For effective coastal aquifers management, it is strongly required to effectively analyze seawater intrusion (SWI). This study used an integrated approach of hydrogeochemical, statistical, geological, and geospatial techniques to assess the extent of SWI in the Ghiss-Nekor aquifer by evaluating the physicochemical parameters of 52 groundwater samples. Two main groundwater facies were identified, Na-Cl (38%) and Ca-Mg-Cl-SO4 (62%). The correlation matrix and a principal component analysis (PCA) depicted that the high salinization in the study area is influenced by both geogenic and anthropogenic factors, including a potential mixing with seawater. A single indicator or a small number of techniques were insufficient to evaluate SWI owing to the multiple causes of salinization in the study area. As a result, we coupled various geochemical indicators with geospatial methods to assess this complicated phenomenon. Accordingly, several ionic ratios (Cl/HCO3, SO4/Cl, Na/Cl, and Mg/Mg + Ca) and SWI indices (GQIswi and SMI) were overlaid to generate the final map that highlights the regions prone to SWI. Most of the SWI spots were discovered within two kilometers or less from the coast. The saline water detected far from the coast was interpreted as the encroachment of seawater from the Souani area being trapped where the clay-marly substratum is deep. These results support the application of geospatial tools to manage groundwater resources in water-stressed areas with complex aquifer systems, by combining various ionic ratios and indices. These findings will assist decision-makers in the Al Hoceima region in developing suitable groundwater management plans and strategies.
Research Article
A geochemical approach in assessing seawater intrusion by integrating geospatial techniques: a case study of Ghiss-Nekor coastal aquifer, Central Rif of Morocco
https://doi.org/10.21203/rs.3.rs-2118023/v1
This work is licensed under a CC BY 4.0 License
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GIS
Overlay analysis
Saltwater fraction
3D geological modeling
Principal component analysis
Al Hoceima
Groundwater is a preeminent freshwater resource. This valuable resource serves as the primary supply of water for more than half of the world's population and is used for a variety of uses in household, agricultural, and industrial areas (Arulbalaji et al., 2019; Satheeskumar et al., 2021). It represents 99% of the globe’s liquid freshwater and provides about 34% of the world’s annual water supplies (Kura et al., 2015). Presently, people are getting increasingly dependent on groundwater resources, particularly in arid and semi-arid areas (Taylor et al., 2013). In these regions, reliance on groundwater is due to the ongoing depletion of surface water sources from consecutive periods of drought because of climate change. The increasing demand for groundwater leads to its over-exploitation and consequently, for coastal aquifers, further threatens the resource through seawater intrusion (Giménez-Forcada et al., 2010; Yeh et al., 2009).
Seawater intrusion (SWI) is defined as a process by which groundwater salinity increases due to a landward movement of seawater and can have multiple causes, including over-pumping, climate change, land use changes, and sea-level changes (Werner et al., 2013; Jiao & Post, 2019; Gomaa et al., 2021). Furthermore, the SWI phenomenon is exacerbated by overpopulation in coastal areas.
In Morocco, the coastal regions are experiencing continuous population growth accompanied by high agricultural and industrial activities which require more freshwater supplies. Therefore, the lack of freshwater resources, due to overexploitation and the impact of climate change, has led to worsening SWI in several coastal aquifers in Morocco (Fadili, 2014). Among these coastal aquifers, the Ghiss-Nekor aquifer, our study area, is the most significant groundwater reservoir in the Al Hoceima region. This semi-arid Mediterranean region is influenced by climate change and is projected to experience periods of prolonged drought, occasional heavy rains, and an abnormal increase in temperature (Tramblay et al., 2012). These climatic conditions make the population prone to an increased lack of surface water and consequently uncontrolled exploitation of the aquifer resources. The overexploitation of groundwater in the Ghiss-Nekor aquifer and the scarcity of rainfall are two main factors that make this precious source of drinking water vulnerable to pollution by SWI. This phenomenon will contribute to a degradation in the quality of groundwater (Boumaiza et al., 2020; X. Hu et al., 2018; Satheeskumar et al., 2021), major source of drinking water, especially during periods of drought and summer seasons. The situation will be accentuated by expected future development because of commitment by the Moroccan government to promote tourism in the Central Rif region.
Thus, the assessment of SWI along Moroccan coastal aquifers is necessary for effective resource management. Numerous authors have addressed the issue of SWI in the Ghiss-Nekor aquifer, including: (1) Salhi (2008) and Chafouq et al.(2016) who used electrical resistivity tomography (ERT) to map the extent of SWI; (2) Iouzzi et al. (2011) employed hydrogeological modeling in a decision support tool to investigate different stresses that an aquifer may face, including possible SWI; (3) Khouakhi et al. (2015) and Kouz et al. (2018) used the GALDIT approach to appraise vulnerability of the aquifer to SWI, and to conduct preliminary assessments of future SWI for 0.5 and 1 m sea-level rise scenarios, respectively; (4) Chafouq et al. (2017) and Nouayti et al. (2022) both utilized hydrochemical and isotopic data to determine the source of salinization in the Ghiss-Nekor aquifer, while (5) Vroey (2012), Ghalit et al. (2017), and El Yousfi et al. (2022) used hydrochemical data to assess the mineralization process in our study area.
Given the geologic heterogeneities of the study area, a single index or a small number of approaches are insufficient to assess SWI. Thus, numerous approaches should be considered to get more accurate results (Jiao & Post, 2019; Kura et al., 2014; Werner et al., 2013; Yusuf et al., 2021).
One of the approaches in which interdisciplinary data are combined with geospatial models is vulnerability mapping using “Groundwater Vulnerability Assessment” (GVA) models such as DRASTIC, GALDIT, and EPIK, which are often used to measure the sensitivity of an aquifer to seawater contamination. However, many authors reported the limitations of this approach, namely Momejian et al. (2019) and Parizi et al. (2019) who proved its limited correlation with SWI indicator measurements (TDS, fsea, GQI), and Momejian et al. (2019) also noted that not all vulnerable areas are necessarily polluted at the time of vulnerability assessments. Furthermore, to prevent erroneous interpretations of electrical resistivity profiles, Kura et al. (2014) developed a GIS-based solution in which the electrical resistivity data were merged with geochemical data to provide a final map delineating the areas undergoing SWI in the Kapas Island peninsula, Malaysia. In order to create a SWI map, (Kazakis et al., 2016) overlaid four ionic ratios. Asare et al. (2021) employed a combination of 10 distinct main ionic ratios and the Base Exchange Index (BEX) to pinpoint the source of salted groundwater in the coastal communities of Ghana's Central Region. These techniques have shown to be successful in producing reliable results, that are relevant for implementing a long-term management strategy for coastal aquifers.
The present work, therefore, attempts to adopt a similar multi-discipline approach to identify the degree of SWI in the Ghiss-Nekor shallow aquifer, but this time by employing an original method of geospatially integrating several geochemical ratios and indices. For this purpose, the ionic ratios (Cl/HCO3, SO4/Cl, Na/Cl, and Mg/Mg + Ca) and SWI indices (Groundwater Quality Index specific for SWI (GQIswi) and Saltwater Mixing Index (SMI)) were computed and integrated into a geographical information system (GIS) to perform a weighted overlay analysis to assess seawater-affected areas. Our results are intended to provide baseline data for optimal groundwater management.
Furthermore, the study also aimed to identify the anthropogenic and geogenic sources contributing to the groundwater chemistry of the study area by interpreting data from statistical analyses : descriptive statistics, correlation matrix of hydrochemical parameters, Principal Component Analysis. A lithologic model and lithology fence diagram, as well as a scatterplot of NO3−/Cl− versus Cl−, were generated to interpret the final map depicting the spatial distribution of SWI. The definition of some acronyms we used in this work are listed in Table 1.
| Acronym | Definition |
|---|---|
| DRASTIC | Index-based seawater intrusion vulnerability model that includes seven criteria: “Depth to water”, “net Recharge”, “Aquifer media”, “Soil media”, “Topography”, “vadose zone Impact”, and “hydraulic Conductivity”. |
| EDTA | Ethylenediaminetetraacetic Acid. |
| EPIK | Index-based seawater intrusion vulnerability model that includes four criteria: “Epikarst”, “Protective cover”, “Infiltration conditions”; and “the Degree of Karstic network development”. |
| FEFLOW | Finite-Element Groundwater Flow and Transport Modeling Tool. |
| GALDIT | Index-based seawater intrusion vulnerability model that uses six input criteria: “Groundwater occurrence”, “Aquifer hydraulic conductivity”, “Depth to groundwater level”, “Distance from shore”, “Impact of existing seawater intrusion”, and “aquifer Thickness”. |
| MCM | Million Cubic Meters |
| MODFLOW | 3-D finite-difference groundwater flow modeling program |
| MT3DMS | Modular Transport 3-D Multi-Species model |
| SEAWAT | 3-D variable-density groundwater flow and transport model |
| SUTURA | 2-dimensional groundwater saturated-unsaturated transport model |
2.1. Description of the study area and its geology
The Ghiss-Nekor plain is situated southeast of the Moroccan Mediterranean city of Al Hoceima between 35°5' N and 35°13' N latitudes, 3°45' W and 3°54' W longitudes (Fig. 1). It is bounded by the Alboran Sea to the north, Mohammed Ben Abdelkarim El Khattabi (MBAK) dam to the south, Temsamane and Ait Touzine mountains to the east, and the National Road number 2 (which crosses the most prominent urban communes of the Al Hoceima province) to the west. The plain slopes on average by about 1% from south to north, has a coastline of almost 14 km, and has an extent of approximately 100 square kilometers shared between five communes: Ait Youssef Ou Ali, Imzouren, and Bni Bouayach in the province of Al Hoceima, and Trougout and Ijermaouas in the province of Driouch.
