This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

  • The study explores the groundwater potential using an integrated approach combining VES techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods.

  • The study highlights the complementarity of VES and RS/GIS in groundwater exploration.

  • The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management.

Water is an essential resource that is necessary for maintaining human existence and facilitating a wide range of activities that are essential for the advancement of socioeconomic conditions. Despite the fact that it is of the utmost significance, ensuring that there is a sufficient supply of safe drinking water continues to be a tough task on a worldwide scale. It has been brought to light by Umoren et al. (2017) and Ugbaja et al. (2021) that communities all over the world continue to struggle with problems that are associated with water scarcity, contamination, and accessibility. When it comes to gaining access to groundwater resources, boreholes are a frequent solution, particularly in regions that do not have an abundance of dependable surface water sources. On the other hand, there is no assurance that boreholes will be successful in satisfying the requirements for water production. Despite the substantial investments that have been made in the water business, a great number of boreholes continue to face difficulties such as low groundwater yields or unexpected failures (Anyanwu & Oguntade 2021).

Communities often face challenges such as low borehole yields or ineffective water sources, leading to insufficient water supply, reliance on alternative sources, and heightened water-related risks. These issues can strain finances through failed projects, exacerbating the problem. Addressing these challenges necessitates robust hydrogeological studies, community involvement, sustainable water management practices, and appropriate technological solutions. By implementing proactive measures and leveraging scientific insights, the reliability of water supply systems can be enhanced, ensuring equitable access to safe and sufficient drinking water (Ajibade et al. 2021).

The growing demand for water for domestic, agricultural, and industrial purposes has become a pressing issue in densely populated and industrialized countries such as India, China, and several nations in Africa. Activities like deforestation, urbanization, and industrial operations significantly impact both the quantity and quality of groundwater (Olusola et al. 2017; Ijioma 2021). Globally, groundwater is heavily utilized, with the residential, agricultural, and industrial sectors consuming 36, 42, and 27% of groundwater resources respectively. Countries with dense populations, such as China, India, and the United States, rely extensively on groundwater to irrigate more than half of their agricultural lands (Döll et al. 2012; Sharma et al. 2021).

Understanding groundwater requires a comprehensive knowledge of water properties and the subsurface elements that potentially contain water. Various techniques are employed to locate groundwater, including geophysical methods and the ‘wild cat’ approach (Arefayne & Abdi 2015). Another critical technique is the use of satellite imagery for RS. RS involves collecting data about an object without direct contact and using satellite images to gather surface data about a region. This technique helps infer geomorphology, drainage patterns, lithology, plant cover, and structural geology of the area (Abdullateef et al. 2021). While RS complements geophysical methods, it does not replace them (Farahani & Aghajani 2019). It reduces the need for extensive geophysical groundwater research over large areas, thereby saving costs (Ahmed & Mansor 2018).

Groundwater resources are increasingly tapped to meet rising population demands for water, underscoring the importance of identifying groundwater potential zones (GWPZs). Traditionally, disciplines such as geology, hydrogeology, and geophysics have been instrumental in delineating GWPZs (Nair et al. 2017). However, advancements in GIS and RS technologies have revolutionized this field, enabling precise mapping of natural resources, including GWPZs (Arulbalaji et al. 2019; Lawal et al. 2022). These technologies are particularly advantageous for data collection in challenging terrains.

In recent years, the growing demand for groundwater due to population growth and industrialization has put immense pressure on water resources, particularly in regions with limited access to surface water. Traditional methods of groundwater assessment have proven insufficient to address the complex environmental and geological factors influencing groundwater availability. Consequently, there is a need for integrated approaches that can more accurately evaluate groundwater potential and identify areas vulnerable to depletion or contamination. This study seeks to address this gap by delineating groundwater susceptibility zones using geospatial analysis, focusing on Aladja in Udu Local Government Area, Delta State, Nigeria. The aim is to provide a comprehensive understanding of the region's groundwater vulnerability, supporting sustainable water resource management and guiding future development initiatives.

Aladja, which is located in the Udu Local Government Area of Delta State, Nigeria, is distinguished by a variety of physical features that have a considerable impact on the climate, terrain, vegetation, relief, and drainage patterns of the area. The town of Aladja is situated in the Niger Delta region, at a latitude of roughly 5.497° North and a longitude of approximately 5.915° East, as shown in Figure 1. This strategic location contributes to the distinctive environmental characteristics of the area.
Figure 1

Map of (a) Nigeria; (b) delta state; and (c) study area with settlements.

Figure 1

Map of (a) Nigeria; (b) delta state; and (c) study area with settlements.

Close modal

The proximity of Aladja to important transportation arteries, such as road networks connecting other towns and cities within the Delta State region, plays a significant role in making the city more accessible. This region is able to reap the benefits of relatively strong connectivity, which improves mobility and makes economic operations easier to carry out (Eyankware et al. 2021). A tropical environment that is distinguished by distinct wet and dry seasons is experienced by Aladja. The region is subject to a substantial amount of precipitation, particularly during the rainy season, which begins in April and continues until October on average. Temperatures and humidity levels are consistently high throughout the year, which is one of the factors that contributes to the region's abundant vegetation and the different ecosystems that can be found there (Igben & Itabita 2020).

The geography of Aladja is characterized by a combination of moderate undulations and occasional high regions. The majority of the land is low lying. As one moves closer to the coast, the topography progressively slopes downward, becoming a portion of the vast plain that is the Niger Delta (Eyankware et al. 2021). Tropical rainforests, mangrove swamps, and freshwater marshes make up the region's vegetation, which is a reflection of the region's great biodiversity and the ecological value of the area.

Relief features in Aladja are comprised of both natural and man-made components interacting with one another. The topography is characterized by shallow depressions, estuary channels, and interconnecting waterways, all of which contribute to the complex drainage network that is found in the Niger Delta region (Eyankware et al. 2021). The existence of rivers, creeks, and tributaries, such as the Forcados and Warri rivers, plays a significant part in the formation of the local hydrology and in the maintenance of a wide variety of aquatic ecosystems.

