This study focuses on the Tongo-Bassa watershed, one of the sub-basin of the Douala Sedimentary Basin (DSB), located on the left bank of the Wouri River. Located between coordinates 4˚01'38" to 4˚05'55" North and 9˚43'03" to 9˚47'50" East, the Tongo-Bassa watershed belongs to the district of Douala V (Figure 1). The Douala V sub-division, is a very active cosmopolitan city covering an area of 210 km², with a population of around 1,000,000 and a population density of 4095 inhabitants per km². The density of its population stems from the fact that the majority of the city of Douala’s infrastructure is found in this sub-division despite the fact that it is not very industrialised (2.5% of Douala’s industrial establishments; [3]).

Figure 1. Location of the study area (A): the Littoral Region of Cameroon; (B) the Tongo-Bassa catchment area in the Littoral Region; (C) map of the Tongo-Bassa catchment area and topographical background of Douala V.

The dynamics of land use show progressive urbanisation invading the small villages, which have gradually been integrated into the modern city. Many authors ([22]; [35]; [29]; [49]) reveals the equatorial nature of the climate which would be influenced by the proximity of the area to the Mount Cameroon and downward trend in rainfall with an average of 4000 mm/year, and average annual temperature of 27˚C, considering data from 1951 to 2016 recorded by Douala meteorological station ([35]). The area has two mains seasons, a long rainy season from March to November and short dry season from December to February. The maximum daily amount of rainfall are recorded in July and August (742.4 mm), while the minimum daily amount are recorded in December and January (34 mm). The DSB has hydrographic network including numerous creeks, mangrove swamps and marshy areas. The urban area is drained by the Tongo-Bassa, Mbopi, Bobongo, Mbanya, Bounadouma and Mgoua rivers, which join the Wouri and Dibamba. The Tongo-Bassa (Figure 1(c)) is the largest catchment in the economic city, with a drainage area of almost 40 km². These 10 km-long catchments have many tributaries, including the Ngongue, Kondi, petit Tongo-Bassa and Moussadi. It flows through several districts, such as Makepe-Missoke, where it becomes a tributary known as the Ngongue, the main watercourse in the sub-catchment bearing that name. The hydrographic network in this dense basin, which is supercharged by numerous drains, is of the subdendritic to subparallel type.

Many authors ([11]; [21]; [25]) highlights in DSB, the predominance of ferralitic soils, divided into two types: yellow ferralitic soils derived from sandy and sandy-clay sedimentary rocks, and the little-represented red ferralitic soils derived from the granite-gneiss bedrock ([27]). In addition to the ferralitic soils, there are hydromorphic soils that are generally clayey, poorly developed soils and rough mineral soils. The Tongo-Bassa river flows through a vast plain with a very gentle slope at altitudes of no more than 62 m. Slope modelling in the study area identifies two types of landscape: flat (92.88%) with slopes between 0˚ and 4.61˚ and undulating (7.12%) with slopes between 4.61˚ and 8.17˚. The DSB covers an area of 19000 km², of which 7000 km² is emerged ([24]). It is bounded to the North by the Cameroon Volcanic Line, to the South by the Northern limit of the Kribi-Campos Sub-basin (KCSB), to the west by the Atlantic Ocean and to the east by the Metamorphic Basement rocks of Central African Fold Belt. According to [50], the history of origin and structure of DSB are associated with the opening of South Atlantic Ocean during the breakup of Gondwanaland. The lithostratigraphy of DSB is made of sequences laying unconformable to the Precambrian basement and ranging from Cretaceous rocks to recent alluviums in the Wouri estuary ([30]; [46]). This lithostratigraphy can be divided in three main units in function of geodynamic and sedimentary evolution ([26]; [31]; [46]). From the bottom to the top we have:

