In the Central Region of Ghana, electromagnetic, electrical resistivity, and magnetotelluric surveys have been used extensively for groundwater exploration for specific locations in the region. In order to determine the spatial distribution of groundwater potential across the region, airborne magnetic and radiometric data, and remote sensing data was used in addition to Weighted Overlay Model in a Geographic Information System’s environment leading to identification of five groundwater potential zones: very low, low, moderate, high and very high. Assessment of the groundwater potential zones mapped, shows that approximately 0.91%, 14.03%, 56.20%, 27.53% and 1.32% of the area respectively were observed to constitutes very low (yield ≤ 0.66 m3/h), low (0.66m3/h < yield ≤ 2.4 m3/h), moderate (2.4 m3/h < yield ≤ 6.3 m3/h), high (6.3m3/h < yield ≤ 14.4m3/h) and very high (> 14.4 m3/h) groundwater potentials zones. Validation potential zones using modified Index of Agreement and Modified Nash-Sutcliffe Error gave 0.81 and 0.74, efficiencies respectively. These efficiencies respectively show very good and good estimates for the hydrological model, showing that the method adopted in delineating groundwater potential is very good, and can be adopted for future detailed groundwater exploration in the area.
Research Article
Integration of Airborne Geophysical Data, and Remote Sensing, in Groundwater Potential Mapping in the Central Region of Ghana.
https://doi.org/10.21203/rs.3.rs-5230811/v1
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Aeromagnetic and radiometric datasets
remote sensing
weighted overlay model
The conventional approach for identifying, delineating, and mapping groundwater is based mostly on ground surveys employing geophysical, geological, and hydrogeological techniques. However, on a regional scale, this approach is costly and time-consuming (Israil et al., 2006). In Central Region of Ghana, the provision of portable water by means of boreholes makes use of exploration techniques such as near-surface geophysical methods (electromagnetic, electrical resistivity and, more recently, magnetotelluric surveys) as well as terrain evaluation. However, there have been numerous cases of dry and low-yielding wells for every major groundwater exploration program. There are also cases where productive wells and low-yielding wells dried up at the peak of the dry season when water demands are high and the recharge of aquifer systems is low.
Despite the successful use of these geophysical methods for the location of boreholes, their use in the mapping of groundwater potential in large regions can be expensive in terms of the time and resources needed for such activity. In addition, groundwater exploration in the country using these methods rarely takes into account all relevant spatial parameters that contribute to groundwater occurrence, accumulation, distribution and storage. In order to spatially map the groundwater potential across the region, there is the need to consider a multi-facet approach that takes into account relevant geospatial parameters that contribute to the accumulation, distribution and storage of groundwater in crystalline terrain like the Central Region of Ghana.
The use of relevant geospatial parameters for groundwater potential studies has been used in different parts of the world. For instance, Simeone et al., (2020) used airborne radiometric, magnetic and electromagnetic to map groundwater potential zones. While aeromagnetic survey in a geographic information system was used for groundwater potential studies, (e.g., Joel et al., 2016; Langenheim and Jachens, 2011), Remote sensing and geographic information systems was used for groundwater potential studies (e.g., Sharma et al., 2012; Khodaei, 2011). In Ghana, studies on groundwater potential zones across the country have been mapped by making use of spatial modelling tools and remote sensing datasets (Guma and Pavelic et al., 2012). In the Central Region, Osiakwan et al., (2022) made use of geological data, borehole yield data and geographic information system to map groundwater potential zones. In the southwestern part of Central Region, Danso and Ma (2023) used geology and remote sensing data to map five groundwater potential zones. Despite the use of these methods to determine groundwater potential in the region, the methods did not use airborne magnetic, radiometric, and remote sensing data to map out spatial groundwater potential zones, let alone predict groundwater potential zones with numerical figures of possible yields across the region.
Geospatial tools are efficient and cost-effective in modelling a variety of valuable geoscience datasets. Various approaches/techniques (e.g., frequency ratio, multi-criteria decision analysis, weights-of-evidence, logistic regression, evidential belief function, artificial neural network model) have been utilized globally to integrate these datasets in delineating groundwater potential zones (e.g., Shabani et al., 2022; Olajide et al., 2022; Nguyen et al., 2020a, b; Guru et al., 2016; Nampak et al., 2014; Ozdemir, 2011; Tahmassebipoor et al., 2016).
Among the various approaches, GIS constitutes an effective tool that provides an efficient means for analyzing and integrating geoscience datasets. The hydrological conditions of the study area show that groundwater can be found in semi-confined to confined aquifers in the deep-seated fractured aquifers in the crystalline rocks, as well as unconfined aquifers, particularly in alluvium, fractured and weathered crystalline rocks. This paper used a GIS-based weighted overlay approach to delineate the groundwater potential zones in the study area. This is because the method is cost-effective and can effectively be used for groundwater exploration before going for detailed and expensive surveying techniques. Thus, our study integrated various evidential layers generated from airborne radiometric, magnetic, and remote sensing using the Weighted Overlay Model to map potential groundwater zones within the Central Region, Ghana. This shall enable policy implementation agencies to focus on areas where successful wells can be obtained by avoiding areas where unsuccessful wells can be encountered.