The extensive network of watercourses and the region's proximity to the Atlantic Ocean both have an impact on the drainage patterns that are found in Aladja due to their respective influences. There are dynamic hydrological processes that are influenced by tidal fluctuations and seasonal variations in river discharge (Balogun & Money-Irubor 2021). These activities have an effect on sediment movement, erosion, and the dynamics of the floodplain environment.

Geoelectrical sounding

The geophysical study utilized the ABEM-Terrameter (SAS1000-Signal Averaging System) Electrical Resistivity instrument employing a Schlumberger configuration to conduct VES. The survey covered a maximum current electrode spread (L) of 800 m, with 400 m (L/2) extending left and right from the midpoint (Jimoh et al. 2023). Ten VES stations were strategically positioned in a grid pattern, although adjustments were necessary due to natural features like gullies, valleys, and human settlements.

The survey was conducted under favorable weather conditions and adhered to all safety protocols for geoelectric measurements. The apparent resistivity (ρa) at each location was determined by systematically varying the electrode spacing using the Schlumberger array.
(1)
The resistance (R) is derived from the current (I) and voltage (V) values using the relation:
(2)
Equation (2) can be written as
(3)
Geometric factor,
(4)

The geometric factor (K) depends on the positions of the electrodes in the ground and can be calculated for any electrode arrangement.

By incrementally adjusting the electrode spacing, potential differences sufficient for precise measurements were achieved. The apparent resistivity at each station was calculated by multiplying the resistance (R) with a geometric factor (K), tailored to each configuration's electrode spacing (Akaolisa et al. 2022). These steps aimed to gather detailed electrical data from beneath the surface, crucial for assessing groundwater potential in the research area.

Data processing

The IP2win software facilitated the analysis of VES data collected during the study. This tool streamlined the input of apparent resistivity values and AB/2 values required for iterative modeling. To enhance accuracy, multiple iterations (typically ranging from 1 to 29) were conducted to minimize errors and optimize the fit of resistivity layers and their respective thicknesses and depths (Ibrahim et al. 2023).

Subsequently, using the IP2win program, crucial Dar-Zarouk parameters were computed to interpret the underlying electrical characteristics and identify GWPZ. This process played a significant role in accurately delineating geological formations and assessing their hydrological significance within the study area. The systematic approach enabled by IP2win underscored its utility in hydrogeological investigations, ensuring robust data interpretation and informed decision-making in groundwater resource management.

The Dar-Zarrouk parameters

To evaluate aquifer properties such as transmissivity and the protective capacity of overlying rock layers, the Dar-Zarrouk criteria play a pivotal role (Bello et al. 2019). These criteria provide essential insights into the aquifer's efficiency in transmitting water and the effectiveness of the overlying geological formations in safeguarding the aquifer from contamination.
(5)
Longitudinal conductance (S)
(6)
Transverse resistance (T)
(7)
Longitudinal resistivity
(8)
Transverse Resistivity
(9)
where ρi and hi represent the resistivity and thickness of each layer, respectively.
The aquifer transmissivity (Tr) is defined as the product of hydraulic conductivity (k) and layer thickness (h) (Ullah et al. 2020) expressed as
(10)

This relationship allows the true resistivity obtained from geoelectric investigations to serve as an estimate for aquifer hydraulic conductivity (k) when pumping test data are unavailable. This method provides a practical approach to assessing aquifer properties based on geophysical data, facilitating informed decisions in groundwater resource management and exploration.

RS and GIS methods

DEM and data from the Shuttle Radar Topography Mission (SRTM) satellite were utilized for spatiotemporal analysis. Satellite imagery was acquired every 16 days under the Global System-2 reference framework. The assessment of water resources benefitted greatly from the integration of Landsat 7′s ETM + sensor, renowned for its enhanced spectral bands. This sensor provided data with a minimum resolution of 100 m, incorporating two thermal bands (TIRS), which were integrated with the higher-resolution 30-m dataset (George et al. 2022). The software tool ArcGIS 10.8 played a crucial role in generating and integrating various thematic layers necessary for identifying potential groundwater locations and conducting comprehensive groundwater studies. The selection of specific photographs and datasets was based on their availability and their relevance to the research objectives.

Materials

The study employed various data sources, including LiDAR-derived DEM for elevation, lineament, drainage, and slope mapping, as shown in Table 1. These data sources enabled a comprehensive assessment of groundwater susceptibility.

Table 1

Data types, sources, parameters, and resolutions used in the study

Data typeSource of dataData parametersResolution
DEM (digital elevation model) Airborne Laser Scanning (LiDAR) Elevation data (topographic surface of the earth) Vertical accuracy: 5 cm, Horizontal resolution: 1 m 
Lineament maps LiDAR-derived DEM, Algorithms for Lineament Detection Length and orientation of linear features – 
Drainage maps LiDAR-derived DEM, Algorithms for Drainage Network Water flow paths, stream length, and density – 
Slope maps LiDAR-derived DEM, Terrain Gradient Calculation Terrain gradients – 
Land use/land Cover maps Sentinel-2 Imagery (European Space Agency) 13 spectral bands (visible to infrared wavelengths) 10-m spatial resolution 
Soil type maps Global datasets (e.g., FAO) Soil properties, texture, nutrient availability Global coverage 
Geological data Nigerian Geological Survey Agency Geological structure, composition, and history National geological coverage 
Rainfall data Meteorological Stations across Nigeria Spatial and temporal variations in precipitation – 
Data typeSource of dataData parametersResolution
DEM (digital elevation model) Airborne Laser Scanning (LiDAR) Elevation data (topographic surface of the earth) Vertical accuracy: 5 cm, Horizontal resolution: 1 m 
Lineament maps LiDAR-derived DEM, Algorithms for Lineament Detection Length and orientation of linear features – 
Drainage maps LiDAR-derived DEM, Algorithms for Drainage Network Water flow paths, stream length, and density – 
Slope maps LiDAR-derived DEM, Terrain Gradient Calculation Terrain gradients – 
Land use/land Cover maps Sentinel-2 Imagery (European Space Agency) 13 spectral bands (visible to infrared wavelengths) 10-m spatial resolution 
Soil type maps Global datasets (e.g., FAO) Soil properties, texture, nutrient availability Global coverage 
Geological data Nigerian Geological Survey Agency Geological structure, composition, and history National geological coverage 
Rainfall data Meteorological Stations across Nigeria Spatial and temporal variations in precipitation – 