The Late Cretaceous units, with thickness ranging from 400 to 2000 m and consists of three formations. Then we has: 1) Moundeck formation (Aptian Cenomanian) made of continental and fluvio-deltaic deposits (clays, coarse-grained sands, sandstones); 2) Logbadjeck formations (Cenomanian-Campagnian) discordant onto Mundeck formation and made of sand, sandstones, limestones and clays; 3) Logbaba formations (Maastrichian) mainly composed of sandstone, sand, and fossils shales;The intermediate units, with thickness varying from 300 to 1000 m and consists of two formations. Firstly, there is Nkappa formation (Paleocene-Eocene), made of calcareous sandstones, marls and clays with lenses of sand and fine to coarse grained sandstone. Secondly we have Souellaba formation (Oligocene), lying unconformably on Nkappa deposits and made of sandstones, marls deposits with some interstratified lenses, shales, clayed sand and gravelly sand;The upper unit, consists of Matanda (Miocene) and Wouri (Pliocene-Pleistocene) formations. Matanda formations are dominated by deltaic interstratified facies with volcano-clastic layers. These volcanic rocks consist of scoria and basalt ([33]). Wouri formations consists mainly of indurate lateritic layers, gravelly and sandy deposits with a clayey or kaolinic matrix.

In this paper, we focus on this last unit of DSB made of Quaternary/Mio-Pliocene deposits which constitute a regional and multi-layered aquifer mainly exploited by wells and borehole in Douala and known as shallow aquifer ([8]; [22]; [29]). This multi-layered aquifer has a maximum thickness of 70 m in the SE (East of the Wouri) where the study area belonging, and consists of sands, sandy clays and lenses of clays ([8]; [29]; [34]; [49]). According to [21], layers with high content of clays has resistivity less than 500 Ω∙m, while those rich in sands where aquifers can be found has resistivity greater than 500 Ω∙m and the right value to identify good aquifer with soft and potable water should be ranged between 1000 and 2000 Ω∙m. In this complex of shallow aquifers two mains levels are distinguishes: unconfined aquifers, with depth less than 20 m (1 to 20 m); and semi confined or confined aquifers, with depth less than 200 m (20 to 200 m). This aquifer is characterized by a high recharge, ranged from 600 to 700 mm/year and low groundwater flows about 5 to 7 m/day ([21]; [35]). Location areas of recharge in Tongo-Bassa watershed correspond to most elevated points (66 and 50 m.a.s.l respectively) as Logbaba, Malangue, Logbessou and Nyalla ([2]; [22]; [35]). According to [21], almost all the boreholes drilled in the DSB tap several aquifers and are not rigorously subjected to pumping tests after completion, which makes it difficult to assess the hydraulic potential of the various aquifers identified. However, several authors estimate that groundwater productivity of these aquifers are variable and show respectively, yields ranged from 1 to 250 m³/h; Transmissivity from 4.6 * 10⁻⁴ m²/h to 6.8 * 10⁻² m²/s; Permeability from 2.6 * 10⁻⁵ to 0.195 m/s and Storage coefficient from 2.4 * 10⁻⁵ to 7.4 * 10⁻⁵ m⁻¹ ([8]; [10]; [21]; [34]). The shallow aquifers in the Tongo Bassa catchment are recharged mainly from rainfall ([29]), with groundwater flows trending predominantly NE-SW and SE-NW regardless of the season. The associated watercourses ensure constant drainage of the aquifers throughout the year. According to [2], [22] and [49], the direct recharge of unconfined aquifers during rainy season makes them more vulnerable to pollution by waste water from anthropogenic activities due to poor sanitation conditions. Monitoring of water quality in the study area suggested that Western and South-eastern part of the Tongo-bassa watershed is more polluted and vulnerable than Eastern and Northern part ([2]; [22]; [29]).

Piezometric study was carried out within the quaternary aquifer which correspond to shallow aquifer, in order to assess the groundwater flow pattern during the rainy and dry seasons. To achieve this, field surveys were carried out and the data obtained was processed in the laboratory. Concerning fields campaigns, during the initial field campaigns, the aim was to identify all the access wells that could be subject to piezometric monitoring and to describe them in order to identify only those that exploited the shallow aquifer. At the end of this campaign, of the 85 wells identified, only 29 were selected for piezometric monitoring (Figure 2), as they met this criterion and were fairly widespread and representative of the site.

Figure 2. Sample map showing piezometric network and VES points.

For the measurements, a 100 m long piezometric probe with dual signalling (light and sound) was used. After measurements at each point, a GARMIN (etrex-20) GPS was used to record the geographical coordinates.