Central region is geographically bounded by latitudes 6.4125° N and 5.0375° S and longitudes 0.125° W and 2.175° W and covers a total area of 9830 km2 approximately. Geologically, the region is underlain by crystalline formations: Tamnean Plutonic Suite, Birimian Supergroup, Tarkwaian Group and Eburnean Plutonic Suite which formed the major formation occupying approximately 90% of the underlying formation, while Togo Structural Unit, Mesozoic, Amissian and Sekondian Groups occurring as traces within the area along the coast (Kesse, 1985; Grifiths, et al., 2002; Doudu et al., 2009; Yaw-Bisianko, 2020; Fig. 1a). Soils in the area generally consist of clayey sand, gravelly sand and sand. Typical soil types such as ferric and haplic lixisols, as well as ferric acrisols, constitute about 80% of the soils within the area. Fluvisols, solanchalks, luxisols, leptsols. leptosols, luvisols, arenosols, and vertisols occur as minor traces in the eastern and northwestern sections of the study (Fig. 1b).
2.1 Hydrogeology
With the exception of major towns that depend on pipe-borne water supply, smaller towns depend mainly on boreholes for their domestic water supply. In some communities, hand-dug wells are the main source of potable water supply. These wells are mostly located near valleys where there are thick layers of sand and gravel accumulate, thereby promoting the infiltration of rainwater for groundwater storage. Depths of hand dug wells rarely exceed 25 m. This is due to the crystalline nature of underlying rocks, which have no primary porosities. The development of secondary porosities such as weathered zones, fractures, contact zones, jointing and fracturing within these basement rocks enhances the development, occurrence, accumulation and storage of groundwater. Assessment of boreholes in the area shows yields that varied from 0.0 m3/h to 24.0 m3/h. Aquifer zones varied between 26 m to 50 m, and depths of wells rarely exceeded 60 m. The water quality of most wells shows fresh water. However, there are isolated cases of saline water in some boreholes, mostly located along the coastal areas, but the salinity decreases as one moves inland.
3.1 Metadata
In order to estimate the spatial distribution of groundwater in the central region, airborne magnetic and radiometric datasets and geo-referenced borehole logs of existing successful and unsuccessful boreholes were correlated and analyzed. Airborne magnetic and radiometric datasets were obtained from the Ghana Geological Survey Authority; a topographic map was obtained from the Survey Department. A-30 m resolution digital elevation model for the study area was deduced from Shuttle Radar Topographic Mission (SRTM) data sourced from the U.S. Geological Survey Earth Explorer (www.earthexplorer.usgs.gov). Other evidential layers generated from SRTM include slope, stream order, stream accumulation, stream density, land use/land cover, and soil types. The potential zones for groundwater were proposed using a knowledge-based approach: the Weighted Overlay Model (WOM). Classes of evidential layers generated from relevant geophysical and remote sensing datasets were scored 1 to 9 based on experts’ opinions on how each class of a thematic layer influences the distribution of groundwater in the study area. The most favourable and significant class was assigned a class score of 9, and the least favourable class was assigned a score of 1(e.g., proximity close to a fault or fault intersection was regarded significant as compared to farther proximities). Hence, the class of closest proximity was assigned the highest score, the class of farthest was assigned the lowest score and the intermediate class was assigned scores between 1 and 9.
3.2 Space Radar Topographic Mission (SRTM)
Digital Elevation Model (DEM) and shaded relief maps were generated from the SRTM data on an ArcGIS platform. The SRTM was subjected to various illuminations (0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°) with a constant inclination of 45° in order to generate different relief maps. During this process, structural lineaments that were orthogonal to the light incident whenever the illumination was varied were delineated and analyzed (e.g., fault and fracture zones). The DEM was subjected to hydrologic modelling methods using the ArcGIS extension toolbox to determine the stream flow direction, stream flow accumulation, watersheds and stream order/network. A slope map was also generated.
3.3 Magnetic data
Various lineament enhancement and edge detection filters were applied to the magnetic data to delineate lithological contacts and structural lineaments (e.g., faults, fractures, dykes) that have the potential to influence or control the groundwater system in the study area. Some of the filters include Reduction to the Pole (RTP), Analytic Signal Amplitude (ASA), First Vertical Derivative (1VD) and Tilt Angle Derivative (TDR).
In order to interpret the geology and structures from the magnetic data, the total magnetic intensity map was reduced to the pole using a magnetic inclination of -15, magnetic declination of -5.8, and total intensity of 31756 nT in order to shift the anomalies directly over the spatial location of the sources (Hansen & Pawlowski, 1989; Blakely, 1995; Rajagopalan, 2003). Since Central Region, and for that matter Ghana, lies within low magnetic latitudes (< 20°), interpreting RTP images at low magnetic latitude (inclinations within 20° of the magnetic equator) presents some difficulties
due to the induced magnetization producing a north-south noise in the data (Rajagopalan, 2003). For example, the transform is not able to completely reconstruct NS-trending anomalies of the dykes and regional faults in the area. The Analytical Signal Amplitude (ASA) filter, which is independent of magnetization direction (Nabighian, 1972; Rajagopalan, 1997), was applied to the Total Magnetic Intensity (TMI, Fig. 2a) to give the ASA image (Fig. 2c). Compared to the RTP (Fig. 2b), this gives a better fit of the anomaly-to-source location. The maxima of the anomalies approximately coincided with the Birimian metavolcanic, with the low coinciding with the Birimian metasediment. The edges of the dykes, faults, and lithological contacts (e.g., Tarkwaian Group and Sekondian Group at the NW part of the study) were enhanced by the ASA filter.
