Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study

Hydrological Sciences Journal Journal des Sciences Hydrologiques ISSN: 0262-6667 (Print) 2150-3435 (Online) Journal homepage: http://www.tandfonline.com/loi/thsj20 Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study Salwa Saidi, Salem Bouri & Hamed Ben Dhia To cite this article: Salwa Saidi, Salem Bouri & Hamed Ben Dhia (2011) Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study, Hydrological Sciences Journal Journal des Sciences Hydrologiques, 56:2, 288-304, DOI: 10.1080/02626667.2011.552886 To link to this article: https://doi.org/10.1080/02626667.2011.552886 Published online: 28 Mar 2011. Submit your article to this journal Article views: 1200 View related articles Citing articles: 16 View citing articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalinformation?journalcode=thsj20

288 Hydrological Sciences Journal Journal des Sciences Hydrologiques, 56(2) 2011 Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study Salwa Saidi, Salem Bouri & Hamed Ben Dhia Water, Energy and Environment Laboratory (LR3E), Ecole Nationale des Ingenieurs de Sfax (ENIS), 3038 Sfax, Tunisia salwa_saidi@yahoo.fr; salem_bouri@yahoo.fr; hamed.bendhia@uss.rnu.tn Received 22 January 2010; accepted 21 October 2010, open for discussion until 1 September 2011 Citation Saidi, S., Bouri, S. & Ben Dhia, H. (2011) Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study. Hydrol. Sci. J. 56(2), 288 304. Abstract The assessment of groundwater vulnerability to pollution has proved to be an effective tool for water resource management, especially in arid and semi-arid regions like Mahdia and Ksour Essaf. The main objective of this study is to assess the aquifer vulnerability by applying the DRASTIC method as well as using sensitivity analysis to evaluate the effect of each DRASTIC parameter on the final vulnerability map. An additional objective is to demonstrate the role of the GIS techniques in the vulnerability assessment. The DRASTIC method assigns a high vulnerability to the coast of the Mahdia-Ksour Essaf. The lowest values are observed in the southern part of the study area. A sensitivity analysis applied in this study suggests that net recharge, aquifer media and depth of groundwater are the key factors determining vulnerability. The model is validated with groundwater quality data and the results have shown strong relationships between modified DRASTIC Vulnerability Index and nitrate and chloride concentrations. Key words vulnerability; DRASTIC method; Mahdia-Ksour Essaf aquifer; sensitivity analysis; GIS Analyse de sensibilité sous SIG de l évaluation de la vulnérabilité des ressources en eaux de l aquifère de Mahdia-Ksour Essaf, Tunisie: étude de validité Résumé L évaluation de la vulnérabilité des eaux souterraines à la pollution apparaît être un outil efficace pour la gestion des ressources en eau, en particulier dans les régions arides et semi-arides telles que celles de Mahdia et de Ksour Essaf. L objectif principal de cette étude est d évaluer la vulnérabilité d un aquifère en appliquant la méthode DRASTIC et en utilisant une analyse de sensibilité pour apprécier l effet de chaque paramètre de DRASTIC sur la carte de vulnérabilité finale. Un objectif supplémentaire est de mettre en évidence le rôle des techniques de SIG dans l évaluation de la vulnérabilité. La méthode DRASTIC attribue une forte vulnérabilité à la zone côtière de l aquifère de Mahdia-Ksour Essaf. Les valeurs les plus faibles sont observées dans la partie méridionale de la zone d étude. L analyse de sensibilité suggère que la recharge nette, la matrice de l aquifère et la profondeur de l eau souterraine sont les facteurs clefs dans la détermination de la vulnérabilité. Le modèle est validé avec des données de qualité des eaux souterraines et les résultats montrent de fortes relations entre l Indice de Vulnérabilité DRASTIC modifié et les concentrations en nitrate et en chlorures. Mots clefs vulnérabilité; méthode DRASTIC; aquifère de Mahdia-Ksour Essaf; analyse de sensibilité; SIG 1 INTRODUCTION Scarce surface water sources, or their unsuitability, make groundwater the only source of water, in certain arid and semi-arid regions. Pollution of groundwater is one of the serious and growing potential problems in most large urban and agricultural areas because aquifers are inherently susceptible to contamination from land use and anthropogenic impacts. Remediation of polluted aquifers is prohibitively costly and often impractical. In this context and in recognition of the need for effective and efficient methods for protecting groundwater resources from future contamination, scientists and resource managers have sought to develop aquifer vulnerability assessment techniques for predicting which areas are more likely than others to become contaminated as a ISSN 0262-6667 print/issn 2150-3435 online 2011 IAHS Press doi: 10.1080/02626667.2011.552886 http://www.informaworld.com