DEM data can be obtained from a variety of RS platforms, depending on the specific requirements of the study. In this study, airborne laser scanning (also known as LiDAR) was used to acquire the DEM data. The acquisition of DEM data involves the use of sensors that can capture the topographic surface of the earth with high accuracy and precision. The LiDAR sensor used in this study is capable of capturing elevation data with a vertical accuracy of up to 5 cm and a horizontal resolution of up to 1 m. The accuracy of the DEM data is crucial for RS and GIS applications, as it affects the accuracy of the derived maps and analyses. The acquisition of DEM data for RS and GIS applications involves the use of sophisticated sensors and software algorithms that can capture and process topographic data with high accuracy and precision (George et al. 2022).

The LiDAR-derived DEM data in this study facilitated the creation of lineament, drainage, and slope maps. Lineament analysis and density calculations were automated using algorithms that detect linear features based on length and orientation criteria (Aladeboyeje et al. 2021). Similarly, drainage networks and their density were delineated from the DEM, employing algorithms to trace water flow paths and quantify stream length and density. Slope maps were generated by computing terrain gradients across the study area.

Sentinel-2 imagery from the European Space Agency enabled the generation of land use/land cover (LULC) maps. This satellite mission provides high-resolution optical images with a 10-m spatial resolution, capturing 13 spectral bands from visible to infrared wavelengths (Aladeboyeje et al. 2021). The detailed spatial and spectral resolution of Sentinel-2 imagery is well-suited for mapping land use and land cover types accurately.

Soil type maps, crucial for understanding soil properties like texture and nutrient availability, were sourced from global datasets such as the world soil type map maintained by organizations like the FAO (Ihediuche et al. 2022). These maps integrate field data, RS, and modeling to provide comprehensive coverage of soil characteristics. Geological data, obtained from the Nigerian Geological Survey Agency, provided insights into the region's geological structure, composition, and history. The agency's comprehensive geological mapping and analysis support various sectors including mining, agriculture, and environmental management, contributing to informed decision-making and resource management strategies.

Rainfall data from meteorological stations across Nigeria were utilized to analyze spatial and temporal variations in precipitation. These data inform studies on vegetation growth, hydrology, and climate dynamics, highlighting rainfall's impact on environmental processes (Saleh & Abdullahi 2023).

GIS/RS preprocessing

RS data underwent geometric correction to ensure precise spatial alignment before processing commenced. Initially, Landsat 8 bands and photos were stacked and combined to create a mosaic. Using ArcGIS's extract tool, the study area's boundary defined by the Delta state shapefile was clipped from this mosaic (Aladeboyeje et al. 2021). Additionally, similar processing steps were applied to the SRTM-DEM data to generate a unique mosaic and subset specific to the study area.

In this study, the selection of theme layers for defining GWPZ was based on the availability of data at the study site. Five thematic layers were identified as crucial for assessing groundwater potential due to their relevance and significant impact on groundwater occurrence and movement. These layers include drainage patterns, lineament density, soil type, slope, and land use/cover. Each layer was chosen for its applicability in providing insights into groundwater dynamics and characteristics within the research area.

Slope map

Adewumi et al. (2023) highlight slope as a critical geomorphological factor influencing groundwater infiltration and recharge in a given area. The slope indicates the steepness of the terrain, typically measured in degrees. Areas with gentle slopes and flat terrain facilitate greater groundwater storage capacity by allowing rainfall to infiltrate and recharge the groundwater system. Conversely, steep terrain and high slopes promote significant runoff and limited infiltration, reducing groundwater storage potential.

Using ArcGIS 10.8′s spatial analyst tool, researchers generated a slope map for the study region. This tool utilizes elevation data to calculate and visualize slope values across the area, providing insights into varying terrain gradients. The slope map's integration into the study's thematic layers allowed researchers to assess its impact on groundwater potential comprehensively. This analysis informs effective groundwater resource management strategies and facilitates informed planning decisions.

Land use/cover

Groundwater occurrence is significantly influenced by land use and land cover, which affect both recharge and depletion processes. By lowering runoff and promoting water retention, land use and cover have been shown to support groundwater recharge (Adewumi et al. 2023). On the other side, assuming a constant interception rate, land use/cover can potentially negatively impact groundwater supplies through processes including evapotranspiration.

There were many different types of land uses and land covers in the research region, including built-up areas, vegetation, arid terrain, agricultural areas, and aquatic bodies. A two-step technique was used to evaluate and categorize the land use and land cover. To begin with, methods of visual interpretation were used to get a general understanding of the various land use categories and their geographical distribution. The researchers utilized the maximum likelihood classifier as a supervised classification method on selected band combinations from available RS data. This approach integrated land use analysis and classification to evaluate the influence of land use and land cover on groundwater potential in the study area (Ijioma 2021). This analysis supports effective planning and groundwater management by providing insights for sustainable use and informed decision-making.

Soil type

In this study, the soil type map for the research region was obtained from a geological map of Delta State. The map was digitized and classified into distinct soil types in order to facilitate groundwater mapping and analysis. By considering the soil characteristics, such as texture, composition, and structure, the researchers aimed to understand the potential for groundwater occurrence and movement within the study area.

