Environ Earth Sci (2016) 75:669 DOI 10.1007/s12665-016-5459-y

ORIGINAL ARTICLE

Aquifer vulnerability and seawater intrusion risk using GALDIT, GQISWI and GIS: case of a coastal aquifer in Tunisia Nadia Trabelsi1 • Ibtissem Triki1 • Imen Hentati1 • Moncef Zairi1

Received: 13 May 2015 / Accepted: 12 February 2016 Ó Springer-Verlag Berlin Heidelberg 2016

Abstract Due to increase in population and agricultural activities, the Sfax coastal aquifer is under tremendous stress and seawater intrusion becomes a serious concern. This situation needs an assessment of aquifer vulnerability and seawater intrusion risk. A new approach for vulnerability mapping using GALDIT, groundwater quality index for seawater intrusion (GQISWI), geostatistics, the Ghyben– Herzberg model and GIS was developed. The model is used to determine the trend of groundwater contamination by seawater intrusion in the coastal aquifer in Sfax basin (Tunisia). Vulnerability assessment method is recognized through assessing weight and ratings of the GALDIT relevant parameter. A second vulnerability map was realized based upon the groundwater quality index for seawater intrusion integrating the water quality parameters for delineating seawater intrusion in the aquifer. The validation of GQISWI and GALDIT vulnerability maps was undertaken through comparison of areas of high Jones Ratio, chloride and TDS concentration and their relative vulnerability index. The Ghyben–Herzberg model has been used to predict the actual seawater intrusion extend and evaluate the freshwater–saltwater dynamics. The results reveal that the coastal area is currently undergoing seawater intrusion and its northern part is the most affected one by this contamination.

& Nadia Trabelsi [email protected] 1

Laboratoire « eau, e´nergie et environnement», e´cole nationale d’inge´nieurs de Sfax, route soukra, BP 1173, 3038 Sfax, Tunisia

Keywords Coastal aquifer  Seawater intrusion  Vulnerability mapping  Risk mapping  GQISWI approach  GALDIT method  Geostatistics  Ghyben–Herzberg model

Introduction Coastal aquifers serve as a major source for freshwater supply in many countries around the world, especially in arid and semi-arid zones (Bear et al. 1999; Kaliraj et al. 2015). Due to the demographic growth, the increase of freshwater demand, especially for agricultural purposes, groundwater resources in these areas are intensively exploited despite their extreme vulnerability to quality degradation by seawater intrusion (Kerrou et al. 2010). This situation is also expected to exacerbate by climate change and associated sea level rise (Pham and lee 2014; Jones et al. 1999; Sivan et al. 2005). The impact of the climatic changes, which generally lead to increased desertification processes, is known to have already affected the Mediterranean arid zone (Rapti-caputo 2010; Mazi et al. 2014). The groundwater vulnerability assessment to seawater intrusion is one of the major techniques used to assist the development of groundwater protection strategies (MadlSzonyi and Fule 1998). Such assessment involves the mapping of coastline areas that are particularly vulnerable (Palmer et al. 2011) and is useful in offering a quick and cost-effective means for those involved with coastal zone management, providing a general overview of where current or future areas of risk might lie. The results could be used by local authorities and decision makers to ensure a durable use of groundwater through the protection of the most vulnerable areas (Kennedy 2012; Polemio 2005).

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Albinet and Margat (1970) defined vulnerability as a degree of protection in the hydrological settings to tolerate against the ingress of pollutants to the aquifer. A similar definition was proposed by Lobo-ferreira and Cabral (1991) for the specific case of seawater intrusion (SWI). They defined the groundwater vulnerability to seawater intrusion as ‘‘the sensitivity of groundwater quality to an imposed groundwater pumping or sea level rise or both in the coastal belt, which is determined by the intrinsic characteristics of the aquifer’’. The groundwater vulnerability assessment methods can be classified into three categories (Evans and Maidment 1995): (1) overlay and index methods, (2) methods employing process-based simulation models, and (3) statistical models. The choice of the appropriate method to determine vulnerability will depend on the purpose and scope of the study, the scale, the data availability, time, cost, and end-user requirements (Liggett and Talwar 2009). Several vulnerability assessment techniques have been developed. The most common are: DRASTIC (Aller et al. 1987): DRASTIC is an acronym standing for depth to water, net recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity; GOD (Foster 1987): GOD is the acronym for three parameters (groundwater occurrence, overlying strata and depth to groundwater table); aquifer vulnerability index (AVI) (Van stempvoort et al. 1993); SINTACS (Civita 1994): the acronym SINTACS originates from the initials (in Italian) of seven factors ‘‘Soggiacenza’’ as depth to water, ‘‘Infiltrazione’’ as net recharge, ‘‘Non saturo’’ as impact of vadose zone, ‘‘Tipologia di copertura’’ as soil media, ‘‘caratteristiche dell’ Acquifero’’ as aquifer media, ‘‘Conduciblita idraulica’’ as hydraulic conductivity, ‘‘acclivita di superficie topografica (S)’’ as slope and the EPIK model (Doerfliger et al. 1999): EPIK is an acronym for Epikarst, Protective cover, Infiltration conditions, and Karst network development. To determine vulnerability of groundwater to seawater intrusion, some modifications and/or new parameters based models were presented; e.g., modified DRASTIC model which uses sea level rise parameter and GALDIT model ¨ zyurt and Ergin 2010). The GALDIT was proposed (O indexing method was specifically developed by Chachadi and Lobo-Ferreira (2001) for the purpose of assessing the spatial vulnerability of hydrogeological settings to SWI. The major drawback of this method is the unawareness of the pumping effect on the seawater intrusion process. Despite this limit, this model still shows many advantages. It has a low cost and can be applied in extensive regions, because of the few and easy to collect, data required. The method gives relatively accurate results for extensive regions with a complex geological structure, despite the absence of measurements of specific parameters that the