The 1VD and TDR filters were applied to the magnetic image to enhance local and subtle fault and dyke anomalies in the study area, as well as isolate diffused anomalies from closely spaced magnetic sources (Blakely, 1995; Cooper and Cowan, 2004, 2006; Ma et al., 2014). The zero values of both filters are placed on the edges of the magnetic sources (Nasuti et al., 2019). These led to an enhanced number of regional structures in the area, 1VD (Fig. 2d) and TDR (Fig. 2e). The TMI image (Fig. 2a) was subjected to the Centre for Exploration Targeting (CET) grid analysis tool in Oasis Montaj platform for magnetic texture analysis and lineament extraction (Holden et al., 2012). The standard deviation, phase symmetry, amplitude threshold, skeletonization, and vectorisation processes guided the extraction of the magnetic lineaments (Fig. 2f). From the Rose diagram in Fig. 2f, it can be seen that most of the structures trend in the NE-SW direction, the main structure trend for Birimian structures (Kesse, 1985; Griffis et al., 2002).
3.4 Radiometric data
Airborne radiometric data was used to ascertain the variation in the concentration of naturally occurring radioelements such as potassium (K) (Fig. 3a), Uranium (eU) (Fig. 3b), and thorium (eTh) (Fig. 3c) in the various lithological units in the area (Anderson & Nash, 1997; Wilford et al., 1997). K (%) is a dominant constituent of most lithology, with mobile eU (ppm) and immobile eTh (ppm) present in trace amounts as mobile and immobile elements, respectively (Elawadi et al., 2004). Derived ratio and ternary maps from the radioelements were used to verify the mapped lithological units, as well as detect weathered zones (Telford et al., 1994; Lo and Pitcher, 1996; Wilford et al., 1997; Elawadi et al., 2004) which have major control on groundwater occurrence.
To delineate K-enrichment zones, high K values related to hydrothermal alteration had to be separated from that of lithology and weathering through the following procedure (de Quadros et al., 2003): First, the deviations or anomalies of measured K were calculated as: \(\:{\text{K}}_{\text{d}}\) = (K map -\(\:{\:\text{K}}_{\text{i}}\)) /\(\:\:{\text{K}}_{\text{i}}\) with \(\:{\text{K}}_{\text{i}}\) = (Kaverage / eThaverage) × eTh map, where the \(\:{\text{K}}_{\text{i}}\) is the ‘ideal thorium-defined potassium’ value for each station having measured thorium values and \(\:{\text{K}}_{\text{d}}\) is the deviations (anomalies) of measured K values from ideal calculated K values (i.e.\(\:\:{\text{K}}_{\text{i}}\)). Secondly, the abundance of measured K related to the ratio of eTh and eU was estimated as F parameter = K map × (eTh map/eU map), where the F parameter defines the abundance of measured K related to eTh/eU ratio. Lastly, a ratio map of K/eTh was generated. The radiometric ternary RGB image (Fig. 3d) was generated to highlight anomalous hydrothermal alteration zones by assigning the following parameters to the following channels: (i) Red channel: \(\:{\text{K}}_{\text{d}}\); (ii) Green channel: F parameter; and (iii) Blue channel: K map/eTh map ratio.
The resulting Kd, F parameter and K/eTh ratio images were then combined in a Principal Component (PC) Analysis, and the high PC1 image values were identified as hydrothermal alteration zones (K-enrichment) (Fig. 4b). The accumulations of K-alteration in the rivers were masked using river channels in the study area. The k-rich alteration zones extracted from the ternary map in Fig. 3d are shown in Fig. 4. These alteration zones overlaying heavily faulted regions were interpreted as prospective zones (IAEA, 2003; Sharma, 2000; Telford, 1994) that are favorable for groundwater accumulation.