Sensitivity analysis in groundwater vulnerability assessment 289 result of activities at or near the land surface (NRC, 1993). The assessment of groundwater vulnerability to pollution has been subject to intensive research and a variety of methods have been developed. One of the most used standard groundwater vulnerability methods is DRASTIC (Aller et al., 1987). This method takes into account seven parameters of both the geological and the hydrological environments. Opinions differ about the DRASTIC method. In fact, some authors, such as Barber et al. (1993) and Merchant (1994), argued that an equivalent DRASTIC result might be obtained using fewer parameters, with several advantages in accuracy, precision and costs (Napolitano & Fabbri, 1996). However, in many studies, all the parameters of the DRASTIC model were quite independent. Therefore, they were representative enough to assess pollution vulnerability (Yang & Wang, 2010). So, the first objective of this study is to evaluate the vulnerability of the shallow aquifer of the Mahdia-Ksour Essaf using the DRASTIC model. The second objective is to determine the influence of each single parameter on the aquifer vulnerability assessment and to calculate new effective weights for each DRASTIC parameter. To achieve this aim, a variety of GIS analyses and geoprocessing framework are used, such as Arc Map and Arc Catalog. The DRASTIC method can also offer an automation of parametric and indexing method (Saidi et al., 2009). 2 STUDY AREA The study area ( 600 km 2 ) is located in the northeastern part of the Mahdia region which is on the eastern coast of Tunisia (Fig. 1). The study area is relatively flat and covered by thin layers of conglomerates and sandstones of marine terraces of Tyrrhenian age in the coastal part. The rest of the study area is covered essentially by a mixing of sand, clay and sandy loam intercalations of Pleistocene age and recent alluvial deposits (Fig. 2) The aquifer has an estimated safe yield of 2.9 10 6 m 3 /year, but annual abstraction by pumping from 1784 wells amounts to 6.50 10 6 m 3 /year (CRDA, 2005). There is also evidence to suggest that the quality of groundwater supplies is under threat as a result of salinization. Thus, salinity levels generally range from 0.6 to 3 g/l in the coastal aquifer, and exceed 7 g/l in some areas in the west and the centre of the study area (CRDA, 2007). The Mahdia region is characterized by a semi-arid climate with an annual precipitation of 308 mm (INM, 2007), an annual temperature of 19 C and a potential evapotranspiration rate of 1118 mm/year. 654000 663000 672000 631000 690000 N 3920000 3920000 3930000 3910000 3910000 3930000 3940000 3940000 A Aquifer boundary Monitoring wells for chemical analysis River network Village A Cross section 3900000 4 km 3900000 654000 663000 672000 681000 690000 Fig. 1 Location of the study area in Tunisia.