The study area has a triangular shape and is crossed by two main rivers; the Ghiss river runs through the area in the northwest, while the Nekor river runs through it in the center. The subsurface of the plain consists of the Ghiss-Nekor aquifer which is believed to be the most important of Morocco’s Mediterranean alluvial aquifers with a surface area of 90 square kilometers. It is fed by rainwater infiltration, irrigation water returning from the MBAK dam, and infiltration from the Ghiss and Nekor rivers, as well as other surrounding streams. The Nekor river is a 5th -order stream belonging to a 905 km2 watershed and a length of 76 kilometers (Bourjila et al., 2020). This river predominantly crosses the impermeable marly flysch, shale bedrock of the Ketama unit, and gypsum strata, which increases the salt content in the groundwater (Benabdelouahab et al., 2019). The Ghiss river, 80 km long, is considered a 6th -order stream with a basin size of around 837 km² (Bourjila et al., 2020, 2021). Both rivers run into the golf of Al Hoceima which is southwest of the Mediterranean Sea. The aquifer's total reserves are estimated to be at 328 MCM (ONEE, 2017).
The climate of the area is of a semi-arid Mediterranean type characterized by two different seasons, mild rainy winters, and hot dry summers. The rainy season lasts between October and April, while the hot and dry season occurs between May and September. In the study area, the mean annual temperature is 18°C, and the mean annual precipitation is 340 mm (Salhi, 2008). The climate in the province of Al Hoceima is distinguished by considerable seasonal and interannual variability, and spatial dispersion. This unpredictability and variability of rainfall is reflected by short and intense floods, as well as extended low water levels (Ghalit et al., 2017; Niazi et al., 2005). The dominant impermeable lithology and sharp terrain amplify these violent floods (El Gharbaoui 1986, as cited by Benabdelouahab et al., 2019). Figure 2 shows the average monthly rainfall and temperature in the Ghiss-Nekor plain from 1981 to 2021.
The study area is bounded by two active normal faults with a strike-slip component: the Imzouren fault (NNW-SSE) to the west and the Trougout fault (N-S) to the east (Medina, 1995 as cited in Taher, 2021) (Fig. 3). These faults were at the origin of the opening of a graben towards the end of the Tertiary, which was filled by the deposition of hundreds of meters of continental alluvial sediments (sands, gravels, and conglomerates) spanning from the Pliocene to the Present. Much of the detrital input came from the two main rivers, Ghiss and Nekor, as well as colluvial deposition from the two watersheds, resulting in the heterogeneous nature of the deposits (Salhi & Benabdelouahab, 2017; Vroey, 2012). Several studies (Guillemin and Houzay, 1982; Morel, 1987; Thauvin, 1971) have reported the undergoing of subsidence in the Ghiss-Nekor plain since at least the Pliocene, and that has been confirmed by Taher (2021) using the DinSAR (Differential Interferometric Aperture Radar) technique based on remote sensing technologies.
The lithology fence diagram (Fig. 4a), and the lithologic model (Fig. 4b), were created from the lithological cross sections generated by Benabdelouahab et al. (2019), from geophysical surveys and available drilling data. The lithology exhibits a deep impermeable bedrock of shale substratum covered by an uneven, discontinuous clay-marly upper substratum. The Ghis-Nekor alluvial plain is composed of considerable deposits of coarse sand and gravel, occasionally containing silt and clay, crossed by sparse clay-marly lenses, and a broad clayey level expanded near the shoreline. These intermediate clay-rich aquitards are not continuous. Consequently, the Ghiss-Nekor aquifer is considered a monolayer aquifer. The heterogeneity of the aquifer has rendered some areas confined and others unconfined (Vroey, 2012). The alluvial deposits have an average thickness of more than 200 meters and a maximum of 450 meters in a depression observed near Souani, and another one approximately a kilometer east of Imzouren with significant deposits of coarse alluvium, which spans about 5.5 km2.
2.2. Sampling and chemical analysis
The current experiment entailed sampling and evaluating the chemistry of groundwater in 52 sampling points spread throughout a coastal region of around 64 square kilometers (Fig. 1). Using Google Earth Pro software, we mapped potential sample sites to identify accessible areas within the region in which to conduct a representative groundwater sampling. Therefore, the chosen wells and shallow boreholes to be investigated were within the first 8 km from the coastline. The potential sample area was reduced to increase the number of the sample density, resulting in a more accurate spatial distribution of the water's physicochemical parameters, and thus, to precisely assess SWI in the Ghiss-Nekor coastal aquifer. The wells were dug for home water supply and irrigation purposes, and their depths varied from 0,78 to 57,67 meters. Their average depth was 17 meters deep.
To ensure methodological coherence and high-quality data, the groundwater samples for this study were collected, stored, and transported, as well as subjected to laboratory analyses in accordance with the standards outlined in “Water analysis (9th edition)” (Rodier et al., 2009). pH, temperature (T), Total Dissolved Solids (TDS), Electrical Conductivity (EC), and concentrations of principal cations and anions, namely Sodium (Na+), Potassium (K+), Magnesium (Mg2+), Calcium (Ca2+), Ammonium (NH4+), Bicarbonates (HCO3−), Nitrates (NO3−), Nitrites (NO2−), Chlorides (Cl−), Sulphates (SO42−), Orthophosphate (PO43−), and silica (SiO2), were the physicochemical parameters of relevance for this investigation. The equipment listed in Table 2 was used to measure these parameters in January 2022. Meteorological conditions this month were a continuation of the previous year's dry spell as the area received no substantial rainfall.
Descriptive statistics, a correlation matrix, and multivariate statistics (PCA using the rotated varimax method) were performed using IBM SPSS STATISTICS software (version 25).
AquaChem 10 software was utilized to automatically generate the hydro-chemical facies of each sample. Afterwards, ArcGIS 10.8 software was used to plot spatially the water types using the Thiessen Polygons tool by which polygonal borders are created to establish and identify the close-proximal zones surrounding individual data points. The ionic ratios and SWI calculation indices, as well as the different graphical plots were performed using both Origin Pro 2021 (Cl/HCO3, SO4/Cl, Na/Cl, Mg/Mg + Ca, Seawater Mixing Index [SMI]) and Microsoft Excel 365 (Groundwater Quality Index SWI [GQIswi].
The spatial interpolation of ionic concentrations, ionic ratios, and the SWI indices were interpolated utilizing the Inverse Distance Weighted (IDW) technique included in ArcGIS. The most common mapping methods in the groundwater sector are IDW and kriging interpolation. According to several earlier researches (Hajji et al., 2022; Yang et al., 2020; Zolekar et al., 2021), the IDW interpolation method is more accurate than the Kriging tool. Additionally, IDW has a track record of being easy to use, quick, and produces accurate results (K. X. Hu et al., 2019; Kura et al., 2014; Rina et al., 2013). For the overlay of the chosen maps, we used the weighted sum technique in ArcGIS to generate the SWI map. Prior to performing this technique, all of the generated maps were reclassified and guaranteed to have the same cell resolution.
| Parameters examined | Measuring equipment and techniques |
|---|---|
| T, pH, EC | PCE-PHD 1 multimeter (PCE instruments) |
| TDS | Calculated by using the formula TDS = Σ cations + Σ anions |
| Na+, K+ | Flame photometer (Model: ELICO CL 378) (accuracy ± 0.5 ppm) |
| Ca2+, Mg2+ | Titration with EDTA |
| Cl− | Titration with silver nitrate (0.1 N) |
| HCO3− | Titration with hydrochloric acid (0.1 N) |
| NO3−, NO2−, NH4+, PO43−, SO42− and SiO2 | The colorimetric determination method using a UV-VIS Spectrophotometer (LANGE HACH DR-6000) (precision ± 1 nm) |
3.1. Analyses of water data
3.1.1. Descriptive statistics
| Parameter | Units | Mean | Median | Min | Max | SD | Skewness |
|---|---|---|---|---|---|---|---|
| pH | - | 7.62 | 7.60 | 7.12 | 8.20 | 0.28 | 0.22 |
| EC | µs/cm | 4076.35 | 3480.00 | 1612.00 | 15300.00 | 2337.80 | 2.88 |
| TDS | mg/L | 3043.60 | 2717.45 | 1449.47 | 10014.78 | 1520.18 | 2.63 |
| Na | mg/L | 529.59 | 436.00 | 137.00 | 2560.00 | 413.85 | 3.08 |
| K | mg/L | 7.20 | 4.30 | 1.70 | 40.40 | 7.65 | 2.79 |
| Mg | mg/L | 142.71 | 131.04 | 56.64 | 395.52 | 65.64 | 1.98 |
| Ca | mg/L | 226.87 | 223.64 | 59.32 | 415.21 | 74.45 | 0.29 |
| NH4 | mg/L | 0.02 | 0.01 | 0.00 | 0.18 | 0.03 | 3.85 |
| Cl | mg/L | 858.58 | 627.52 | 155.99 | 5140.69 | 823.63 | 3.48 |
| SO4 | mg/L | 873.15 | 857.45 | 235.11 | 1592.25 | 312.59 | 0.13 |
| HCO3 | mg/L | 377.85 | 359.90 | 152.50 | 713.70 | 104.09 | 0.70 |
| NO3 | mg/L | 29.53 | 19.79 | 0.00 | 123.36 | 26.68 | 1.58 |
| NO2 | mg/L | 0.04 | 0.00 | 0.00 | 1.37 | 0.20 | 6.37 |
| PO4 | mg/L | 0.08 | 0.04 | 0.00 | 0.54 | 0.11 | 2.76 |
| SiO2 | mg/L | 20.51 | 15.29 | 5.54 | 81.04 | 16.65 | 2.79 |
Table 3 provides an overview of the fundamental data for the physicochemical characteristics of the Ghiss-Nekor groundwater for the month of January 2022. The pH is alkaline and ranges from 7.12 to 8.20 with an average of 7.62. It was determined that the pH's standard deviation (SD) was extremely low (0.28), demonstrating the common source of each sample (Kura et al., 2014). The EC values vary from 1612 to 15300 µS/cm with an average of 4076.35 µS/cm. TDS values vary from 1449.47 to 10014.78 mg/L with an average of 3043.60 mg/L. The abundance of cations was discovered to be in the following order: Na+ > Ca2+ > Mg2+ > K+> NH4+, whereas the order of anions was SO42− > Cl− > HCO3− > NO3−> PO42− > NO2−. In a research conducted by Ghalit et al. (2017) upstream of the Ghiss-Nekor plain, the same order of abundance was obtained.
