. This study demonstrates that combining VES TLC analysis provides a cost-effective and practical approach for delineating aquifer vulnerability in regions lacking detailed hydrogeological data. The findings support the prioritization of groundwater protection measures and contribute to broader goals such as Sustainable Development Goal 6 on clean water and sanitation.\u003c/p\u003e","manuscriptTitle":"Evaluation of Aquifer Vulnerability Using Geo-Electrical Surveys: Case Study of Obbo Payam, South Sudan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:40:33","doi":"10.21203/rs.3.rs-7411257/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f7995787-4b9e-4521-bc08-ed204b11c913","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T09:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:40:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7411257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7411257","identity":"rs-7411257","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}