Groundwater exploration in basement terrain can be somewhat challenging. Aquifer parameters like hydraulic conductivity and transmissivity can help in predicting groundwater potential zones in basement terrains. The vertical electrical sounding investigation that involved the Schlumberger configuration was employed to map the subsurface layers within the crystalline basement of the Obudu Complex, southeastern Nigeria. Secondary electrical resistivity data (Dar Zarrouk parameters) and a few pumping test-derived hydraulic parameters (i.e., transmissivity and hydraulic conductivity) were employed to develop empirical models. These models were used to predict hydraulic parameters at locations where only geoelectrical parameters (i.e., aquifer layer thickness and electrical resistivity) exist. Results showed that the northeastern part of the study area and areas located within zones of major faults displayed relatively higher values of hydraulic conductivity and transmissivity. The study area was classified into good, moderate, and poor groundwater potential aquifer zones. This integrated approach can be adopted in other areas with similar geology, where pumping test information is scarce or limited, as an alternative means of predicting aquifer properties and delineating groundwater potential zones for sustainable development and management of groundwater resources.

  • Electrical resistivity technique was used to estimate aquifer parameters in the Obudu basement.

  • Empirical models derived were used to deduce these parameters elsewhere within the basement.

  • Results show a good fit where both pumping test-derived and geoelectrical predicted values exist.

  • It provided reliable site-specific and cost-effective alternatives to pumping test-derived parameters.

The residents of the northeastern parts of Cross River State, Nigeria, domiciled within the basement terrain of the Obudu plateau, have over the years suffered from water scarcity problems. This critical situation is due to the nature of groundwater occurrence in basement terrain (Sarwade et al. 2007; Puranik 2009; Mondal 2021; Hassan et al. 2022), the low specific capacity of boreholes, consistent variations and discontinuities in aquifer properties (Limaye 2010), and other complexities such as irregular subsurface structural characteristics) associated with the exploration and quantitative assessment of the resources (Darko & Krásny 2003). Several boreholes within the area under investigation have failed due to limited knowledge and understanding of the structural, hydrogeological, hydrological, and geomorphological conditions of the basement environment by some borehole contractors (Mondal et al. 2008; Guevara-Mansilla et al. 2020; Ige et al. 2021). Groundwater transmission in fractured rocks depends mainly on the interconnectivity and extent of fractures, which are products of local and regional-scale tectonic activities, weathering, and lithology. Recent studies have shown that hard rock lithologies have specific individual hydrodynamic properties (Chandra et al. 2010; Courtois et al. 2010; Dewandel et al. 2011; Mondal et al. 2016; Ebong et al. 2021a; Lachassagne et al. 2021). For instance, the hydrodynamic properties of the saprolite layer have been reported to be capacitive, whereas the stratiform fissured layer displays transmissive properties (Lachassagne et al. 2021). In addition, unweathered granitic rocks possess low primary porosity and low hydraulic conductivity in contrast to metamorphic rocks that have lost their original hydrodynamic characteristics through metamorphic processes and have acquired secondary porosity that can facilitate groundwater transmission (Achtziger-Zupancik et al. 2017).

In this study, we propose to estimate aquifer parameters to facilitate groundwater exploration decisions and sustainable groundwater resources management within the northeastern part of Cross River State that constitutes the study area. These parameters include porosity, hydraulic conductivity, and transmissivity. Some of these parameters are derived conventionally from pumping tests, tracer test experiments, and grain size analysis from drilled boreholes. Although these methods are relatively expensive (i.e., the cost of borehole drilling and pumping tests), they have been used over the years and are relied upon to produce dependable results. Besides, the hydrogeologic conditions of interest for groundwater to occur in basement terrain, such as faults and fractures are to a certain extent localized (i.e., laterally limited) within the subsurface. Thus, aquifer properties derived from the pumping test of a particular borehole can result in inaccurate characterization when used to extrapolate or predict aquifer properties some few meters to a kilometer away from the borehole point (Sanderson & Zhang 1999). However, with few pumping test-derived hydraulic parameters and adequately constrained electrical resistivity sounding at these pumping test locations, empirical models can be developed and used to predict these parameters in the vicinity of the borehole and elsewhere within the study area where geoelectrical measurement information exists. The vertical electrical sounding (VES) technique when combined with hydrogeological measurements can provide a reliable alternative approach for the estimation of aquifer parameters. This approach has been utilized in resolving a variety of hydrological, hydrogeological, environmental and engineering problems (Obianwu et al. 2015; Guevara et al. 2017; Akpan et al. 2018; George et al. 2018; Arétouyap et al. 2019; Ndubueze et al. 2019; Asfahani & Ahmad 2020; Ebong et al. 2021b, 2023; Haq et al. 2022; Karthik et al. 2022), vadose zone infiltration rates, groundwater protective capacity and vulnerability assessments (Mhamdi et al. 2015; Akpan et al. 2016; Hussain et al. 2017), and geohydraulic parameter estimation (Ebong et al. 2014; Vogelgesang et al. 2020; Mahmud et al. 2022). More so, Asfahani & Al-Fares (2021) provided reliable estimates of hydraulic parameters within the Quaternary basaltic aquifer in the Deir Al-Adas area, southern Syria. The less-expensive geoelectrical resistivity method constitutes a rapid and versatile procedure that does not disturb the environment in any significant manner and can provide reliable results when adequately constrained (Akpan et al. 2015; Ebong et al. 2021a). This cost-effective technique is frequently used to resolve groundwater-related problems where good electrical resistivity contrasts exist between the saturated and unsaturated layers. Also, it is suitable for environmental and hydrogeological surveys in both sedimentary basins and hard rock terrains (Aleke et al. 2018; Mahmud et al. 2022). In spite of these advantages, geophysical data are fraught with errors, albeit the data acquisition may have been performed under favorable conditions. The errors are introduced into the results due to the unresolved ambiguity problem associated with converting geophysical data into representative geological models (Ebong 2012). The application of lithologic and other information derived from nearby boreholes as constraints during the VES forward modeling process remarkably minimize the ambiguity problems associated with one–dimensional (1D) modeling (Ebong et al. 2017; Bahammou et al. 2021).

This study aims to improve water security by effectively locating sustainable groundwater target zones and developing an empirical-based technique for estimating aquifer parameters in fractured rocks. The objective of the study is to generate aquifer empirical models based on modeled electrical resistivity data and few available pumping test information and apply the empirical models to estimate aquifer hydraulic properties (such as transmissivity, hydraulic conductivity, and fractional porosity) of fractured basement aquifer in other locations within the study area.