3.5 Evidential layers and class scores
In this study, thirteen evidential layers, namely, geology, soil, lineament, lineament intersection, lineament density, lineament intersection density, dyke, alteration zones, land use/cover, digital elevation model, slope, stream flow accumulation and drainage density, were used. The evidential layers were each assigned a weight based on the relevant importance of the layer to groundwater occurrence, accumulation and storage (Table 1). The assigned scores were done such that the total weight scores add up to 100%. Mapped lineaments, which are possible fracture zones and weathered zones, have major control on groundwater occurrence (Joel et al., 2016). Therefore, areas with lineament (Fig. 5a) were given a score of 10. Also, lineament density, which influences the availability of trap zones for infiltration of rainwater for groundwater storage, was given a score of 8 (Fig. 5b). Lineament intersection (Fig. 5c) was given the highest score of 15 because groundwater occurred within developed secondary porosities in crystalline terrain. Furthermore, high groundwater potential areas are associated with areas of high lineament intersections (Reddy and Raju, 1998). Lineament intersection density (Fig. 5d), which is the total length of lineaments per unit area, was given a score of 10. Proximity to mafic dyke (Fig. 5e) was assigned a lower score of 5 since dykes primarily act as barriers to groundwater flow and proximity to alteration zones (Fig. 5f), derived from the airborne radiometric dataset) was given a score of 7. Geology (Fig. 1a) was also given a score of 10 because it plays a significant role in the weathering and fracturing of rock types, similar to lineaments. This makes geology and lineament each to be assigned a score of 10 since both have a significant influence on groundwater availability. Soil types (Fig. 1b) in the study were assigned a score of 8 since the soil types will influence the rate of infiltration. Land use/cover and lineament density were each assigned a score of 8. This is because the soil type will influence the rate of infiltration, and the land use/cover determines to what extent the land has been put to use. Thus, land use/cover (Fig. 6a) is dependent on the soil type underlying the area and was given a score of 8, just like soil types. The digital elevation model (Fig. 6b) was assigned a score of 5. Slope (Fig. 6c) was given a score of 7. Stream flow accumulation (Fig. 6d) and drainage density (Fig. 6e) were assigned scores of 4 and 6, respectively (Table 1). The stream flow accumulation (Fig. 6d) was given the lowest score of 4 because the important of stream does little in the weathering or fracture for groundwater accumulation except for bank storage or when the stream flow tends to recharge a weathered zone or some extensive fracture extending across some section of the stream. The drainage density was given score of 6 because drainage reflects the inability of surface flow to infiltrate into the subsurface environment. Areas of high drainage density are low groundwater potential zones since they have high surface flow and vice versa (Al-Djazouli et al., 2020).
|
No. |
Evidential layer |
Weight |
|---|---|---|
|
1 |
Geology |
10 |
|
2 |
Soil type |
8 |
|
3 |
Proximity to lineament |
7 |
|
4 |
Density of lineaments |
8 |
|
5 |
Lineament Intersection |
15 |
|
6 |
Density of lineament intersection |
10 |
|
7 |
Proximity to mafic dyke |
5 |
|
8 |
Proximity to alteration zone |
7 |
|
9 |
Land use/cover |
8 |
|
10 |
Digital elevation model |
5 |
|
11 |
Slope |
7 |
|
12 |
Flow accumulation |
4 |
|
13 |
Drainage Density |
6 |
The classes of each evidential layer deduced from the geology, magnetic, radiometric and remote sensing datasets (e.g., DEM) were given scores ranging from 1 to 9. Nine was chosen because, for each evidential layer, the maximum number of classes is 9. For each class, the subcategory that has the least contribution to groundwater occurrence was given a score of 1, and the most favourable a score of 9. The intermediaries that lie between the two extremes were given scores between 1 and 9 according to their relative importance to groundwater occurrence. The geology layer was reclassified into 8 classes due to the fact that there are 8 groups/structural units within the study area. The regions of the Sekondian Group, the Tamnean Plutonic Suite and the Eburnean Plutonic Suite were regarded as favourable classes. They were assigned high scores as compared to the overlying Tarkwaian Group and Birimian Supergroup. This is based on the composition and ability of the rocks contained in the group, formation or structural unit to have varied porosities and can undergo weathering. Groups/structural units containing rocks with high porosities and can undergo rapid weathering are given higher scores. On the other hand, groups containing rocks with low porosities and can undergo less weathering state are given lower scores (Shama, 2002; Telford et al., 1994; Blyth & de Fraites, 1984). The intrusive suites serve as an aquitard to the overlying aquifers. High scores were assigned to the land cover layer classes for grass without dispersed trees, moderately closed trees, and moderately dense herbs with scattered trees. These locations are low-elevation and easily accessible.
The soil cover layer was used to identify regions with high water retention capability. The favourable classes were the leptosols, lixisols and acrisols, with high scores of 7, 8, and 9, respectively. Lower values were assigned to other soils with lower porosities. The favourable classes for the DEM and slope layers were the 35–63 m class (lowest elevated region) and the 0-10.45o class (low lying-gentle slope). The stream flow accumulation layer was classified into eight classes, and the favourable class, 128, was assigned the highest score of 9. This is because the 128 class represents the area with the concentrated flow and stream channels. Lower scores were assigned to flow accumulations for 64, 32, 16, 8, 4,2, and 1. The stream order/drainage density layer is defined as a ratio of the total length of all streams to the total area of the basin. The favourable drainage density classes are 0.59–0.69, 0.69–0.82, and 0.82–1.18, with class scores of 9, 8, and 7, respectively. Lower scores were accordingly assigned to less favourable drainage densities. The proximity layers generated were proximity to the faults layer, proximity to the fault intersection layer, proximity to dykes, and proximity to the hydrothermal alteration zones layer. The favourable proximity classes were 100, 200, and 300 m, with 9, 8, and 7 class scores, respectively. Less favourable proximity classes: 400, 500, 1000, 2000, 3000 and > 3000 m was assigned scores of 6, 5, 4, 3, 2 and 1, respectively. The lineament density layers generated were the fault density layer and fault intersection density layer. The proximity and lineament density layers were used to identify regions with favourable permeability. Higher scores were assigned to areas with close proximity, and lower scores were assigned to areas with less proximity. Since dykes serve as barriers to groundwater occurrence (Comte et al., 2017), areas closest to dyke were given lower scores and areas farthest were given the highest score (Table 2.0). The closest proximity to dyke was given a score of 8 instead of 9 since Manso dyke in the study may have some substantial thickness (Antonio et al., 2019).