Static levels were measured over a one-year period (from January to December 2022) with a constant 30-day interval like suggest many authors ([12]). The data were used to calculate the piezometric level of each water point through the formula (Equation (1)):

where Np = Piezometric level, Z = altitudinal elevation of the point, Ns = Static level.

In order to reduce the margin of error when taking the water level and altitude of the points, the reference level for the altitude of a point is that of the surface of the ground. Thus, for a well with a curbstone, the static level and altitude data taken at the curbstone were corrected by subtracting the value of the curbstone from the values of the water level obtained from the probe and the altitude obtained from the GPS. On the other hand, level of altitude was corrected through analysis of SRTM images by Arcgis software and raster image of topographic map of Douala produced by [6]. Once the calculations had been made, the data were used to produce two iso-piezometric maps of the Tongo-Bassa catchment (one in the dry season and another in rainy season), using kriging methods in Surfer 16 software.

For this study, 32 vertical electrical soundings (VES) were carried out at pre-selected stations in the watershed (Figure 2), using the WDJD-3 resistivity meter with its accessories (04 coils, 04 electrodes, 02 connectors and 03 hammers). The Schlumberger quadrupole configuration was used for each VES, with half current electrodes (AB/2) separation of 1.5 to 90 m, and half potential electrodes (MN/2) separation of 0.5 to 5 m. According to many authors ([17]; [41], [39]) who used this configuration, its well adapted for the field where structure are complex vertically as horizontally like in DSB. In the field, the measurement procedure for a given VES consisted to send a low-intensity current through injection electrodes AB, and measure the potential difference through potential electrodes MN located on either side of central axis point (O). During the various measurement, the depth of investigation was increased by gradually varying the positions of electrodes A and B from AB/2 = 1.5 m to AB/2 = 90 m. Electrodes M and N were kept fixed for a series of measurements. In order to avoid the phenomena associated with energy losses generally known as “breeze blowing”, two disengagements are carried out during each sounding. When the clutch is disengaged, the position of M and N is changed from MN/2 = 0.5 m to MN/2 = 5 m. The purpose of disengagement is to increase the distance MN in order to increase the current intensity and obtain better measurement values while eliminating noise. The resistivity metre measured the potential difference generated and converted it to apparent resistivity (ρ_a) which are used to plot a graph manually or by computer software. The manual procedure consisted to plot in a bi-logarithmic graph, apparent resistivity against half-electrodes spacing and curves obtained were smoothened to remove a noisy signature or the effects of lateral inhomogeneities ([9]). Practically, the values of apparent resistivity were imputed into computer software program (Ipi2win) for the computer modelling which generate a set of 1-D curves of vertical electrical sounding (VES) from where the values of true resistivity, thickness and depth of each layer were obtained. It is noticed that, this software applies one-dimensional geophysical inversion using the damped least square method through which the observed data is compared to a synthetic model. The acceptability of the result model is based on the fitness criteria between the observed and calculated apparent resistivity curves. Qualitative interpretation of VES curves and quantitative interpretation of the result (Figure 3) was based on field observations of wells and lithological log of boreholes available (case of F1 in the study area). This procedure helps to reduce the ambiguities in the interpretation stage and enhanced the reliability and quality of the modelled results ([17]; [41]).

Figure 3.Sample of VES results obtained after modelling correlated with borehole lithology. (A) Curve model; (B) table obtained from modelization showing layers structures with through resistivity and layer thickness; (C) correlation of geo-electrical section with borehole lithology; 1: Sandy clay material; 2: Fine to Medium sand with or no little clay; 3: Coarse grain sand (aquifers); 4: Grey Clayey sand; 5: Medium sand with little or no clay; 6: Mottled clays.