290 Salwa Saidi et al. Fig. 2 Geological map (extracted from El Jem map no. 12). The study area is mostly used for intensive cultivation of crops such as cereals and olive. The use of inorganic fertilizers on these crops has a great polluting effect, which adds to the environmental pressure on the groundwater of the Mahdia-Ksour Essaf. 3 MATERIALS AND METHODS 3.1 Vulnerability assessment Numerous approaches to modelling groundwater vulnerability can be considered. A comprehensive groundwater vulnerability model must include parameters to describe how much a site is likely to be contaminated and how the contaminant moves from the site of contamination to the aquifer. Here, the vulnerability rating used is the DRASTIC index. It is the most commonly used of the point count and matrix rating methods because of its simple and easyto-understand characteristics (Chen & Fu, 2003). In fact, the DRASTIC model has been used as a valuable tool in many parts of the world for assessing the vulnerability of groundwater (Zhou et al., 1999). The DRASTIC model was developed by the US Environmental Protection Agency (EPA) to evaluate groundwater pollution potential for the entire United States. It was based on the following hypotheses: the potential contamination sources are in the surface of the soil; the potential contamination sources reach the aquifer by the infiltration mechanism; the pollutant has the same mobility as the groundwater; and the hydrogeological unit considered has a surface of greater than 40 ha (Aller et al., 1987). The acronym DRASTIC stands for the seven parameters used to calculate the index number. These parameters describe the hydrogeological setting, which affects the groundwater movement into, through and out of the area. The factors are: Depth of water, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and the hydraulic Conductivity of the aquifer. The most important feature of the DRASTIC model is its numerical relative rating and weighting system. In fact, each rating range has been evaluated with respect to the others and to their potential pollution. The numerical relative rating varies from 1 to 10. The weighting which varies from 1 to 5 represents an attempt to define the relative importance of each factor in its ability to affect pollution transport to and within the aquifer. From these parameters, a DRASTIC index (DI) or vulnerability rating can be obtained according to the following equation (1): DRASTIC index = D R D W + R R R W + A R A W + S R S W + T R T W + I R I W + C R C W (1) where D, R, A, S, T, I, andc are the seven factors of the DRASTIC method and subscripts R and W represent, respectively, the rating and weight of the factors.

Sensitivity analysis in groundwater vulnerability assessment 291 The higher the value of the DRASTIC index, the greater is the vulnerability to pollution of that part of the aquifer. 3.2 Development of the DRASTIC vulnerability index The application of DRASTIC is based on existing, multidisciplinary data. Table 1 gives an overview of the data sources used and the mode of processing in Arc GIS for each parameter, as briefly described below. 3.2.1 Depth of groundwater (D) This represents the depth from the land surface to the first groundwater aquifer (Witczak et al., 2007). It determines the thickness of material through which infiltrating water must travel before reaching the aquifer-saturated zone. Consequently, the depth of the groundwater impacts on the degree of interaction between the percolating contaminant and sub-surface materials (air, minerals, water) and, therefore, on the degree and extent of physical and chemical attenuation, and degradation processes (Rahman, 2008). The distribution of the depth of groundwater parameter D was established by subtracting the groundwater level, measured in thirty wells in Mahdia-Ksour Essaf aquifer, from the topographic elevation in the corresponding cell location. 3.2.2 Net recharge (R) To calculate the distribution of the recharge parameter, the water table Table 1 Raw data sources and mode of processing of DRASTIC index (VI) parameters. Parameter Raw data sources Mode of processing D Measured by an electric sounder in 33 shallow wells (monthly monitoring of the CRDA, 2007a,b). R Precipitation, evapotranspiration (ETP) (I.N.M., 2007). A Geological information and published well logs (CRDA, 2007a,b). S Soil maps (scale 1:50 000) (CRDA, 2008). T Topographical maps (scale 1:50 000) (CRDA, 2008). I Analysis of published well logs and geological maps (CRDA, 2007a,b). C Pumping tests of 30 water wells (CRDA, 2007a,b). Interpolation Interpolation Interpolation Digitalization Digitalization Interpolation Interpolation fluctuations method (WTF) was used. This method estimates groundwater recharge as the product of specific yield and the annual rate of water table rise plus the total groundwater draft (Sophocleous, 1991). 3.2.3 Aquifer media (A) and the impact of the vadose zone (I) These factors were represented by the equivalent permeability, which is found from well logs (Saidi et al., 2010). 3.2.4 Topography (T) This refers in this case to the percent slope of the land surface which was determined directly from the topographic maps (scale 1:50 000). 3.2.5 Soil media (S) The soil parameter (S) was obtained by digitizing the existing soil maps, with a scale of 1:50 000 acquired from Regional Agency of Agriculture Laboratory CRDA, covering the region. 3.2.6 Hydraulic Conductivity (C) In DRASTIC, C is defined as the ability of aquifer materials to transmit water, which in turn, controls the rate at which groundwater will flow under a given hydraulic gradient. The rate, at which the groundwater flows, also controls the rate at which it enters the aquifer (Saidi et al., 2009). The hydraulic conductivity was calculated based on the following equation: K = t/b (2) where K is the hydraulic conductivity of the aquifer (m/s), b is the thickness of the aquifer (m) and t is the transmissivity (m 2 /s), measured from the field pumping test data. The thematic maps representing the depth of groundwater (D), recharge (R), aquifer media (A), impact of the vadose zone (I) and hydraulic conductivity (C) parameters were created by interpolation of the raw data used for each one (Table 1). An exact interpolation scheme is appropriate for generating a smooth-surface representation for the high degree of spatial continuity of the D, R, A, I and C parameters in an aquifer (Saidi et al., 2009) and the inverse distance moving average interpolation technique was used (Babiker et al., 2005). However, the soil type (S) and topography (T) maps were geo-referenced and digitized from different data files (Table 1 and Fig. 3).