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most specialized methods would require (Panagopoulos et al. 2006). The simplicity of the GALDIT method makes it attractive for SWI vulnerability mapping (Ivkovic et al. 2013). However, geochemistry prospecting techniques are required to determine the status of SWI into groundwater system. The groundwater chemistry has been utilized as a tool to outlook water quality for various purposes (Rao 2006). Boyacioglu (2007) classify the methods of assessing water quality on traditional methods, based on the comparison of experimentally determined parameter values with existing guidelines, and ‘‘water quality index’’ (WQI) methods. The WQI improves understanding of water quality issues by integrating complex data and generating a score that describes the water quality status and evaluates water quality trends (Boyacioglu 2007). The WQI also permits to assess the changing of water quality and to identify water dynamics (Kaswanto et al. 2012). The specific groundwater quality index (GQI) is helpful in understanding the extent of saline water intrusion. ElFadel et al. (2013) developed a saltwater intrusion-specific GQI from various water quality indicators. It accounts for hydrogeochemical processes associated with saltwater intrusion (El-Fadel et al. 2013). Tomaszkiewicz et al. (2014) uses common water quality parameters indicative of seawater intrusion to develop a representative index for seawater intrusion (GQISWI), for the purpose of aggregating data into a comprehensible format that allows spatial analysis. Application of the above-mentioned methods requires a minimum of geochemical and groundwater head data. In many cases, especially in low and middle income countries, the rarity or absence of data constitute the main difficulty for the use of these approaches. The application of the theoretical approach for evaluating the sharp interface position constitutes a good alternative to overcome this limit. The initial model was developed independently by Ghyben (1888) and by Herzberg (1901). This simple model is known as the Ghyben–Herzberg model (Fares and El Kadi 2008). This approach is often used as a first approximation when estimating the depth to the saltwater interface (Ivkovic et al. 2013). Studies on seawater intrusion require a multidisciplinary approach, because single approaches were generally unsuccessful in providing reliable results (Werner and Gallagher 2006). Therefore, this work will attempt to integrate GALDIT model, GQISWI approach, geochemistry, geostatistics and Ghyben–Herzberg model with the aim of assessing seawater intrusion in Sfax coastal aquifer. The integration of several approaches can be a more effective and consistent way of assessing the extent and magnitude of seawater intrusion.

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To overcome the problem associated to groundwater decline and quality degradation, it is crucial to know and quantify the vulnerability magnitude of the coastal phreatic groundwater of Sfax, which will be utilized to set up preliminary management options (Almasri 2008; Selmi 2013).

Study area The Sfax basin is located in eastern part of Tunisia (Fig. 1). It is bounded between Lat 35°400 to 34°100 N and Long 9°330 E to 11°100 E. The region covers an area of approximately 8000 km2 and limited in the eastern side by the Mediterranean Sea with a coastline of about 150 km, in the west by the North–South axis mountain chain (Burollet 1956), in the north by a SW–NE alignment structures of Kordj, Bouthadi (Haller 1983) and in the south by the Mezzouna mountain. A number of sebkhas (salt plain) are spread west. The study area is characterized by a semi-arid climate with an annual precipitation of 230 mm, and an annual mean temperature of 20 °C. The geology of the area has

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been described by Castany (1953), Burollet (1956), Zebidi (1989), Maliki (1994) and Trabelsi et al. (2007). The stratigraphic series are composed from bottom to top as follows: the Eocene composed of sediments deposited in a marine environment. The Oligocene consists of a lower marine unit and an upper continental sandy one. The Miocene presents an important thickness and it composed of an alternation of sand, sandstone and clay units. The Pliocene consists of marl units. The Quaternary composed of sandy loam, sandy clay and clay. The Sfax coastal aquifer consists of unconfined layers with a main geological material composed of sand and silty clay of the upper Miocene, Pliocene and Quaternary. The coastal zones in Sfax are of importance in terms of economical, industrial and agricultural activities and the groundwater is fragile in terms of their vulnerability to sea level rises and an eventual salinization by seawater intrusion. Climatic factors (low rainfall recharge, high temperature) and the high reliance on groundwater resources make the problem more severe (Steyl and Dennis 2010).