The root means square error (RMSE) less than 3% was used in assessing the extend of fit, and the interpreted curves. Based on auxillary curve matching techniques ([45]), to deduce the approximate number of layers, three to five layers was assigned to the curve models obtained after a number of iterations made by the software.Figure 3 shows an example of curve with table model obtained, geoelectrical section and correlation of the results with borehole lithologs. Based on litho-logs correlation to VES modelled and previous study on DSB area ([8]; [21]; [29]; [34]), there is multi-layered aquifer, but two main aquifers are distinguish: unconfined aquifer, less than 20 m depth and semi-confined/confined aquifer between 20 - 35 m depth (Figure 3). In addition to this observation, it can be seen that in these aquifers, layers rich in sand have the highest resistivities, whereas when clays are abundant, resistivity values drop as well as in the sands aquifers.

Iso-apparent resistivity maps help to provide information on the lateral variation of apparent resistivity values at a defined depth. These maps indicate the distribution of apparent resistivity in the area against distance of current electrodes. Interpretation of these maps was based on lithology and where there is low value of resistivity is considered as a dominant clayey or saturated material; while where there is high value of resistivity is considered as an aquifer made of gravel or as sandy dry material. For this step, sixteen maps where drawn, but only Six maps are presented base on change observed, respectively at AB/2 = 1.5; 13.2; 24.8; 40; 70 and 90 m.

The various parameters Hydraulic conductivity (K), Transmissivity (T), Porosity (Ø) and Longitudinal conductance (Lc) are obtained, based on true resistivity and thickness of aquifer after modelling VES data. These geohydraulic parameters are used to estimate the potential and protective capacity of different identified aquifers. Indirect calculation methods have been proposed by [42], [13] and [32] for basement area. However, recent studies conducted by many authors ([15]; [39]; [44]) to determine the values of these parameters for a given aquifer (Equations (2)-(6)), show that these equations are applicable in sedimentary areas. This is done on condition that a calibration has been made between a borehole and a lithological section to determine the aquifer characteristics, as carried out in this study (Figure 3). Assessment of the aquifer vulnerability was done through determination of hydraulic conductivity of the aquifer protective layers. Its value is expressed in metres per day (m/day) and is obtained from Equation (2).

where Rw is the resistivity of the aquifer in Ohm∙m (Ω∙m).

Hydraulic conductivity (K) is the ability of a porous medium to allow a fluid to pass through it under the effect of a pressure gradient. Aquifer vulnerability index (AVI) where describes as a right method to quantifies vulnerability by calculation of hydraulic resistance (C) to vertical flow of water through the covering layers ([48]). The estimation of AVI requires to use two parameters to determine hydraulic resistance (C): the estimated hydraulic conductivity (Ki) and the thickness (hi) of the protective layers. C was computed by using Equation (3):

After hydraulic resistance (C) obtained, values were compared to those in Table 1 which gives the relationship between C and AVI and helps to determine the level of vulnerability.

Table 1. Relationship between C and AVI after [48].

The transmissivity (Tr) of an aquifer represents its capacity to mobilise the water it contains. It is a parameter that governs the rate of water flow per unit width of an aquifer (h), under the effect of the hydraulic gradient (K). Its value is expressed in metres square per day (m²/day) and is obtained from Equation (4).

Computed transmissity values were comparing to standards (Table 2) classification proposed by [23].

Table 2.Classification of T magnitude of Krasny.

Longitudinal conductance (S in mhos) is a geoelectric parameter used to assess the protective capacity of the aquifer. It is expressed as results obtained by the ratio of the thickness of the aquifer (h in m) and the resistivity through Equation (5).

Longitudinal conductance is a crucial parameter in assessment of groundwater potential target. Its help to assess the protective capacity of an aquifer through comparison between values obtained and those given in Table 3 ([37]). Spatialization of this parameter in a given area helps identify areas with good groundwater potential to those with poor groundwater potential.

Porosity (Ø) expressed in %, is the percentage of voids per unit volume in a sediment or rock. It is deduced from Equation (6).

where K is the conductivity of the aquifer in m/day.

Table 3.Classification of protective capacity based on S rating.

All geohydraulic parameters obtained were spatialized and discussed in agreement with studies carried out in a similar context with regard to the difficulty of validation by pumping tests in the aquifers, given that many boreholes drilled tap several aquifers. However, the aquifer vulnerability index deduced from the geohydraulic parameters and used to determine the vulnerable zones by spatialization, were validated on the basis of known previous study ([2]; [8]; [21]; [22]; [51]) and observations on the field in August 2022.