3.6 Weighted Overlay Model
In order to integrate thirteen (13) evidential layers (proximity to faults, fault density, fault intersection, faults intersection density, dykes, radiometric alteration zones, stream order, stream flow density, stream flow accumulation, geology, land use/ land cover, slope, and digital elevation model) derived from processed airborne magnetic and radiometric datasets and relevant remote sensing data, Weighted Overlay Model in a GIS environment was employed. In such a suitability study, the weighted overlay modelling is the intersection of standardised and differently weighted layers to solve multicriteria problems such as site selection for potential groundwater (Steinel et al., 2016). The assigned weights quantify the relative importance of the evidential layers. The weighted evidential layers were combined through the equation below, which calculates an average weighted output score (\(\:\overline{\text{S}}\)) for each pixel (Bonham-Carter, 1994; Yousefi and Carranza, 2016):
where \(\:{\text{S}}_{\text{i}\text{j}}\) is the class score assigned to each of the jth classes of the ith evidential layer according to their relative importance based on experts’ knowledge of the study area, and \(\:{\text{W}}_{\text{i}}\) is the weight assigned to the evidential layers based on relative importance as compared to other layers. The output score (\(\:\overline{\text{S}}\)) is written to new cells in an output layer. The symbology in the output layer is based on these output scores.
These suitability models of the thirteen evidential layers are superimposed on georeferenced borehole yields to obtain the area's groundwater potential. To validate the groundwater potential map obtained, a set of new geo-referenced boreholes are used to determine the dispersion patterns of borehole yields and the distribution of such yields of boreholes based on categorized yields of boreholes defined within the study.
3.8 Validation of Results
The groundwater potential map was validated using borehole yields in the area to determine the distribution of borehole yields or categories that constitute the study area. Two validations were done using Modified Index of Agreement (Mod., IOA) and Modified Nash-Sutcliffe Error (Mod., NSE) to determine the accuracy of groundwater potential map. According to Krause et al., (2005), the Modified Index of Agreement(dj) and Modified Nash-Sutcliffe Error (Ej) are given as:
where M is the mean, \(\:{O}_{i}\), the observed value, \(\:{P}_{i}\) is the predicted or modeled value and jЄN, ∀=1. The statistical determination of the errors associated with the groundwater potential map obtained is then classified as unsatisfactory, satisfactory, good, and very good for error or modified values of x: x ≤ 0.5, 0.5 < x ≤ 0.65, 0.65 < x ≤ 7.5 and 0.75 ≤ x < 1.0 respectively (Bouslihim et al., 2016). A conclusion is then drawn as to the error associated with using the model and the map as a basis for future groundwater exploration in the study area.
The summary class scores of the thirteen evidential layers: geology, soil, lineament, lineament intersection, lineament density, lineament intersection density, dyke, alteration zones, land use/cover, digital elevation model, slope, stream flow accumulation and drainage density using the weighted Overlay Model are indicated in Table 2. For each thematic layer, class scores varied from 0.04 to 0.90, while the thematic map value for each evidential layer varied from 1.8 to 6.75 for drainage density and lineament intersection, respectively. Least and maximum class scores occurred in proximity to dyke and lineament intersection density, respectively. Least and maximum scores for each evidential layer are associated with stream flow accumulation and lineament intersection density, respectively.
| No | Parameter | Class | Class score | Weight (%) | Favourability | Suitability Value |
|---|---|---|---|---|---|---|
| 1 | Geology | Group/Structural Unit: MG; TSU; AG; TG; BSP; SG; TPS; EPS. | 1, 2,3,4,6,7,8,9 | 10 | 0.1;0.2;0.3;0.4; 0.6;0.7 | 4.00 |
| 2 | Soil | Soil types: Flu.; Sol.; Are.;Ver.; Luv.;Lep.; Lix.; Acr.; | 1;2;3;5;6;7;8;9 | 8 | 0.08;0.16;0.24;0.40;0.48; 0.56;0.64;0.72 | 3.28 |
| 3 | Proximity to lineament(m) | Buffer distance: 100100;200;300;400; 500;1000; 2000;3000; > 3000 | 9;8;7;6;5;4;3;2;1 | 7 | 0.63;0.56;0.49;0.42;0.35;0.28;0.210.14;0.07;0.8; | 3.15 |
| 4 | Lineament density | 0-0.13; 0,13-0.31;0.31–0.46; 0.46–0.59;0.59–0.72;0.72–0.86; | 1;2;3;4;5;6;7;8;9 | 8 | 0.08;0.16;0.24;0.32;0.40;0.48;0.56;0.64;0.72 | 3.60 |