Drainage density

The underlying lithology dictates drainage patterns, texture, and density, making them crucial indicators of groundwater potential easily observable through RS imagery (Falebita et al. 2020). Drainage patterns illustrate how precipitation infiltrates the ground versus running off the surface. High drainage density areas indicate poor groundwater conditions, with limited infiltration and storage capacity. Conversely, low or absent drainage density areas suggest favorable conditions for groundwater occurrence, while moderate density areas indicate intermediate potential.

Using the SRTM-DEM, researchers can analyze and map drainage patterns to calculate density. This analysis involves studying water flow patterns derived from elevation data to create a drainage density map. Such maps are essential for identifying GWPZ by visualizing the distribution and intensity of drainage networks across the study area. The drainage density is typically expressed as the length of drainage channels per unit area, calculated using specific equations tailored to the study's needs.
(11)
where A is the study's area in square kilometres and L is the channel's total length in kilometres.
Lineaments

The study of lineaments and their intersections provides valuable insights into the occurrence, movement, and storage of groundwater, making them valuable tools for identifying areas with high groundwater potential. In numerous countries, recent groundwater exploration endeavors have experienced improved success rates by employing lineament mapping to guide drilling locations (Arshad et al. 2020). A spatial analyzer tool was utilized to create and classify lineament density maps.

Weighting of thematic maps using the Analytic Hierarchy Process

Analytic Hierarchy Process (AHP) is a mathematical matrix-based technique designed to aid decision-making by breaking down complex choices into manageable components and evaluating them independently (Ifediegwu 2021; Ghosh et al. 2022). It organizes decisions hierarchically, placing the primary aim at the top. Unique to AHP is its systematic checks for consistency, which help identify and correct inconsistencies when the consistency ratio (CR) exceeds 0.1 (Thapa et al. 2017; Ghosh et al. 2022). This study utilized AHP to evaluate thematic preferences for groundwater occurrence, transit, and storage through pairwise comparison matrices. The process involved the normalization of weights using Saaty's scale, matrix construction, and CR calculation to ensure consistent, robust decision support in groundwater assessment and management (Wind & Saaty 1980).

The study's CR of 0.037, well below the threshold of 0.10, reflects a high level of consistency in paired comparisons, indicating reliable judgments for assessing groundwater potential. A CR above 0.10 would have necessitated reassessment for accuracy. To consolidate findings, a final map was created using weighted overlay techniques on thematic maps, as detailed by Tamiru & Wagari (2021) and Girma (2022). ArcGIS 10.8 was utilized to compute the GWPZ, integrating multiple data layers for a comprehensive spatial evaluation of groundwater potential. This spatial analysis supports informed decision-making in water resource management and environmental planning.
(12)
where is the weight for each thematic layer and is rates for the classes within a thematic layer derived from AHP.

AHP offers a structured method for comparing and ranking options based on multiple criteria, used in this study to assign weights to thematic layers. Themes like rainfall, geology, slope, drainage density, LULC, lineament density, and soil type were evaluated as criteria. Pairwise comparison matrices were created, with themes compared on a scale from 1 to 9, indicating relative importance (Ajibade et al. 2021). Weights were calculated using AHP, reflecting each theme's significance in assessing groundwater potential (Ogungbade et al. 2022). This method enhances objectivity and rigor in GIS analysis for groundwater assessment.

Apparent resistivity

The electrical resistivity technique assesses the resistance of subsurface materials by running an electric current through the ground and measuring the resulting electrical potential differences. According to Umoren et al. (2017), subsurface resistivity distribution is often correlated with physical attributes such as lithology, porosity, water saturation levels, and the presence of rock cavities. With its wide application in hydrogeological research for aquifer delineation, rock analysis, and geological formation identification, this technique offers vital subsurface data, especially for groundwater exploration, especially in sedimentary basins and complicated terrains (Ojo et al. 2024).

The results of field geophysical surveys are shown in Table 2 and Figure 2 along with the matching Schlumberger array and apparent resistivity, longitude, latitude, and elevation data. A gauge is an apparent resistivity in relation to subsurface resistivity.
Table 2

Aquifer parameters and characterization of the study area

VESLongitudeLatitudeResistivity (Ωm)Depth (m)Thickness (m)
VES 1 5.791 5.478 224.70 62.51 12.59 
VES 2 5.781 5.472 222.00 60.20 6.50 
VES 3 5.757 5.482 290.00 70.40 19.20 
VES 4 5.752 5.469 460.50 62.80 18.90 
VES 5 5.771 5.464 184.00 66.50 27.10 
VES 6 5.783 5.458 147.00 61.90 26.60 
VES 7 5.778 5.497 266.00 49.60 23.20 
VES 8 5.785 5.594 333.00 87.90 36.30 
VES 9 5.794 5.465 220.00 76.40 34.70 
VES 10 5.774 5.481 265.00 77.50 24.70 
VESLongitudeLatitudeResistivity (Ωm)Depth (m)Thickness (m)
VES 1 5.791 5.478 224.70 62.51 12.59 
VES 2 5.781 5.472 222.00 60.20 6.50 
VES 3 5.757 5.482 290.00 70.40 19.20 
VES 4 5.752 5.469 460.50 62.80 18.90 
VES 5 5.771 5.464 184.00 66.50 27.10 
VES 6 5.783 5.458 147.00 61.90 26.60 
VES 7 5.778 5.497 266.00 49.60 23.20 
VES 8 5.785 5.594 333.00 87.90 36.30 
VES 9 5.794 5.465 220.00 76.40 34.70 
VES 10 5.774 5.481 265.00 77.50 24.70 
Figure 2

Computer modeled curves of VES (a) 2, (b) 4, (c) 6, and (d) 8 showing modelled curve, theoretical curve, and layered iso-resistivities.

Figure 2

Computer modeled curves of VES (a) 2, (b) 4, (c) 6, and (d) 8 showing modelled curve, theoretical curve, and layered iso-resistivities.