Location

The study area falls within the Obudu Basement Complex (OBC) area in the northeastern part of Cross River State, Nigeria. It lies between Latitudes 6° 15′ 0″ and 6° 45′ 0′N and Longitudes 8° 56′ 0″ and 9° 30′ 0″ E; and occupies ∼1,465 km2 area (Ebong et al. 2021a) within southeastern Nigeria (Figure 1). It falls within the semi-temperate climatic region of West Africa, characterized by two dominant seasons, i.e., rainy and dry seasons. While the rainy season starts in April and gradually subsides in September, the dry season begins in October and terminates in March (Ebong et al. 2021a). During the dry season, the temperature within the area ranges from 26 to 32 °C and can drop to ∼4 °C within the crest of plateau (∼1,716 m above m.s.l); and ∼10 °C in the adjourning area during the rainy season (Ebong et al. 2021a). Relative humidity varies from 40 to 80% (Edet & Okereke 2005). Annual rainfall ranges from 2,000 to 2,500 mm within adjourning areas and can rise to ∼4,200 mm at the peak of the Plateau. The study area is composed of interlocking hill networks and elevation increases from about 200 m to approximately 1,800 m above m.s.l at the crest of the plateau (Figure 2).
Figure 1

Geologic map of study area showing VES points (adapted from Ebong et al. 2021a).

Figure 1

Geologic map of study area showing VES points (adapted from Ebong et al. 2021a).

Close modal
Figure 2

Digital elevation model of the study area.

Figure 2

Digital elevation model of the study area.

Close modal

Geology and hydrogeology

The dominant rocks within the OBC are grouped into the Migmatitic Gneiss Complex and the Pan-African Older Granites (Ebong et al. 2021a). These rocks include migmatites, gneisses, undifferentiated granites, and schists. Details of the nature and characteristics of these rocks are well documented (Ekwueme 1990, 2003; Ukwang et al. 2003). The gneissic rocks have been intruded by pegmatite in several locations (Ekwueme 1994). At other locations, the gneisses have been folded and refolded with enclosed xenoliths while in some locations where schists are dominant, intense shearing, and fracturing due to thermal contacts have been reported (Edem et al. 2016). Several weakly foliated boulders of garnet gneisses, charnockites, dolerite dykes on banded amphibolites having sharp contacts due to brittle deformation and massive quartz veins with multiple fractures have been reported (Oden et al. 2012; Ebong et al. 2021a).

Hydrogeologically, the study area depends on weathering and secondary tectonic activities for groundwater transmission and storage. Ebong et al. (2021a) identified potential aquifer types namely, the regolith aquifer type and the dolerite-intrusion-induced fracture-aquifer type within the OBC. Hence, the weathered and fractured zones constitute the main aquifer zones within the area and were mapped in this study. Since the aquifer zones are limited to the weathered and fractured column, which occur at relatively very shallow depths, the inhabitants of the area tend to depend on hand-dug wells and very few deep boreholes in the area which are often not functional, especially during the dry season. Edet & Okereke (2005) reported that the weathered overburden regolith (∼38 m thick on average) and fractured basement rock aquifers can provide average permeability and transmissivity of ∼7.5 × 10−1 m/day and 3.8 × 10−1 m2/day, respectively. Groundwater-specific capacity and yield estimated from lineament density within the regolith wells were in the range of 40–270 m3/day/m and 700–4,050 m3/day, respectively (Edet & Okereke 2005). Static water levels in the area vary between 5 and 15 m and depths of hand-dug wells range from a few meters to ∼15 m while tube wells may extend to ∼50 m (Okereke et al. 1995).

Materials and methods

The VES technique that involves measuring the difference in potential between the potential electrode pair arising from current injected into the ground through the current electrode pair was employed in this investigation (Bhattacharya & Patra 1968; Binley 2015; Ahmed et al. 2022; Chibuike et al. 2023). The Integrated Geo Instruments and Services (IGIS) signal enhancement resistivity meter (model SSR-MP-ATS) was utilized in measuring the earth resistance within the shallow subsurface. Twenty-one electric soundings were carried out using the Schlumberger array with current electrode separation (AB) that ranged from 2 to 500 m and sometimes up to 600 m in areas where accessibility is unlimited. Consequently, the potential electrode pair (MN) separation ranged between 0.5 and 20 m. The quality of the VES data was generally good, but in dry grounds where the contact resistance was high, water was used to moisten the ground around the electrodes, to reduce the contact resistance and enhance the current input in such areas (Uhlemann et al. 2018; Ebong et al. 2021a). Water samples from boreholes and hand-dug wells closest to the VES points were collected and the electrical conductivity (EC) was measured using a hand-held conductivity meter.

Data analysis and interpretation

The apparent electrical resistivity () values were plotted against their corresponding half-current electrode spacing (AB/2) on a bi-log graph paper. The resulting VES curves were interpreted in terms of layer electrical resistivity and actual depths and/or thicknesses of rocks through computer-assisted forward modeling. The electrical resistivity and depth values of each geoelectric layer used as starting model during the forward modeling phase were constrained using lithologic data (i.e., layer electrical resistivities and thicknesses) from available boreholes closest to the VES points. The geologic constraints were meant to reduce the ambiguity problem associated with the non-uniqueness associated with VES data interpretation (LaBrecque et al. 1996). The observed apparent electrical resistivity values for each VES point were compared during the iteration process with the theoretically generated values until good fits were realized after a certain number of iterations with minimum root mean square errors of <2.5% (Inman 1975). The 1D geoelectric models were produced using the RESIST code (Vander Velpen 1988; Vander Velpen & Sporry 1993). Figure 3 shows samples of the 1D electrical resistivity models and their correlation with lithologic logs. The interpreted output provides geoelectric layer parameters (i.e., electrical resistivity and thickness) that were used in estimating hydrogeological parameters like transmissivity and hydraulic conductivity.
Figure 3

Samples of VES curves and 1D electrical resistivity models correlated with some lithologic logs from drilled boreholes. (1) The highly weathered rocks (top layer) constitute predominantly reddish at top from 0.2 to 2.0 m composed of gravely-clays and grades into reddish-yellow sandy-clay (regolith) with lots of quartz grains. (2) The moderate/slightly weathered layer constitutes the laminated saprolite layer composed of brownish coarse sand clastic texture with preserved fissures from orthogneissic granites and about ∼10 m thick. (3) The fractured basement constitutes fissured granite gneisses with numerous fragments of porphyritic granite clastics. (4) The fresh basement constitutes unweathered granite gneisses, migmatites, and porphyritic granites. The apparent electrical resistivities were interpreted based on information from Telford et al. (1990); Reynolds (2011) and Ebong et al. (2021a).