292 Salwa Saidi et al. Raw data Parameter maps Processing Final product Water table Depth of water Rainfall, ETP Net recharge Well logs published, geological map reports Soil data Aquifer media Soil media GIS DRASTIC map Sensitivity analysis Real or effective weight Modified DRASTIC map Topography Slope Well logs, published geological map reports Impact of vadose zone Transmissivity, saturated thickness Hydraulic conductivity Fig. 3 Flow chart of methodology for validation test of groundwater vulnerability using the DRASTIC model and sensitivity analysis in GIS. 3.3 Calculation and mapping of DRASTIC After mapping all the parameters, the vulnerability maps were obtained by overlaying the individual maps in a GIS and calculating the indices on a grid map (cells of 300 m 300 m). For each grid cell, the DRASTIC index was calculated as the weighted sum of the parameters according to equation (1). The resultant vulnerability map was subdivided into classes in relation to each degree of vulnerability according to the classification of Engel et al. (1996). 3.4 Sensitivity analysis The DRASTIC model is characterized by the use of a high number of parameters (Evan & Myers, 1990), which is believed to limit the impact of errors and uncertainties in the individual parameters on the final output (Rosen, 1994; Babiker et al., 2005). However, some authors (Barber et al., 1993; Merchant et al., 1987) have claimed that a result equivalent to DRASTIC can be obtained using a lower number of input parameters (McLay et al., 2001). Because it is not possible at present to avoid subjectivity, one way to deal with this is by performing a sensitivity analysis (Napolitano & Fabbri, 1996). 3.4.1 Map removal sensitivity analysis This is defined by Lodwick et al. (1990) and describes the sensitivity of the vulnerability map when removing one or more maps from the suitability analysis, and is calculated by: S = 100 (V/N V /n)/v (3) where V and V are the unperturbed and the perturbed vulnerability indices, respectively; N and n are the number of data layers used to calculate V and V.