Fig. 1 Location of the study area

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Materials and methods Description of the available data A various multidisciplinary data are highly needed for the considered approach. In fact, a range of data is required to provide quantitative information for the groundwater vulnerability assessment (Saidi et al. 2009). The examination of groundwater chemistry was performed using data collected from direction of water resources of Sfax (CRDA 2006). Chemical components such as EC, TDS, major cations and anions (Table 1) were used to identify seawater intrusion. The groundwater level was measured from field surveys conducted during 2006. The geometry of the costal aquifer was recognized and characterized from the aspect of its water potentials using geological and geophysical available data. A database was established to input the collected data. Once created, it is then possible to register all data as data layers with a common coordinate system and manipulate them to produce thematic maps, including the overall study area vulnerability map. Methodology The methodology combining an indicator-based model, hydrochemistry, Ghyben–Herzberg approach, geostatistics and geographic information system (GIS) to assess the seawater intrusion is developed in this work. GALDIT method The GALDIT method considers different hydrogeological parameters influencing the seawater intrusion in the aquifer and is commonly used for vulnerability assessment (Recinos et al. 2014). The GALDIT map is a result of the overlay of six weighted maps (Table 2).

Table 1 Statistical summary of the parameters and major chemical constituents in the coastal superficial aquifer of Sfax in 2006

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Groundwater occurrence (aquifer type) (G) Aquifer type: unconfined, confined and leaky confined. This basic nature of groundwater occurrence has an influence on the extent of seawater intrusion (Chachadi and Lobo-Ferreira 2001). The unconfined aquifer is characterized with low pressure which makes it more vulnerable to seawater intrusions as compared to confined aquifer (Sundaram et al. 2008). Aquifer hydraulic conductivity (A) The parameter A is used to represent the flow rate of water in the aquifer. A coastal aquifers with low hydraulic gradients are associated with increased SWI risk (Sophiya and Syed 2013). Height of groundwater level above sea level (L) Height of groundwater level above sea level constitutes an important factor in the evaluating seawater intrusion because it determines the hydraulic pressure eventually able to push back the seawater front. Distance from the shore (D) The impact of seawater intrusion generally decreases as one move inland at right angles to the shore (Agarwadkar 2005). Impact of existing status of seawater intrusion (I) Groundwater is invariably under stress, and this stress has modified the natural hydraulic balance between seawater and fresh groundwater. The hydrochemical ratio rCl/ rHCO3 will be used to show the impact of existing status of seawater intrusion in the area. Thickness of aquifer being mapped (T) Aquifer thickness plays an important role in preventing seawater intrusion into a given aquifer because the thicker the aquifer, the lesser the vulnerable to seawater intrusion and the thinner the aquifer, the more likely to be affected by seawater intrusion (Chachadi and Lobo-Ferreira 2001; Najib et al. 2012; Kura et al. 2014). In determining the GALDIT index, each parameter is assigned a numeric rating between 1 (least effective on

Parameters

Minimum

Maximum

Mean

Standard deviation (SD)

EC (ls/cm)

1060

14,690

5396.9

2398.5

Ca (mg/L)

50.06

1802.2

566.9

230.07

Mg (mg/L)

12.63

703.49

158.7

88.04

Na (mg/L)

57

2045

641.72

398.84

K (mg/L)

0.47

47

10.52

6.72

Cl (mg/L)

34.4

4476

894.2

763.7

SO4 (mg/L)

270

4590

2043.5

709.77

HCO3 (mg/L)

42.7

408.7

135.05

48.57

TDS (mg/L)

689

9548.5

3523.1

1558.3

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Table 2 Summary of GALDIT parameter weights, rates, and ranges (Chachadi and Lobo-Ferreira 2001) Weight

Rating Very low 2.5

Low 5

Medium 7.5

High 10

G groundwater occurrence (aquifer type)

1

Bounded aquifer

Leaky-confined aquifer

Unconfined aquifer

Confined aquifer

A aquifer hydraulic conductivity (m/day)

3

\5

5–10

10–40

[40

L height of groundwater level above sea level (m)

4

[2

1.5–2

1–1.5

\1

D distance from the shore (m)

4

[1000

1000–750

750–500

\500

I impact of existing status of seawater intrusion

1

\1

1–1.5

1.5–2

[2

T thickness of aquifer being mapped (m)

2

\5

5–7.5

7.5–10

[10

vulnerability) and 10 (most effective on vulnerability). Each parameter is also assigned a weighting factor ranging between 1 and 10, based on their relative influence in affecting the intrusion. Calculation of the GALDIT index then involves multiplying each parameter weight by its site rating then summing the total and can be expressed as follows (Chachadi and Lobo-Ferreira 2005): , 6 6 X X GALDIT index ¼ ððWi ÞRi Þ Wi ð1Þ i¼1

i¼1

where Wi is the weight of the ith indicator and Ri is importance rating of the ith indicator. The details of weights and rating values for all the factors used in computation of the GALDIT index for the study area given in Table 2.

the the the are

    Ca2þ þ Mg2þ HCO 3 þ Total cations Total anions  50ðin meq=LÞ     HCO ðNaþ þ Kþ Þ 3 þ ¼ Total cations Total anions  50ðin meq=LÞ

GQIPiper ðdomÞ

GQISWI ¼

GQIPiper ðmixÞ þ GQIfsea 2

ð4Þ

ð5Þ ð6Þ

In this study, an excel-based algorithm, utilizing both indices (Eqs. 4, 5), was used to define the hydrogeochemical water domains based on measured water quality data (Tomaszkiewicz et al. 2014). Geostatistical analysis