Close modal

Aquifer and Dar-Zarrouk parameters

Table 3, sourced from Table 2, provides a detailed breakdown of the aquifer and Dar-Zarrouk characteristics. It explores key parameters including aquifer resistivity, conductivity, thickness, transmissivity, longitudinal conductance, transverse resistance, and hydraulic conductance. These meticulously compiled attributes offer both a broad perspective and essential insights, forming the cornerstone for understanding the complex interactions within groundwater systems and hydrogeological processes.

Table 3

Dar-Zarrouk parameters of the aquifers in the study area

VESLongitudinal conductance (Ω/m)Transverse resistance (Ωm)Hydraulic conductivity (m/day)Transmissivity (m2/day)
VES 1 0.06 2828.97 0.5054 6.3624 
VES 2 0.03 1443.00 0.5071 3.2964 
VES 3 0.07 5568.00 0.4642 8.9132 
VES 4 0.04 8703.45 0.3719 7.0296 
VES 5 0.15 4986.40 0.5328 14.4393 
VES 6 0.18 3910.20 0.5591 14.8713 
VES 7 0.09 6171.20 0.4789 11.1114 
VES 8 0.11 12087.90 0.4390 15.9353 
VES 9 0.16 7634.00 0.5085 17.6434 
VES 10 0.09 6545.50 0.4796 11.8452 
VESLongitudinal conductance (Ω/m)Transverse resistance (Ωm)Hydraulic conductivity (m/day)Transmissivity (m2/day)
VES 1 0.06 2828.97 0.5054 6.3624 
VES 2 0.03 1443.00 0.5071 3.2964 
VES 3 0.07 5568.00 0.4642 8.9132 
VES 4 0.04 8703.45 0.3719 7.0296 
VES 5 0.15 4986.40 0.5328 14.4393 
VES 6 0.18 3910.20 0.5591 14.8713 
VES 7 0.09 6171.20 0.4789 11.1114 
VES 8 0.11 12087.90 0.4390 15.9353 
VES 9 0.16 7634.00 0.5085 17.6434 
VES 10 0.09 6545.50 0.4796 11.8452 

Aquifer resistivity

Aquifer resistivities vary due to the inherent characteristics and composition of rock formations. Typically, aquifers exhibit higher resistivity values compared to other types of rocks. Determining specific aquifer resistivity values involves analyzing VES data shapes, modeling, and evaluating geological attributes at the site (Okoli et al. 2024). As a result, the range of aquifer resistivities lacks a predefined standard. In this study, aquifer resistivities range from 147.00 Ωm at VES 6 to 460.50 Ωm at VES 4.

The data from Figure 3 illustrates that the highest apparent aquifer resistivity values are situated within the southwestern region (VES 4) of the study area, whereas the lowest apparent aquifer resistivity values are observed in the southern part (VES 5 and 6) of the study area.
Figure 3

Aquifer apparent resistivity map of the study area.

Figure 3

Aquifer apparent resistivity map of the study area.

Close modal

Aquifer thickness and depth

Aquifer thickness, depicted on isopach maps by regions with similar thicknesses, measures the groundwater volume in a specific area. Figure 4(a) illustrates the distribution of aquifer thickness across the research area. Among the VES sites, VES 2, located centrally, has the lowest aquifer thickness at 6.50 m, while VES 8 in the southeast has the highest at 36.30 m. Notably, the northeastern part of the research area features thinner aquifers compared to the southern region.
Figure 4

Map of the aquifer: (a) thickness and (b) depth of the study area.

Figure 4

Map of the aquifer: (a) thickness and (b) depth of the study area.

Close modal

The vertical distance between the water table's saturation zone, located below, and the aeration zones above, defines aquifer depth. Water infiltrates the earth through pores and cracks in rocks and soil following rainfall. Due to gravity, this water gradually percolates deeper into the soil (Akiang et al. 2024). Consequently, the saturation zone accumulates water at its lowest point. Changes in precipitation patterns influence the movement of the water table: it rises as more water enters the saturation zone and declines when water is consumed or extracted from this zone.

Figure 4(b) presents the distribution of aquifer depths across the research area. Among the VES sites, VES 7 has the shallowest depth at 49.60 m, while VES 8 reaches the deepest depth at 87.90 m. This visualization aids in comprehending the spatial variations in aquifer depths within the study area.

Aquifer longitudinal conductance and transverse resistance

Longitudinal conductance, a secondary geoelectric parameter, emerges from the interplay of two primary geoelectric factors: layer thickness and resistivity. Within the investigated region, longitudinal conductance values exhibit a spectrum, ranging from 0.03 m/Ω at VES 2 to 0.18 m/Ω at VES 6, as depicted in Figure 5(a). This parameter serves as a key indicator of the subsurface's electrical conductivity, offering insights into the composition and structural characteristics of the geological formations. The variability observed underscores the heterogeneity of the subsurface environment and underscores the significance of longitudinal conductance in geophysical investigations.
Figure 5

Map of the aquifer: (a) longitudinal conductance and (b) transverse resistance of the study area.

Figure 5

Map of the aquifer: (a) longitudinal conductance and (b) transverse resistance of the study area.

Close modal

Aquifer transverse resistance serves as a pivotal parameter, offering insights into the spatial variation of resistance across diverse locations. This parameter is derived by multiplying aquifer resistivity with its thickness over a specific area, as outlined by Umoren et al. (2017). Figure 5(b) provides a visual representation of the distribution of aquifer transverse resistance across the research area.

Among the VES locations, VES 2 registers the lowest aquifer transverse resistance, measured at 1443.00 Ωm, whereas VES 8 shows the highest value, reaching 12087.90 Ωm. This significant variation emphasizes the importance of aquifer transverse resistance in delineating the subsurface hydrogeological characteristics within the study area. Particularly notable are the elevated levels of aquifer transverse resistance observed in the southeastern regions, suggesting potential differences in groundwater flow dynamics across the study area. Understanding these spatial variations is crucial for assessing groundwater movement and availability in different parts of the study region.