Sensitivity analysis in groundwater vulnerability assessment 293 3.4.2 The single parameter sensitivity The DRASTIC index is highly sensitive to the parameter scores and weightings, and the numerical values assigned to its parameters are essentially arbitrary (Al-Adamat et al., 2003). For this reason, single parameter sensitivity was introduced by Napolitano & Fabbri (1996), with which a comparison can be made between the real weight and theoretical weight used in DRASTIC. The real or the effective weight is computed by: W = 100 P r P w /V (4) where W refers to the effective weight of each parameter, P r and P w are the rating value and the weight for each parameter, and V is the overall vulnerability index. 3.5 Modified DRASTIC computation Using the effective weight in the DRASTIC computation could help to obtain a more realistic vulnerability map; i.e. replacing the theoretical weight with the calculated effective weight after rescaling, in the DRASTIC equation (1). 3.6 Validation testing The use of methods that are not validated can result in erroneous conclusions and subjective vulnerability assessment. To avoid subjectivity, parameter comparison testing and mapping of validation alternatives are necessary (Ramos-Leal & Rodriguez-Castillo, 2003). To test vulnerability assessment using DRASTIC, chloride and nitrate were selected as pollution indicators. Existing nitrate and chloride data (July 2007) collected from 40 wells in the Mahdia-Ksour Essaf aquifer were used to compare relative NO 3 - and Cl - concentrations to vulnerability values. To illustrate this validation test, a crosssection A A was created on the vulnerability, the chloride and the nitrate maps. The comparison of these cross-sections enabled evaluation of the validity of the vulnerability map. 4 RESULTS AND DISCUSSION 4.1 DRASTIC parameters and aquifer vulnerability The rate and weighting variation of each parameter of DRASTIC are illustrated in Table 2. The depth to groundwater varies between 4 and 37 m across the aquifer, as shown in (Fig. 4). According to the classification of Aller et al. (1987), the rates range between 1 and 9. The net recharge calculated by the WTF method shows high recharge (>225 mm) zones in the extreme north, in the southern portion of the aquifer, and near Boumerdes (Fig. 5). The latter zone is due to the injection of water, derived from the wastewater purification service, into the Zrata River (Fig. 1; Maaref and Ltaief, 2004); thus, it was assigned a rating of 9 (Fig. 5). The aquifer media in the east is constituted of permeable sediments, including the Tyrrhenian gravel in the coastal area. Thus it has high permeability rates of 8 and 9 (Fig. 6). In the southwestern part of the region, infiltration capacity is low and expressed by permeability rates of 1, 2 and 4. The soil map (Fig. 7) shows great soil variability, with soils in eight different rate classes. The highest rate (9) was assigned to mineral soils and the lowest rate (1) was assigned to soil in the built-up areas. The topography layer (Fig. 8) displayed a very gentle slope of 0 3% over most of the study area, which has been assigned the DRASTIC rating score of 10. The impact of the vadose zone layer is represented by the vertical equivalent permeability (Saidi et al., 2010). The gravel/sand coastal deposits were assigned a high rating value (6) and the value 1 was assigned to the clayey sand deposits in the west and the south (Fig. 9). The hydraulic conductivity shows high variability and rates of 1, 2, 4, 6 and 8 were assigned (Fig. 10), All the GIS coverage was in raster format and the values for each overlay were summed in ArcGis according to the pixel value of each area that resulted from multiplying the ratings with the appropriate DRASTIC weight (Table 2). The DRASTIC map resulting from overlaying the seven thematic maps shows three classes, as indicated in Fig. 11. The highest class of vulnerability (DI: 140 184) covers 27% of the total surface in the east of the study area. This is due to the high permeability of the aquifer and the vadose zone sediments there, i.e. the combination of Tyrrhenian gravels, shallow groundwater (<9 m), high recharge (>250 m) and high hydraulic conductivity. This results in a low capacity to attenuate the contaminants. The low vulnerability (DI: 66 100), which is represented by 20% of the total Mahdia-Ksour Essaf surface, is essentially

294 Salwa Saidi et al. Table 2 Rate and weight of the seven DRASTIC parameters (Aller et al., 1987); R: Rate. Depth of groundwater, D (m) Net recharge, R (m) Topography (slope), T (%) Hydraulic conductivity, C (m/s) Aquifer media, A Impact of the vadose zone, I Soil media, S Interval R Interval R Interval R Interval R Permeability classes 4 4.5 9 0 0.05 1 0 3% 10 10-6 5 10-5 1 Sandy clay (10-6 10-5 ) 4.5 9 7 0.05 0.10 3 3 5% 9 5 10-5 2 10-4 2 Silty sand and sand (10-5 10-4 ) 9 15 5 0.10 0.18 6 5 10% 5 2 10-4 4 10-4 4 Sand, gravel and sandy clay (10-4 10-3 ) R Permeability classes R Soil classes R 1 Confined aquifer (1.1 10-7 10-5 ) 2 Sandy clay and sand (10-5 10-3 ) 1 Mineral soil 9 5 Isohumic chestnut soil 4 Sand (10-3 5 10-3 ) 6 Rendzina 7 15 23 3 0.18 0.25 8 10 15% 3 4 10-4 5 10-4 6 Sand (10-3 10-2 ) 6 10 Calcareous brown soil 23 31 2 >0.25 9 15 25% 1 5 10-4 10-3 8 Sandy gravel (10-2 10-1 ) 8 Soil with little evolution 31 37 1 Gravel (10-1 4.8) 9 Polygenetic soil 4 Halomorphic soil 2 Urban zones 1 Weight 5 Weight 4 Weight 1 Weight 3 Weight 3 Weight 5 Weight 2 8 6 5

Sensitivity analysis in groundwater vulnerability assessment 295 Fig. 4 Depth of groundwater of Mahdia-Ksour Essaf. Fig. 5 Net recharge of Mahdia-Ksour Essaf.