The standard deviation of the hydrochemical parameters revealed broad variations of the measured concentrations, especially for chloride which exhibited the greatest variation of all the examined ions (SD = 823.63 mg/L), with a range of 155.99 to 5140.69 mg/L and an average of 858.58 mg/L. The considerable variations in the hydrochemical parameters indicate that salinization could have several different sources in our study area (Sarker et al., 2022). SWI might be one of the potential factors of salinization which is noticeable from the large variations in EC (SD = 2337.80 µs/cm) and TDS (SD = 1520.18 mg/L) (Kura et al., 2014). The previous observations are corroborated by the skewness values, which, except for SO42−, pH, Ca2+ and HCO3−, depict the hydro-physicochemical parameters' distant dispersion from normal and are substantially greater than zero (Table 3). The different physicochemical characteristics evaluated in the current investigation are shown spatially in Fig. 5.
Figure 5. Spatial distribution of hydrochemical parameters: (a) EC, (b) TDS, (c) Cl, (d) HCO3−, (e) NO3−, (f) SO42+, (g) Na2+, (h) Ca2+, (i) Mg2+, (j) K+, (k) NO2−, (l) PO43−, (m) NH4+, (n) SiO2.
Figure 5. (Continued).
3.1.2. Correlation of hydrochemical parameters
The correlation matrix was used to determine the level of connection between several physicochemical parameters (Table 4). A strong correlation between the parameters is shown by values of + 1 (positive correlation) or -1 (negative correlation), whereas a value of zero denotes no link. A correlation coefficient of 0.5 to 0.7 indicates variables that are moderately correlated, and between 0.7 and 0.9 are highly correlated, whereas those with values of 0.9 or higher are very highly correlated. These three categories of correlation coefficients were bolded in the correlation matrix table. The high and the very high positive correlations of Na+, Cl−, Mg2+, and K+ with EC and TDS reveal that the observed high salinity is mainly controlled by these major ions which are most likely to originate from the same sources (Kura et al., 2014), such as the occurrence of SWI (Kim et al., 2003) and the dissolution of salts (Askri et al., 2022; Gebeyehu et al., 2022). The very high correlation between Na+ and Cl− (r = 0.99) emphasizes the effects of chemical weathering, secondary salt leaching, and SWI(Abu-alnaeem et al., 2019; Prasanna et al., 2010; Srinivasamoorthy et al., 2011). The very high correlation between Ca2+ and SO42− (r = 0.92) may indicate that the source for these two ions is gypsum (CaSO4. 2H2O) or anhydrite (CaSO4) dissolution (Abu-alnaeem et al., 2019; Askri et al., 2022; Dobrzyński, 2007). However, the high correlation of Mg2+ with Ca2+ (r = 0.74), Na+ (r = 0.84) and Cl− (r = 0.85) strongly suggests the same sources for these elements, primarily SWI and carbonate dissolution (Abu-alnaeem et al., 2019). The strong correlations of K+ with Na+ and Cl− point to a shared source, especially given that these ions are found in high quantities close to the shoreline, suggesting that saltwater incursion may be a potential cause of salinization along the seaside (Gebeyehu et al., 2022). The SiO2 is highly correlated with PO43− (r = 0.94) and both are moderately correlated with NO3− (r = 0.60 and r = 0.61, respectively). SiO2 is geogenic, however, the occurrence of PO43− and NO3− is mainly anthropogenic which can originate from septic systems, manure use, and commercial fertilizers (Ağca et al., 2014). This high correlation between SiO2, PO43− and NO3− can be explained by the fact that the silicate weathering can be enhanced by the nitrification process (Batsaikhan et al., 2021). The weak to very weak correlations between NH4+, NO3−, NO2−, PO43− with most of the variables (EC, TDS, Na+, K+, Mg2+, Ca2+, Cl−) might be explained by the dissimilar sources of these ions and indicates that human activities, including sewage leaks and agricultural operations, have had an impact on the chemistry of groundwater (Abu-alnaeem et al., 2019). A very weak negative correlation between pH and all the other parameters (-0.37 < r < 0.05) implies that pH fluctuation has little impact on groundwater salinity (Gebeyehu et al., 2022).
The examination of the correlation matrix exhibits a strong probability of SWI along with other sources of salinity in the Ghiss-Nekor aquifer.
| Ions | Na | K | Mg | Ca | NH4 | Cl | SO4 | HCO3 | NO3 | NO2 | PO4 | SiO2 | EC | TDS | pH |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Na | 1,00 | ||||||||||||||
| K | 0,79 | 1,00 | |||||||||||||
| Mg | 0,84 | 0,56 | 1,00 | ||||||||||||
| Ca | 0,49 | 0,20 | 0,74 | 1,00 | |||||||||||
| NH4 | 0,14 | 0,24 | 0,23 | 0,18 | 1,00 | ||||||||||
| Cl | 0,99 | 0,77 | 0,85 | 0,50 | 0,16 | 1,00 | |||||||||
| SO4 | 0,38 | 0,14 | 0,69 | 0,92 | 0,20 | 0,36 | 1,00 | ||||||||
| HCO3 | 0,19 | 0,02 | 0,26 | -0,01 | -0,09 | 0,14 | 0,02 | 1,00 | |||||||
| NO3 | 0,21 | 0,17 | 0,04 | -0,15 | 0,03 | 0,19 | -0,16 | 0,11 | 1,00 | ||||||
| NO2 | 0,14 | 0,04 | 0,13 | 0,10 | -0,10 | 0,14 | 0,04 | 0,06 | 0,39 | 1,00 | |||||
| PO4 | 0,18 | 0,25 | -0,06 | -0,29 | -0,03 | 0,16 | -0,36 | 0,16 | 0,61 | 0,03 | 1,00 | ||||
| SiO2 | 0,18 | 0,22 | -0,08 | -0,26 | -0,04 | 0,16 | -0,37 | 0,14 | 0,60 | 0,05 | 0,94 | 1,00 | |||
| EC | 0,99 | 0,75 | 0,91 | 0,60 | 0,18 | 0,99 | 0,49 | 0,19 | 0,19 | 0,14 | 0,12 | 0,12 | 1,00 | ||
| TDS | 0,97 | 0,71 | 0,93 | 0,68 | 0,18 | 0,96 | 0,58 | 0,21 | 0,15 | 0,14 | 0,07 | 0,07 | 0,99 | 1,00 | |
| pH | -0,22 | -0,04 | -0,18 | -0,26 | 0,32 | -0,19 | -0,18 | -0,37 | -0,03 | 0,05 | -0,18 | -0,23 | -0,22 | -0,25 | 1,00 |
3.1.3. Principal component analysis (PCA)
Multivariate statistics were calculated using PCA to investigate the sources of variation for hydrochemical variables. PCA reduces a large number of independent parameters to a smaller set while retaining their original properties (Sarker et al., 2022). As a result, the primary variables influencing groundwater chemistry could be emphasized. PCA was applied in this study using orthogonal varimax rotation with Kaiser normalization. This rotation method is most often used in the literature and can decrease the impact of less significant parameters of groundwater quality found through PCA analysis (Said & Salman, 2021). The analysis was carried out on a subset of 13 selected variables that represent the geochemical framework. To determine the suitability of the data set for PCA, Kaiser Meyer Olkin (KMO) and Bartlett's tests were used and Table 5 shows the calculated results. A KMO value of more than 0.5 and a Bartlett's test significance threshold of less than 0.05 indicate that there is a substantial correlation in the data. In the present study, the KMO adequacy value is 0.642 and the significance level equals 0.000.
We selected the four most significant components (Eigenvalues > 1). These four components explain 81.05% of the total variance with 40.12% for component 1, 23.21% for component 2, 9.60% for component 3, and 8.12 for component 4. Table 6 lists the corresponding loadings for each of the four components. The loadings with values surpassing 0.65 are bolded in Table 6 and utilized to appraise the relationships between the components and the hydrochemical parameters (Sarker et al., 2022). Component 1 can be termed “the salinization component” because the major elements are characteristic of seawater. Component 1 exhibits positive loadings for EC (0.984), Na (0.943), Cl (0.959), Mg (0.923), and moderately positive loadings of K (0.778), Ca (0.627), and SO4 (0.537) indicating a potential influence by SWI (Badmus et al., 2020; Balasubramanian et al., 2022; Kumar et al., 2020; Tiwari et al., 2019; Wang et al., 2022). This inference is reinforced by the high concentrations of Cl− and K+ found near the shoreline. However, evaporation could be one of the main sources of mineralization in the study area because, according to Said and Salman (2021), evaporation is enhanced in dry and unconfined conditions as is the case in our study area. Furthermore, weathering of feldspar minerals such as albite releases sodium and could be a prominent source of Na+ concentrations (Kaur et al., 2019). Dissolution of gypsum and anhydrite in the recharge area upstream of the Ghiss-Nekor plain could also explain the moderately positive loading of Ca2+ and SO42− in the first component (Kumar et al., 2020). In summary, the salinization component in our study area could originate from geogenic processes such as evaporation, water-rock processes, and SWI.