Based on the dynamics of the water table, the piezometric monitoring enabled classification into 5 periods: January to February; March to June; July to September; October to November and December. The variation in static levels and piezometric values recorded during piezometric monitoring allow to produce two maps. Examination of the piezometric maps show that the onset of rainfall at the end of February led to an increase in the piezometric depression and the multiplication of piezometric domes following the infiltration of water. The piezometric level rise from 23 m in January to 28 m in February, an increase of 5 m marking the end of the low-water period. Low water peaked in January (Figure 4(a)). During the period from March to June, numerous rainfall events gradually led to saturation of the ground by infiltration, resulting in the stabilisation of the piezometric level at 28 m and an increase in the volume of the depression and the piezometric domes. During the period from July to August, the intensity of rainfall almost tripled, and the piezometric level reached 30 m, recharging the water table. The piezometric maps show that the volume of the piezometric domes is at its maximum. This period of high water in the basin reaches its peak in August (Figure 4(b)).

Figure 4. Piezometric maps (A) in dry and (B) rainy periods.

In the field, epidermal run-off and upwelling of more than 30 cm above the surface were observed, resulting in the widening and persistence of marshy areas. Rainfall became scarce between November and October, marking the end of the high-water period in the basin. The water table begins to empty, a process that continues until December, when the dry season begins. The maps illustrate the regression of the piezometric domes and in the field we can see a gradual regression until the marshy areas disappear. Analysis of the static levels shows that some wells reacted suddenly as soon as the first rains began in February, while others reacted late or slowly. The sudden rises reflect areas of low porosity and high permeability: these areas would be the most vulnerable, unlike the areas with late rises as suggested [21], and [51]. This rapid infiltration of rainwater contributes not only to the direct recharge of the water table but also to its pollution through the contribution of organic and mineral matter from the surface or effluents of waste waters ([2]; [8]; [10]; [22]; [29]; [49]). The beat of the Tongo-Bassa water table is punctuated by alternating seasons. The fluctuating height of the groundwater varies from 0 m to 1.4 m in the wet and dry seasons respectively. The piezometric map of the Tongo-Bassa catchment show two sides: West side with very low piezometric levels and East side with high values of piezometric levels. Based on previous study cited above, all wells of this West side are the most vulnerable to the nitrate and bacteriological pollution than those located at the East side where piezometric level are high. Only the wells located in the centre of this catchment have an average piezometric level. According to [2] and [35], many factors, including the thickness of the unsaturated zone and the depth of the piezometric surface, plays a key role in the time to takes for a pollutant to be transferred to the water table. As the depth of the piezometric surface in the study area is shallow, groundwater pollution will be very rapid. The Tongo-Bassa soils are mainly composed of sands, clays and silts; so pollution is highly variable. The communication between the groundwater recharge zones (piezometric domes) in the South and South-East and the piezometric depressions in the North-West with the groundwater, accentuates the vulnerability of the latter, as these areas are densely populated. In the Douala V district, 56% of households dispose of wastewater in the open, 54.52% of households dispose of rubbish in the open and 14% dispose of rubbish in fields or ravines ([5]). Recharge of the Tongo-Bassa water table during rainfall by infiltration into accumulation zones is accompanied by pollution of the latter by transport and dissolution of these multiple mineral and organic pollutants. For the population, the various rivers in the catchment area are a receptacle for various types of waste and excreta. Water pollution in this case is influenced by hydrogeochemical and anthropogenic processes ([2]; [8]; [34]).

The maps (six) resulting from the data processing (Figure 5) have apparent resistivity values ranging from 19.46 to 98136.5 Ω∙m, and are divided into three domains:

conductive domain, with low apparent resistivity (19.46 to 2862.5 Ω∙m) and represented by area with blue color; intermediaries domains with medium apparent resistivity (203.2 to 24285.7 Ω∙m), and represented by yellow to orange color;resistive domain, with medium apparent resistivity (403.98 to 98136.5 Ω∙m), and represented by red color.