296 Salwa Saidi et al. Fig. 6 Aquifer media of Mahdia-Ksour Essaf. Fig. 7 Soil media of Mahdia-Ksour Essaf.

Sensitivity analysis in groundwater vulnerability assessment 297 Fig. 8 Topography of Mahdia-Ksour Essaf. Fig. 9 Impact of vadose zone of Mahdia-Ksour Essaf.

298 Salwa Saidi et al. Fig. 10 Hydraulic conductivity of aquifer of Mahdia-Ksour Essaf. Fig. 11 Groundwater vulnerability of Mahdia-Ksour Essaf.

Sensitivity analysis in groundwater vulnerability assessment 299 due to the deep groundwater table (>25 m depth), the low permeability of the aquifer and vadose zone sediments and especially the low hydraulic conductivity, as well as the low recharge rate. The moderate vulnerability (DI: 101 139) applies to 53% of the study area. This vulnerability pattern is mainly dictated by the variation of the permeability of both the aquifer and the vadose zone, and less by the recharge and the depth of groundwater. 4.2 Sensitivity of the DRASTIC model Table 3 presents a statistical summary of the seven rated parameters of the DRASTIC. The highest risk of contamination originates essentially from topography (mean: 9.73), aquifer media (mean: 8.36), and net recharge (mean: 7.22), and to a lesser extent from depth of groundwater (mean: 4.61) and soil media (mean: 6.23). The hydraulic conductivity and the impact of the vadose zone impose a low risk of aquifer contamination (3.25 and 1.85, respectively). The coefficients of variation (CV) indicate that a high contribution to the variation of the vulnerability index is made by the impact of the vadose zone (96.2%) and a moderate contribution is due to hydraulic conductivity (58.2%) and depth of groundwater (37.5%). Aquifer media is the least variable parameter (10.4%), which implies a smaller contribution to the variation of the vulnerability index across the study area (Babiker et al., 2005). 4.3 Map removal sensitivity analysis The sensitivity analysis carried out in this study helped to validate and evaluate the consistency of the analytical results and is the basis for proper evaluation of the vulnerability maps. Using sensitivity analysis, a more efficient interpretation of the vulnerability index can be achieved (Raj Pathak et al., 2008). Table 4 illustrates the variation of the vulnerability index as a result of removing one layer from Table 3 A statistical summary of the DRASTIC parameter maps. D R A S T I C Minimum 1 1 2 1 1 1 1 Maximum 9 9 9 10 9 6 8 Mean 4.61 7.22 8.36 9.73 6.23 1.85 3.25 SD 1.73 1.94 0.87 1.09 1.75 1.78 1.89 CV (%) 37.5 26.9 10.4 11.2 28 96.2 58.2 SD: standard deviation; CV: coefficient of variation. Table 4 Statistics of the one-map removal sensitivity analysis. Parameter removed Variation index (%) Mean Minimum Maximum SD D 19.36 5 40 6.52 R 24.51 4 39 6.39 A 21.64 6 34 3.27 S 10.79 1 23 3.36 T 8.48 1 15 1.75 I 7.19 3 25 5.5 C 8.09 2 22 4.1 SD: standard deviation. the assessment. It is clear that high variation of the vulnerability index is expected on removal of the net recharge, R (mean variation index: 24.51%). This can be attributed to the high theoretical weight assigned to this layer (4) and the high recharge rate derived essentially by wastewater additions to the rivers and irrigation applications in this area, Furthermore, the vulnerability index seems to be sensitive to the removal of aquifer media (A) asthe mean variation index is 21.64%, although the low real weight (13%) (Table 6) relative to removing the depth of groundwater (D) caused a variation of 19.36%. The least sensitive parameter is the impact of the vadose zone, I (7.19%), in spite of the high theoretical weight assigned to it. Table 5 demonstrates the variation of the vulnerability index due to the removal of one or more data layers at a time from the DRASTIC model computation. The layers which cause less variation of the vulnerability index were removed one-by-one, to leave just one. The highest variation is associated with the removal of the aquifer media (A) and the recharge (R) parameters and the least variation is observed after removing the impact of the vadose zone parameter, I. The variation index increased as the number Table 5 Statistics of map removal sensitivity analysis. Parameters used Variation index (%) Mean Minimum Maximum SD D, R, A, S, T, C 7.19 3 25 5.5 D, R, A, S, T 7.6 3 20 3.63 D, R, A, S 7.92 4 16 2.14 D, R, A 8.75 5 15 1.6 R, A 14.03 7 23 3.21 R 16.34 14 19 0.97 SD: standard deviation.

