The Assessment of the Groundwater Quality in the Coastal Aquifers of the Essaouira Basin, Southwestern Morocco, Using Hydrogeochemistry and Isotopic Signatures

Abstract

Because of anthropogenic activity and seawater intrusion, coastal aquifers worldwide frequently face a threat to their water supply due to salinization. This paper investigates the assessment of the groundwater quality in coastal aquifers of the Hauturivien aquifer in the Essaouira basin. In this study, 56 groundwater samples collected from the Hauturivian aquifer across four campaigns in 2017, 2018, 2019, and 2020 were subjected to multivariate analyses involving principal component analysis (PCA) and cluster analysis (CA) using SPSS software. Among the three main water types, the mixed Ca-Mg-Cl classification was predominant in the investigated aquifer. In addition to the natural processes (such as the water–rock interaction, ion exchange, dissolution/precipitation dynamics, and evaporation) that govern groundwater quality, current land use practices have increased salinization in this poorly drained semi-arid area. Based on assessments using Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI), the water quality is suitable for human consumption, but its use for irrigation is limited to crops that can tolerate high salt levels. The stable isotopes (δ²H and δ¹⁸O) of groundwater demonstrated that local precipitation is the primary recharge source. Nonetheless, the evaporation process, influenced by various geological conditions, affects groundwater recharge, regardless of the topographical differences in the study area.

1. Introduction

Sustainable water resource management is gradually gaining importance with the increased water consumption in recent years due to population growth, industrial development, and climate change worldwide [1,2,3,4]. Water resource management is one of the current global challenges, as groundwater is a vital source of human life and needs. Numerous anthropogenic and natural activities affect the quality of groundwater [5,6,7,8] Groundwater quality may be impacted by natural causes, such as geology and geochemical processes [3]. Groundwater quality has dramatically declined due to population growth, urbanization, and industry due to sewage seepage, industrial wastewater infiltration, and manure for weed growth [9]. Various water quality indices have been developed to date. However, most of these indexes target drinking water. IWQI, involving a set of indicators, has been used to represent water quality with a single value [10,11].

Salinization is a severe problem in many coastal regions worldwide, particularly in semi-arid and arid zones [12,13,14,15,16,17]. It is considered the most critical and apparent phenomenon that challenges future water use and decreases water quality. Because it limits the amount of water used for agriculture and urban water supply [18,19,20,21,22,23], groundwater salinization is critical to the resilience and sustainability of coastal. Increasing total dissolved solids (TDS) or chloride (Cl⁻) content signifies salinization [24]. It may be brought on by mining, waste disposal, excessive fertilizer use, or seawater intrusion [25]. The balance between groundwater recharge and discharge plays a crucial role in determining various factors, such as the infiltration of saltwater, the piezometric level of the aquifer, the distance between the aquifer and saltwater sources, as well as the geological structure of the area [26]. Water challenges, particularly groundwater salinization, have captured the attention of scientists, managers, and policymakers on a national scale [26,27]. It has shown up in numerous national research studies [28,29,30,31]. Because of the overuse and overexploitation of fertilizers, the Mediterranean and Atlantic coastal plains of Morocco are primarily affected by the issue of saltwater intrusions; as a result, the renewable water resources could decrease from 800 m³ in 1990 to 400 m³ in 2020 for each resident, making Morocco a country with chronic water stress [32].

Multivariate statistical techniques refer to a set of methods used to analyze data that involves multiple variables [33]. These techniques are particularly useful when dealing with complex data matrices, such as those encountered in environmental studies. In the context of groundwater quality assessment, multivariate statistical techniques can help identify the potential sources of contamination and their impact on the environment [34,35].

One commonly used multivariate technique is principal component analysis (PCA), which reduces the dimensionality of a dataset by identifying the most important variables that explain the variance in the data. PCA can be useful in identifying the underlying factors that contribute to the observed patterns in groundwater quality data.

Another important technique is cluster analysis (CA), which groups similar observations into clusters based on their similarities or dissimilarities [35]. This can be useful in identifying natural groupings in the data and in identifying potential contamination sources responsible for the observed patterns.

Environmental isotope tracers and age indicators have been used in arid regions, such as North Africa, for assessing groundwater systems, particularly in terms of residence time and characterization of groundwater movement [36,37,38,39]. In fact, the use of stable and radioactive isotopes is considered an essential approach for identifying the cause of salinization in coastal aquifers in Morocco [40,41,42].

The Essaouira basin in Morocco is mainly fed by surface water from rainfall [43,44,45,46,47,48,49,50,51]. However, the region has been experiencing prolonged drought in recent years, which has led to a decrease in water resources. To address this situation, measures have been taken to improve water management, including promoting efficient irrigation techniques and developing seawater desalination projects.

The main goal of this study was to assess whether the groundwater quality in the research location is appropriate for drinking and agricultural purposes. To achieve this, the study aimed to accomplish the following objectives: creating piezometric maps of the area, analyzing the hydrochemical characteristics of groundwater, evaluating the quality parameters of irrigation water and the IWQI, and determining the suitability of groundwater for human consumption by calculating WQI values.

In this context, the physical and chemical characteristics for the four campaigns (2017, 2018, 2019, and 2020) with 56 samples within the Hauturivien aquifer were used for calculations (Table S1). GIS was used to map the spatial distributions of the calculated WQI and IWQI.

2. Study Area

The area under investigation experiences a semi-arid climate and has two clear seasons: a rainy season stretching from November to March and a dry season that spans from April to October. The yearly and monthly average precipitation does not surpass 300 mm, as illustrated in Figure 1, temperatures are around 20 °C, and the average evapotranspiration is about 910 mm per year. The Igouzouline River flows through the study area. The present study area covers 229 km². Precipitation is the primary source of groundwater recharge. Groundwater is extracted using wells and boreholes.

This aquifer is bounded north by the Amssettène anticline, south by the Igouzoullene wadi, east by the Lower Cretaceous rocks, and west by the Atlantic Ocean (Figure 1). Its surface area is about 100 km², and its inhabitants are about 40,000, distributed in several localities.

The Amssettène anticline to the north, Igouzoulene wadi to the south, Lower Cretaceous rocks to the east, and the Atlantic Ocean to the west, as illustrated in Figure 1, delineate the boundaries of the aquifer, which spans an estimated area of 100 km² and sustains roughly 40,000 inhabitants living in diverse localities.