Figure 3

Samples of VES curves and 1D electrical resistivity models correlated with some lithologic logs from drilled boreholes. (1) The highly weathered rocks (top layer) constitute predominantly reddish at top from 0.2 to 2.0 m composed of gravely-clays and grades into reddish-yellow sandy-clay (regolith) with lots of quartz grains. (2) The moderate/slightly weathered layer constitutes the laminated saprolite layer composed of brownish coarse sand clastic texture with preserved fissures from orthogneissic granites and about ∼10 m thick. (3) The fractured basement constitutes fissured granite gneisses with numerous fragments of porphyritic granite clastics. (4) The fresh basement constitutes unweathered granite gneisses, migmatites, and porphyritic granites. The apparent electrical resistivities were interpreted based on information from Telford et al. (1990); Reynolds (2011) and Ebong et al. (2021a).

Close modal
Different combinations of layer electrical resistivity (ρ) and thickness (h) have been applied in hydrogeological investigations. Maillet (1947) referred to these combinations as Dar Zarrouk (D-Z) parameters. These D-Z parameters consist of the longitudinal conductance (S) that relates to electric conductance parallel to the face of a unit cross-sectional area and the transverse resistance (TR) which corresponds to the resistance normal to the face for a unit cross-sectional area (Maillet 1947; Batte et al. 2010; Mahmud et al. 2022). According to Gowd (2004), S can be used to adequately assess the properties of a thin conducting layer and is quantitatively expressed as
(1)
where h and are the layer electrical resistivity and thickness, and i = 1, 2, 3…Nth layer. Such thin and highly conductive structures in hard rock environments are usually fractures/faulted rock columns and can store and transmit water. Such locations within the hard rock terrain have been reported to have high groundwater potential (Naidu et al. 2021). TR, the product of h and ρ (Niwas & Singhal 1981; Okonkwo & Ugwu 2015) is represented by
(2)
where h and are the layer electrical resistivity and thickness, and i = 1, 2, 3…Nth layer. The average longitudinal resistivity () (Equation (3)) and average transverse resistivity () (Equation (4)) are other geoelectrical parameters that can be derived from the D-Z parameters (Maillet 1947) and are defined as;
(3)
(4)
The variation expressed by these parameters is a measure of the anisotropic nature of the environment. Considering the resistivity across the bedding plane, i.e., transverse resistivity () and that parallel to the bedding (Maillet 1947; Mazáč et al. 1985), i.e., the longitudinal resistivity (), the dimensionless electrical anisotropy () is given as
(5)

In anisotropic geological units, is >1 since electrical resistivity is greatest in the transverse direction (Yeboah-Forson & Whitman 2014). These parameters are used to characterize groundwater potential in hard rock environments where groundwater availability depends on weathered and fractured conduits.

The formation factor (F) is a fundamental parameter when considering the electrical properties of rocks. It provides a measure of the influence that the pore structure offers on the flow and/or resistance of fluid. Studies have shown that changes in F are occasioned by temperature, lithological characteristics, porosity (ϕ), extent of saturation and pore fluid resistivity and can affect the electrical resistivity response of the formation (Ziarani & Aguilera 2012; Ebong et al. 2014). Archie (1942) empirical relationship of F for a saturated rock is given by
(6)
where a represents the tortuosity factor and m is the cementation exponent. However, in fractured rock aquifers, the relationship between a, and that describes the nature of the saturated formation is a complicated phenomenon, since rocks are not homogeneously fractured equally in all directions. Consequently, it becomes difficult to effectively predict or determine these parameters from well-cuttings. Core analysis that can serve as an alternative approach is expensive to perform. However, Equation (7) relates F, the electrical resistivity of bulk saturated geologic formation (), and electrical resistivity of pore water .
(7)
This relation provides a rather simplistic but relevant way of determining F since the EC of borehole water can be measured using a hand-held conductivity meter in micro-mho per centimeter (μmho/cm). The inverse of conductivity is resistivity; hence, the measured borehole water EC can be converted to electrical resistivity in ohm-meter (Ωm), using Equation (8). Since can be derived from geoelectrical resistivity measurements and from the water wells, F of saturated rock formation can be estimated using the following equation.
(8)

One of the significance of the formation factor is that it can be employed in the estimation of hydraulic conductivity of the saturated medium (Kelly 1977). Hydraulic conductivity is the capacity of an aquifer to transmit water through fractures and pore spaces under the influence of hydraulic gradient (Lobo-Ferreira et al. 2005). It determines the ability of a geologic formation to transmit a unit volume of groundwater in unit time at the existing viscosity through a unit cross-sectional area, measured perpendicularly to the flow direction, under a hydraulic gradient of unit change in the head through a unit flow length (Lohman 1979; Maliva & Missimer 2012). In other words, it is a coefficient of permeability that measures the ease with which water flows through a medium and depends on the properties of the medium, such as pore fluid density and viscosity, and acceleration of gravity (Vukovic & Soro 1992). In fractured basement aquifers, hydraulic conductivity may vary from 0 to 1,000 m/day (Kruseman & de Ridder 1990; Gnanachandrasamy et al. 2019; Saravanan et al. 2019) and depends on the intrinsic permeability, fracture width, density and interconnectivity of fractures, and hydraulic gradient. The Kozeny–Carman method is widely used to derive hydraulic conductivity but falls short in heterogeneous strata (Zhu et al. 2016).

In order to adequately characterize the Obudu fractured basement aquifer, we considered the relationships between pumping test transmissivity and the measured and computed secondary aquifer parameters, i.e., , , TR, and S (Figure 4). The correlation revealed a strong positive correlation between T vs S (0.9937) and vs TR (0.9849), a strong negative correlation between T vs TR (−0.9606), TR vs S (−0.9234), T vs (−0.8978), and S and (−0.8429). A medium positive correlation was observed between vs (0.6358) while poor positive correlation was observed between TR and (0.4924). Also, a poor negative correlation was observed between T vs (−0.2309) and vs S (−0.1205). Hence, the parameters with the strongest positive were used to establish the empirical relationship, i.e., T and S derived from VES (Equation (9)). The second empirical related k and F derived from and (Equation (10)).
(9)
(10)
where T is the pumping test-derived transmissivity, i.e., the product of hydraulic conductivity and aquifer thickness (Younger 2007) expressed in m2/day, and k is pumping test-derived hydraulic conductivity expressed in m/s. Equations (9) and (10) were used to determine transmissivity and hydraulic conductivity for all VES locations where there are no boreholes or the aquifer hydraulic parameters are unknown. However, the apparent limitation of the empirical equations lies in the few pumping test information available within the area. In areas where more pumping test-derived information (i.e., transmissivity and hydraulic conductivity) are available, they can be included in generating the models to provide better estimates of these parameters.
(11)
Figure 4

Pearson correlation matrix showing the relationship between the aquifer parameters.