300 Salwa Saidi et al. of layers excluded from the DRASTIC computation increased. The removal of some layers (A, R and D) affects the vulnerability assessment and this is demonstrated by all the sensitivity tests. However, the interpretation of some average variation indexes needs further investigation, but through this sensitivity analysis it is clear that a considerable variation in the vulnerability assessment is expected if a few parameters have been integrated. 4.4 Single-parameter sensitivity analysis While the map removal sensitivity analysis presented (Section 4.3)has confirmed the significance of the seven parameters in the assessment of the DRASTIC vulnerability index for the study area, the single parameter sensitivity analysis compares their effective weights with their theoretical weights (Babiker et al., 2005). The effective weight is a function of the other six parameters as well as the weight assigned to it by the DRASTIC model. The effective weights of the DRASTIC parameters exhibited some deviation from the theoretical weight (Table 6). The net recharge, R, tends to be the effective parameter in the vulnerability assessment with an average weight of 24.5% against the theoretical weight (17.4%). The effective weight of the aquifer media parameter, A (21.64%) greatly exceeds the theoretical weight assigned by DRASTIC (13%). Similarly, the calculated weights of the soil media, S, and topography, T (10.79% and 8.48%, respectively) exceed the theoretical weights (8.7% and 4.3%, respectively). The depth of groundwater, D, the hydraulic conductivity, C, and especially the impact of the vadose zone, I, reveal lower effective weights (19, 8.1 and 7.19%), than the theoretical weights (21.7, 13 and 21.7%, respectively). Comparing with the theoretical weights, the effective ones show huge differences in the case of the net recharge and the impact of the vadose zone parameters. This pattern is due to the high recharge and the low variability of the vadose zone permeability. 4.5 Modified DRASTIC map The modified DRASTIC map classifies much of the aquifer (64%) as highly vulnerable (Fig. 12). This classification is due to the high permeability of the aquifer media and the high recharge, both of which also have high calculated weights (4.97 and 5.63, respectively) (Table 7). The least vulnerable areas are located in the south, covering 8% of the total surface of the Mahdia-Ksour Essaf aquifer, and the remainder (28%) of the total has a moderate vulnerability (Fig. 12). 4.6 Validation testing The comparison between maps of nitrate and chloride concentrations, and the vulnerability maps show that some maximum concentrations correspond with some highest DRASTIC vulnerability values (Table 8, Figs 11, 12, 13 and 14). This coincidence is very clear in the east (A ), especially between the chloride and vulnerability maps (Figs 11, 12 and 13). The cross-section data (Fig. 15) show that the best correspondence is observed where there is a high density of wells (high density of information). Thus to obtain a better validation, other monitoring wells should be analysed, to cover the area with more data. 5 SUMMARY AND CONCLUSIONS The GIS-based DRASTIC model has been used to assess the potential groundwater vulnerability in the Table 6 Statistics of single parameter sensitivity analysis. Parameter Theoretical weight Theoretical weight (%) Effective weight (%) Mean Minimum Maximum SD D 5 21.7 19 5 40 19.36 R 4 17.4 24.5 4 39 6.39 A 3 13 21.64 6 34 3.27 S 2 8.7 10.79 1 23 3.36 T 1 4.3 8.48 1 15 1.75 I 5 21.7 7.19 3 25 5.5 C 3 13 8.1 2 22 4.1 SD: standard deviation.