The sedimentary succession of the Essaouira basin, which extends from the Triassic to the Quaternary, includes several aquifers, including the Hauterivian [52,53]. It comprises fractured hard siliceous limestones, marly siltstones, altered and fractured marly limestones, and fractured dolomitic limestones alternating with altered and fractured marly and silty clay levels (Figure 1) [54,55,56].

3. Materials and Methods

3.1. Sample Collection and Physiochemical Analysis

Four groundwater sampling campaigns were conducted between 2017 and 2020, during which 56 samples were collected. Water levels were measured using a 200-meter piezoelectric probe, while temperature, pH, and electrical conductivity were measured in situ using a HANA multiparameter (HI9828).

Water is often pumped for a few minutes before sampling to ensure that the sample collected represents the water source being sampled. This is because stagnant water can contain different characteristics and contaminants than flowing water.

Groundwater samples were labeled, stored in polyethylene bottles, and transported to the laboratory for chemical analysis. The samples were then preserved at 4 °C. The major chemical element analysis for the 2020 campaign was conducted at the International Water Research Institute (IWRI) using a SKALAR San⁺⁺ Continuous Flow Analyzer (CFA) located at Mohammed VI Polytechnic University in Morocco.

The analysis of major chemical elements of the campaigns (2017, 2018, 2019) was analyzed in ENS (Ecole Normale Supérieure, Marrakech, Morocco). The volumetric analysis of TH (as CaCO₃) and Ca²⁺ was performed using standard EDTA. The Mg²⁺ was calculated, taking the difference value between TH and Ca²⁺. A flame photometer was used for the estimation of Na⁺ and K⁺ ions. The volumetric method was used to estimate the HCO₃⁻ and CO₃²⁻ using HCl as a standard solution. The Cl was analyzed by titrating with standard AgNO₃. The turbidimetric method was used to determine SO4²⁻, and the colorimetric method was used to determine NO₃⁻. The EC is expressed in micro-siemens per centimeter (μS/cm) at 25 °C. The chemical ions are expressed in milligrams per liter (mg/L) and milliequivalents per liter (meq/L).

The ionic balance method is used to assess the dependability of the obtained findings, with only those falling within the acceptable threshold of 10% [57] being deemed as approved outcomes.

Stable water isotopes were collected from 17 samples (9 wells, 5 springs, 2 surface water, and 1 dam) of the 2020 campaign (Figure 1) were analyzed in the laboratory at the Center “Centro de Ciencias e Tecnologias Nucleares (C²TN)”, Campus Tecnológico e Nuclear” in Lisbon (Portugal), using the SIRA 10 VG-ISOGAS mass spectrometer for δ²H and δ¹⁸O determinations.

To improve analytical accuracy, each sample was measured three times using the procedures proposed by Friedman [58] and Epstein and Mayeda [59] for stable isotopes (δ²H and δ¹⁸O). Using the standard method, the water samples were equilibrated with CO₂ and H₂ to obtain quantities of δ¹⁸O and δ²H values, respectively. Then, the instrument was calibrated to determine the composition of δ¹⁸O and δ²H by analyzing the IAEA standards, i.e., Vienna Standard Mean Ocean Water (VSMOW) with an accuracy range of ±1.0‰ for δ²H and ±0.1‰ for δ¹⁸O. Isotope results are expressed in terms of per thousand (‰) relative to the VSMOW using the notation ‘δ’ and Equation (1) [60]:

δ (‰) = ((R_sample − R_standard)/R_standard) × 100

(1)

where R_sample is the ratio of δ¹⁸O/ δ¹⁶O and ²H/H isotopes for the collected groundwater sample and R_standard is the ratio of δ¹⁸O/δ¹⁶O and ²H/H isotopes for the standard water sample.

The IDW (Inverse Distance Weighted) algorithm in the ArcGIS 10.3 Spatial Analysis Module (free trial period) generated a spatial distribution of water quality parameters.

3.2. Drinking Water Quality Index (DWQI)

The DWQI is a practical approach for evaluating the overall quality of groundwater for drinking purposes. It employs an arithmetic weighting system that assigns weights ranging from 1 to 5 based on the significance of different factors in determining the quality of groundwater. These weights are utilized to calculate the DWQI using Equation (2).

It is worth noting that several studies have used the DWQI method to evaluate the quality of groundwater for drinking purposes, including [61,62,63,64,65,66]. The method is useful in providing an overall assessment of groundwater quality for drinking purposes, taking into account various parameters that can affect water quality [67].

$$DWQI={\displaystyle \sum _{i=1}^n{q}_i}{W}_i$$

(2)

Equation (3) defines n as the number of parameters, where each parameter has a weight unit of W_i and a sub quality index of q_i:

$$q_i=\frac{V_i-V_0}{S_i-V_0}\times 100$$

(3)

Each parameter’s analyzed value is denoted as V_i, while Si represents the standard permissible limit of each parameter. The ideal value of each parameter is V_o, which is zero for all parameters except pH = 7.0 [23]. To calculate W_i for each parameter, recommended standards [23] are utilized through Equation (4) based on the

Table 1. Weight and relative weight of each parameter used for the WQI calculation.

Physico-Chemical Parameters	WHO Standard [67]	Weight (wi)	$\mathbf{Relative}\mathbf{Weight}({\mathit{W}}_{\mathit{i}}){\mathit{W}}_{\mathit{i}}=\frac{{\mathit{w}}_{\mathit{i}}}{{\displaystyle \sum _{\mathit{i}}^{\mathit{n}}\mathit{w}\mathit{i}}}$
pH	8.5	4	0.114
EC (µS/cm)	1500	4	0.114
TDS (mg/L)	1000	5	0.142
Cl− (mg/L)	250	3	0.086
SO42− (mg/L)	250	4	0.114
NO3− (mg/L)	45	5	0.142
HCO3− (mg/L)	120	3	0.086
Na+ (mg/L)	200	2	0.057
Ca2+ (mg/L)	75	2	0.057
Mg2+ (mg/L)	50	1	0.029
K+ (mg/L)	12	2	0.057
		35	0.998

:

$$W_i=K/S_i$$

(4)

where K is the proportionality constant.

3.3. Irrigation Water Quality Index (IWQI)

Soil structure and agricultural yield can be significantly affected by the levels of salt in water. Elevated salt levels in irrigation water can result in degraded soil quality and decreased crop productivity. Hence, it is crucial to assess the appropriateness of water for irrigation, and the irrigation water quality index (IWQI) is an effective instrument for accomplishing this objective.