Seawater intrusion groundwater quality index (GQISWI) Vulnerability maps were developed based upon the GALDIT method and groundwater quality index for seawater intrusion (GQISWI) developed by Tomaszkiewicz et al. (2014). The author used common water quality parameters indicative of seawater intrusion to develop representative index. This methodology can translate information from the Piper diagram into a format that can be mapped spatially and can also be spatially analyzed under a GIS framework. The newly proposed index GQISWI (Eq. 6) translates information from the Piper diagram (Eq. 4) and the fraction of seawater (Eqs. 2, 3) to develop a new two-stage numerical indicator for seawater intrusion (Tomaszkiewicz et al. 2014). The GQISWI can range between 0 and 100, where 0 is indicative of seawater and 100 represents freshwater. mClðsampleÞ  mClðfreshwaterÞ fsea ¼ ð2Þ mClðseawaterÞ  mClðfreshwaterÞ GQIfsea ¼ ð1  fsea Þ  100



GQIPiper ðmixÞ ¼

ð3Þ

The geostatistical analysis was performed to determine spatial distribution of the GQISWI in the study area using the ESRI geostatistical analyst as it has been used in groundwater quality studies (Nas 2009; Nas and Berktay 2010; Dash et al. 2010; Agoubi et al. 2013; Triki et al. 2014). The main tool in geostatistics is the variogram which expresses the spatial dependence between neighboring observations. The experimental semivariogram is a graphical representation of the mean square variability between two neighboring points of distance h as shown in Eq. 7. N ðhÞ

1 X c ð hÞ ¼ ½ðZ ðxi Þ  Z ðxi þ hÞÞ2 2N ðhÞ i¼1

ð7Þ

where c(h) is the semivariogram expressed as a function of the magnitude of the lag distance or separation vector h, N(h) is the number of observation pairs separated by distance h, and Z(xi) is the regionalized variable at location xi (Dash et al. 2010). The most commonly used models applicable to compute theoretical variogram are spherical, Gaussian, and

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exponential models. These models were evaluated by determining the nugget (c0), the semivariance at h = 0, the range (a0), the lag distance where the semivariance becomes constant, known as the sill (c) (Tomaszkiewicz et al. 2014). The optimal fitting will be chosen on the basis of the cross-validation procedure, which checks the compatibility between the data and the structural model, by considering each data point in turn, by removing it temporarily from the data set and by using its neighboring information to predict the value of the variable at its location. The estimate is compared with the measured value by calculating the experimental error, i.e., the difference between estimate and measurement, which can also be standardized by estimate standard deviation (Buttafuoco et al. 2011). The mean error (ME) and the root mean square standardized (RMSS) were used to evaluate the best model performance in the cross-validation test. In the estimation, the degree of bias is determined by the ME as represented in Eq. 8: n 1X ME ¼ Z  ðx i Þ  Z ðx i Þ ð8Þ n i¼1 The ratio between the square of the experimental estimation error and the kriging variance is measured by RMSS, as in Eq. 9: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ) u( n u 1X 2 t RMSS ¼ ð9Þ ½ðZ  ðxi Þ  Z ðxi ÞÞ=rðxi Þ n i¼1 The terms Z*(xi), Z(xi) and rðxi Þ are, respectively, the estimated value, observed value and the calculated kriging estimation error variance for Z*(xi), at points xi; and n is the sample size. The best model with the accurate prediction is represented by the ME being close to zero and the RMSE nearer to one (Narany et al. 2014). Kriging technique is an exact interpolation estimator used to find the best linear unbiased estimate. The best linear unbiased estimator must have minimum variance of estimation error (Ahmadi and Sedghamiz 2007). A more detailed explanation of the kriging method is given by Journel and Huijbregts (1978); Goovaerts (1997). The general equation of linear kriging estimator is: Z  ðx 0 Þ ¼

N X

ki Z ð x i Þ

ð10Þ

i¼1

where Z(x0) is the value to be estimated at the location of x0; Z(xi) is the known value at sampling site xi, and ki is weight. Today, a number of variants of kriging are in general use, and these are simple kriging, ordinary kriging (OK), universal kriging, block kriging, cokriging, and disjunctive kriging (Nas and Berktay 2010).

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Among the various forms of kriging, the OK method was used in the present study because of its simplicity and prediction accuracy in comparison to other kriging methods (Nas 2009). The Ghyben–Herzberg approach The Ghyben–Herzberg model has been widely used to study seawater intrusion and estimate the depth of the fresh/salt water interface (Essaid 1986; Steinich and Marin 1996; Salameh and El-Naser 2000; Yechieli 2005). The Ghyben–Herzberg principle postulates that in a homogeneous and isotropic aquifer, the depth of the interface between saltwater and freshwater is directly proportional to the elevation of the water table above mean sea level (Ghyben 1888; Herzberg 1901). The relationship, for an unconfined coastal aquifer, is expressed in the following form: qw z¼ h ð11Þ qs  qw where qw is the freshwater density, qs the saltwater density, z the depth of the saltwater–freshwater interface, and h the freshwater head above sea level. In this study, the Ghyben–herzberg approach is used to examine the present status of seawater intrusion into groundwater system. This approximation for depth of fresh/saline interface was estimated by assuming that hydrodynamic dispersion is negligible, vertical flow is insignificant and is all horizontal in the aquifer, saline groundwater is at rest, and the interface is a sharp interface with no mixing zone. Spatial analysis with GIS analyses This work emphasizes the use of water quality index approach, GALDIT model and the Ghyben–Herzberg method in the assessment of groundwater quality of Sfax. All these approaches were integrated with GIS environment. Based on the compiled database (vector or raster), GIS is used extensively in analysis for mapping purpose (Sundaram et al. 2008). Hence, spatial changes can be evaluated more easily and quickly. In this study, GIS has been used to collect diverse spatial data to represent spatially variable phenomena by applying a series of overlay analysis of data layers that are in spatial register (Bonham-Carter 1996). The GIS is also utilized in the map classification according to the rates and weights of each parameter and to analyze and validate the results of groundwater vulnerability (Pandey et al. 2013; Sarma and Saraf 2002).