Aquifer hydraulic conductivity and transmissivity

As illustrated in Figure 6(a), hydraulic conductivity in the study area ranges from 0.3719 to 0.5591 m/day. The highest values, approximately 0.5591 m/day, are found near VES 6 in the southern part of the research region. Hydraulic conductivity is influenced by several factors, including aquifer thickness or depth, transmissivity, resistivity, transverse resistance, and lithology. This complex parameter measures how easily groundwater can flow through subsurface materials, significantly impacting hydrogeological processes and groundwater flow dynamics (Joshua et al. 2023). The area's groundwater circulation is shaped by a complex interplay of geological factors and a diverse subsurface environment, as highlighted by Ifeanyichukwu et al. (2021).
Figure 6

Map of the aquifer: (a) hydraulic conductivity and (b) transmissivity of the study area.

Figure 6

Map of the aquifer: (a) hydraulic conductivity and (b) transmissivity of the study area.

Close modal

Conversely, transmissivity is a crucial metric that assesses an aquifer's ability to transmit groundwater across its entire saturated thickness. Figure 6(b) illustrates the spatial distribution of aquifer transmissivity throughout the study area, providing valuable insights into groundwater movement at various hydraulic gradients. This visualization helps us understand how groundwater flows through the aquifer under different conditions.

Geospatial analysis

Lineament and drainage density maps

Lineaments, which are linear surface features, serve as crucial indicators of subsurface groundwater fissures, highlighting secondary porosity and detectable through tone variations in satellite imagery compared to other topographic features (Sahu et al. 2022). These lineaments encompass various geological formations such as cracks, faults, fractures, master joints, and elongated linear structures, influencing topographic linearity and the course of streams. Extensive research consistently links lineaments to groundwater occurrence and movement (Aladeboyeje et al. 2021; George et al. 2022; Adewumi et al. 2023).

Figure 7(a) graphically presents the lineament density map for the study region. This map calculates the length of linear features per unit area, providing essential information for assessing groundwater potential, as lineaments often represent permeable strata or units. The line density tool in ArcGIS was combined with the lineament map to determine lineament density across the study area. This integration allowed for a detailed analysis of the spatial distribution of lineaments, which are crucial indicators of groundwater pathways and storage potential.
Figure 7

Map of (a) lineament and (b) drainage density of the study area.

Figure 7

Map of (a) lineament and (b) drainage density of the study area.

Close modal

Areas with higher lineament density polygons generally correlate with greater recharge capacity, indicating better groundwater potential. This correlation arises because lineaments, which include fractures, faults, and other linear features, enhance the permeability of the subsurface materials, facilitating the infiltration and movement of groundwater. Consequently, regions with a higher density of these features are likely to have more effective groundwater recharge and storage capabilities.

The lineament density map is an invaluable tool for hydrogeologists and water resource managers as it highlights zones with potentially high groundwater yield. Such information is critical for the planning and development of sustainable water extraction methods, ensuring that groundwater resources are efficiently utilized and preserved. By identifying areas with high recharge potential, efforts can be directed toward regions that promise the most significant returns in terms of groundwater availability and quality (Adewumi et al. 2023).

By assigning the highest rating value to areas with the most significant lineament density, potential groundwater storage sites can be identified. Consequently, regions characterized by high lineament density likely harbor substantial groundwater resources (George et al. 2022). Furthermore, these lineament intersections serve as potential groundwater storage sites. Therefore, areas with elevated lineament density boast a heightened likelihood of hosting considerable groundwater reserves.

The drainage system of the research area, as delineated in Figure 7(b), offers crucial insights into the landscape's hydrological characteristics. This picture, which was made using DEM data, shows how different things, like vegetation cover, soil permeability, infiltration capacity, and slope gradient, affect the drainage pattern. Among these factors, the drainage system stands out as a fundamental indicator of hydrogeological dynamics (Ashaolu et al. 2020).

Indeed, low-drainage density regions are widely recognized as favorable zones for groundwater availability (George et al. 2022). Figure 7(b) reveals a range of drainage densities within the study area, spanning from 1 to 5 km−1, with a notable portion featuring low-density zones around the 1 km−1 mark. This distribution underscores the significant groundwater potential of these areas, highlighting their suitability for groundwater development and management initiatives. The insights gleaned from the analysis of drainage density patterns are invaluable for informed decision-making in sustainable water resource management practices.

Slope and rainfall maps

In the present study, the slope map of the research area (depicted in Figure 8(a)) was generated using the DEM data and the spatial analyst tool within ArcGIS. The resulting slope values portrayed in the map generally hover around 1°, indicating favorable conditions conducive to high levels of infiltration and recharge (Magesh et al. 2012). This observation suggests an enhanced water potential within the study area, bolstering prospects for sustainable water resource management and utilization. Regions characterized by gentle slopes exhibit minimal surface runoff, affording ample time for rainwater to permeate the soil and recharge groundwater reservoirs. Conversely, areas with steep slopes frequently experience rapid surface runoff, limiting the time available for infiltration and recharge processes to occur effectively (Naves et al. 2021).
Figure 8

Map of (a) slope and (b) rainfall of the study area.

Figure 8

Map of (a) slope and (b) rainfall of the study area.

Close modal

In geomorphological studies, rainfall data assumes critical importance, aiding in the identification of locations characterized by varying levels of groundwater potential. Areas experiencing high annual rainfall tend to boast elevated groundwater potential, as the abundance of water provides ample opportunity for infiltration and aquifer recharge (Shah & Lone 2019). Conversely, regions with limited annual rainfall face diminished groundwater potential, as the scarcity of water restricts opportunities for recharge.