The overall quality of irrigation water can be indicated by a numerical value known as IWQI. This value is derived from five water quality parameters, namely electrical conductivity (EC), sodium adsorption ratio (SAR), sodium ion concentration (Na⁺), chloride ion concentration (Cl⁻), and bicarbonate ion concentration (HCO₃⁻), as calculated according to reference [10] (

Table 2. Weights for the IWQI parameters according to Meireles [10].

Parameters	Weights (wi)
EC	0.211
Na+	0.204
HCO3−	0.202
Cl−	0.194
SAR	0.189
Total	1

).

The ability of water to conduct electricity, also known as electrical conductivity (EC), is closely associated with the concentration of dissolved salts present in it. The concentration ratio of sodium ions to calcium and magnesium ions in water is referred to as the Sodium Adsorption Ratio (SAR). In addition to SAR, specific ions, such as Na⁺, Cl⁻, and HCO₃⁻, can have an impact on the quality of irrigation water.

To calculate the IWQI, each parameter is assigned a weight or importance factor based on its impact on water quality. The weights are then multiplied by the measured values of each parameter, and the resulting products are added together. The sum is then divided by a normalization factor to obtain the IWQI value.

Meireles et al [10] suggested defining the values of accumulation weights (w_i) in the first step based on their relative importance for irrigation water quality. These values were normalized, and their total was set to one, as presented in

Table 3. Limiting values of (q_i) calculations [10].

HCO3−	Cl−	Na+	SAR	EC	Qi
meq/L			meq/L	μS/cm
1–1.5	1–4	2–3	2–3	200–750	85–100
1.5–4.5	4–7	3–6	3–6	750–1500	60–85
4.5–8.5	7–10	6–9	6–12	1500–3000	35–60
<1 or ≥8.5	<1 or ≥10	<2 or ≥9	<2 or ≥12	<200 or ≥3000	0–35

. The second step involved estimating the Q_i value based on the parameters recommended by Ayers and Westcot [68], as displayed in

Table 4. Irrigation water quality index characteristics [68].

Recommendation		Water Use Restrictions	IWQI
Plant	Soil
No toxicity risk for most plants	Indicating no specific restrictions or limitations for using a particular irrigation water on most soils. However, it is important to note that this designation assumes that the soil has a low probability of causing salinity and sodicity problems. If there is a higher risk of these issues, additional measures, such as leaching, may be necessary.	No restriction (NR)	85–100
Avoid salt sensitive plants	Recommended in irrigated soils with a light texture or moderate permeability. These soils allow water to infiltrate and move through them easily, which helps prevent the accumulation of salts and other minerals that can harm plant growth. However, in heavy-texture soils, using LR water may lead to soil sodicity, which is the accumulation of sodium ions in the soil. This can result in soil structure degradation, reduced water infiltration, and reduced plant growth.	Low restriction (LR)	70–85
Plants with moderate tolerance to salts may be grown	Refers to a soil classification based on permeability, which determines the soil’s ability to absorb and transmit water. MR soils have moderate-to-high permeability values, meaning they can easily absorb and transmit water.	Moderate restriction (MR)	55–70
Should be used for the irrigation of plants with moderate-to-high tolerance to salts with special salinity and control practices, except water with low Na+, Cl−, and HCO3− values	It is typically used in soils with high permeability, which allows water to move quickly through the soil without forming compact layers. When using water with an electrical conductivity (EC) level above 2000 µS cm−1 and a sodium adsorption ratio (SAR) level above 7.0, a high-frequency irrigation schedule should be adopted to ensure that the salt levels do not accumulate in the soil. This means that the irrigation should be done frequently in smaller doses rather than less frequently in larger doses to prevent the salt concentration from becoming too high.	High restriction (HR)	40–55
Only plants with high salt tolerance, except for waters with extremely low values of Na+, Cl−, and HCO3−	Under normal conditions, water with severe restrictions (SR) should be avoided for irrigation. However, in special cases, it may be used occasionally. Gypsum application can be helpful if low salt levels and high SAR are present in the water. Soils must have high permeability in highly salinized water, and excessive water should be applied to avoid salt accumulation.	Severe restriction (SR)	0–40

. The Q_i value, a non-dimensional number that indicates better water quality when it is higher and vice versa, was calculated using Equation (5):

$$q_i=q_{i\max }-\left[(x_{ij}-x_{\inf })\times q_{iamp}\right]/q_{amp}$$

(5)

where:

q_max is the upper-class value of q_i, X_ij represents the data points of the parameters shown in Table 1 (observed value of each parameter), X_inf denotes the lower limit value of the class to which the observed parameter belongs, q_iamp Represents the amplitude of the classes for the q_i classes, and x_iamp is the amplitude of the class to which the parameter belongs.

Finally, the IWQI was determined using the following relationship (Equation (6)):

$$IWQI={\displaystyle \sum _i^n{q}_i\times W_i}$$

(6)

Meireles [10] stated that in this case, with five parameters considered (represented by n), the values presented in Table 4 were multiplied by the corresponding weight of each parameter specified in Table 3. The Irrigation Water Quality Index (IWQI) is a useful instrument for assessing the suitability of irrigation water for agricultural applications. It takes into account various factors, such as the influence of water quality on soil and plant toxicity. The IWQI generates a distinct classification of irrigation water quality based on its score, with five categories ranging from no restriction to severe restriction.

The quality of water for irrigation can be assessed using the IWQI score, which is a scale that ranges from 0 to 100. A higher score on the IWQI scale indicates better water quality for irrigation. The IWQI score falls into different categories, which are based on the ranges set by Meireles et al. (2010) as shown in

Table 5. Summary of the results of physicochemical parameters of the entire campaign (2017, 2018, 2019, and 2020) in the coastal zone of the Essaouira basin in the Hauterivian aquifer.