Component 2 exhibits strong positive loadings of PO43− (0.905), SiO2 (0.895) and NO3− (0.667). The association of both NO3− and PO43− indicates an anthropogenic source such as domestic sewage that contains detergents and cleaning products, as well as agricultural run-off that contains nitrogen and phosphorus fertilizers (Batsaikhan et al., 2021; Tiwari et al., 2019; Wang et al., 2022). Water-rock interactions are the most important sources of SiO2, however, the high correlation between this element and the two anthropogenic contaminants (PO43− and NO3−) can be explained by silicate weathering mediated by nitrification (Batsaikhan et al., 2021). Because most of the variables linked to component 2 correlate to domestic sewage inputs and fertilizers this component can be called "the anthropogenic contamination component".
Strong positive loading of NO2− is described by component 3. The high levels of NO2− in groundwater refer to pollution caused by human activities. Moreover, the high concentration of this element is observed in the area surrounding the urban city of Imzouren which strongly suggests that it originated from sewage leachate. It is worth noting that traditional pit latrines are more commonly used in Morocco's rural and semi-rural areas (Mchiouer et al., 2022).
Component 4 describes the moderately positive loading of NH4. Its occurrence in groundwater might be attributed to anthropogenic operations such as the excessive use of fertilizers or sewage seepage (Wang et al., 2022). However, the fact that this element is found in significant quantities in places where agriculture is actively practiced leads us to believe that this fourth component has an agricultural origin. HCO3− is negatively correlated with the above-mentioned element which can be explained by its natural occurrence rather than anthropogenic activities. This natural process might typically be the dissolution of carbonate minerals in the rock formations that the Nekor river crosses since the lithology of the surrounding region is mostly composed of shale and limestone (Badmus et al., 2020; Salhi & Benabdelouahab, 2017).
Based on these PCA results, complex hydrogeochemical processes were exhibited together with weathering of recharge area material (including dissolution of gypsum, weathering of silicates, and weathering of sodium-containing feldspar), evaporation process, SWI, and anthropogenic effects like agriculture and seepage of residential sewage. These same conclusions have been reported by several authors (Chafouq et al., 2017; Ghalit et al., 2017; Nouayti et al., 2022; Vroey, 2012), and Chafouq et al. (2017) identified SWI as slightly contributing to the salinity of the Ghiss-Nekor aquifer.
| Tests | Measured value | |
|---|---|---|
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy | 0.642 | |
| Bartlett's Test of Sphericity | Approx. Chi-Square | 987.711 |
| Degrees of freedom ( df ) | 78 | |
| Significance | 0.000 | |
| Variable | Component 1 | Component 2 | Component 3 | Component 4 |
|---|---|---|---|---|
| Name | Salinization | Anthropogenic | Sewage origin | Agriculture origin |
| EC | 0,984 | 0,022 | 0,088 | -0,020 |
| Na | 0,966 | 0,120 | 0,044 | -0,041 |
| Cl | 0,959 | 0,101 | 0,041 | 0,001 |
| Mg | 0,923 | -0,239 | 0,128 | -0,062 |
| K | 0,778 | 0,291 | -0,149 | 0,194 |
| Ca | 0,627 | -0,564 | 0,280 | 0,100 |
| PO4 | 0,125 | 0,905 | 0,074 | -0,053 |
| SiO2 | 0,116 | 0,895 | 0,095 | -0,050 |
| NO3 | 0,126 | 0,667 | 0,567 | 0,032 |
| SO4 | 0,537 | -0,641 | 0,256 | 0,100 |
| NO2 | 0,049 | 0,050 | 0,873 | -0,083 |
| HCO3 | 0,224 | 0,096 | -0,029 | -0,778 |
| NH4 | 0,247 | -0,016 | -0,100 | 0,679 |
| Eigenvalue | 5,215 | 3,018 | 1,248 | 1,056 |
| Variance (%) | 40,118 | 23,214 | 9,598 | 8,120 |
| Cumulative (%) | 40,118 | 63,332 | 72,930 | 81,051 |
| The bolded values represent positive loadings of the related variables in extracted components. | ||||
3.1.4. Water type (facies) spatial distribution
The primary ionic components of a sample are described by its water facies, commonly referred to as water type. The way that water reacts with minerals it has come into contact will affect the water type of a specific sample. Precipitation, dissolution, ion exchange, geological structure, and watershed/aquifer mineralogy are examples of these interactions.
The water type is identified by calculating the equivalents per liter contribution of each cation and anion to the overall concentration of ions in solution. We computed the "short" water type in AquaChem by concatenating the cation and anion with the greatest equivalent concentrations. We then used the GIS environment to map the results. Figure 6 depicts the geographical distribution of short water facies in the Ghiss-Nekor shallow aquifer. These calculations led to the identification of two major groundwater groups: NaSO4 and NaCl accounting for 56 and 38 percent of the total number of water samples examined, respectively. Most of the examined area is characterized by NaSO4-type water. The Mg-SO4-types account for only 6% of the total.
According to a research conducted by Ghalit et al. (2017), the predominant groundwater type found upstream of the study area were the Ca-SO4 and Mg-SO4 facies, which were formed by the dissolution of anhydrite or gypsum. The transition from the upstream (Ca-SO4) facies to the downstream (Na-SO4) facies might be explained by the ongoing enrichment of groundwater by sodium cation, which could be caused by the following mechanisms:
1- the hydrolysis of sodium-rich silicates, such as albite, following the reaction mentioned below (Earle, 2015) :
2NaAlSi3O8 + 9H2O + 2H+ ◊ Al2Si2O5(OH)4 + 2Na + 4H4SiO4
albite + water ◊ kaolinite + dissolved Sodium + silicic acid
2- If gypsum-containing water contacts or washes sodium-type clays from their formations, part of the calcium would replace the ion exchanger’s sodium and produce Na-SO4 (E. Garrett, 2001).
3- CaCO3 precipitation results in the creation of HCO3− ions, and in the case of sodium plagioclase water, Na+ and HCO3− ions are released. When NaHCO3 waters are combined with CaSO4 waters, the outcome might be the NaSO4 facies due to CaCO3 oversaturation.
These three processes might explain the abundance of Na-SO4 groundwater facies in the Ghiss-Nekor aquifer.
3.1.5. Ionic ratios
The above-mentioned methods and techniques prove the influence of SWI in the salinization of the Ghiss-Nekor aquifer. Since the relative proportion of individual ions in seawater has a tendency to be noticeably different from that of terrestrial waters (Jiao & Post, 2019), the ionic ratios Cl/ HCO3, SO4/Cl, and Na/Cl (in meq/L) were determined in order to assess the degree to which saltwater has impacted the study area's freshwater aquifer (Abdalla, 2015; Slama et al., 2022). Based on these calculations, scatter plots and their corresponding spatial distribution maps were created. Cl− is the predominant ion in seawater, but it is only present in extremely tiny amounts in groundwater, whereas HCO3−, which is present in big amounts in groundwater, is present only in very small amounts in seawater. A key indicator of SWI into the freshwater aquifer is the Cl/ HCO3 ratio, often known as Simpson's ratio (Wang et al., 2022). According to the hydrochemical analysis, the Cl/HCO3 ratio of the groundwater samples ranges from 0.57 to 25.77. Six categories of SWI effects might be identified using the Cl/HCO3 ratio (Table 7) (Todd & Mays, 2004). The results obtained are presented in Fig. 7 and exhibit six water points that exceed 6.6, i.e., are highly and severely contaminated.
| Simpson ratio | Contamination degree | Percentage (Number of samples) |
|---|---|---|
| ≤ 0.5 | Not polluted | 0% (0) |
| 0.5–1.3 | Slightly polluted | 6% (3) |
| 1.3–2.8 | Moderately polluted | 34% (18) |
| 2.8–6.6 | Injuriously polluted | 48% (25) |
| 6.6–15.5 | Highly affected | 10% (5) |
| > 15.5 | Severely affected | 2% (1) |
The molar ratios of SO4/Cl in the 52 sample points were computed to evaluate the effect of SWI in the Ghiss-Nekor aquifer, (Fig. 8). The computed ratios range from 0.17 for well number 47, which is located closer to the beach, to 2.19 for well number 16, which is located 6 km from the coastline. The SO4/Cl ratio found in 42% of the samples below the value 1 confirms that SWI has contaminated the aquifer since it shows that Cl− predominates over SO42− (Abdalla, 2015).
The Na/Cl molar ratio has frequently been utilized in order to determine the source of the salinity in groundwater (Aladejana et al., 2021; Telahigue et al., 2020). The Na/Cl molar ratio of our groundwater samples ranges from 0.76 to 1.46. When ratios go below the theoretical seawater ratio of 0.86, it means that fresh groundwater has become polluted by the seawater exchanger (Abdalla, 2015; Schoeller, 1956; Tran et al., 2020), i.e. inverse cation exchange takes place and Na+ is taken by the exchanger. Na/Cl ratios of less than 0.86 were discovered in 19% of the groundwater samples, indicating that the maritime environment affects the salinity of the water (Fig. 9).