Observation of the iso-resistivity maps (Figure 5) reveals that the different resistivity classes show a change in the resistivity field as you go deeper.Figure 5 show six maps that better expresses lateral variation of the apparent resistivity at different depths in the study area, with two mains parts (North-Eastern and South-Western parts) in function of resistivity from surface (AB/2 = 1.5 m) to depth (AB/2 = 90 m). In general, South-Western part have low apparent resistivity values while North-Eastern part have high apparent resistivity. The zone with moderate resistivity values covers the centre and most of the sector.

Base on correlation with borehole logs (Figure 3) and previous study made by [21], conductive areas with resistivity less than 200 Ω∙m are made of Clay or Clayey sand material; while on this same domain, when resistivity belongs to 200 to 800 Ω∙m, material are made of Sandy clay or Sand. When resistivity is over 800 Ω∙m, materials are essentially medium to coarse Sand.

Figure 5. Apparent iso-resistivity maps at different investigation depths.

This observation implies that the North-Eastern part of watershed are sandier than the South-Western part; so, these areas should be more adapted to find sandy aquifers. The validation of this results was made with litho-logs of many boreholes (Bependa (Dla34), polyplast (Dla 8), Tropic (Dla9), Unalor (Dla 13) and CCC (Dla17) studied by [21]) and new borehole drilled at Makepe, in this area.

Interpretation of the 1D inversion of the VES data revealed 10 mains types of curves (Table 4) and grouped as: HK (39.39%); KH (15.15%); 12.12% of HA and HKH; H (6.06%) while 3.03% represents K, AK, QH, KQ, and HKQ. Sounding data show three (H and K) to five geo-electric layers (HKH and HKQ).

Table 4. Synthesis of measured electrical resistivity data in the study area after inversion.

Base on the analysis of VES data and curve type with lithology, two main aquifers are identified: first aquifer made of unconfined aquifer, and second one made of semi-confined or confined aquifer. These two aquifers are used by population through open wells or boreholes. Then their potentiality and protective capacity in the study area was evaluated by geohydraulic parameters, and the results are analysed separately with illustration of their spatial variation.

4.4.1. Case of Unconfined Aquifer

Geohydraulic parameters (porosity ϕ, hydraulic conductivity K, transmissivity Tr, longitudinal conductance S, and hydraulic resistance C) of the unconfined aquifer were estimated (Table 5) using the combination of resistivity and thickness through Equations (2)-(6). The AVI was determined in term of Log C to assess the level of vulnerability. Aquifer resistivity and thickness of this unconfined aquifer ranged from 57.45 to 8458 Ω∙m and 0.30 to 12.9 m respectively. The low value of resistivity can be explained by the presence of argillaceous formation facies which combine to weak thickness may lower the aquifer potentials like shows many authors ([41]; [37]; [39]).

High resistivity values combine to sandy formations which combine to very thick layers show great aquifer potentials. Porosity range from 14.35 to 35.30%; hydraulic conductivity from 0.08 to 8.83 m/day; longitudinal conductance from 3.4 * 10⁻⁵ to 0.0289 mhos; transmissivity from 0.02 to 14.67 m²/day; and hydraulic resistance from 0.265 to 29.29 day⁻¹. These parameters in accordance to standards (Table 1, Table 2 and Table 3) show that these unconfined aquifers are extremely vulnerable (87.88%) and groundwater supply potential are fairly adapted for local level with limited consumption for private water supply due to less permeable materials or discontinuous aquifer formations ([38]). In comparison to study made by [20], average yields of this aquifer can be estimated as less than 6 l/s for 51.51% of data, and ranged between 6 - 30 l/s, for 36.36% of data. Spatialization by kriging method, shows a contour map displaying the distribution of these geohydraulic parameters (Figure 6) which divide watershed in two particular domains:

North to North-Eastern domain, which have weak geohydraulic values; and South to South-western domain with high geohydraulic values.

Table 5. Estimated geohydraulic parameter for the unconfined aquifer.

The different maps obtained through spatialization, show that groundwater potential decreasing from this South and South-Western domain, to North and North-Eastern domain; while AVI increasing in the same direction.

Figure 6.Spatial variation of geohydraulic parameters of unconfined aquifer (A) porosity; (B) conductivity, (C) transmissivity; (D) longitudinal conductance.