Campaign	H	pH	T	EC	Ca2+	Mg2+	Na+	K+	HCO3−	Cl−	SO42−	NO3−	WQI	IWQI	IB
	m		°C	µS/cm	mg/L										%
April 2017 Campaign
Min	117.4	6.9	19.4	1134.0	106.2	59.6	50.6	3.9	244.1	127.6	57.7	6.2	85.9	26.5	−9.0
Max	154.4	8.4	24.8	2203.0	158.3	178.7	216.1	11.7	518.7	638.1	514.1	18.6	145.8	77.5	−1.0
Average	137.9	7.3	22.9	1647.6	126.7	90.3	114.8	7.8	445.4	345.8	189.2	10.5	110.8	51.4	−5.4
May 2018 Campaign
Min	117.5	7.1	18.1	668.0	51.3	20.4	45.0	2.7	207.5	113.6	38.4	2.3	50.0	20.0	−8.0
Max	326.8	8.3	24.3	3688.0	246.9	186.6	196.7	9.0	549.1	894.6	340.8	15.3	176.8	81.8	3.0
Average	222.3	7.6	21.9	1803.6	126.8	83.1	92.0	5.3	425.2	302.6	152.0	8.5	100.1	59.2	−3.8
March 2019 Campaign
Min	117.4	7.1	18.0	687.0	82.2	38.9	27.6	3.9	244.1	120.5	43.2	0.0	60.4		−8.0
Max	325.7	8.2	24.8	2405.0	190.4	109.4	115.0	15.6	659.0	411.2	374.8	18.6	128.2		−1.0
Average	207.8	7.5	22.5	1558.5	155.3	80.8	79.7	7.2	498.8	290.1	176.2	8.8	107.8		−5.3
July 2020 Campaign
Min	0.8	6.6	21.5	481.0	60.7	25.6	39.2	1.5	178.1	51.7	66.0	0.2	55.9	22.4	−9.0
Max	324.8	8.5	32.0	27000.0	341.1	817.5	7117.6	231.0	510.0	11574.5	2320.0	940.5	1244.1	76.5	4.0
Average	126.4	7.6	24.8	3136.0	145.8	97.4	507.7	25.3	372.7	894.4	274.3	66.1	182.7	53.4	−2.5

Note: WQI: Water Quality Index. IWQI: Irrigation Water Quality Index. IB: Ionic Balance.

. The categories are as follows: No restriction (IWQI = 85–100), Low restriction (IWQI = 70–85), Moderate restriction (IWQI = 55–70), High restriction (IWQI = 40–55), and Severe restriction (IWQI = 0–40).

3.4. Data Analysis

The statistical techniques used in the analysis of groundwater data are as follows:

• Descriptive statistics: These are used to summarize and describe the characteristics of a dataset. The mean, standard deviation, minimum, and maximum are common measures of central tendency and spread used in descriptive statistics.
• Spearman correlation: This is a non-parametric test used to measure the strength and direction of the relationship between two variables. The Spearman correlation coefficient ranges from −1 to +1, where −1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation [69].
• Principal component analysis (PCA): This is a multivariate statistical technique used to reduce the dimensionality of a dataset by identifying the underlying patterns and relationships among variables. PCA can help to identify the most important variables and can be used to create new variables (components) that capture most of the variation in the original data [70]
• Cluster analysis (CA): This technique is used to group data objects into clusters based on similarities among them. CA can be used to identify patterns and relationships in the data and help identify subgroups within the data that may be of interest [71].

4. Results and Discussion

4.1. Groundwater Geochemistry

The physico-chemical assessments of the groundwater samples collected in the study area are shown in Table 5, and the extension of all 56 samples are available in the Supplementary Material. The temperature readings for the groundwater samples from the Hauterivian aquifer range from 21.5 to 32 °C, with an average temperature of 24.8 °C recorded for the year 2020.

Water samples typically have pH values ranging from 6.6 to 8.5, with an average pH of 7.60 for the year 2020 (Table 1). The electrical conductivity (EC) at 25 °C varies from 481 to 27,000 µS/cm, with an average of 3136 µS/cm for groundwater in the Hauterivian aquifer during the same year. According to Table 1, these findings suggest that the groundwater in the Hauterivian aquifer has the lowest level of mineralization.

Groundwater samples from the Hauterivian aquifer exhibit a chemical composition primarily influenced by the chloride ion, followed by bicarbonate for anions. Sodium and calcium are the dominant cations present in the samples. Such a composition is characteristic of limestone regions, where the rock formations contain carbonates that dissolve in water, resulting in the formation of bicarbonate and calcium ions.

The cations present in the samples were ordered in descending abundance as follows: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺, with sodium (Na⁺) being the dominant cation at most sample sites, ranging from 39.15 to 7117 mg/L (mean value of 507.68 mg/L). Calcium (Ca²⁺) (60.67–341.06 mg/L) and magnesium (Mg²⁺) (25.57–817.47 mg/L) were also significant contributors to the mineralogical composition of the samples. Potassium (K+) had an average concentration of 25.31 mg/L, with a range of 1.46–230.98 mg/L.

The descending order of concentration for the main anions (in mg/L) was as follows: Cl⁻ > HCO₃⁻> SO₄²⁻ > NO₃⁻. Chloride (Cl⁻) was the prevailing anion among all the sampled sites, ranging from 51.74 to 11,574.52 mg/L.

Boxplots were used to analyze the relative influence of different chemical parameters on groundwater chemistry. The boxplots showed each variable’s median, lower and upper quartiles, and non-outlier range. The analysis revealed that EC and TDS showed the most significant variability, while pH, Ca²⁺, Mg²⁺, K⁺, SO₄⁻, and NO₃⁻ showed the least variability. Na⁺, HCO₃⁻, and Cl⁻ showed moderate variability (Figure 2).

These findings suggest that local contamination plays a significant role in regional groundwater chemistry processes. It is important to identify the sources of contamination and take measures to prevent further contamination from affecting groundwater resources.

In conclusion, groundwater in the Hauterivian aquifer is slightly alkaline and relatively mineralized, with a chemical composition dominated by chloride, bicarbonate, sodium, and calcium ions. These results are consistent with the geologic characteristics of the region and may provide important information for the management and protection of groundwater resources.

4.2. Multivariate Statistical Analysis

4.2.1. Descriptive Statistics and Pearson’s Correlation Matrix

The hydrochemistry process was analyzed in a study of 56 groundwater samples using multivariate techniques, such as correlation coefficients, cluster analyses, and factor analyses. The study examined variables such as total dissolved solids (TDS), electrical conductivity (EC), pH, cations, and anions, which were standardized to eliminate scale differences based on reference [72].

Table 6. Correlation coefficient matrix.