The impact of SWI in the Ghiss-Nekor aquifer was determined using the Mg/Mg + Ca ionic ratio (Hounslow, 1995; Kura et al., 2014). Mg/Mg + Ca is present in seawater above a value of 0.5. The salinity in the aquifer, however, is thought to be caused by weathering of the dolomite or carbonate if the Mg/Mg + Ca is less than 0.5. These classifications revealed that weathering was occurring at all other monitoring locations, and the SWI was affecting 50% of the sampling points (Fig. 10).
3.1.6. Seawater intrusion indices
3.1.6.1. Groundwater quality index for SWI (GQIswi)
Many researchers have used solely the saltwater fraction (fsea) (Eq. 2) to assess SWI (Daniele et al., 2022; El Yousfi et al., 2022; Slama et al., 2022; Sonkamble et al., 2014), however, this index may not be sufficient on its own due to its numerous flaws (Tomaszkiewicz et al., 2014). Alternatively, Tomaszkiewicz et al. (2014) developed the Groundwater Quality Index specific to SWI (GQIswi) (Eq. 3), which is based on both the GQI of the saltwater fraction (fsea) (Eq. 4) and the GQIPiper(mix) (Eq. 5). Decision-makers may utilize the GQIswi to compile data into an easily transferable format for subsequent processing and spatial analysis inside a GIS framework (Rachid et al., 2017). In their research, Aladejana et al. (2021), Amiri et al. (2016), and Trabelsi et al. (2016) all had success using this approach. The GQIswi values can be in the range of 0 to 100, with 0 being saltwater and 100 denoting freshwaters. Index values are typically above 75 for freshwater and below 50 for saltwater and saline groundwater (Amiri et al., 2016). The calculation of GQIswi was processed using the downloadable Excel calculation sheet provided by Tomaszkiewicz et al. (2014) and Aladejana et al. (2021). Figure 11 shows the spatial distribution of GQIswi in the Ghiss-Nekor aquifer.
where Cl− sample is the Cl− concentration of the sampled water, Cl− freshwater corresponds to the Cl− content of the freshwater sample, and Cl− seawater is the chloride concentration of the Mediterranean seawater.
3.1.6.2. Seawater mixing index (SMI)
The relative level of seawater and freshwater mixing is measured using the seawater mixing index (SMI) (Abu Salem et al., 2022; Aladejana et al., 2021; Boumaiza et al., 2020; Khan et al., 2021), in which the major elements of seawater namely Cl−, SO42−, Na+, and Mg2+ were employed to compute this index using the Eq. (6):
$$SMI=\left(a\times \frac{{C}_{Na}}{{T}_{Na}}\right)+\left(b\times \frac{{C}_{Mg}}{{T}_{Mg}}\right)+\left(c\times \frac{{C}_{Cl}}{{T}_{Cl}}\right)+\left(d\times \frac{{C}_{{SO}_{4}}}{{T}_{{SO}_{4}}}\right)$$
6
where C indicates the concentration of the measured parameter in milligrams per liter; the letters a, b, c, and d correspond to the relative degree concentration proportions of sodium, magnesium, chlorine, and sulfur in seawater, their values are respectively a = 0.31, b = 0.04, c = 0.57, and d = 0.08. The letter T stands for the regional threshold values which are extrapolated from the interpretation of the probability curves (Edet, 2017), which are employed to distinguish between the samples in relation to the effects of seawater mixing (Park et al., 2005). The obtained threshold values (TNa= 741 mg/L, TMg= 182 mg/L, TCl= 912 mg/L, TSO4= 676 mg/L) (Fig. 12) were used to calculate SMI for each sample and then were geospatially interpolated (Fig. 13). The water is assumed to be impacted by saltwater mixing if the SMI is greater than 1, while freshwater is indicated by SMI values less than 1 (Abu Salem et al., 2022; Aladejana et al., 2021; Edet, 2017). The calculations revealed twelve places (W05, W19, W26, W27, W28, W31, W35, W38, W39, W50, and W52) had SMI values greater than one. This indicates that saltwater may have impacted these wells.
3.2. The GIS-based overlay analysis
The ionic ratio maps (Cl/HCO3, SO4/Cl, Na/Cl, and Mg/Mg + Ca), as well as the GQIswi and SMI maps, were overlaid together, by using the weighted sum tool in ArcGIS (Fig. 14a), to generate a map of the saltwater affected areas (Fig. 14b). The initial step in the reclassification process for the six maps was to use the Reclassify tool as part of the Spatial Analyst license in ArcGIS, where values of 1 and 0 were assigned to represent the presence or the absence of SWI, respectively. The values of each pixel in the six maps are summed up, i.e., each pixel in the final map has a value between 0 and 6, where 0 means “freshwater” and 6 means “seawater”. To prevent any layer from being favored over others and resulting in a biased outcome, we gave each map the same weight (Kura et al., 2014). The final map, which was created using the overlay analysis, clearly shows the area that has been most heavily polluted by seawater intrusion. SWI was mainly detected in coastal areas: surrounding the Ghiss river and along the coast between the Nekor river and the village of Hdid. Our findings are consistent with those of Salhi (2008), Vroey (2012), Chafouq et al. (2017), Ghalit et al. (2017), Benabdelouahab et al. (2019), and El Yousfi et al. (2022) in that the oceanfront areas east of the Nekor river have been affected by marine intrusion and extends in some areas two kilometers inward. In the present study, a marine intrusion phenomenon was also detected at the mouth of the Ghiss river.
Benabdelouahab et al. (2019) attributed the lack of evidence of an intrusion in this location at the time of their geophysical campaign to the Ghiss river's substantial water flow, which helped to maintain the balance of freshwater/saltwater in this area. Today, however, the region has seen a severe lack of precipitation, heavy irrigation pumping, and a sharp decline in water flow in the Ghiss river, which may explain the signs of SWI in this region.
The deposits along the seashore are generally made up of impermeable layers of silts and clay. Therefore, it is possible that SWI has occurred in the far eastern part of the Ghiss-Nekor plain due to the Trougout normal fault, which crosses the aquifer system (Chafouq et al., 2016, 2017). SWI was also observed in areas containing permeable deposits like in the case of the east side of the Nekor river mouth and the east side of the Ghiss river mouth where no faults have been identified by the geophysical campaign conducted by Benabdelouahab et al. (2019). Since the study area is known for recurrent tectonic activity, SWI may be intensified by the movement of seawater due to seismic waves caused by earthquakes (Won et al., 2019).
In other sites far inland, saline water was also found, including the urban area of Imzouren (W05, W27, and W28) (3.5 to 5 km far from the coast) and the rural area of Azrou (W19) (around 6 km far from the coast). In order to interpret the occurrence of saline water in these two regions, the scatter plot of NO3−/Cl− versus Cl− (Fig. 15) was created to help detect whether salinization is caused by SWI or by anthropogenic pollution (Wang et al., 2022). This technique demonstrated that three-quarters (W05, W19, and W27) of the aforementioned wells were prone to anthropogenic contamination, such as home sewage and agricultural operations (water irrigation inputs and excessive fertilizer usage), rather than SWI. Therefore, the area exposed to SWI did not surpass the first two kilometers from the shore, with the exception of well W28 (3.4 km from the coast), which demonstrated a facies of groundwater polluted by seawater. Vroey (2012) refuses to directly deduce the occurrence of SWI, using hydrochemical analyses, for wells located above 3 km far from the coast especially when the surrounding wells do not show the same facies, and instead assumes the intervention of other salinity causes linked to anthropogenic pollution such as wastewater discharges which can be deduced from the high levels of nitrate found in these areas. However, based on our simplified stratigraphic model of the Ghiss-Nekor aquifer, the high salinity in the area surrounding the urban city of Imzouren (W28) can be related to seawater that has encroached from the Souani area and became trapped where the clay-marly substratum is deep, significantly raising the salinity of the groundwater (Fig. 16). The absence of groundwaters showing seawater facies surrounding the contaminated wells may be due to the heterogenic nature of the aquifer which has different depths of impermeable layers (Salhi, 2008). The geophysical survey (Electrical resistivity tomography) carried out by Salhi (2008) and the use of isotopic analyses conducted by Chafouq et al. (2017) did not reveal any sign of SWI in this area, though additional and intensified campaigns in the area surrounding Imzouren would be of high interest to corroborate our hypotheses.
Rainwater, which resembles diluted saltwater, could not be ignored as another factor contributing to the increased salinity in coastal aquifers (Jiao & Post, 2019). Consequently, rains in the study area, combined with other salt sources, can raise the salinity of Ghiss-Nekor groundwater.
The use of ionic ratios or SWI indices independently to evaluate whether groundwater is susceptible to SWI is not practicable since each measure produces somewhat misleading or erroneous findings (Kura et al., 2014; Vroey, 2012; Werner et al., 2013). Therefore, by combining the suggested indices using the overlay approach, we were able to partially eliminate biased findings. Thus, this study emphasizes the need of employing several integrated techniques with a geographic information system to examine the possible contamination of groundwater by SWI and get accurate results.
An integrated approach of hydrogeochemical, statistical, geological, and geospatial techniques was used in this study to better comprehend the contribution of geogenic and anthropogenic sources in controlling groundwater salinization and to assess the extent of SWI in the Ghiss-Nekor coastal aquifer.
According to the interpretation of the correlation matrix, the studied variables, and the PCA using orthogonal varimax rotation, the high salinization phenomenon in the Ghiss-Nekor aquifer is caused by both geogenic factors (such as salt dissolution, chemical weathering, secondary salt leaching, evaporation process, and SWI) and anthropogenic factors (such as domestic sewage leaks and the excessive use of fertilizers in agriculture and irrigation water return).
The groundwater facies in the Ghiss-Nekor aquifer were divided into two types: 1) sodium-chloride and 2) chlorinated calcium and magnesium sulfated, which accounted for 38% and 62% of the total water samples, respectively.