Figure 4

Pearson correlation matrix showing the relationship between the aquifer parameters.

Close modal

Based on the strong statistical significance of these models (i.e., the strong relationship between the variables), they can be applied at any location within the Obudu area and environs where geoelectrical data exist, to directly evaluate the transmissivity and hydraulic conductivity of the aquifer to enhance the selection of appropriate sites that can provide optimum groundwater yield to boreholes. The application of the models can be summarized in the following steps;

  • Step 1: Acquire geoelectrical data for the proposed survey area

  • Step 2: Compute the longitudinal conductance (S) of the formation

  • Step 3: Determine the transmissivity of the aquifer formation by substituting the computed longitudinal conductance value into Equation (9).

  • Step 4: Determine the hydraulic conductivity of the aquifer formation using Equation (11) from known saturated aquifer thickness.

However, if the formation factor is estimated based on Equations (7) and (8), then Equations (10) and (11) can be used to evaluate the hydraulic conductivity and transmissivity, respectively.

The fractured basement aquifer fractional porosity () was determined by combining Equations (6) and (7)
(12)
where a and m are equivalent to 1.4 and 1.58, respectively, for rocks with <4% porosity including igneous and metamorphosed sediments (Keller 1987). Porosity is a measure of the capacity by which a saturated thickness can store and transmit water. This procedure that involves estimating transmissivity from apparent formation factor eliminates the effect of aquifer water resistivity changes while utilizing the information provided by these changes in its estimation (K'Orowe et al. 2011; Ebong et al. 2014).
The electrical resistivity models provided the geoelectric layer parameters and h values that were used in evaluating the D-Z parameters (Table 1). The D-Z parameters i.e., TR and longitudinal conductance range from 3,266.64 to 64,338.35 Ωm2 and 0.04 to 0.85 Ω−1, respectively. Figure 6(a) and 6(b), reveal the spatial distribution of both TR and longitudinal conductance values. The values ranged from 54.33 to 587.73 Ωm while varied between 91.84 and 1,238.50 Ωm (Table 2). The electrical anisotropy that describes the variation of electrical resistivity of subsurface rocks and formations laterally and vertically (Youssef 2020) ranged between 1.0 and 2.0 and was observed to have been influenced by geologic and topographic conditions. It describes the directional dependence of EC or its inverse electrical resistivity of subsurface rocks and formations. The farther away the value of the coefficient of the electrical anisotropy of a medium is from unity, the greater the heterogeneity of the medium. Table 3 presents the results of aquifer parameters estimated from hydrogeologic measurements and 1D electrical resistivity models, and the summary of the statistics. Empirical relationships between the pumping test-derived parameters available in three locations where VES were performed, were used to develop the two site-specific models. These models were used to predict transmissivity and hydraulic conductivity at other unknown locations where VES data were acquired. The standard deviation statistic indicator was used to measure the reliability of the hydrogeologic parameters. It shows that the estimated parameters are clustered closely around their respective mean values (i.e., low standard deviations), hence, indicating more reliability of the estimates (Table 3). The empirical models show a good correlation with R2 of 0.99 and 0.87 for the transmissivity and hydraulic conductivity models, respectively (Figure 5(a) and 5(b)). The aquifer electrical resistivity value was compared graphically with aquifer water resistivity measured from existing boreholes adjacent to the VES locations to establish an empirical model for hard rock aquifers (Figure 5(c)). The correlation coefficient revealed a strong relationship between both parameters. From the plot, the slope represents the average value of F. The predicted transmissivity also shows a good correlation with the TR computed for the area with a correlation coefficient of 0.76 (Figure 5(d)). The spatial distributions of aquifer thickness, TR, longitudinal conductance, predicted transmissivity and hydraulic conductivity, and fractured rock fractional porosity were represented in Figure 6. The fractured basement aquifer thickness map shows the spatial distribution across the area with minimum aquifer thickness at VES-17 while maximum aquifer thickness was observed at VES-2. The hydraulic conductivity within the hard rock area is ranged between 1.14 × 10−5 and 1.76 × 10−5 m/s (Table 3). The observed results were within the range of 10−6–10−5 m/s for fractured metamorphic rocks (Freeze & Cherry 1979; Shapiro et al. 2015). The range of values for fractional porosity and predicted transmissivity as shown in Table 3 are 0.20–0.29 and 8.1–91.7 m2/day, respectively. The observed values of are adequate for fractured basement aquifers (Table 3). However, this range of porosity value is higher than the 0.00–0.10 range reported by Freeze & Cherry (1979) for fractured rocks. These high values are expected, since the estimation was based on the saturated thickness. However, based on conventional porosity classification of Etu-Efeotor (1997), is within the very good (i.e., 0.10–0.30) range (Table 4).
Table 1