Campaign 2017	TDS	Ca2+	Mg2+	Na+	K+	HCO3−	Cl−	SO42−	NO3−
TDS	1
Ca2+	0.59	1
Mg2+	0.67	0.35	1
Na+	0.87	0.55	0.38	1
K+	0.06	−0.21	0.27	0.06	1
HCO3−	0.76	0.13	0.39	0.66	−0.06	1
Cl−	0.92	0.60	0.43	0.96	0.05	0.70	1
SO42−	−0.04	0.14	0.61	−0.31	0.14	−0.35	−0.31	1
NO3−	0.43	0.29	−0.16	0.51	−0.27	0.58	0.46	−0.62	1
Campaign 2018	TDS	Ca2+	Mg2+	Na+	K+	HCO3−	Cl−	SO42−	NO3−
TDS	1
Ca2+	0.91	1
Mg2+	0.93	0.73	1
Na+	0.92	0.75	0.88	1
K+	−0.16	−0.42	0.02	0.01	1
HCO3−	0.51	0.33	0.63	0.54	0.07	1
Cl−	0.97	0.89	0.88	0.91	−0.23	0.50	1
SO42−	0.68	0.70	0.60	0.54	−0.02	−0.10	0.55	1
NO3−	0.31	0.04	0.58	0.33	0.30	0.83	0.29	−0.15	1
Campaign 2019	TDS	Ca2+	Mg2+	Na+	K+	HCO3−	Cl−	SO42−	NO3−
TDS	1
Ca2+	0.78	1
Mg2+	0.70	0.76	1
Na+	0.39	0.49	0.07	1
K+	0.09	−0.02	−0.10	0.32	1
HCO3−	0.60	0.68	0.39	0.73	0.01	1
Cl−	0.62	0.81	0.67	0.58	−0.27	0.70	1
SO42−	0.25	0.29	0.53	−0.38	0.02	−0.40	−0.06	1
NO3	0.70	0.48	0.46	0.09	0.10	0.49	0.26	0.10	1
Campaign 2020	TDS	Ca2+	Mg2+	Na+	K+	HCO3−	Cl−	SO42−	NO3−
TDS	1
Ca2+	0.72	1
Mg2+	0.98	0.66	1
Na+	1.00	0.67	0.99	1
K+	0.80	0.89	0.73	0.76	1
HCO3−	−0.38	−0.03	−0.32	−0.42	−0.23	1
Cl−	1.00	0.68	0.99	1.00	0.76	−0.41	1
SO42−	0.98	0.65	0.99	0.99	0.73	−0.41	0.99	1
NO3−	0.09	0.60	−0.04	0.01	0.64	0.07	0.01	−0.03	1

presents the descriptive statistics for the 56 groundwater samples. Additionally, a detailed correlation matrix analysis was conducted to demonstrate the role and impact of each parameter in the hydrochemistry process [73,74,75].

The correlation matrix is an effective statistical tool. It reveals the linear correlation between different physical and chemical variables and the influence of each parameter on the overall hydrochemical data (Table 6).

Table 6 shows the Pearson’s correlation matrix, which indicates significant correlations between groundwater variables. TDS displayed a strong positive correlation with Na⁺, SO₄²⁻, and Cl⁻ concentrations, while Mg²⁺, Na⁺, K⁺, and Cl⁻ also exhibited significant correlation. Moreover, Cl⁻, Ca²⁺, and K⁺ showed a moderately positive correlation, whereas NO₃⁻ concentration demonstrated a weak correlation with SO₄²⁻, Cl⁻, HCO₃⁻, and Na⁺ parameters.

Overall, the results suggest complex relationships among the studied groundwater variables, and the multivariate analyses used in this study may provide valuable insights into these relationships.

4.2.2. Hierarchical Cluster Analysis (HCA)

HCA can potentially classify groundwater samples into different water quality groups or hydrochemical facies. HCA applies the Euclidean separation technique as a similarity measure to distinguish between groundwater samples based on their hydrochemical characteristics [74,75].

Hierarchical cluster analysis was performed to evaluate the spatial separability of the water samples originating from different locations. The nearest neighbor cluster method was used to calculate the similarity between the variables. The results are presented as a dendrogram (Figure 3).

All variables were logarithmically transformed, and nearly matched normally distributed data were used. Three primary groups have been identified in the dendrogram of the nine physicochemical parameters (EC, TDS, pH, Ca²⁺, Na⁺, K⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and NO₃⁻) (Figure 3). A specific phenon line was selected at a connection distance of 5, and the specified phenon line [76] was selected.

The hydrochemical characteristics of the groups are separated at this distance. Based on the results, the height variables were divided into two clusters managed by the EC (Figure 3).

The second group, with the lowest medium linking distance, is composed by TDS and Cl⁻, which are associated with Na⁺. The third group, with the highest medium linking distance between all variables identified, is composed by Ca²⁺, K⁺, Mg²⁺, SO₄²⁻, HCO₃⁻, NO₃⁻, and pH.

4.2.3. Principal Component Analysis (PCA)

PCA is a pattern recognition method that reduces data dimensionality to facilitate visualization and analysis. The extracted principal components are orthogonal to one another.

Variable projection on the F1–F2 plane, which explains 89.24% of the total variance, indicates that all the variables, except HCO₃, are positively correlated with the F1 axis (Figure 4). As a result, this axis represents the cluster responsible for the mineralization of water in the study area. However, the more the F1 axis components are positive, the more the wells have a high concentration of major ions (Figure 4). NO₃⁻ and HCO₃⁻ are positively correlated with the F2 axis; therefore, the positive components on this axis are enriched by the nitrates and the bicarbonate. This reflects the presence of another phenomenon responsible for these ions.

The dominance of water quality parameters and the existing correlation reveals that a mineral weathering process has taken place [74].

Factor 2 suggests that water chemistry is affected by natural as well as anthropogenic factors, which can be justified by high positive loading of HCO₃⁻ and NO₃⁻.

Factor 1 shows high positive loading of TDS, Cl⁻, Na⁺, Mg²⁺, and SO₄²⁻, which could possibly be due to carbonate weathering and seawater intrusion.

PCA analysis suggests that water chemistry of the study region is affected by both geogenic and anthropogenic factors in the study area.

4.3. Groundwater Levels

Groundwater depth varies between 3.07 and 30.3 m in the study area in July 2020 (Table 1). Piezometric maps (Figure 5) show that the piezometric contour is regularly distributed, with groundwater heads varying from 0.8 to 324.8, and the flow direction is northeast to southwest. The hydraulic gradient is high in the western part and low in the eastern part. The water table is recharged by runoff from the Molay Abdellah Dam, precipitation, and the return of irrigation water.

Furthermore, the piezometric study of the groundwater in the Hauterivian aquifer from 2017 to 2020 indicates that the flow direction is from NE to SW towards the Atlantic Ocean outlet. This suggests that the water in the aquifer is flowing towards the ocean in the southwest direction.

4.4. Hydrochemical Facies

Based on the analysis of samples and the Piper diagram [77], the Hauterivian aquifer’s groundwater can be divided into three categories: mixed Ca-Mg-Cl, Na-Cl, and Ca-HCO₃. Additionally, surface waters can be grouped into Ca-Mg-Cl and Na-Cl (Figure 6). These variations in chemical composition are likely due to differences in the underlying geologic formations in the study area. The distinct groundwater types are associated with specific geological formations. The relationship between surface water and groundwater could play a role in their chemical makeup, such as by recharging groundwater from surface water sources.