Due to the numerous sources of salinization in the study area, a single index or a small number of approaches are insufficient to evaluate SWI. Therefore, it is more reliable to evaluate this complex phenomenon by combining different indices with the help of geospatial tools. Consequently, several ionic ratios and SWI indices were superimposed to create a final map highlighting the areas subjected to SWI in the Ghiss-Nekor coastal aquifer. Most of the locations with SWI were found to be within two kilometers of the seashore, such as the regions east of the Nekor river mouth and surrounding of the mouth of the Ghiss river. The saline water detected in the area surrounding the urban city of Imzouren was understood as the encroachment of seawater from the Souani area being trapped where the clay-marly substratum is deep.
Our results also support the application of geospatial tools to manage groundwater resources in water-stressed areas, such as the Ghiss-Nekor coastal aquifer and other aquifer systems all over the world. These conclusions will aid in the adoption of appropriate strategies, planning, and management of groundwater usage by decision-makers. Potential strategies to slow the progression of seawater into the Ghiss-Nekor aquifer include temporarily seeking alternatives to groundwater, as well as artificial replenishment.
Despite the promising results of the method used in this study as well as the results of the previous studies conducted in the Ghiss-Nekor plain, additional multidisciplinary studies utilizing the combination of map data from hydrochemical, isotopic, and geophysical studies will be critical for accurate assessment of SWI. Furthermore, it might be necessary to include an offshore geophysical survey (airborne electromagnetic survey) to delineate geologic units offshore and aid in modeling saltwater contamination through numerical modeling tools (MODFLOW, SEAWAT, SUTURA, FEFLOW, or MT3DMS).
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Acknowledgments
We gratefully acknowledge Mr. Vincent Pellerito and Mr. Zakariaa El Farhi for editing the text, providing excellent advice, and providing critical comments that contributed to improve the quality of this manuscript. We are grateful to Professor Elena Giménez Forcada for her unconditional support, which helped us to select adequate methodologies adapted to the characteristics of the study area. We also want to express our gratitude to Abdellah El Omrani and Mohamed Ettoukouki for their help with the fieldwork.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Competing Interests
We declare that we have no relevant financial or non-financial interests to disclose.
- Abdalla, F. (2015). Ionic ratios as tracers to assess seawater intrusion and to identify salinity sources in Jazan coastal aquifer, Saudi Arabia. Arabian Journal of Geosciences, 9(1), 40. https://doi.org/10.1007/s12517-015-2065-3
- Abu Salem, H. S., Gemail, K. S., Junakova, N., Ibrahim, A., & Nosair, A. M. (2022). An Integrated Approach for Deciphering Hydrogeochemical Processes during Seawater Intrusion in Coastal Aquifers. Water, 14(7), 1165. https://doi.org/10.3390/w14071165
- Abu-alnaeem, M., Yusoff, I., Ng, T., Alias, Y., May, R., & Haniffa, M. (2019). An integrated multi-techniques approach for hydrogeochemical evaluation of ion exchange processes and identification of water types based on statistical analysis: Application to the Gaza coastal aquifer, Gaza Strip, Palestine. Groundwater for Sustainable Development, 9, 100227. https://doi.org/10.1016/j.gsd.2019.100227
- Ağca, N., Karanlık, S., & Ödemiş, B. (2014). Assessment of ammonium, nitrate, phosphate, and heavy metal pollution in groundwater from Amik Plain, southern Turkey. Environmental Monitoring and Assessment, 186(9), 5921–5934. https://doi.org/10.1007/s10661-014-3829-z
- Aladejana, J. A., Kalin, R. M., Sentenac, P., & Hassan, I. (2021). Groundwater quality index as a hydrochemical tool for monitoring saltwater intrusion into coastal freshwater aquifer of Eastern Dahomey Basin, Southwestern Nigeria. Groundwater for Sustainable Development, 13, 100568. https://doi.org/10.1016/j.gsd.2021.100568
- Amiri, V., Nakhaei, M., Lak, R., & Kholghi, M. (2016). Assessment of seasonal groundwater quality and potential saltwater intrusion: A study case in Urmia coastal aquifer (NW Iran) using the groundwater quality index (GQI) and hydrochemical facies evolution diagram (HFE-D). Stochastic Environmental Research and Risk Assessment, 30(5), 1473–1484. https://doi.org/10.1007/s00477-015-1108-3
- Arulbalaji, P., Padmalal, D., & Sreelash, K. K. (2019). GIS and AHP Techniques Based Delineation of Groundwater Potential Zones: A case study from Southern Western Ghats, India. Scientific Reports, 9(1). Scopus. https://doi.org/10.1038/s41598-019-38567-x
- Asare, A., Appiah-Adjei, E. K., Ali, B., & Owusu-Nimo, F. (2021). Assessment of seawater intrusion using ionic ratios: The case of coastal communities along the Central Region of Ghana. Environmental Earth Sciences, 80(8), 307. https://doi.org/10.1007/s12665-021-09601-x
- Askri, B., Ahmed, A. T., & Bouhlila, R. (2022). Origins and processes of groundwater salinisation in Barka coastal aquifer, Sultanate of Oman. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103116. https://doi.org/10.1016/j.pce.2022.103116
- Badmus, G. O., Akinyemi, O. D., Gbadebo, A. M., & Oyedepo, J. A. (2020). Hydrochemical analysis of groundwater quality along the coastal aquifers in part of Ogun Waterside, Ogun State, southwestern Nigeria. Heliyon, 6(12), e05661. https://doi.org/10.1016/j.heliyon.2020.e05661
- Balasubramanian, M., Sridhar, S. G. D., Ayyamperumal, R., Karuppannan, S., Gopalakrishnan, G., Chakraborty, M., & Huang, X. (2022). Isotopic signatures, hydrochemical and multivariate statistical analysis of seawater intrusion in the coastal aquifers of Chennai and Tiruvallur District, Tamil Nadu, India. Marine Pollution Bulletin, 174, 113232. https://doi.org/10.1016/j.marpolbul.2021.113232
- Batsaikhan, B., Yun, S.-T., Kim, K.-H., Yu, S., Lee, K.-J., Lee, Y.-J., & Namjil, J. (2021). Groundwater contamination assessment in Ulaanbaatar City, Mongolia with combined use of hydrochemical, environmental isotopic, and statistical approaches. Science of The Total Environment, 765, 142790. https://doi.org/10.1016/j.scitotenv.2020.142790
- Benabdelouahab, S., Salhi, A., Himi, M., Stitou El Messari, J. E., & Casas Ponsati, A. (2019). Geoelectrical investigations for aquifer characterization and geoenvironmental assessment in northern Morocco. Environmental Earth Sciences, 78(6). https://doi.org/10.1007/s12665-019-8221-4
- Boumaiza, L., Chesnaux, R., Drias, T., Walter, J., Huneau, F., Garel, E., Knoeller, K., & Stumpp, C. (2020). Identifying groundwater degradation sources in a Mediterranean coastal area experiencing significant multi-origin stresses. Science of The Total Environment, 746, 141203. https://doi.org/10.1016/j.scitotenv.2020.141203
- Bourjila, A., Dimane, F., EL Ouarghi, H., Nouayti, N., Taher, M., EL Hammoudani, Y., Saadi, O., & Bensiali, A. (2021). Groundwater potential zones mapping by applying GIS, remote sensing and multi-criteria decision analysis in the Ghiss basin, northern Morocco. Groundwater for Sustainable Development, 15, 100693. https://doi.org/10.1016/j.gsd.2021.100693
- Bourjila, A., Dimane, F., Nouayti, N., Taher, M., & El Ouarghi, H. (2020). Use of GIS, Remote Sensing and AHP Techniques to Delineate Groundwater Potential Zones in the Nekor Basin, Central Rif of Morocco. Proceedings of the 4th Edition of International Conference on Geo-IT and Water Resources 2020, Geo-IT and Water Resources 2020, 1–7. https://doi.org/10.1145/3399205.3399219
- Chafouq, D., El Mandour, A., Elgettafi, M., Himi, M., Bengamra, S., Lagfid, Y., & Casas, A. (2016). Assessing of Saltwater Intrusion in Ghiz-Nekor Aquifer (North Morocco) Using Electrical Resistivity Tomography. Near Surface Geoscience 2016-22nd European Meeting of Environmental and Engineering Geophysics.
- Chafouq, D., El Mandour, A., Elgettafi, M., Himi, M., Chouikri, I., & Casas, A. (2017). Hydrochemical and isotopic characterization of groundwater in the Ghis-Nekor plain (northern Morocco). Journal of African Earth Sciences, 139, 1–13. https://doi.org/10.1016/j.jafrearsci.2017.11.007
- Daniele, L., Tardani, D., Schmidlin, D., Quiroga, I., Cannatelli, C., & Somma, R. (2022). Seawater intrusion and hydrogeochemical processes in the Ischia Island groundwater system. Journal of Geochemical Exploration, 234, 106935. https://doi.org/10.1016/j.gexplo.2021.106935
- Dobrzyński, D. (2007). Chemical diversity of groundwater in the Carboniferous-Permian aquifer in the Unisław Ślaski—Sokołowsko area (the Sudetes, Poland); a geochemical modelling approach. Acta Geologica Polonica, 57, 97–112.