| 5 | Lineament intersection(m) | Buffer distance: 100100;200;300;400; 500;1000; 2000;3000; > 3000 | 9;8;7;6;5;4;3;2;1 | 15 | 1.35;1.2;1.05;0.9;0.75; | 6.75 |
| 6 | Lineament intersection Density | 0-0.07; 0.07–0.15; 0.15–0.23; 0.23–0.31; 0.31–0.39; 0.39–0.47; 0.47–0.55; 0.55–0.71 | 1;2;3;4;5;6;7;8;9 | 10 | 0.1; 0.2;0.3;0.4;0.5; 0.6; 0.7; 0.8;0.9; | 4.3 |
| N.B: ‘MS = Mesozoic group; TSU = Togo Structural Unit; AG = Amissian Group; TG = Tarkwaian Group;BG = Birimian Group; SG = Sekondian Group; TPU = Tamnean Plutonic Suite; EPS = Eburnean Plutonic Suite. Flu = Fluvisol; Sol = Solonchaks; Are = Arenosols; Ver = Vertisols; Luv = Luvisol; Lep = Leptisols; Lix = Lixisols; and Acri = Acrisols’. | ||||||
| No | Parameter | Class | Class score | Weigh t(%) | Favourability | Suitability Value |
|---|---|---|---|---|---|---|
| 7 | Dyke | Buffer distance (m): 100100;200;300;400; 500;1000; 2000;3000; > 3000 | 8;9;7;6;5;4;3;2;1 | 5 | 0.40;0.45;0.35;0.30;0.25;0.20;0.15;0.10;0.05 | 2.25 |
| 8 | Alteration Zone | Buffer distance (m): 100100;200;300;400; 500;1000; 2000;3000; > 3000 | 9;8;7;6;5;4;3;2;1 | 7 | 0.63;0.56;0.49;0.42;0.35;0.28;0.21;0.14;0.07 | 3.15 |
| 9 | Land use/cover | Closed Forest < 60% trees; SW; MT; Mosaic Thickets; Grass; GC; MDT | 1;2;4;6;7;8;9 | 8 | 0.08;0.16;0.32;0.48;0.56;0.64;0.72. | 2.96 |
| 10 | Digital Elevation Model (DEM) | 35–63;63–88;88–110;110–151; 151–174; 174–214;214–399 | 9;8;7;6;5;4;3.1 | 5 | 0.45;0.40;0.35;0.30.0.25;0.20;0.15;0.10; 0.05 | 2.10 |
| 11 | Slope | 0-10.45;10.45–17.42; 17.42–23.70; 23.70.29.71;29.71–39.68;39.68–42.09; | 9;8;7;6;5;4;3;2;1. | 7 | 0.63;0.56;9.49;0.42;0.35;0.28;0.21;0.14;0.07 | 3.28 |
| 12 | Stream flow accumulation | 1;2;4;8;16;32;64;128 | 1;2;3;4;5;6;7;8;9 | 6 | 0.06; 0.12; 0.18; 0,24;0.30; 0.36;0.42; | 2.40 |
| 13 | Drainage Density | 0-.08;0.08–0.19;0.19–0.29;0.29–0.39; 0.39–0.48; 0.48–0.59;0.59–0.69;0.69–0.82; | 1;2;3;4;5;6;9;8;7 | 4 | 0.04;0.08;0.12;0.16; 0.20;.24;0.36;0.32;0.28 | 1.80 |
| NB: SW = Savanna Woodland; MT = Mosaic Thickets; Grass, scrubs, scattered trees; MCT = Moderately closed trees; MDT = Moderately dense trees | ||||||
Assessing the percentage contributions of the evidential layer parameters responsible for the spatial distribution of groundwater in the study area using WOM depicts 7.32%, 8.36%, 15.69%, 9.99%, 9.29%, 5.23%, 7.62%, 7.32%, 5.57%, 4.18%, 6.88%,4.88% 7.62% respectively for lineament, lineament density, lineament intersection, lineament intersection density, geology, dyke, soil, radiometric alterations, stream flow accumulation, drainage density, land use, digital elevation model and slope. These values were obtained by first finding the map value, which is equal to Σ (each class score * weight/100). Then dividing each map value by the total map values and multiply by 100 percent. The density of lineament intersection constitutes the highest contribution of 15.69% for the favorability and groundwater potential map, whereas stream order density (drainage density) provided the least contribution of 5.23%. The contributions of other evidential layers lay between the two extremes. Soil and slope have equal contribution of 7.62% to groundwater potential in the area. This suggests that the presence of slopes leads to the formation of soil along slopes. Similarly, radiometric alterations, which are basically weathered zones (IAEA, 2003; Sharma, 2002; Telford et al., 1994), contribute equally (7.32%) like lineament. The equal contribution of weathered zones and lineaments suggests that lineaments in the study area play contributing roles in the formation of weathered zones
4.1 Groundwater Potential Map
Groundwater serves as a source of water for domestic, industrial, and agricultural uses and other developmental initiatives (Arulbalaji et al., 2019). The groundwater potential zone map generated from the GIS-based weighted overlay approach was overlaid with the spatial location of available borehole yields. The borehole yields are categorized into five (5) groups as shown in Table 3.0 below: (i) very low groundwater potential could be considered for yields between a dry well (0 m3/h) and 0.66 m3/h (11 litres per minute); (ii) low groundwater potential for yields between 0.66 m3/h and 2.4m3/h (i.e. 11.0 and 40 litres per minute); (iii) moderate groundwater potential for yields ranging between 2.4m3/h and 6.3 m3/h (i.e. between 40 litres per minute and 105 litres per minute); (iv) high groundwater potential for yields between 6.3 m3/h and 14.4 m3/h (i.e. 105 litres per minute and 240 litres per minute); and (v) very high groundwater potential for yield ranging between 14.4 m3/h and 24 m3/h (i.e. 240 litres per minute and to 400 litres per minute).