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Results and discussion The GALDIT groundwater vulnerability map In the study area, the coastal area covers approximately 2200 km2 (Fig. 1). Its thickness varies from 8 to 70 m, with an average of 41 m. The aquifer is recharged by direct infiltration and the recharge area is located in the middle of the study area (Triki et al. 2013). The groundwater flows from the higher topographic area in the west toward the area of lower topography in the east and the natural discharge limits coincide with the Mediterranean shore line. The aquifer characteristics of the study area were (G) carried out by examining exploratory data of 24 wells. The aquifer is mainly unconfined and the rating of groundwater occurrence corresponds to 7.5. The hydraulic conductivity data (A) was extracted from pumping test on 12 wells covering all the study area. The values are ranging from 0.04 to 19.14 m/day (Fig. 2a). The hydraulic conductivity was classified, according to Table 2, into three classes and was rated 2.5–7.5 with the lowest hydraulic conductivity at the southern part and the highest within the northern part of the study area. The height of groundwater above mean sea level (L) is calculated from groundwater well measurements during 2006. The values ranged from -8 to 64.5 (Fig. 2b). The minimum height is recorded closed to the sea, indicating likelihood of more pressure from the seawater to break the equilibrium interface that lies between the fresh and seawater. The maximum height is recorded at the western part of the study area which gives the aquifer more pressure to push away the seawater from coming in. The rating (R) of the parameter (L) varied between 2.5 and 10 (Table 2). The distance from the shore (D) was estimated according to three distances to the coast line ranges (500, 750 and 1000 m). The maximum estimate of 10 is adopted for a distance lower than 500 m whereas the minimal (2.5) is allotted for distance higher than 1000 m. In fact, the impact of the intrusion of seawater decreases when moving perpendicularly from shore towards the land. The further away the groundwater is from shore, the lesser the pressure from seawater to infiltrate into the aquifer. Seawater and freshwater have differing hydrochemistry, with the former being characterized by nearly uniform chemistry where chloride (Cl) and sodium (Na) make up 84 % of the total ionic composition. On the other hand, freshwater composition varies widely, calcium (Ca) and bicarbonate (HCO) commonly dominate (Richter and Kreitler 1993). Chachadi and Lobo-Ferreira (2001) adopted the Revelle index (Cl/(HCO ? CO) to evaluate seawater intrusion.

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The minimum ratio of Cl/HCO is recorded at the central part of the study area, corresponding to the recharge area. The remaining area shows a ratio greater than one indicating the presence of seawater intrusion (Fig. 2c). The impact of parameter I was divided into four classes from least affected to the most affected and rated, respectively, 2.5, 5, 7 and 10 (Table 2). The thickness of aquifer (T) is calculated from 24 groundwater well data (Fig. 2d). The class with a thickness less than 10 m was rated 7.5 while most of the study area showed a saturated thickness more than 10 m and was assigned a rate value of 10. All the parameter layers (i) were then superposed in GIS environment and weights (Wi) are assigned to each layer based on Eq. (1) to produce the GALDIT map. Once GALDIT index has been computed, it is possible to identify areas more likely to be susceptible to seawater intrusion. The higher the index, the greater the vulnerability for seawater intrusion. The range of the index score are categorized into three classes: 4–5, 5–7.5 and 7.5–10 denoted, respectively, as low, moderate and high. Figure 3 illustrates the spatial distribution of GALDIT index in the study area. The percentage distribution of low, moderate and high vulnerability zones are 77.7, 13.18 and 9.12 %, respectively. The northeastern part of the study area is the most vulnerable area. Seawater groundwater quality index map The parameters considered for the calculation of the GQISWI included concentration in groundwater of calcium (Ca), magnesium (Mg), sodium (Na), bicarbonate (HCO3), chloride (Cl), sulfate (SO4), and nitrate (NO3). The calculated GQISWI, using the excel-based algorithm (Tomaszkiewicz et al. 2014) and the coordinates of each sample were imported into the GIS environment. For mapping the GQISWI, an experimental variogram of the index values was computed in different directions to detect any anisotropy of the spatial variability. However, there were no distinct differences among the structures of the calculated variograms in different directions. Therefore, only omnidirectional variogram was considered. Then, an appropriate theoretical variogram model must be determined. The experimental semi-variogram is compared with different theoretical models such as spherical, Gaussian, exponential models. From the cross-validation statistics, it can be concluded that the exponential model, which resulted in close to zero ME and close to 1 RMSS, for the GQISWI is chosen as the final model. The semivariogram parameters (including sill and range) that defines amplitude, and the distance beyond which autocorrelation is negligible are provided in Fig. 4.