- E. Garrett, D. (2001). Sodium sulfate: Handbook of deposits, processing, properties, and use GARRETT Donald E. Librairie Lavoisier. https://www.lavoisier.fr/livre/chimie/sodium-sulfate-handbook-of-deposits-processing-properties-and-use/garrett/descriptif_1075790
- Earle, S. (2015). 5.2 Chemical Weathering. https://opentextbc.ca/geology/chapter/5-2-chemical-weathering/
- Edet, A. (2017). Hydrogeology and groundwater evaluation of a shallow coastal aquifer, southern Akwa Ibom State (Nigeria). Applied Water Science, 7(5), 2397–2412. https://doi.org/10.1007/s13201-016-0432-1
- El Yousfi, Y., Himi, M., El Ouarghi, H., Elgettafi, M., Benyoussef, S., Gueddari, H., Aqnouy, M., Salhi, A., & Alitane, A. (2022). Hydrogeochemical and statistical approach to characterize groundwater salinity in the Ghiss-Nekkor coastal aquifers in the Al Hoceima province, Morocco. Groundwater for Sustainable Development, 100818. https://doi.org/10.1016/j.gsd.2022.100818
- Fadili, A. (2014). Etude hydrogéologique et géophysique de l’extension de l’intrusion marine dans le sahel de l’Oualidia (Maroc): Analyse statistique, hydrochimie et prospection électrique. https://hal.archives-ouvertes.fr/tel-01294119
- Gebeyehu, A., Ayenew, T., & Asrat, A. (2022). Hydrogeochemistry of the groundwater system of the transboundary basement and volcanic aquifers of the Bulal catchment, Southern Ethiopia. Journal of African Earth Sciences, 194, 104622. https://doi.org/10.1016/j.jafrearsci.2022.104622
- Ghalit, M., Yousfi, E. B., Zouhairi, M., Gharibi, E. K., & Taupin, J.-D. (2017). Hydrochemical characterization of groundwater in the Nekor basin located in the North-East of the Rif of Morocco. Moroccan Journal of Chemistry, 5(2), 5-2 (2017) 272-284.
- Giménez-Forcada, E., Bencini, A., & Pranzini, G. (2010). Hydrogeochemical considerations about the origin of groundwater salinization in some coastal plains of Elba Island (Tuscany, Italy). Environmental Geochemistry and Health, 32(3), 243–257. https://doi.org/10.1007/s10653-009-9281-2
- Gomaa, S. M., Hassan, T. M., & Helal, E. (2021). Assessment of seawater intrusion under different pumping scenarios in Moghra aquifer, Egypt. Science of The Total Environment, 781, 146710. https://doi.org/10.1016/j.scitotenv.2021.146710
- Guillemin, M., & Houzay, J.-P. (1982). Le Néogène post-nappes et le Quaternaire du Rif nord-oriental ; Stratigraphie et tectonique des bassins de Melilla, du Kert, de Boudinar et du piedmont des Kebdana. Editions du Service géologique du Maroc.
- Hajji, S., Allouche, N., Bouri, S., Aljuaid, A. M., & Hachicha, W. (2022). Assessment of Seawater Intrusion in Coastal Aquifers Using Multivariate Statistical Analyses and Hydrochemical Facies Evolution-Based Model. International Journal of Environmental Research and Public Health, 19(1), 155. https://doi.org/10.3390/ijerph19010155
- Hounslow, A. (1995). Water Quality Data: Analysis and Interpretation. CRC Press.
- Hu, K. X., Awange, J. L., Kuhn, M., & Saleem, A. (2019). Spatio-temporal groundwater variations associated with climatic and anthropogenic impacts in South-West Western Australia. Science of The Total Environment, 696, 133599. https://doi.org/10.1016/j.scitotenv.2019.133599
- Hu, X., Ma, C., Qi, H., & Guo, X. (2018). Groundwater vulnerability assessment using the GALDIT model and the improved DRASTIC model: A case in Weibei Plain, China. Environmental Science and Pollution Research, 25(32), 32524–32539. https://doi.org/10.1007/s11356-018-3196-3
- Iouzzi, N., Larabi, A., & Faouzi, M. (2011). Modélisation hydrodynamique de la nappe de Rhis-Nekor (Maroc) et impact de l’intrusion marine. 6.
- Jiao, J., & Post, V. (2019). Coastal Hydrogeology (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781139344142
- Kaur, L., Rishi, M. S., Sharma, S., Sharma, B., Lata, R., & Singh, G. (2019). Hydrogeochemical characterization of groundwater in alluvial plains of river Yamuna in northern India: An insight of controlling processes. Journal of King Saud University - Science, 31(4), 1245–1253. https://doi.org/10.1016/j.jksus.2019.01.005
- Kazakis, N., Pavlou, A., Vargemezis, G., Voudouris, K. S., Soulios, G., Pliakas, F., & Tsokas, G. (2016). Seawater intrusion mapping using electrical resistivity tomography and hydrochemical data. An application in the coastal area of eastern Thermaikos Gulf, Greece. Science of The Total Environment, 543, 373–387. https://doi.org/10.1016/j.scitotenv.2015.11.041
- Khan, A. F., Srinivasamoorthy, K., Prakash, R., & Rabina, C. (2021). Hydrochemical and statistical techniques to decode groundwater geochemical interactions and saline water intrusion along the coastal regions of Tamil Nadu and Puducherry, India. Environmental Geochemistry and Health, 43(2), 1051–1067. https://doi.org/10.1007/s10653-020-00713-0
- Khouakhi, A., Niazi, S., Raji, O., & Fahchouch, A. N. E. (2015). Vulnerability assessment of seawater intrusion using hydro-geological indices in Moroccan Mediterranean aquifers. International Journal of Hydrology Science and Technology, 5(2), 133. https://doi.org/10.1504/IJHST.2015.070087
- Kim, Y., Lee, K.-S., Koh, D.-C., Lee, D.-H., Lee, S.-G., Park, W.-B., Koh, G.-W., & Woo, N.-C. (2003). Hydrogeochemical and isotopic evidence of groundwater salinization in a coastal aquifer: A case study in Jeju volcanic island, Korea. Journal of Hydrology, 270(3), 282–294. https://doi.org/10.1016/S0022-1694(02)00307-4
- Kouz, T., Cherkaoui Dekkaki, H., Mansour, S., Hassani Zerrouk, M., & Mourabit, T. (2018). Application of GALDIT Index to Assess the Intrinsic Vulnerability of Coastal Aquifer to Seawater Intrusion Case of the Ghiss-Nekor Aquifer (North East of Morocco). In M. L. Calvache, C. Duque, & D. Pulido-Velazquez (Eds.), Groundwater and Global Change in the Western Mediterranean Area (pp. 169–177). Springer International Publishing.
- Kumar, P. J. S., Jegathambal, P., Babu, B., Kokkat, A., & James, E. J. (2020). A hydrogeochemical appraisal and multivariate statistical analysis of seawater intrusion in point calimere wetland, lower Cauvery region, India. Groundwater for Sustainable Development, 11, 100392. https://doi.org/10.1016/j.gsd.2020.100392
- Kura, N. U., Ramli, M. F., Ibrahim, S., Sulaiman, W. N. A., & Aris, A. Z. (2014). An integrated assessment of seawater intrusion in a small tropical island using geophysical, geochemical, and geostatistical techniques. Environmental Science and Pollution Research, 21(11), 7047–7064. https://doi.org/10.1007/s11356-014-2598-0
- Kura, N. U., Ramli, M. F., Sulaiman, W. N. A., Ibrahim, S., & Aris, A. Z. (2015). An overview of groundwater chemistry studies in Malaysia. Environmental Science and Pollution Research, 25(8), 7231–7249. Scopus. https://doi.org/10.1007/s11356-015-5957-6
- Mchiouer, F., Boughrous, A. A., & Ouarghi, H. E. (2022). Groundwater Quality Assessment for Human Drinking in Rural Areas, Al-Hoceima Province (Northern Morocco). Ecological Engineering & Environmental Technology, 23(3), 138–147. https://doi.org/10.12912/27197050/147450
- Momejian, N., Abou Najm, M., Alameddine, I., & El-Fadel, M. (2019). Can groundwater vulnerability models assess seawater intrusion? Environmental Impact Assessment Review, 75, 13–26. https://doi.org/10.1016/j.eiar.2018.10.003
- Morel, J.-L. (1987). Évolution récente de l’orogène rifain et de son avant-pays depuis la fin de la mise en place des nappes (Rif, Maroc) [These de doctorat, Paris 11]. http://www.theses.fr/1987PA112504
- Niazi, S., Snoussi, M., & Foutlane, A. (2005). Impact des aléas climatiques sur la qualité des eaux d’un hydrosystème aménagé en zone semi-aride: Cas du bassin-versant du Nekkor (Maroc). Science et Changements Planétaires / Sécheresse, 16(3), 183–187.
- Nouayti, N., Cherif, E. K., Algarra, M., Pola, M. L., Fernández, S., Nouayti, A., Esteves da Silva, J. C. G., Driss, K., Samlani, N., Mohamed, H., Azzirgue, E. M., Krawczyk, D. A., & Rodero, A. (2022). Determination of Physicochemical Water Quality of the Ghis-Nekor Aquifer (Al Hoceima, Morocco) Using Hydrochemistry, Multiple Isotopic Tracers, and the Geographical Information System (GIS). Water, 14(4), 606. https://doi.org/10.3390/w14040606
- ONEE. (2017). L’étude de l’adduction d’eau brute de la station de traitement d’Al Hoceima à partir du barrage projeté sur l’Oued Ghiss (Evaluation environnementale II; p. 135). Office National de l’électricité et de l’Eau potable-Branche Eau, Direction Technique et Ingénierie (DTI).