However, it's essential to recognize that the timing and distribution of rainfall also exert significant influence on groundwater dynamics. Intense rainfall events occurring over short durations may overwhelm the soil's capacity to absorb water, leading to runoff and reduced recharge efficiency. Similarly, irregular rainfall distribution throughout the year can pose challenges to maintaining stable groundwater levels, particularly during dry periods.

Soil type and geology map

In this research, the lithology layer was established by digitizing a preexisting soil type map. RS data analysis revealed that the predominant soil types in the research area encompass various forms of clay, including loam clay, sandy clay, and silty clay (depicted in Figure 9(a)). Factors such as soil texture, structure, depth, and drainage collectively shape the groundwater potential inherent in different soil types.
Figure 9

Map of (a) soil type and (b) geology of the study area.

Figure 9

Map of (a) soil type and (b) geology of the study area.

Close modal

Of notable significance in groundwater systems is the Benin Formation, comprising lignite, claystone, and shale. While lignite is a potential groundwater source, claystone and shale typically have low permeability, serving as barriers or confining layers that impede groundwater flow as seen in Figure 9(b). The composition and properties of the Benin Formation can influence groundwater quality and the occurrence of contaminants, underscoring the importance of comprehensive geological analysis in groundwater studies.

LULC Map

The land cover and land use patterns in the studied area are closely related to the groundwater dynamics. Figure 10, reveals the makeup of the landscape (Koko et al. 2022). Patterns of water runoff are significantly influenced by land use and vegetation. Different land uses and types of cover affect water flow in various ways; some allow water to seep into the soil, reducing runoff, while others may cause water to remain on plant surfaces. Ultimately, water droplets captured by vegetation descend to the earth and replenish groundwater (Daramola et al. 2022).
Figure 10

Land use and land cover map of the study area.

Figure 10

Land use and land cover map of the study area.

Close modal

Groundwater dynamics are significantly influenced by both land cover and land use. While certain practices enhance groundwater recharge, others may have detrimental effects, particularly concerning processes like evapotranspiration. Vegetation can cause ongoing evaporation and transpiration of water, potentially increasing groundwater loss and counteracting the beneficial effects of infiltration and recharge. For instance, agricultural land with high irrigation demands might lead to excessive water withdrawal, affecting the natural recharge processes. Conversely, forested areas typically promote better infiltration and reduce surface runoff, enhancing groundwater recharge.

Urbanization also plays a crucial role in groundwater dynamics. Paved surfaces in urban areas prevent water from infiltrating the soil, leading to increased surface runoff and reduced groundwater recharge. Industrial activities can contribute to groundwater contamination, further complicating the management of water resources.

To effectively manage and sustain groundwater supplies, a comprehensive understanding of the dynamics of land use and land cover is essential (Aladejana et al. 2020). This understanding allows for the implementation of strategies that promote sustainable water use and mitigate adverse effects on groundwater resources.

Groundwater potential map

Thematic maps pertaining to variables affecting groundwater availability, transport, storage, and replenishment were integrated to construct a groundwater potential index (GPI) map (Figure 11). Using a weighted overlay technique, several thematic maps, including lineament density, slope, land use, and soil type, were analyzed and combined to create this GPI map for the research region. The region was subsequently divided into three zones based on groundwater potential: exceptional, good, and moderate.
Figure 11

Groundwater potential of the study area.

Figure 11

Groundwater potential of the study area.

Close modal

The northern regions exhibit high groundwater potential due to their moderate slopes and high lineament density. These areas are more conducive to groundwater infiltration and storage, making them ideal for sustainable water resource management. Conversely, regions with steeper slopes and lower lineament density tend to have reduced groundwater potential due to increased surface runoff and diminished infiltration capacity.

The GPI in the research area ranges from substantial to modest overall. This comprehensive approach underscores the importance of incorporating multiple variables to accurately assess groundwater potential. By evaluating these factors collectively, researchers can identify areas with the highest groundwater recharge potential and those that may require more careful management to ensure sustainable water use.

The GPI map serves as a valuable tool for decision-makers in planning and managing groundwater resources. It highlights the areas where groundwater development can be most effective and sustainable, guiding the allocation of resources and the implementation of conservation measures. Furthermore, this method can be applied to other regions with similar hydrogeological conditions, providing a framework for assessing and managing groundwater resources on a broader scale.

To evaluate the groundwater potential of the research region, a range of variables were analyzed for their contributions. Rainfall emerged as the most critical factor, accounting for 37.6% of the total influence. Other significant contributors included geology (24.6%), slope (13.5%), drainage density (8.9%), land use/coverage (6.2%), lineament density (4.8%), and soil type (3.9%). The computed CR value of 0.037, which is much lower than the acceptable threshold of 0.10, suggested a high level of consistency in the weighting of these factors.

To estimate the area coverage of the GPI, potential values were converted into shapefiles. The geometry calculator tool in ArcGIS 10.8 was then utilized to construct a table with the attributes of each potential zone, facilitating accurate mapping and classification of GWPZ.

The study incorporated seven thematic maps illustrating the factors influencing groundwater occurrence and migration: rainfall, geology, slope, drainage density, land use/coverage, lineament density, and soil type. Using the AHP within a GIS framework, these indicators were weighted according to their significance. A weighted overlay approach was then employed to integrate the weighted maps into a comprehensive picture of groundwater potential.

Rainfall was identified as the primary factor affecting groundwater availability and recharge. Areas with heavy rainfall were more likely to have higher groundwater potential. Geology played a crucial role, as certain rock types and formations facilitated the movement and storage of groundwater. Slope impacted infiltration and runoff processes, with gentler slopes promoting better infiltration and steeper slopes leading to increased runoff. Drainage density, representing the network of streams and rivers, influenced groundwater recharge, with higher densities indicating more substantial infiltration opportunities.