4.5. Ion Ratio and Hydrogeochemical Evolution

Various geochemical reactions and processes taking place within the subsurface environment have an impact on the chemistry of groundwater. To assess its quality and suitability for different purposes, it is crucial to comprehend the physicochemical characteristics of groundwater.

Analyzing the correlation between physicochemical elements present in water is critical in identifying the controlling mechanisms of groundwater geochemical evolution. A positive correlation between two ions suggests that they have a common source or origin, whereas a negative correlation indicates that these ions do not have the same origin. The correlation matrix can provide insights into water mineralization and help us understand its quality. Different binary graphs (Figure 7) can explain the various controlling mechanisms of groundwater geochemical evolution.

The correlation diagram between Na⁺ and Cl⁻ can provide useful information about the origin of water salinity in arid and semi-arid environments. In the case of the Hauterivian aquifer and surface waters, the significant positive correlation between Na⁺ and Cl⁻ (R² = 0.90. and Figure 7a) suggests that these two ions have the same origin, which could be the dissolution of halite and the evaporation phenomenon.

Halite is a mineral composed of sodium chloride (NaCl), and its dissolution can contribute significantly to Na⁺ and Cl⁻ levels in the water. In arid and semi-arid environments, where evaporation rates are high, water bodies can become concentrated with dissolved salts, increasing salinity. This evaporation phenomenon can also contribute to a water’s Na⁺ and Cl⁻ levels.

Indeed, the negative saturation indices for halite suggest halite dissolution contributes to water mineralization, as explained by the following reaction (Equation (7)):

NaCl + 2H₂O → Na⁺ + Cl⁻ + 2H₂O

(7)

However, some samples show that halite is highly undersaturated, indicating that it cannot be the only source of Na⁺ and Cl⁻ ions. Other possible sources of these ions include surface contamination or saline sources due to advancing marine intrusion into the aquifer. As indicated by the Gibbs diagram, the increase in Na⁺ and Cl⁻ ions may also be attributed to evaporation. Additionally, the water–rock interaction may contribute to groundwater mineralization.

Figure 7b illustrates that most samples exhibit a surplus of calcium, indicating a positive relationship between calcium and sulfate (R² = 0.31). This implies that these two elements might have the same source linked to the dissolution of sulfate minerals (gypsum and anhydrite). The negative saturation indices (Figure 8) of all groundwater samples indicate that they are undersaturated with respect to the confirmed presence of gypsum and anhydrite and their dissolution process. Therefore, the high calcium content and low sodium content can be attributed to the cation exchange reaction, which considerably impacts the chemical makeup of groundwater. This reaction involves the adsorption of Na⁺ cations by clay minerals on their surface, leading to the release of Ca²⁺ according to Equation (8):

Ca-clay(s) + 2Na⁺→ Na²-clay(s) + Ca²⁺

(8)

The cation exchange process is confirmed by the relationship between (Na⁺ + K⁺)-(Cl⁻) versus Ca²⁺ + Mg²⁺-(SO₄²⁻ + HCO₃⁻) studied in Figure 9a.

The relationship between Ca²⁺ and Mg²⁺ can be deduced from the scatter plot. As shown in Figure 7c. This plot shows that most of the samples collected in the Hauterivian aquifer are above the reference line (1:1). While a few are below the reference line. This suggests that the samples above and below the reference line are rich in Ca²⁺ and Mg²⁺, respectively.

The Ca²⁺ versus SO₄²⁻ diagram (Figure 7b) reveals that the majority of samples have an excess of calcium, indicating a positive correlation between calcium and sulfate (R² = 0.31). This indicates the inability of groundwater to dissolve calcite and dolomite due to supersaturation (SI > 0) and steady states (SI = 0) relative to these minerals (Figure 8). Calcium is mainly derived from anhydrite and gypsum, which are dissolved in evaporites. This dissolution is confirmed by the undersaturation state of all gypsum and anhydrite samples, indicating that they are dissolving in the water. The amount of calcium derived from cationic exchange and fertilizers can also play a significant role.

The excess of Ca²⁺ over SO₄²⁻ and HCO₃⁻ could be related to another process, such as reverse base exchange reactions by Na⁺ fixation and Ca²⁺ and Mg²⁺ release (Figure 9a).

4.6. The Main Geochemical Processes

In Figure 9a, the concentration levels of cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺) and anions (Cl⁻, SO₄²⁻, and HCO₃⁻) in groundwater samples obtained from extensively fractured aquifers are displayed. The plot indicates that the majority of the samples are situated on a linear pattern with a slope of −1.73.

This suggests that the reverse ion exchange process is an important factor in enriching Ca²⁺ or Mg²⁺ in these aquifers relative to Na⁺. The process works by Na⁺ being adsorbed onto exchange sites in the aquifer matrix and replaced by Ca²⁺ or Mg²⁺. This can be explained using Equation (9):

2Na⁺ + Ca²⁺(Mg²⁺) – Clay ↔ Na⁺ − Clay + Ca²⁺(Mg²⁺)

(9)

The scatter plot between Na⁺/Cl⁻ versus Cl⁻ (Figure 9b) suggests that the excess of Na⁺ over Cl⁻ in groundwater is due to ion exchange. However, some samples show a Na⁺/Cl⁻ ratio < 1 with increasing salinity concentration, indicating the effect of dissolution processes.

The decrease in groundwater discharge is attributed to high Cl⁻ concentration as indicated on the springs (E72, E76, HT26, P9, and HT25), which suggests the influence of groundwater chemistry on dissolution. The semi-arid climatic conditions in the region may also contribute to an increase in the ionic concentration in the groundwater due to evaporation.

4.7. Water Mineralization

The primary processes contributing to groundwater mineralization are evaporation and the weathering of rocks and soils. The products of these processes can be transported through various pathways, such as surface water, subsurface groundwater, atmospheric precipitation, and even biological mechanisms. Internal inputs such as diffusion and dissolution processes in sedimentary and particulate phases can also play a role in groundwater mineralization [78].

In Figure 10, the Gibbs diagram illustrates a few key processes, including dilution, rock dominance, and evaporation. The majority of the samples are projected towards the left corner of the graph, indicating that the dominant processes are likely to be evaporation and water-rock interaction

The statement highlights the significance of the water–rock interaction in the study area by emphasizing that the primary groundwater chemistry of the aquifer is controlled by carbonate dissolution.