Results of 1D electrical resistivity models

VES pointsCoordinates in degrees
Resistivity (Ωm)
Thickness (m)
LongitudeLatitudeρ1ρ2ρ3ρ4ρ5h1h2h3h4
9.248 6.638 715.6 44.4 150.5 1,508.7 – 0.7 12.8 27.5 – 
9.178 6.621 147.3 38.6 214.1 9,427.5 – 1.3 4.8 49.5 – 
9.041 6.524 149.2 28.2 437.7 1,039.7 1,426.7 1.1 5.1 10.3 25.6 
9.208 6.642 181.2 567.8 143.0 2,401.5 – 0.3 4.6 35.4 – 
9.080 6.539 430.6 3,020 270.0 943.1 1,684.3 0.9 4.1 17.3 36.3 
9.200 6.611 215.3 58 208.1 1,146.2 – 1.8 12.6 45.9 – 
9.294 6.619 232 137.8 30.0 137.1 438.5 0.6 2.6 15.7 25.9 
9.311 6.634 157.1 14.1 100.0 1,535.0 – 3.3 6.6 36.5 – 
9.138 6.637 105.3 805.9 252.0 2,278.6 – 0.2 12.5 36.3 – 
10 9.051 6.566 1,381 353.3 142.8 1,468.8 – 2.4 9.0 19.7 – 
11 9.196 6.597 327.4 37.3 272.7 1,802.7 – 2.2 4.3 35.1 – 
12 9.095 6.554 1,073.5 273.1 154.7 1,127.3 4,546.2 1.3 4.3 34.2 50.1 
13 9.099 6.568 1,281.4 321.2 420.0 362.0 2,099.8 1.0 4.3 18.7 38 
14 9.417 6.518 336.9 889.7 495.3 3,329.3 – 0.2 13.8 24.3 – 
15 9.126 6.591 1,493.1 144.7 1,624.2 1,348.8 – 5.9 11.0 27.3 – 
16 9.191 6.558 744.5 228.3 1,139.0 878.3 – 1.2 35.9 6.0 – 
17 9.184 6.545 685.5 236.39 1,558.1 1,911.4 – 2.2 7.5 14.0 – 
18 9.245 6.687 214.6 62.2 277.3 681.4 – 1.1 7.0 28.1 – 
19 9.251 6.699 161.2 51.3 155.4 3,632.6 – 1.4 6.5 35.5 – 
20 9.193 6.649 884.6 483.3 156.4 2,295.3 – 1.9 8.4 16.0 – 
21 9.025 6.637 34.0 130.0 225.2 1,240.6 – 2.9 6.7 10.2 – 
VES pointsCoordinates in degrees
Resistivity (Ωm)
Thickness (m)
LongitudeLatitudeρ1ρ2ρ3ρ4ρ5h1h2h3h4
9.248 6.638 715.6 44.4 150.5 1,508.7 – 0.7 12.8 27.5 – 
9.178 6.621 147.3 38.6 214.1 9,427.5 – 1.3 4.8 49.5 – 
9.041 6.524 149.2 28.2 437.7 1,039.7 1,426.7 1.1 5.1 10.3 25.6 
9.208 6.642 181.2 567.8 143.0 2,401.5 – 0.3 4.6 35.4 – 
9.080 6.539 430.6 3,020 270.0 943.1 1,684.3 0.9 4.1 17.3 36.3 
9.200 6.611 215.3 58 208.1 1,146.2 – 1.8 12.6 45.9 – 
9.294 6.619 232 137.8 30.0 137.1 438.5 0.6 2.6 15.7 25.9 
9.311 6.634 157.1 14.1 100.0 1,535.0 – 3.3 6.6 36.5 – 
9.138 6.637 105.3 805.9 252.0 2,278.6 – 0.2 12.5 36.3 – 
10 9.051 6.566 1,381 353.3 142.8 1,468.8 – 2.4 9.0 19.7 – 
11 9.196 6.597 327.4 37.3 272.7 1,802.7 – 2.2 4.3 35.1 – 
12 9.095 6.554 1,073.5 273.1 154.7 1,127.3 4,546.2 1.3 4.3 34.2 50.1 
13 9.099 6.568 1,281.4 321.2 420.0 362.0 2,099.8 1.0 4.3 18.7 38 
14 9.417 6.518 336.9 889.7 495.3 3,329.3 – 0.2 13.8 24.3 – 
15 9.126 6.591 1,493.1 144.7 1,624.2 1,348.8 – 5.9 11.0 27.3 – 
16 9.191 6.558 744.5 228.3 1,139.0 878.3 – 1.2 35.9 6.0 – 
17 9.184 6.545 685.5 236.39 1,558.1 1,911.4 – 2.2 7.5 14.0 – 
18 9.245 6.687 214.6 62.2 277.3 681.4 – 1.1 7.0 28.1 – 
19 9.251 6.699 161.2 51.3 155.4 3,632.6 – 1.4 6.5 35.5 – 
20 9.193 6.649 884.6 483.3 156.4 2,295.3 – 1.9 8.4 16.0 – 
21 9.025 6.637 34.0 130.0 225.2 1,240.6 – 2.9 6.7 10.2 – 
Table 2

Estimated Dar–Zarrouk parameters and electric anisotropy

VES pointsCoordinates in degrees
TR (Ωm2)S−1)ρL (Ωm)ρT (Ωm)
LongitudeLatitude
9.248 6.638 5,207.99 0.47 86.87 127.02 1.2 
9.178 6.621 10,974.72 0.36 152.59 197.39 1.1 
9.041 6.524 31,432.57 0.24 178.10 746.62 2.0 
9.208 6.642 7,728.44 0.26 156.62 191.77 1.1 
9.080 6.539 51,675.07 0.11 552.77 881.83 1.3 
9.200 6.611 10,670.13 0.45 135.15 176.95 1.1 
9.294 6.619 4,519.37 0.73 61.06 100.88 1.3 
9.311 6.634 4,261.49 0.85 54.33 91.84 1.3 
9.138 6.637 19,242.41 0.16 303.49 392.70 1.1 
10 9.051 6.566 9,307.26 0.17 188.29 299.27 1.3 
11 9.196 6.597 10,452.44 0.25 165.93 251.26 1.2 
12 9.095 6.554 64,338.35 0.28 318.26 715.67 1.5 
13 9.099 6.568 24,272.56 0.16 378.83 391.49 1.0 
14 9.417 6.518 24,381.03 0.07 587.73 636.58 1.0 
15 9.126 6.591 54,741.65 0.10 456.71 1,238.50 1.6 
16 9.191 6.558 15,923.37 0.16 262.60 369.45 1.2 
17 9.184 6.545 25,094.43 0.04 539.59 1,058.84 1.4 
18 9.245 6.687 8,463.59 0.22 165.30 233.80 1.2 
19 9.251 6.699 6,075.83 0.36 119.29 140.00 1.1 
20 9.193 6.649 8,242.86 0.12 215.87 313.42 1.2 
21 9.025 6.637 3,266.64 0.18 108.72 164.98 1.2 
Minimum   3,266.64 0.04 54.33 91.84 1.0 
Maximum   64,338.35 0.85 587.73 1,238.50 2.0 
VES pointsCoordinates in degrees
TR (Ωm2)S−1)ρL (Ωm)ρT (Ωm)
LongitudeLatitude
9.248 6.638 5,207.99 0.47 86.87 127.02 1.2 
9.178 6.621 10,974.72 0.36 152.59 197.39 1.1 
9.041 6.524 31,432.57 0.24 178.10 746.62 2.0 
9.208 6.642 7,728.44 0.26 156.62 191.77 1.1 
9.080 6.539 51,675.07 0.11 552.77 881.83 1.3 
9.200 6.611 10,670.13 0.45 135.15 176.95 1.1 
9.294 6.619 4,519.37 0.73 61.06 100.88 1.3 
9.311 6.634 4,261.49 0.85 54.33 91.84 1.3 
9.138 6.637 19,242.41 0.16 303.49 392.70 1.1 
10 9.051 6.566 9,307.26 0.17 188.29 299.27 1.3 
11 9.196 6.597 10,452.44 0.25 165.93 251.26 1.2 
12 9.095 6.554 64,338.35 0.28 318.26 715.67 1.5 
13 9.099 6.568 24,272.56 0.16 378.83 391.49 1.0 
14 9.417 6.518 24,381.03 0.07 587.73 636.58 1.0 
15 9.126 6.591 54,741.65 0.10 456.71 1,238.50 1.6 
16 9.191 6.558 15,923.37 0.16 262.60 369.45 1.2 
17 9.184 6.545 25,094.43 0.04 539.59 1,058.84 1.4 
18 9.245 6.687 8,463.59 0.22 165.30 233.80 1.2 
19 9.251 6.699 6,075.83 0.36 119.29 140.00 1.1 
20 9.193 6.649 8,242.86 0.12 215.87 313.42 1.2 
21 9.025 6.637 3,266.64 0.18 108.72 164.98 1.2 
Minimum   3,266.64 0.04 54.33 91.84 1.0 
Maximum   64,338.35 0.85 587.73 1,238.50 2.0 
Table 3