- Parizi, E., Hosseini, S. M., Ataie-Ashtiani, B., & Simmons, C. T. (2019). Vulnerability mapping of coastal aquifers to seawater intrusion: Review, development and application. Journal of Hydrology, 570, 555–573. https://doi.org/10.1016/j.jhydrol.2018.12.021
- Park, S. C., Yun, S. T., Chae, G. T., Yoo, I. S., Shin, K. S., Heo, C. H., & Lee, S. K. (2005). Regional hydrochemical study on salinization of coastal aquifers, western coastal area of South Korea. Journal of Hydrology, 313(3–4), 182–194. https://doi.org/10.1016/j.jhydrol.2005.03.001
- Prasanna, M. V., Chidambaram, S., & Srinivasamoorthy, K. (2010). Statistical analysis of the hydrogeochemical evolution of groundwater in hard and sedimentary aquifers system of Gadilam river basin, South India. Journal of King Saud University - Science, 22(3), 133–145. https://doi.org/10.1016/j.jksus.2010.04.001
- Rachid, G., El-Fadel, M., Abou Najm, M., & Alameddine, I. (2017). Towards a framework for the assessment of saltwater intrusion in coastal aquifers. Environmental Impact Assessment Review, 67, 10–22. https://doi.org/10.1016/j.eiar.2017.08.001
- Rina, K., Datta, P. S., Singh, C. K., & Mukherjee, S. (2013). Isotopes and ion chemistry to identify salinization of coastal aquifers of Sabarmati River Basin. Current Science, 335–344.
- Rodier, J., Legube, B., Merlet, N., & Brunet, R. (2009). L’analyse de l’eau - 9ème édition - Eaux naturelles, eaux résiduaires, eau de mer: Eaux naturelles, eaux résiduaires, eau de mer (9e édition). Dunod.
- Said, I., & Salman, S. A. (2021). Salinization of groundwater under desert reclamation stress at Qena region, Egypt. Journal of African Earth Sciences, 181, 104250. https://doi.org/10.1016/j.jafrearsci.2021.104250
- Salhi, A. (2008). Géophysique, hydrogéologie et cartographie de la vulnérabilité et du risque de pollution de l’aquifère de Ghis-Nekor (Al Hoceima, Maroc). http://rgdoi.net/10.13140/RG.2.2.34803.78887/2
- Salhi, A., & Benabdelouahab, S. (2017). Etude hydrogéologique de la nappe alluviale de Ghis-Nekor (Maroc). Noor Publishing.
- Sarker, Md. M. R., Van Camp, M., Hossain, D., Islam, M., Ahmed, N., Karim, Md. M., Bhuiyan, Md. A. Q., Ahsan, Md. A., & Walraevens, K. (2022). Hydrochemical characterization and groundwater potential of the deep aquifer system in southwest coastal region of Bangladesh. Journal of Asian Earth Sciences, 234, 105271. https://doi.org/10.1016/j.jseaes.2022.105271
- Satheeskumar, V., Subramani, T., Lakshumanan, C., Roy, P. D., & Karunanidhi, D. (2021). Groundwater chemistry and demarcation of seawater intrusion zones in the Thamirabarani delta of south India based on geochemical signatures. Environmental Geochemistry and Health, 43(2), 757–770. https://doi.org/10.1007/s10653-020-00536-z
- Schoeller, H. (1956). Géoochimie des eaux souterraines application aux eaux des gisements de pétrole. Institut Français du Pétrole.
- Sinclair, A. J. (1974). Selection of threshold values in geochemical data using probability graphs. Journal of Geochemical Exploration, 3(2), 129–149. https://doi.org/10.1016/0375-6742(74)90030-2
- Slama, F., Nasri, N., & Bouhlila, R. (2022). Delineating the origins and processes of groundwater salinization and quality degradation in a coastal irrigated plain, Korba (Northeastern Tunisia). Marine Pollution Bulletin, 181, 113914. https://doi.org/10.1016/j.marpolbul.2022.113914
- Sonkamble, S., Chandra, S., Ahmed, S., & Rangarajan, R. (2014). Source speciation resolving hydrochemical complexity of coastal aquifers. Marine Pollution Bulletin, 78(1), 118–129. https://doi.org/10.1016/j.marpolbul.2013.10.052
- Srinivasamoorthy, K., Nanthakumar, C., Vasanthavigar, M., Vijayaraghavan, K., Rajivgandhi, R., Chidambaram, S., Anandhan, P., Manivannan, R., & Vasudevan, S. (2011). Groundwater quality assessment from a hard rock terrain, Salem district of Tamilnadu, India. Arabian Journal of Geosciences, 4(1), 91–102. https://doi.org/10.1007/s12517-009-0076-7
- Taher, M. (2021). Sismotectonique du bassin inférieur du Nekor (Rif central) – Maroc—: Apport de la télédétection, du SIG et de la topométrie.
- Taylor, R. G., Todd, M. C., Kongola, L., Maurice, L., Nahozya, E., Sanga, H., & MacDonald, A. M. (2013). Evidence of the dependence of groundwater resources on extreme rainfall in East Africa. Nature Climate Change, 3(4), 374–378. https://doi.org/10.1038/nclimate1731
- Telahigue, F., Souid, F., Agoubi, B., Chahlaoui, A., & Kharroubi, A. (2020). Hydrogeochemical and isotopic evidence of groundwater salinization in a coastal aquifer: A case study in Jerba Island, southeastern Tunisia. Physics and Chemistry of the Earth, Parts A/B/C, 118–119, 102886. https://doi.org/10.1016/j.pce.2020.102886
- Thauvin, J.-P. (1971). Ressources en eau du Maroc: Vol. Tome I. https://fr.scribd.com/document/494796627/Ressources-en-Eau-Du-MAROC-Tome-II
- Tiwari, A. K., Pisciotta, A., & De Maio, M. (2019). Evaluation of groundwater salinization and pollution level on Favignana Island, Italy. Environmental Pollution, 249, 969–981. https://doi.org/10.1016/j.envpol.2019.03.016
- Todd, D. K., & Mays, L. W. (2004). Groundwater Hydrology. John Wiley & Sons.
- Tomaszkiewicz, M., Abou Najm, M., & El-Fadel, M. (2014). Development of a groundwater quality index for seawater intrusion in coastal aquifers. Environmental Modelling & Software, 57, 13–26. https://doi.org/10.1016/j.envsoft.2014.03.010
- Trabelsi, N., Triki, I., Hentati, I., & Zairi, M. (2016). Aquifer vulnerability and seawater intrusion risk using GALDIT, GQISWI and GIS: Case of a coastal aquifer in Tunisia. Environmental Earth Sciences, 75(8), 1–19. https://doi.org/10.1007/s12665-016-5459-y
- Tramblay, Y., Badi, W., Driouech, F., El Adlouni, S., Neppel, L., & Servat, E. (2012). Climate change impacts on extreme precipitation in Morocco. Global and Planetary Change, 82–83, 104–114. https://doi.org/10.1016/j.gloplacha.2011.12.002
- Tran, D. A., Tsujimura, M., Vo, L. P., Nguyen, V. T., Kambuku, D., & Dang, T. D. (2020). Hydrogeochemical characteristics of a multi-layered coastal aquifer system in the Mekong Delta, Vietnam. Environmental Geochemistry and Health, 42(2), 661–680. https://doi.org/10.1007/s10653-019-00400-9
- Vroey, P. D. (2012). Etude des conditions hydrogéologiques et de l’intrusion salée dans les eaux souterraines de la plaine du Rhiss‐Nekor à Al Hoceima (Maroc).
- Wang, H., Yang, Q., & Liang, J. (2022). Interpreting the salinization and hydrogeochemical characteristics of groundwater in Dongshan Island, China. Marine Pollution Bulletin, 178, 113634. https://doi.org/10.1016/j.marpolbul.2022.113634
- Werner, A. D., Bakker, M., Post, V. E. A., Vandenbohede, A., Lu, C., Ataie-Ashtiani, B., Simmons, C. T., & Barry, D. A. (2013). Seawater intrusion processes, investigation and management: Recent advances and future challenges. Advances in Water Resources, 51, 3–26. https://doi.org/10.1016/j.advwatres.2012.03.004
- Won, B., Hamm, S.-Y., Kim, K.-Y., Ha, K., Shin, J., Hwang, S., & Lee, S.-H. (2019). Response Analysis of Multi-Layered Volcanic Aquifers in Jeju Island to the 2011 M9.0 Tohoku-Oki Earthquake. Water, 11(5), 942. https://doi.org/10.3390/w11050942
- Yang, X., Tan, Y., Li, K., Zhang, H., Liu, J., & Xiang, C. (2020). Long-term changes in summer phytoplankton communities and their influencing factors in Daya Bay, China (1991–2017). Marine Pollution Bulletin, 161, 111694. https://doi.org/10.1016/j.marpolbul.2020.111694
- Yeh, H.-F., Lee, C.-H., Hsu, K.-C., & Chang, P.-H. (2009). GIS for the assessment of the groundwater recharge potential zone. Environmental Geology, 58(1), 185–195. https://doi.org/10.1007/s00254-008-1504-9
- Yusuf, M. A., Abiye, T. A., Ibrahim, K. O., & Abubakar, H. O. (2021). Hydrogeochemical and Salinity Appraisal of Surficial Lens of Freshwater Aquifer along Lagos Coastal Belt, South West, Nigeria. Heliyon, e08231. https://doi.org/10.1016/j.heliyon.2021.e08231
- Zolekar, R. B., Todmal, R. S., Bhagat, V. S., Bhailume, S. A., Korade, M. S., & Das, S. (2021). Hydro-chemical characterization and geospatial analysis of groundwater for drinking and agricultural usage in Nashik district in Maharashtra, India. Environment, Development and Sustainability, 23(3), 4433–4452. https://doi.org/10.1007/s10668-020-00782-2
No competing interests reported.