4.8. Groundwater and Saline Water Interaction in the Hauterivian Aquifer

Bivariate plots utilizing ion ratio diagrams were employed to distinguish between freshwater, recycled water, and saline water based on the various sources of salinization and the controlling factors of geo-chemical processes within the aquifer. As such, this methodology was utilized to pinpoint the underlying reasons for salinization in the study area.

Many of the waters found in our study suggest that several processes have influenced the evolution of groundwater in the study area. Cation exchange, contamination by agricultural inputs, water–rock interaction, irrigation returns, and seawater intrusion are among them. Na⁺/Cl⁻ molar ratios may also implicate the latter concluded that ratios below 0.86 indicate significant seawater mixing, and more than two-thirds (72%) of the wells studied fell below this criterion (Figure 11a). A positive correlation between Na⁺ and Cl⁻ (R² = 0.90) indicates a common origin, indicating seawater intrusion.

All samples with a Ca²⁺/Mg²⁺ versus Ca²⁺ ratio (Figure 11b) greater than 0.9 were from seawater contamination. However, in one sample where Cl⁻ concentration exceeded 10 meq/L, there was a higher Mg²⁺ content than Ca²⁺. It is shown that most of the points in Figure 9b were contaminated by marine intrusion in 2020.

Seawater contains higher chloride ions (Cl⁻) and freshwater contains higher bicarbonate ions (HCO₃⁻) [79].

The plot Cl⁻/HCO₃⁻ versus Cl⁻ diagram (Figure 12a) shows that most points are contaminated by marine intrusion by some degree, depending on the distance from the Atlantic Ocean.

By using Cl⁻/HCO₃⁻ ratios, the plot of Cl⁻/HCO₃⁻ against Cl⁻ (as shown in Figure 12b) can be utilized to differentiate between freshwater and seawater types. Revelle [80] established classifications for water salinization types based on their level of influence by seawater intrusion or freshwater. These classifications are as follows: not influenced (less than 0.5), slightly influenced (between 0.5 and 1.3), moderately influenced (1.3–2.8), detrimentally affected (2.8–6.6), and strongly affected (above 6.6). The diagram classifies the water into fresh water, mixed water, and seawater. The marine intrusion contaminates most points.

4.9. Groundwater Quality Assessment

4.9.1. Water Quality for Drinking Purposes

The physicochemical parameter-based Water Quality Index (WQI) (Figure 13) is used to evaluate the suitability of groundwater for consumption. It consists of five classes (

Table 7. Results of WQI and its percentage of the four campaigns (2017, 2018, 2019, and 2020).

WQI Values	Water type	Campaign 2017		Campaign 2018		Campaign 2019		Campaign 2020
		Sample No.	%	Sample No.	%	Sample No.	%	Sample No.	%
<50	Excellent water
50–101	Good water	2	15.4	4	30.8	8	66.7	8	44.5
100–200	Poor water	11	85.6	9	69.2	4	33.3	8	44.5
200–300	Very poor water							1	5.5
>300	Unsuitable water for drinking							1	5.5

) that determine the quality of the water. Class (1) denotes excellent water with WQI values < 50, while class (2) indicates good quality water with WQI values between 50 and 100. On the other hand, classes 3 and 4 are assigned to poor and very poor-quality water, respectively, with WQI values between 100 < WQI < 200 and 200 < WQI < 300. Lastly, water with WQI values > 300 (class 5) is considered unsuitable for consumption [23,81].

For the Hauterivian aquifer for the campaign 2020, the WQI values range from 63.7 to 1504.58. Only one sample has WQI values above 300, which indicates unsuitable water for drinking. One sample has WQI values between 200 and 300, which indicates very poor water.

Eight samples have WQI values between 100 and 200, indicating poor-quality water, and the remaining eight samples have WQI values between 50 and 101, indicating good water, such as the Molay Abdellah dam, wadi, and a well (Figure 13).

The poor quality of groundwater in the HT aquifer can be attributed to a variety of factors, both natural (such as water interacting with the geological environment) and anthropogenic (such as infiltration of agrochemicals and wastewater and overexploitation of wells, resulting in seawater intrusion). This is supported by the increase in major element contents, as indicated by the index.

4.9.2. Water Quality for Irrigation Purposes

The IWQI (Irrigation Water Quality Index) results for the Hauterivian aquifer indicate that groundwater quality is unsuitable for unrestricted irrigation use. Based on the analysis, only one well out of 19 was classified as having low restriction, which suggests that caution should still be exercised even when using groundwater for irrigation.

Most of the wells (63.2%) were categorized as having moderate restriction, meaning groundwater can only be used for plants with moderate tolerance in moderately to highly permeable soils, with consideration given to moderate soil leaching processes. About 21.1% of the wells were in the severe restriction category, which limits groundwater use for irrigation to only plants with high salt tolerance. The remaining 10.5% were in the high restriction category, which allows irrigation of moderate-to-high-tolerance plants in permeable soil only if the irrigation water’s electrical conductivity and sodium adsorption ratio is within certain limits.

The IWQI values range from 22.36 to 76.54 for the 2020 campaign (

Table 8. Classification of groundwater quality for the investigated sites based on IWQI for the four campaigns.

IWQI Values	Type of Restriction	Campaign 2017		Campaign 2018		Campaign 2019		Campaign 2020
		Sample No.	%	Sample No.	%	Sample No.	%	Sample No.	%
85–100	No restriction
70–85	Low restriction	2	15.4	3	23.1	2	16.7	1	5.3
55–70	Moderate restriction	4	30.8	8	61.5	4	33.3	12	63.2
40–55	High restriction	5	38.5			2	16.7	2	10.5
0–40	Severe restriction	2	15.5	2	15.4	4	33.3	4	21.1

). In general, 31.3% of the surveyed wells have a high to severe restriction rating for irrigation purposes. In comparison, 68.7% of the surveyed wells received in low-to-moderate restriction ratings (Table 8).

The GIS-IWQI map represents the spatial distribution of the groundwater quality in the study area. The map shows different zones of groundwater quality, which are highlighted in different colors. The western zone of the study area, highlighted in brown, is suitable for salt-tolerant plants. The central and northeastern areas of the Hauterivian aquifer, highlighted in blue and green, are suitable for low-to-moderate-restriction plants. The severely restricted areas along the Atlantic Ocean are highlighted in red.