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Fig. 2 Parameters of the GALDIT method: a Aquifer hydraulic conductivity (A); b height of groundwater level above sea level (L); c impact of existing status of seawater intrusion in the area (I), d thickness of aquifer (T)

Finally, using the best-fit theoretical model and corresponding semivariogram parameters, spatial variability map of the GQISWI was generated using the OK. The result of OK interpolation showed that the GQISWI values are ranged from 30 to 71 (Fig. 5). The lowest values were localized mainly in the northeast part of the study area, in Sidi Mansour, Sfax, Hachichina and Cha`al localities. These low indices are associated with the seawater intrusion into the aquifer. The highest values interest the middle part, particularly in Mahares and Nakta localities. It corresponds to the presence of less saline water, due to the dilution of groundwater by direct infiltration through Chaffar stream.

seawater (Hydro Metrics 2012). Few hydrogeochemical parameters have been suggested as indicators for seawater intrusion. These parameters include levels of TDS, Cl concentrations and Jones Ratio (Khmaj et al. 2014). A data base made up of 399 laboratory analyses of groundwater samples was used (DGRE, 2006). Spatial distribution maps of TDS, Cl and Na/Cl ratio were produced (Figs. 6, 7, 8). These parameters are considered valuable indicators for the assessment and classification of fresh water salinisation (Gounari et al. 2014; Sherif et al. 2011; Kura et al. 2013). A detailed discussion of these maps is given in the following sections. TDS distribution

Groundwater vulnerability maps validation To assess the groundwater vulnerability maps validity, a comparison with hydrochemical maps was undertaken. It is recognized that no single variable definitively identifies seawater intrusion, however by looking at various analyses we can ascertain when fresh groundwater mixes with

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The TDS in a water sample is a measure of all dissolved solid materials whether ionized or not. It does not include suspended sediments, colloids or dissolved gases and is an indication of groundwater salinity (Sherif et al. 2011). The extent of seawater intrusion into coastal aquifer is commonly estimated by a salinity rise in observation wells

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Fig. 3 GALDIT vulnerability map of the Sfax superficial coastal aquifer

Fig. 4 GQISWI semivariogram for 2006

(Melloul and Zeitoun 1999). The TDS contents in the studied aquifer range from 689 to 9548 mg/L (Fig. 6). This wide variation can be attributed to seawater intrusion which is likely to affect some parts of the study area. TDS values of less than 6000 mg/L were identified in most parts of the region. Low values of this parameter (less than

2000), corresponding to a good quality of the ground water resources, are observed in the central sector of the investigated area. Elevated TDS concentration levels ([4000 mg/L) are located in the northeast and southeast parts along the coast particulary close to El Amra, La louza and Djebeniana localities.

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Fig. 5 GQISWI map of the Sfax superficial coastal aquifer

Fig. 6 TDS concentrations (in mg/L) in the superficial coastal aquifer of Sfax in 2006

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Fig. 7 Chloride concentrations (in mg/L) in the superficial coastal aquifer of Sfax in 2006

Fig. 8 Jones ratio distribution in the study area

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Chloride ions (Cl) repartition The most obvious indication of seawater intrusion is an increase in Cl concentrations (Korfali and Jurdi 2010). In the study area, the chloride ion concentration varied from 34 to 4476 mg/L with an average of 895 mg/L (Fig. 7). Considering the fact that Cl is the dominant ion in seawater; therefore, its variation is most likely to be influenced by seawater intrusion (Kura et al. 2013). The distribution of the chloride shows a trend similar to that of the TDS. The minimum values are observed in the central sector, while the highest values are shown in the northeast, southeast parts of the study area and specifically close to the shore line. This increased Cl ion concentration gives a good indication of seawater encroachment (Simpson 1946; Fass et al. 2007). Jones ratio (JR) mapping The Jones ratio can be used to differentiate seawater intrusion and other sources of saltwater in groundwater. It is expressed as the ratio JR = (Na?/Cl-). The seawater has a uniform chemistry with the prevalence of Cl- and Na possessing a molar ratio of 0.86 (Korfali and Jurdi 2010). In the process of a seawater intrusion front progress, sodium often replaces calcium on the aquifer matrix through ion exchange and this ratio will be below 0.86 (Vengosh et al.1999; Lagudu et al. 2013). This distinguishes seawater intrusion from domestic wastewater, which typically has sodium/chloride ratios above 1 (Jones et al. 1999). In the study area, the Na/Cl ratio ranges between 0.3 and 7.34 (Fig. 8). Low ratios (below 0.86) are noticed in northeast and in the south coastal area. The rest of the groundwater has ratios consistently above 1. The ratio much higher than seawater ratio [1, include ion exchange with clay material (Appelo and Willemsen 1987; Jalali 2010; Reddy et al. 2012) or domestic waste particularly sewage pollution (Moujabber et al. 2006).