4.4.2. Case of Semi-Confined/Confined Aquifer

In second aquifer level identify after VES computing, many geohydraulic parameter has varying as presented in Table 6 while the graphical presentation depicting the spatial variations of some of these geohydraulic parameters with respect to VES points shown in Figure 7. Resistivity and thickness ranged from 172.7 (VES 28) to 9980 Ω∙m (VES 22) and from 1 to 38.2 m respectively. Porosity and Hydraulic conductivity values ranged from 13.65 to 30 % and from 0.072 to 3.16 m/day respectively. Spatial distribution of these two parameters over the study area (Figure 7(A) and Figure 7(B)) indicated that the western and eastern part, had a high value, implying ability of these section to get a high groundwater potential which can move easily through the pore spaces. According to [52], space of a given area with high value of hydraulic conductivity can be linked to the space with a high groundwater potential and where there is a great level of hydraulic flow and groundwater recharge. Conversely the center part of the study area showing low values of hydraulic conductivity, implying low groundwater potential and difficulties of groundwater flowing and recharge. Based on the study made by [20], 33.34% of data could had yield less than 6 l/s due to K < 0.3 m/day; while 63.64% should have yield ranged between 6 - 30 l/s. In the Tongo-Bassa watershed, transmissivity values of confined or semi-confined aquifer ranged from 0.077 m²/day to 42.04 m²/day, with an average of 7.606 m²/day. Wide range of transmissivity show high heterogeneities of lithology and groundwater potential and flowing. Classification of this transmissivity in the study area show that this aquifer is adapted for groundwater supply for small communities at local scale for 72.73% of data and more represented in Southern sector (Figure 7(C)), while 27.27% of data are adapted for private water supply in Northern sector. Longitudinal conductance which is a key geohydraulic parameter used to assess the protective capacity of aquifers, ranged from 0.00011 to 0.0726 Ω⁻¹with average of 0.0128 Ω⁻¹. This longitudinal conductance confirms also poor protective capacity, in line with AVI which remain High to Extremely high (excepted VES 11 which show a moderate AVI), compared to results obtained in the same environments at the Southern side of Benue State in Nigeria ([9]), where good protective capacity of deep and confined aquifer were observed. Figure 7(D) shows that Northern part of the watershed (in blue color), near the west part of the outlet, are more vulnerable.

These various maps showing a correlation between changes in porosity and permeability. In fact, the most porous areas are also the most permeable. However, the transmissivity maps are similar to those showing longitudinal conductance, where the areas with potential for rapid transmission are the least vulnerable. This contrast with previous hydrogeochemical studies in the area, where the most polluted wells were found by many authors ([8]; [21]; [29]). However, this contrast can be explained by the combined action of topography, slope and the nature of the material as suggested by [2]. In fact, if the high areas near the ridges are made up of sand, whereas the low areas are rich in clay, the action of the slope means that the water will circulate quickly upstream and slowly downstream, with water levels in the wells almost invariant in dry and rainy season. It goes without saying that the critical discrepancy between the low calculated vulnerability of the deep aquifer and the pollution observed in previous studies could be linked to the intertwined structure of the layers, which, due to lateral circulation as observed in piezometric studies, could bring the confined aquifer waters into contact with pollutants from surface waters. Similarly, this situation could be explained by the fact that on the field, when drilling wells, populations capture both surface and deep groundwater in order to obtain high flow rates; which makes the confined/semi-confined aquifer more vulnerable.

This means that if a pollutant is introduced into the environment, it will be rapidly transported laterally downstream, and when circulation is slow, the wells analysed downstream will be more polluted than those upstream. This situation explains why in the study area, many polluted wells which exploited unconfined aquifer are identified where it’s supposed to be less vulnerable while in the same environment, boreholes which exploited confined or semi-confined aquifer are less polluted due to their relative protection by clayey material.

Table 6. Estimated geohydraulic parameter for the semi-confined/confined aquifer.

Figure 7. Spatial variation of geohydraulic parameters of confined/semi-confined aquifer (A) porosity; (B) conductivity; (C) transmissivity; (D) longitudinal conductance.