According to the statement, the GIS zoning map for the parameter IWQI (Figure 14) shows no change in groundwater quality for irrigation during the four years sampled. This suggests that the groundwater quality remained relatively stable over the four years. The GIS-IWQI map can be useful for decisionmakers to identify hot spots that suffer from over-extraction and provide guidance for sustainable groundwater management. Using this map, decisionmakers can take appropriate actions to ensure the sustainable use of groundwater resources.

4.10. Origin, Modes, and Elevation of Groundwater Recharge in the Hauterivian Aquifer

Stable isotope ratios of the water molecule range from −5.32 to 4.79‰ relative to SMOW (average of −3.01) for δ¹⁸O and from −25.9 to 20.3‰ relative to SMOW (average of −15.12) for δ²H (

Table 9. Results of isotopic analysis of samples collected in the Hauterivian aquifer for campaign 2020.

Sample	Nature	X	Y	Z	pH	EC	Cl−	δ18O	δ2H
		m				μS/cm	meq/L	Vs SMOW (‰)
E70	Well	79,032	64,190	4	7.1	1407	5.9	–3.3	–12.4
E71	Well	78,523	61,058	100	7.7	4094	30.5	–3.01	–15.9
HT120	Well	89,536	64,080	179	7.2	1541	7.0	–4.37	–23.3
HT102	Well	89,177	64,188	156	7.5	1551	6.9	–4.34	–24.5
HT24	Well	89,239	63,810	123	8.2	1837	9.1	–1.03	–8.2
E83	Well	92,802	63,545	163	8.12	867	1.5	–1.34	–6.4
HT115	Well	98,271	68,747	335	7.9	1896	8.9	–4.75	–21.6
HT117	Well	94,198	68,475	287	7.7	1161	3.0	–5.32	–25.9
HT118	Well	90,605	67,791	267	8.1	481	6.1	–4.78	–19.1
E72	Spring	82,073	63,394	46	7.6	2224	12.4	–2.81	–16.9
E76	Spring	82,525	67,811	250	8.05	1862	10.6	–3.89	–20.8
HT26	Spring	89,651	63,823	135	7.2	1579	7.0	–3.56	–19.3
P9	Spring	90,126	63,897	143	7.3	1635	7.1	–4	–20.3
HT25	Spring	87,764	64,270	148	7.4	1812	9.2	–5.25	–21.6
E73	Surface water	82,107	63,464	47	7.4	2340	13.7	–1.59	–11.1
E74	Surface water	79,021	63,948	4	8.5	27,000	326.5	4.79	20.3
HT29	Dam	94,921	64,380	195	8.2	1050	1.5	–3.16	–10.1
				Min	7.1	481.0	1.5	–5.3	–25.9
				Max	8.5	27,000.0	326.5	4.8	20.3
				Mean	7.7	3196.3	27.5	–3.0	–15.1

). Figure 15 shows the presence of two isotopic groups.

In the text, two sample groups are being discussed. The initial group (G1) is situated above both the global meteoric water line (GMWL: δ²H = 8 × δ¹⁸O + 10; [82]) and the local meteoric water line of the Essaouira basin (LMWL: δ²H = 7.96 × δ¹⁸O + 11.30; [83]). It is distinguished by a significant influence of direct rainwater infiltration on aquifer recharge, primarily in the vicinity of the hydrographic network, indicating a speedy and current recharge.

Samples in the second group (G2) have isotopic compositions that are more enriched than those found below the GMWL and LMWL. This indicates that the samples are primarily affected by evaporation, which could be due to either slow infiltration of rainwater as a result of low permeability soil or the return of evaporated irrigation water.

The Cl⁻ vs. δ¹⁸O diagram (Figure 15b) confirms the presence of two main processes contributing to the mineralization of the Hauterivian aquifer: dissolution of evaporating rocks and evaporation. The diagram shows some samples with isotopic enrichment, such as HT29 (dam), HT24 (well), E73, and E74 (wadis), which confirm that the evaporation process contributes to groundwater mineralization.

Isotopic enrichment refers to the increase in the concentration of an isotope relative to another in a substance. In this case, the samples with isotopic enrichment suggest that the water has undergone some form of evaporation, leading to an increase in the concentration of the heavier isotope of oxygen (δ ¹⁸O) relative to the lighter isotope (δ¹⁶O).

Evaporation is a process by which water changes from a liquid to a gas, leaving behind dissolved salts and minerals. As the water evaporates, the concentration of dissolved salts and minerals in the remaining water increases, leading to mineralization. The presence of isotopic enrichment in some samples suggests that evaporation is a significant process contributing to mineralization in the Hauterivian aquifer.

To determine the recharge elevation of the Hauterivian aquifer, one possible method is to analyze the altitude line graph of precipitation and water sources in Morocco. This graph is likely to illustrate how the isotopic composition of precipitation changes with altitude, which can be used to infer the altitude of the aquifer's recharge area. This approach is based on the assumption that groundwater's isotopic composition is related to precipitation at the time of recharge [84], which has a gradient of 0.25‰ per 100 m for δ¹⁸O. This value is consistent with that defined for the High Atlas (0.27‰ per 100 m) [85] and that of the Essaouira Basin our study area (−0.26‰ per 100 m) [86]; the projection (Figure 16) reveals that the waters originate from altitudes between 120 (E70) and 900 m (HT117).

5. Conclusions

Cluster analysis and PCA are efficient tools for evaluating the association between the stable chemical elements and the physical-chemical parameters studied in this paper.

Although seawater intrusion into coastal aquifers is a well-known problem on a global scale, no practical measures have been taken to prevent it. The restoration of water quality is typically an expensive or futile endeavor once seawater intrusion has begun. It takes flushing for a long time with a lot of fresh water. For efficient water management and effective remediation, monitoring and early detection of the source of the salinity are essential.

In terms of water quality, the Hauterivian aquifer’s overall pattern of operation is primarily influenced by its geological and extraction contexts. The area has a semi-arid environment with 300 mm or less of yearly rainfall on average.

The results of our study indicate that the major cations and anions present in the sub-basin have a relative abundance as follows: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺, while the concentration of the primary anions is in the order of Cl⁻ > HCO₃⁻ > SO₄²⁻ > NO₃⁻.

Thus, the recharge area calculated by stable isotope analysis confirms the location of a recharge zone over a wide range of altitude between 120 and 900 m.

It is necessary to control water management at the irrigated perimeter and implement suitable management strategies to preserve the sustainability of water resources in this region.

In this water-scarce region, the ongoing stress on land and water resources has changed the natural equilibrium and sped up the salinization process. An integrated approach to managing water resources is required to ensure the availability of fresh water in the future. This approach should account for groundwater balance calculations in coastal aquifers.