Estimated aquifer parameters from geoelectrical and pumping test data and their basic statistics

LocationVES pointsCoordinates in degrees
ρb (Ωm)ρw (Ωm)ha (m)FϕPumping test
Predicted
LongitudeLatitudeT (m2/day)k (m/s)Tp (m2/day)kp (m/s)
Ablesang 9.248 6.638 150.5 9.8 28 15.4 0.22 – – 52.3 1.56 × 10−5 
Agasham 9.178 6.621 214.1 18.5 50 11.6 0.26 – – 41.2 1.26 × 10−5 
Alege 9.041 6.524 437.7 40.0 10 10.9 0.27 – – 28.0 1.21 × 10−5 
Amunga 9.208 6.642 143.0 11.4 35 12.6 0.25 – – 30.1 1.35 × 10−5 
Ashikpe 9.080 6.539 270.0 18.5 17 14.6 0.23 – – 14.5 1.50 × 10−5 
Bayayam 9.200 6.611 208.1 19.2 46 10.8 0.28 49.6 1.25 × 10−5 49.6 1.20 × 10−5 
Beegbong 9.294 6.619 137.1 11.4 26 12.1 0.26 – – 79.2 1.30 × 10−5 
9.311 6.634 100.0 9.9 37 10.1 0.29 – – 91.7 1.14 × 10−5 
Begiatsul 9.138 6.637 252.0 22.7 36 11.1 0.27 18.5 1.19 × 10−5 20.2 1.22 × 10−5 
Biuhwe 10 9.051 6.566 142.8 10.2 20 14.0 0.23 – – 20.6 1.46 × 10−5 
Bukemanya 11 9.196 6.597 272.7 25.6 35 10.6 0.28 – – 29.5 1.19 × 10−5 
Karo 12 9.095 6.554 154.7 11.2 34 13.8 0.24 – – 32.7 1.44 × 10−5 
Kikong 13 9.099 6.568 362.0 23.3 38 15.6 0.22 – – 20.5 1.58 × 10−5 
Kundeve 14 9.417 6.518 495.3 31.3 24 15.8 0.22 – – 10.3 1.60 × 10−5 
Ohong 15 9.126 6.591 144.7 14.1 11 10.3 0.28 – – 13.6 1.16 × 10−5 
Okambi 16 9.191 6.558 228.3 16.9 36 13.5 0.24 22.3 1.43 × 10−5 20.5 1.42 × 10−5 
17 9.184 6.545 236.39 16.9 13.9 0.23 – – 8.1 1.45 × 10−5 
Shikpeche 18 9.245 6.687 277.3 15.4 28 18.0 0.20 – – 26.2 1.76 × 10−5 
19 9.251 6.699 155.4 15.4 36 10.1 0.29 – – 41.1 1.14 × 10−5 
Udigie 20 9.193 6.649 156.4 12.2 16 12.8 0.25 – – 16.2 1.37 × 10−5 
Ukpada 21 9.025 6.637 225.2 20.0 10 11.3 0.27 – – 22.4 1.24 × 10−5 
Minimum    100.0 9.8 10.1 0.20 18.5 1.19 × 10−5 8.1 1.14 × 10−5 
Maximum    495.3 40.0 50 18.0 0.29 49.6 1.43 × 10−5 91.7 1.76 × 10−5 
Average    226.8 17.8 28 12.8 0.25 30.1 1.29 × 10−5 31.8 1.36 × 10−5 
Standard Deviation    102.1 7.6 12 2.2 0.03 17.0 1.27 × 10−6 21.6 1.76 × 10−6 
LocationVES pointsCoordinates in degrees
ρb (Ωm)ρw (Ωm)ha (m)FϕPumping test
Predicted
LongitudeLatitudeT (m2/day)k (m/s)Tp (m2/day)kp (m/s)
Ablesang 9.248 6.638 150.5 9.8 28 15.4 0.22 – – 52.3 1.56 × 10−5 
Agasham 9.178 6.621 214.1 18.5 50 11.6 0.26 – – 41.2 1.26 × 10−5 
Alege 9.041 6.524 437.7 40.0 10 10.9 0.27 – – 28.0 1.21 × 10−5 
Amunga 9.208 6.642 143.0 11.4 35 12.6 0.25 – – 30.1 1.35 × 10−5 
Ashikpe 9.080 6.539 270.0 18.5 17 14.6 0.23 – – 14.5 1.50 × 10−5 
Bayayam 9.200 6.611 208.1 19.2 46 10.8 0.28 49.6 1.25 × 10−5 49.6 1.20 × 10−5 
Beegbong 9.294 6.619 137.1 11.4 26 12.1 0.26 – – 79.2 1.30 × 10−5 
9.311 6.634 100.0 9.9 37 10.1 0.29 – – 91.7 1.14 × 10−5 
Begiatsul 9.138 6.637 252.0 22.7 36 11.1 0.27 18.5 1.19 × 10−5 20.2 1.22 × 10−5 
Biuhwe 10 9.051 6.566 142.8 10.2 20 14.0 0.23 – – 20.6 1.46 × 10−5 
Bukemanya 11 9.196 6.597 272.7 25.6 35 10.6 0.28 – – 29.5 1.19 × 10−5 
Karo 12 9.095 6.554 154.7 11.2 34 13.8 0.24 – – 32.7 1.44 × 10−5 
Kikong 13 9.099 6.568 362.0 23.3 38 15.6 0.22 – – 20.5 1.58 × 10−5 
Kundeve 14 9.417 6.518 495.3 31.3 24 15.8 0.22 – – 10.3 1.60 × 10−5 
Ohong 15 9.126 6.591 144.7 14.1 11 10.3 0.28 – – 13.6 1.16 × 10−5 
Okambi 16 9.191 6.558 228.3 16.9 36 13.5 0.24 22.3 1.43 × 10−5 20.5 1.42 × 10−5 
17 9.184 6.545 236.39 16.9 13.9 0.23 – – 8.1 1.45 × 10−5 
Shikpeche 18 9.245 6.687 277.3 15.4 28 18.0 0.20 – – 26.2 1.76 × 10−5 
19 9.251 6.699 155.4 15.4 36 10.1 0.29 – – 41.1 1.14 × 10−5 
Udigie 20 9.193 6.649 156.4 12.2 16 12.8 0.25 – – 16.2 1.37 × 10−5 
Ukpada 21 9.025 6.637 225.2 20.0 10 11.3 0.27 – – 22.4 1.24 × 10−5 
Minimum    100.0 9.8 10.1 0.20 18.5 1.19 × 10−5 8.1 1.14 × 10−5 
Maximum    495.3 40.0 50 18.0 0.29 49.6 1.43 × 10−5 91.7 1.76 × 10−5 
Average    226.8 17.8 28 12.8 0.25 30.1 1.29 × 10−5 31.8 1.36 × 10−5 
Standard Deviation    102.1 7.6 12 2.2 0.03 17.0 1.27 × 10−6 21.6 1.76 × 10−6 
Table 4