The weighted overlay model (WOM), which is a method of modelling suitability, was used to generate a predictive map for the groundwater potential zone (GWPZ) in the study area. The thirteen (13) evidential layers were classified as shown in Table 1, and the classes were assigned class scores from 1, the least favourable class, to 9, the highly favourable class. The classified evidential layers with class scores were assigned weights based on experts’ opinions, and the weights were evaluated based on how each layer influenced the distribution and accumulation of groundwater in the study area. The average weights assigned to the classified evidential layers are shown in Table 2. The weighted evidential layers were summed and divided by the total assigned weight to generate a WOM for the GWPZ map shown in Fig. 7.
The GWPZ map in Fig. 7 generated using WOM is classified into five classes from very low probability, represented by Class 1 (0.05–0.25), to very high probability, represented by Class 5 (0.65–0.74). The intermediate probabilities are low, moderate, and high and are represented by Class 2 (0.25–0.38), Class 3 (0.38–0.59), and Class 4 (0.59–0.65), respectively. The area covers (in terms of percentage) each of the classes with respect to the study area is shown in Table 3. Delineated groundwater potential zones superimposed on borehole yield map for the study show that Class 1, 2, 3, 4 and 5 corresponds to very low (yield ≤ 0.66 m3/h), low (0.66 m3/h < yield ≤ 2.4 m3/h), moderate (2.4 m3/h < yield ≤ 6.3 m3/h), high (6.3 m3/h < yield ≤ 14.4 m3/h) and very high (14.4 m3/h ≤ yield ≤ 24.0 m3/h) groundwater potential zones respectively.
| No | Yield(m3/h) | Classification |
|---|---|---|
| 1 | Yield ≤ 0.66 | Very Low Groundwater Potential |
| 2 | 0.66 < Yield ≤ 2.4 | Low Groundwater Potential |
| 3 | 2.4 ≤ Yield < 6.3 | Moderate Groundwater Potential |
| 4 | 6.3 ≤ Yield < 14.4 | High Groundwater Potential |
| 5 | 14.4 ≤ Yield ≤ 24.0 | Very High Groundwater Potential |
The delineated potential zones in the GWPZ map in Fig. 7 largely show high and very high potential zones associated with the midland and lowland regions and the coastal areas. High and very high groundwater potential zones are generally confined to moderate dense trees and moderate dense herbs areas with high infiltration potential. Superimposing the GWPZ map on the DEM maps shows that intermediate potential zones generally occur in areas occupied by valleys and high drainage density. The very low and low groundwater potential zones are spatially distributed across highlands and lowlands compared to the midlands.
The majority of the high (Class 4) to very high (Class 5) potential zones are located at the central and western parts of the study area, which are associated with the Eburnean plutonic suite and the Tarkwaian Group, as well as dominated by lixisols and acrisols soil types. These parts of the study area host numerous cross-cutting dykes with intense lineament density. The very high and high potential zones, as shown in Table 3, occupy approximately 2616.5 and 125.7 km2, which is approximately 1.3 and 27.5% of the total area of the study area, respectively. The groundwater potential zones delineated in the central part of the study area, as shown in Fig. 7, are seen to trend in the N-S and NE-SW directions associated with the faults and dyke lineaments. The superimposed borehole yield map in Fig. 7 shows that the central part of the study area is associated with yields ranging from 0.66 m3/h to 2.4 m3/h (i.e., 11.0 and 40 litres per minute). The flow direction of groundwater potential zones in the south and southeast part of the GWPZ map in Fig. 7 is in the N-S direction associated with trends of the mafic dykes towards the southeast in the northwestern part and towards the south in the northeastern part. At the western part of the GWPZ map, the flow direction of the delineated potential zone is in both the N-S and NW-SE directions.
4.2 Validation of Groundwater Potential Zones
The location of 91 boreholes and associated yields on the WOM map in Fig. 7 showed that the very low and low groundwater potential zones (Class 1 and Class 2) intercepted 1 and 28 boreholes with yields of 0 and 3–400 litres per minute, respectively. Subsequently, the intermediate groundwater potential zone (Class 3) intercepted 52 boreholes with yields ranging from 3–240 litres per minute. However, the high groundwater potential zone (Class 4) intercepted 8 boreholes with yields ranging from 18–95 litres per minute. The very high groundwater potential zone (Class 5) intercepted 2 boreholes with yields ranging from 18–34 litres per minute.
The northern part of the GWPZ map (Fig. 7) is associated with very low (Class 1) to low (Class 2) potential zones, which occupy approximately 0.9 and 14.0% of the study area, constituting about 86 and 1334 km2 of the total area (9503.04 km2) of the study area, respectively, as shown in Table 3.0. The areas associated with these very low to low potential zones, although they have high drainage density and high potassic alteration, are characterized by low lineament density, steep slope, and lixisols. However, the overlaid borehole yield map in Fig. 7 shows that the low and very low potential zones in the northern part of the GWPZ map are associated with high yields greater than 2.4 m3/h (i.e., > 40 litres per minute).