Discussion The GALDIT method assesses the state of the studied area, based on hydrogeological characteristics (Giupponi and Vladimirova 2006). However, it provides little idea about water quality contaminant loads and water pollution (Saidi et al. 2009). A combination of both vulnerability and water quality can be considered as an efficient tool for groundwater management. The GALDIT map indicated a high contamination potential in the costal band particularly in the northeast area and the shore line, the moderate vulnerability to

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contamination interests the southeast part and the south of Hachichina location. The GQISWI map shows similarities with that of GALDIT vulnerability. The differences are clear in the south of Hachichina city. The most saline waters were encountred in this sector. The hydrochemical data show relativity low salinity levels in the central part of the coastal areas. Elevated level of salinity with total dissolved solids greater than 4000 mg/L and high chloride in the range of 1000–4476 mg/L were observed on the North and South of the coastal strip zone, in the south of Hachichina city. The lower Na/Cl ratio is found to increase, due to seawater intrusion, mainly eastward toward the shore line and to a lesser extent southward. The excel algorithm (Eqs. 4, 5) predicted water type to be 67 % as Ca–Cl, 10 % as Na–Cl and chloride facies characterize the coastal area (Fig. 9). The various results (GALDIT map, GQISWI map, hydrochemical indicators) clearly support the presence of seawater intrusion in the Sfax coastal aquifer, particularly in the northward and southward of the coastal strip zone. However, the saline water (lowest indices in GQISWI map) noticed in Hachichina sector can be attributed to the water quality deterioration and not to seawater intrusion. In fact the low hydraulic conductivity (0.5 m/day) and the high piezometric level (9 m) in Hachichina sector, exclude the possible seawater intrusion and the saline water can be explained by water rock interaction (Polemio et al. 2006; Freeze and Cherry 1979). As deduction, the GQISWI approach cannot solely explain the origin of groundwater salinity and the GALDIT model provided a better understanding of the SWI processes in the aquifer. Seawater intrusion risk assessment Groundwater contamination risk assessment is a useful tool for its management. These assessment could help to screen areas threatened by groundwater contamination, which could be an important basis for decision-making, such as land planning and groundwater monitoring (Wang et al. 2012). Compared to SWI vulnerability assessment, there are fewer general methods used for SWI contamination risk assessment. The risk of groundwater contamination depends on the hazards and the vulnerability of the aquifer system (Andreo et al. 2006). In this study, the risk of SWI contamination of Sfax groundwater is found to be dependent on two elements: (1) the hazard due to human activities represented by heavy consumption and pumping rates which constitute the principal causes of salt water intrusion processes acceleration (Rapti-caputo 2010) and a key factor noted in leading

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Fig. 9 Groundwater facies distribution in the study area

to SWI within Africa coastal aquifer systems (Steyl and Dennis 2010) and (2) the intrinsic vulnerability of groundwater to seawater contamination using the GALDIT model. The spatial distribution of pumping wells, all active in the time of investigation, is defined using kernel density. Figure 10 gives the density area of the pumping wells. The highest density areas are in the northeast (Djebenyana, El Amra), in the middle eastern and in the southeast (Skhira), indicating that these areas have a large number of pumping wells and consequently large volumes of extracted water where the hazard index classification shows three ranges low (\2 wells/km2), moderate (2–4 wells/km2) and high (7–12 wells/km2). The combination of vulnerability and hazard maps provided the groundwater SWI risk map of Sfax aquifer in the coastal areas (Fig. 11). The high- and medium-risk zones comprise the northeast, southeast and eastern parts. In the study area, the Sfax aquifer is accessible by more than 4000 wells. The rate of abstraction and recharge of groundwater quantity are not in balance. A lowering of the groundwater table obviously occurs with the direct consequence of triggering saltwater intrusion phenomena. In the rest of the aquifer, the low levels of hazards and vulnerability implicate a low level of risk.

Assessment of the current extent of seawater intrusion Due to the scarcity of the geochemical data and continuously monitored head data, the Ghyben–Herzberg approach has been used to expect the actual seawater intrusion extend. The freshwater–saltwater interface depths were calculated using (Eq. 11), assuming a freshwater density of 1.000 g/cm3 and a saltwater density of 1.025 g/cm3. With these densities, the depth to the theoretical interface below sea level is 40 times the height of the freshwater head above sea level. The prediction of interface location requires only the water level in freshwater wells (Bear et al. 1999). A total of 33 observed hydraulic head values (h) for 2012 are used (CRDA, 2012). To verify the validity of the used approach, we calculated the depth to the interface (z) for the 2006 water table elevation. A comparison between 2006 Ghyben–Herzberg depth interface and GALDIT map was undertaken. The relative positions of the freshwater to saline water interface are depicted in two maps. The first map shows the 2006 fresh/saline water interface position. To examine the closeness of the extent of

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Fig. 10 Hazard map represented by the spatial distribution of pumping wells in the study area

Fig. 11 SWI risk map of the superficial coastal aquifer of Sfax

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Fig. 12 Comparison of the extent of seawater intrusion for the Ghyben–Herzberg and GALDIT models

seawater intrusion for the sharp interface and GALDIT models, a visual comparison approaches is provided in Fig. 12. The two methods show similar results. However, the limit fresh/salt water in some regions of the studied area is over-estimated, when utilizing the sharp interface approach. The saltwater intrusion extent according to the Ghyben–Herzberg model corresponds to the high and medium GALDIT vulnerability zones (eastern strip coast, northeast and southeast parts). Some areas with low SWI contamination potential show high TDS and chloride concentrations (Figs. 6, 7, 12). This fact may be related to the evaporitic materials dissolution and the infiltration of fertilizers used in the irrigation district (Trabelsi et al. 2007) and not to seawater intrusion. Figure 13 illustrates the 2006–2012 saltwater front position evolution. The Ghyben–Herzberg interface was farther inland on the 2012 map compared to the 2006 map, particularly in the northern sector. However, in the southern part, the fresh/saline interface is shown to be in the same general location as it was in 2006. This farther inland location in 2012 is mainly due to an increased pumping and a lowering of groundwater levels. In 2012, the interface advanced inland and seawater intrusion extends more than 9 km in the northern sector and reaches 5 km in the southeast sector (Fig. 13).