Groundwater potential classification

S/NLocationsParameters
Groundwater potential
Range of resistivity (Ωm)Aquifer thickness range (m)Transmissivity (m2/day)
VES-1, VES-2, VES-6, VES-7, VES-8 and VES-19 100–214 26–50 >40 Good 
VES-3, VES-4, VES-5, VES-9, VES-10, VES-11, VES-12, VES-13, VES-16, VES-18, VES-20 and VES-21 143–438 10–38 <40–15 Marginal 
VES-14, VES-15 and VES-17 145–495 8–24 <15 Poor 
S/NLocationsParameters
Groundwater potential
Range of resistivity (Ωm)Aquifer thickness range (m)Transmissivity (m2/day)
VES-1, VES-2, VES-6, VES-7, VES-8 and VES-19 100–214 26–50 >40 Good 
VES-3, VES-4, VES-5, VES-9, VES-10, VES-11, VES-12, VES-13, VES-16, VES-18, VES-20 and VES-21 143–438 10–38 <40–15 Marginal 
VES-14, VES-15 and VES-17 145–495 8–24 <15 Poor 
Figure 5

Empirical relationships between (a) pumping test-derived transmissivity and longitudinal conductance, (b) pumping test-derived hydraulic conductivity and electrical resistivity formation factor, (c) aquifer resistivity and aquifer water resistivity, and (d) transverse resistance and predicted transmissivity.

Figure 5

Empirical relationships between (a) pumping test-derived transmissivity and longitudinal conductance, (b) pumping test-derived hydraulic conductivity and electrical resistivity formation factor, (c) aquifer resistivity and aquifer water resistivity, and (d) transverse resistance and predicted transmissivity.

Close modal
Figure 6

Maps showing the spatial distribution of some aquifer parameters; (a) aquifer thickness, (b) transverse resistance, (c) longitudinal conductance, (d) predicted transmissivity (e) predicted hydraulic conductivity and (f) fractured basement aquifer fractional porosity.

Figure 6

Maps showing the spatial distribution of some aquifer parameters; (a) aquifer thickness, (b) transverse resistance, (c) longitudinal conductance, (d) predicted transmissivity (e) predicted hydraulic conductivity and (f) fractured basement aquifer fractional porosity.

Close modal

Table 5 presents the results of correlation analysis performed on transmissivity and hydraulic conductivity data from pumping test experiments at three specific borehole locations, juxtaposed with the corresponding values predicted via the hydro-geoelectrical technique at the same spatial coordinates, yielded correlation coefficients (R2) of 0.98 and 0.89, respectively. Furthermore, an error analysis was performed on the hydraulic parameters, specifically transmissivity and hydraulic conductivity. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were calculated to evaluate the accuracy of the predictions. For transmissivity, the RMSE and MAPE were determined to be 1.439 and 5.807, respectively. In the case of hydraulic conductivity, the corresponding values were and 0.027, respectively. These low error values demonstrate the high precision of the predictive models used in estimating these hydraulic parameters. The accuracy of the predictions was assessed by calculating the percentage accuracy, which was observed to be 94.19 and 99.97%, respectively, for transmissivity and hydraulic conductivity. These results highlight the robustness and reliability of the employed predictive models in accurately characterizing the hydraulic parameters within the area under investigation.

Table 5

Available aquifer parameters, their corresponding predicted values and statistical analysis

S/NParameterVES-6VES-9VES-16R2RMSEMAPEAccuracy (%)
Aquifer electrical resistivity, ρb (Ωm) 208 252 228.3 – – – – 
Saturated aquifer thickness, ha (m) 46 36 36 – – – – 
Longitudinal conductance, S (Ω−10.45 0.16 0.16 – – – – 
Transverse resistance, TR (Ωm210,670.1 19,242.4 15,923.37 – – – – 
Electrical formation factor 10.8 11.1 13.5 – – – – 
Aquifer transmissivity derived from pumping test, T (m2/day) 49.6 18.5 22.3 0.99 1.43895 5.807 94.19 
Predicted aquifer transmissivity from geoelectric sounding, Tp (m2/day) 49.6 20.2 20.5 
Aquifer hydraulic conductivity derived from pumping test, k (m/s) 1.25 × 10−5 1.19 × 10−5 1.43 × 10−5 0.89 3.5 × 10−7 0.027 99.97 
Predicted aquifer hydraulic conductivity from geoelectric sounding, kp (m2/day) 1.20 × 10−5 1.22 × 10−5 1.42 × 10−5 
S/NParameterVES-6VES-9VES-16R2RMSEMAPEAccuracy (%)
Aquifer electrical resistivity, ρb (Ωm) 208 252 228.3 – – – – 
Saturated aquifer thickness, ha (m) 46 36 36 – – – – 
Longitudinal conductance, S (Ω−10.45 0.16 0.16 – – – – 
Transverse resistance, TR (Ωm210,670.1 19,242.4 15,923.37 – – – – 
Electrical formation factor 10.8 11.1 13.5 – – – – 
Aquifer transmissivity derived from pumping test, T (m2/day) 49.6 18.5 22.3 0.99 1.43895 5.807 94.19 
Predicted aquifer transmissivity from geoelectric sounding, Tp (m2/day) 49.6 20.2 20.5 
Aquifer hydraulic conductivity derived from pumping test, k (m/s) 1.25 × 10−5 1.19 × 10−5 1.43 × 10−5 0.89 3.5 × 10−7 0.027 99.97 
Predicted aquifer hydraulic conductivity from geoelectric sounding, kp (m2/day) 1.20 × 10−5 1.22 × 10−5 1.42 × 10−5 

R2, correlation coefficient; RMSE, root mean square error; MAPE, mean absolute percentage error.