Land use and cover also altered groundwater potential by changing surface permeability and recharge rates. Vegetated areas allowed greater infiltration compared to urbanized areas with impermeable surfaces. Lineament density indicated subsurface cracks and faults, facilitating groundwater flow and storage. Soil type affected infiltration rates and water retention capacity, with different soils having varied permeability levels.

Superimposing the thematic maps provided an accurate depiction of the research region's groundwater potential. The findings revealed a substantial zone of exceptional groundwater potential throughout much of the studied area. The northwest part of the research area, near VES 3 and 10, exhibited the highest GPI. This region's favorable characteristics included high rainfall, suitable geology, low to medium slope, high drainage density, high lineament density, and good land use/cover.

Validation of VES with RS and GIS

The findings from vertical resistivity survey investigations were instrumental in validating results obtained through RS and GIS methodologies. By integrating multiple thematic maps and overlaying data from 10 geophysical survey locations, the groundwater potential map was generated. Table 4 offers a comprehensive summary of the study area's characteristics, affirming the accuracy and reliability of the study's findings.

Table 4

Comparison of groundwater potentiality assessment methods: VES vs. RS/GIS

VESClassification (VES)Classification (RS and GIS)
VES 1 Low Good 
VES 2 Low Poor 
VES 3 Low Excellent 
VES 4 Low Good 
VES 5 Intermediate Fair 
VES 6 Intermediate Poor 
VES 7 Intermediate Good 
VES 8 Intermediate Good 
VES 9 Intermediate Fair 
VES 10 Intermediate Good 
VESClassification (VES)Classification (RS and GIS)
VES 1 Low Good 
VES 2 Low Poor 
VES 3 Low Excellent 
VES 4 Low Good 
VES 5 Intermediate Fair 
VES 6 Intermediate Poor 
VES 7 Intermediate Good 
VES 8 Intermediate Good 
VES 9 Intermediate Fair 
VES 10 Intermediate Good 

In Table 4, the groundwater potentiality in the study area is evaluated using two different methods: VES and RS coupled with GIS. Each method assigns a classification to indicate the groundwater potential in the respective locations.

The VES classification categorizes the groundwater potentiality into three levels: low, intermediate, and high. On the other hand, the RS/GIS classification provides a more detailed assessment, ranging from poor to excellent. A comparison of the classifications reveals some disparities between the two methods. VES locations classified as low or intermediate are often rated as having good or excellent potentiality when assessed using RS/GIS. Conversely, VES locations classified as having intermediate potentiality may sometimes be rated as poor or fair using RS/GIS.

This comparison underscores the importance of utilizing multiple methods for groundwater potentiality assessment. While VES provides valuable direct measurements, RS/GIS integration offers a broader spatial perspective and can incorporate additional data layers for a more comprehensive evaluation. Integrating these methods can enhance the accuracy and reliability of groundwater potentiality assessments, ultimately supporting more informed decision-making in water resource management and planning efforts.

In hydrogeological research, the electrical resistivity technique proves invaluable for delineating aquifers, analyzing rock formations, and identifying geological structures. By measuring the resistance of subsurface materials through the passage of electric current and subsequent measurement of electrical potential differences, this method correlates with physical properties such as lithology, porosity, water saturation, and the presence of rock voids. Particularly in complex terrains and sedimentary basins, it provides crucial data for groundwater studies.

At Aladja, Udu Local Government Area, Delta State, Nigeria, VES was employed to assess the vertical distribution of conductive zones. The obtained resistivity values ranging from 147.00 to 460.50 Ωm provided comprehensive insights into aquifer parameters, revealing significant variations influenced by underlying rock properties and compositions. Aquifer depths ranged from 49.60 to 87.90 m, while thickness varied from 6.50 to 36.30 m.

Key Dar-Zarrouk parameters including transmissivity, hydraulic conductivity, longitudinal conductance, and transverse resistance were analyzed. Transverse resistance ranged from 1443.00 to 12087.90 Ωm, with longitudinal conductance values varying from 0.03 to 0.18 m/Ω. Transmissivity varied from 3.2964 to 17.6434 m²/day, reflecting diverse hydrogeological conditions, while hydraulic conductivity values ranged from 0.3719 to 0.5591 m/day, with higher values in specific zones.

The geophysical findings were corroborated by RS and GIS analyses. Maps of drainage density and lineament density highlighted the correlation between groundwater potential and geological formations, with higher lineament density and lower drainage density indicating greater groundwater prospects. Similarly, slope and rainfall maps underscored those areas with higher rainfall and gentler slopes exhibited better groundwater recharge potential.

RS and GIS research provided an in-depth understanding of the spatial distribution of groundwater potential. Soil type and geology maps emphasized the prevalence of different clay types and highlighted the significance of formations like the Benin Formation, which includes shale, claystone, and lignite, in influencing groundwater flow dynamics. Analysis of land use and land cover revealed how various activities and vegetation impacted water runoff and groundwater recharge, with vegetated areas demonstrating superior recharge capability compared to urbanized zones.

The groundwater potential evaluation integrated all thematic maps, categorizing the region into zones of moderate, good, and exceptional groundwater potential. Rainfall emerged as the most influential factor, followed by soil type, geology, slope, drainage density, land use/cover, and lineament density. While discrepancies existed, validation of RS and GIS results against VES data underscored the importance of combining multiple approaches for comprehensive assessment.

By merging several thematic maps and overlaying data from 10 geophysical survey locations, the groundwater potential map was generated. This holistic approach confirmed the hydrogeological diversity of Aladja, and Udu and validated the accuracy of the findings. The combined use of electrical resistivity, RS, and GIS methodologies provides an effective framework for assessing groundwater potential, offering essential insights for groundwater exploration and sustainable water resource management. This integrated method facilitates the optimal distribution of water resources by delineating aquifer boundaries, assessing groundwater quality, and identifying areas with high potential for groundwater extraction.

All relevant data are available from an online repository at https://earthexplorer.usgs.gov/.

The authors declare there is no conflict.

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