As the demand for groundwater pumping intensifies, it can be expected that the extent of seawater intrusion in the future may be more severe than the model prediction.

Summary and conclusions This study built a method for assessing SWI groundwater contamination risk through the integration of GALDIT method, GQISWI approach, geostatistics, Ghyben–Herzberg model and GIS. Two types of vulnerability maps were produced. The deteriorative condition of Sfax coastal aquifer is evident and is highly aggravated in the northern sector. The vulnerability ranges and corresponding regions are compared for both approaches. Both models determine high vulnerability for the northern part and southern coastline of Sfax and lower vulnerability ranges at the middle part, it corresponds to the presence of less saline water, due to the dilution of groundwater by direct infiltration through Chaffar stream. On the other hand, for the eastern part in south of Hachichina city, the GQISWI method predicts high vulnerability, contrary to the GALDIT model, which predicts moderate vulnerability. The difference is believed to be related to the fact that the first method is based only on the geochemical

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Fig. 13 Positions of the Ghyben–Herzberg salt-fresh water interface in the superficial coastal aquifer of Sfax (map comparing the interface position in the year 2006 with the year 2012)

indicators, however, GALDIT methodology includes hydrodynamics conditions (piezometry, permeability). The results show the applicability of the GQISWI method in the Sfax coastal area and are in agreement with observations for other regions where the approach is applied. However, seawater intrusion entails complex hydrogeochemical processes that cannot be fully captured through use of the GQISWI (Tomaszkiewicz et al. 2014). The two approaches have converged to both assess and characterize seawater intrusion as well as the extent of saltwater wedge encroachment. However, GQISWI approach based only on geochemical parameters cannot solely solve the main problematic. The deduction of seawater intrusion through GALDIT map seems more convincing than the GQISWI map. The present research allowed to emphasize the use of the GQISWI as a complementary methodology to the GALDIT one. The first constitute a tool to verify, discuss and compel the shortcoming of the GALDIT model. The use of the seawater indicators and chemical facies for the validation of a groundwater vulnerability map yields good results in identifying the spatial distribution of zone with different groundwater salinity. This validation, confirmed the high vulnerability of northeast, southeast and eastern strip coast of the studied area.

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The GALDIT vulnerability method does not consider the effect of the pumping factor (Wetzelhuetter 2013). Indeed, a risk map is used to assess the impact of the over extraction on Sfax costal groundwater. The different areas with high intensity of pumping wells coincide with the vulnerable areas. This confirms that the excessive pumping to meet the increasing water demand is the main reason of saltwater intrusion in coastal Sfax groundwater. The risk map revealed that about 20 % of the studied area is under high-risk and 24 % is under medium risk. The application of the Ghyben–Herzberg model illustrates sharp interface approach for the examination of freshwater–saltwater dynamics in Sfax coastal aquifer. The 2006 theoretical interface fresh/saline water calculated, using the Ghyben–Herzberg model, is in accordance with GALDIT vulnerability map. Despite the fact that the results obtained by Ghyben–Herzberg approach on the position of the interface are estimates, the use of this model can provide interesting informations such as the temporal evolution of the interface. The evolution of the interface for the period 2006–2012 is positive, particularly in the northeast. The potential for saltwater contamination of freshwater in the coastal aquifer system tends to increase in this sector. The sharp interface is dynamic, moving laterally in

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response to changes in groundwater level and over pumping. This paper constitutes the first study that has used the integrated method of GALDIT model, GQISWI approach, hydrochemical indicators, geostatistics, Ghyben–Herzberg model and GIS to investigate seawater intrusion in semiarid region. Hence, the proposed methodology is effective for the diagnosis of the state of the aquifer quality and the eventual occurring risks. The most apparent advantage of the combination technique is the time and cost effective. The methodology provides a helpful and excellent decision-making tool for groundwater management. The results of the study can serve as a primary target for the introduction of future mitigation and adaptation strategies. Further steps could be made towards prediction of future trends and the ability to simulate different water management scenarios that can be provided by groundwater modeling. The present research emphasizes the importance of integrated water resource management plan to ensure sustainability and viability of coastal aquifers. This necessitates the reduction in pumping to eliminate the ongoing problem of saltwater intrusion, installation of a monitoring network. Indeed, a continuous monitoring of the salinization process will lead to a better management of the water resources in coastal areas (Rapti-caputo 2010). Acknowledgments We would like to thank the anonymous reviewer for his/her constructive comments and suggestions to improve the quality of the manuscript. We are also thankful to the personnel of the direction of water resources of Sfax (CRDA) for providing us with the valuable observation data.

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