Aquifer characterization of the OBC

Due to the cost-effectiveness and speed of the electrical resistivity technique, it permits greater spatial coverage and provides valuable pilot information that is needed for rapid aquifer characterization decisions. Aquifer characterization provides information on aquifer parameters and numerical models required for groundwater resources management, sustainability and prediction. These parameters that including the D-Z parameters, hydraulic conductivity, porosity, and transmissivity were evaluated within the OBC. TR values were observed to increase from the northern segment toward the south (Figure 6(b)). The low TR values within the northern portion correlated geologically with the portion of the study area dominated by migmatitic rocks. The migmatites have been subjected to intense weathering processes leading to reduced resistivity values of the formations within this area. TR values tend to show high values toward the south, around the massive porphyritic granite dominant terrain. On the other hand, S values were observed to increase from the western segment toward the northeastern part of the study area where it attained its maximum value (Figure 6(c)). The high S values are dominant within the parts where gneissic rocks have been intensely fractured and the aquifer formation electrical resistivity values within these areas have been reduced due to groundwater transmission. Crystalline basement terrains are usually dominated by episodes of faulting, fracturing, and intrusions leading to weathering of preexisting rocks. These processes alter the configuration and characteristics of crystalline rocks leading to heterogeneity in their electrical resistivity signatures. This phenomenon is responsible for the variations in electrical anisotropy within the area.

Figure 5(c) provides an empirical model of the aquifer formation due to and in the area, presented as
(13)

Equation (13) gives the average F value as 11.65 – the slope of the equation. The lowest value of F was observed around VES-8 and VES-19 while relatively high values were observed at area (VES-18). The northern segment and areas located along major fault zones were notable portions with relatively high hydraulic conductivity (Figure 6(e)). The networks of fractures that provide semi-confined aquifers within the area are interconnected adequately which allows groundwater circulation (Ebong et al. 2014, 2021a; Ekwok et al. 2020). Lower values of k were observed around some locations within the central part sandwiched between the two dominant parallel faults and the western segments of the study area. The lower values that are dominant, especially within the western part of the landscape are due to minor occurrence of fracture networks. Also, the central and western segments have been identified to have relatively little or sparse lineament structures (Ebong et al. 2021a).

Generally, T and k are related linearly which implies that an increase in k can result in a corresponding increase in the value of T. The northeastern portion and locations around major fault zones show relatively high values (i.e., T > 40 m2/day) and decreased to >10 m2/day toward the south (Figure 6(d)). Additionally, the migmatite and gneisses within the northeastern part have been intensely fractured resulting in relatively thick and partially saturated formations that can increase T values. Also, the relatively high transmissivity observed can be traced to the presence of localized productive fractures/faults that are connected to some nearby surface water and/or rivers that constantly recharge the semi-confined aquifers. Conversely, locations, where charnockitic rocks abound, show low k and T values, due to its resistance to weathering and fracturing. Furthermore, the transmissivity values derived from the hydro-geoelectrical measurements were correlated with TR (Figure 5(d)) and an empirical model with a correlation coefficient (R² = 0.76) was obtained.
(14)

This R2 value implies that the estimated relationship between these two parameters is good. However, the TR values tend to considerably decrease as T increases because the magnitude of resistance offered by the crystalline basement rock is high, except in VES-21 where the layer electrical resistivities were relatively low and correspond to the thick regolith layer. Hence, the relatively low transmissivity observed within the area under investigation (Figure 6(d)).

Aquifer potential delineation

The resistivity of the fractured-aquifer formation and the estimated transmissivity were used to delineate aquifer potential zones. Locations with ρ range of 100–214 Ωm, T > 40 m2/day and h of the range of 26–50 m were categorized as having good aquifer potential (Table 4). The locations include VES-1, 2, 6, 7, 8, and 19. Moderate potential aquifer zones were observed within VES-3, 4, 5, 9, 10, 11, 12, 13, 16, 18, 20, and 21 with ρ that range between <143 and 438 Ωm, T that range between <40 and 15 m2/day and h between 10 and 38 m while poor potential aquifer zones that have ρ that range between 145 and 495 Ωm, T < 15 m2/day and h that range from 8 to 24 m were observed in VES-14, 15, and 17. In general, the groundwater potential classification indicates that a decrease in electrical resistivity values of aquifer formation reflects higher fracture density and water content. Hence, adequate structural elements (i.e., fractures and faults) and thickness of the saturated formation are important factors to also consider in siting productive boreholes within such areas.

Hydrogeologic and electrical resistivity techniques were used to study the OBC for the purpose of developing empirical models used in predicting aquifer hydraulic parameters and delineating potential aquifer zones. The VES technique applied in this study involved the Schlumberger electrode configuration. This technique provided layer parameters used in computing secondary geoelectrical information (i.e., Dar Zarrouk parameters). The D-Z parameters, i.e., longitudinal conductance and TR range between 0.04 and 0.85 Ω−1 and between 3,266.64 and 64,338.35 Ωm2, respectively. These wide variations are due to the intrinsic electrical anisotropic nature of the basement terrain. This variation in electrical anisotropy results from the influence of electrical current flow by some factors like geologic structures (i.e., faults and fractures) and topographic conditions. The combination of these primary and secondary parameters alongside water electrical resistivity measurements, and a few pumping test-derived hydraulic parameters were used to develop empirical models used in evaluating aquifer parameters such as fractional porosity of the fractured-aquifer layer (0.20–0.30), hydraulic conductivity (1.14 × 10−5 to 1.76 × 10−5 m/s), and transmissivity (8.1–91.7 m2/day). Based on the estimated aquifer parameters, groundwater potential zones were delineated within the study area. The results of this investigation provided good, moderate, and poor groundwater potential zones that were semi-confined (i.e., fractured basement aquifers). Additionally, this approach can be applied in hard rock terrains elsewhere to estimate aquifer parameters and delineate suitable sites for productive boreholes. This study develops empirical models for the estimation of fractured-aquifer hydrogeological, physical, and geohydraulic parameters where pumping test information is limited which enables the delineation of groundwater potential zones required for adequate management of groundwater resources and sustainability.

The first author wishes to thank Prof. I. Othman, General Director of the Syrian Atomic Energy Commission for allowing Prof. J. Asfahani to participate in this research paper.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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