Statistical assessment of the groundwater potential zones using the Modified Index of Agreement and Nash-Sutcliffe Error stood at approximately 0.81 and 0.74, respectively. These values respectively fall within very good region and good region of classification (Bouslihim et al., 2016), suggesting the reliability of using the map as a basis for detailed groundwater exploration within the study area.
In order to determine the spatial variability of groundwater potential in the Central Region, airborne magnetic and radiometric datasets obtained from Ghana Geological Survey Authority were processed using Oasis Mantaj (Geosoft) Software to obtain alteration zones (associated with weathered zones), lineaments, lineament intersection, lineament density, lineament intersection density, and dykes. Remote sensing data was processed using ESRI software to delineate stream order, drainage density, land use/cover, and slope digital elevation model. The geology of the area was also obtained by modifying the existing geology in a GIS environment. A soil map of the area was obtained for the study area. These thirteen (13) evidential layers were used as input parameters in a GIS environment where individual evidential layers were assigned scores depending on the contribution of each evidential layer to groundwater occurrence. Each evidential layers were classified, and each class was given a score from 1 to 9, the least and most favourable to groundwater occurrence based on the evidential layer. The results using weighted overlay Model and geo-referenced borehole yields led to delineation of groundwater potential zones that varied (x) from very low (x ≤ 0.66 m3/h) through low (0.66 m3/h < x ≤ 2.4 m3/h), moderate (2.4 m3/h < x ≤ 6.3 m3/h), high (6.3 m3/h < x ≤ 14.4 m3/h), and very high (14.4 m3/h < x ≤ 24.0 m3/h), groundwater potential zones in the area. This study reflects the importance of using a multi-faceted approach in resolving complex issues relating to groundwater occurrence.
The results show the suitability of using airborne magnetic and radiometric datasets, remote sensing techniques, and geo-referenced boreholes’ yields to delineate the spatial distribution of groundwater potential in the region. Five (5) classes (1, 2, 3, 4, and 5) and corresponding five (5) groundwater potential zones: very low, low, moderate, high and very high groundwater potential have been delineated in the area. The numerical values for the five classes are 0.0- 0.25 for Class 1, 0.25–0.38 for Class 2, 0.38–0.59 for Class 3, 0.59–0.65 for Class 4 and 0.65–0.74 for Class 5. The five (5) groundwater potential zones are. very low (x ≤ 0.66 m3/h), low (0.66 m3/h < x ≤ 2.4 m3/h), moderate (2.4 m3/h < x ≤ 6.3 m3/h), high (6.3 m3/h < x ≤ 14.4 m3/h), and very high (> 14.4 m3/h). The distributions of the groundwater potential zones within the study area suggest that very low groundwater potential, constituting 0.91%, occupies 86.12 km2; low groundwater potential constitutes approximately 14.03%, occupies 13344.04 km2; moderate groundwater potential formed about 56.19%, occupies 5340.68 km2 of the land surface; the high groundwater potential zones that formed about 27.53% occupies about 2616 .48 km2 of the land. The very high groundwater potential zones constituted approximately 1.32%, occupying 125.72 km 2. Moderate groundwater zones in the study are the most prominent, constituting approximately 56.2% of the wells drilled in the area. The cumulative large percentage (83.72%) of moderate and high groundwater potentials in the study suggests that groundwater can play a key role for extensive irrigation in the region. The low percentage of very high groundwater potential zones suggests that very high yields for small-town water supply may be rare. For every 100 boreholes drilled, there is a likelihood of a well with a yield ≥ 14.4 m3/h. There is a gradual percentage increase from very low potential to moderate potential, where it reaches a peak and decreases to a very high groundwater potential zone. The spatial distribution of groundwater potentials over the area, show the possible yields of wells that can be obtained prior to detailed groundwater exploration. Thus, the method used improved the traditional method by providing possible locations for borehole yields thereby serving as a bedrock for detailed explorations within the study area. However, this method of integrating various geophysical methods using weighted Overlay Model for groundwater mapping is evolving and future research needs to look at Fuzzy Logic Models which corrects inconsistencies associated with the Weighted Overlay Model (Azeez and Shifri, 2021; Papadopoulou, and Hatzichristos, 2019; Raid et al., 2011). Additionally, Geometric Average Method (GAM) which determines accurately a single average from several heterogeneous sources (Youselfi, and Corranza, 2015b) can be considered in future studies. Assessment of error associated with groundwater potential map delineated using Modified Index of Agreement and Modified Nash-Sutcliffe Error gives values of 0.81 and 0.74 respectively. These values respectively lie within very good and good classes performance based on the classification of the hydrological model (Bouslihim, 2016), thereby making the model and the potential map respectively suitable for adoption in other areas of similar geo-environmental conditions and as a basis for detailed groundwater exploration in the study area.
Conflicts of interest/Competing interests
On behalf of all the authors, the corresponding author states that there is no conflict of interest.
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