Sensitivity analysis in groundwater vulnerability assessment 301 Fig. 12 Groundwater vulnerability map of the Mahdia-Ksour Essaf Aquifer, using a Modified DRASTIC index. Table 7 Comparison between theoretical weight and effective weight. DRASTIC parameter Theoretical weight D 5 4.37 R 4 5.63 A 3 4.97 S 2 2.46 T 1 1.95 I 5 1.63 C 3 1.86 Calculated effective weight after rescaling Mahdia-Ksour Essaf aquifer. The seven DRASTIC parameters: depth of groundwater, net recharge, aquifer media, soil media, topography, impact of the vadose zone and hydraulic conductivity, were used to calculate the vulnerability of the study area. The results show that groundwater in Mahdia Ksour-Essaf is located under low to high pollution vulnerability zones. The DRASTIC index varied between 66 and 184, and was divided into three vulnerability classes: low (66 100), moderate (101 139) and high (140 184). The eastern part of the aquifer is highly vulnerable because of its sandy and gravel lithology (with high permeability), the high net recharge and the low depth of groundwater. The sensitivity analysis applied reveals that the intrinsic vulnerability of the Mahdia-Ksour Essaf aquifer is largely due to the net recharge, the aquifer media and the topography because the aquifer Table 8 Chemical analyses for the validation study (CRDA, 2007). Sample Concentration (mg/l) Sample Concentration (mg/l) (a) Nitrate: 1 67 9 1 2 126 10 0.17 3 37 11 0.1 4 150 12 56 5 0.6 13 48 6 52 14 2 7 0.1 15 0 8 0.2 (b) Chloride: 1 1161 14 597 2 811 15 1225 3 1051 16 1160 4 1165 17 1324 5 900 18 735 6 1426 19 1297 7 916 20 1076 8 3168 21 1202 9 977 22 2579 10 506 23 964 11 1333 24 1040 12 644 25 950 13 970 has permeable sediments with flat topography, and receives a high rate of recharge. The map removal sensitivity analysis indicated that the vulnerability index is highly sensitive to the removal of net recharge, aquifer media and depth

302 Salwa Saidi et al. Fig. 13 Chloride concentration map. Fig. 14 Nitrate concentration map.

Sensitivity analysis in groundwater vulnerability assessment 303 (a) (b) (c) (d) Fig. 15 Cross-section A A with chemical and vulnerability profiles: (a) chloride concentration; (b) nitrate concentration; (c) the DRASTIC index; and (d) the modified DRASTIC index. of groundwater layers, but is least sensitive to the removal of the impact of the vadose zone layer. The single parameter sensitivity analysis has shown that net recharge, aquifer media and depth of groundwater are the most significant hydrogeological factors determining the high vulnerability of the Mahdia-Ksour Essaf aquifer. Therefore, the sensitivity analysis was very useful to revise the weight factors to obtain more realistic results. This finding suggests that a rescaling of the DRASTIC rating is proposed and justified based on the analysis of its effective weight. The vulnerability map using the calculated effective weight, demonstrates that the majority of the Mahdia-Ksour Essaf is highly vulnerable. In order to reduce the subjectivity of the vulnerability maps, a cross-section, A A, on the vulnerability, chloride and nitrate maps, was analysed. The cross-section revealed that maximum concentrations of chloride and nitrate did correspond with some of the highest DRASTIC vulnerability values. However, low values of nitrate occurred in other highly vulnerable zones (in the centre and the coast), although this was not the case of the modified DRASTIC vulnerability map. The latter shows a high similarity between the highly vulnerable zones and the high concentrations of nitrate and chloride (Fig. 15). Without GIS capabilities, the vulnerability assessment and the sensitivity analysis could not have been rigorously demonstrated. Thus, GIS techniques should be considered in the implementation and validation of the maps focusing on exploration for groundwater in this and similar localities of arid to semi-arid lands. Acknowledgements The authors are grateful to Professor Weldon A. Lodwick (Denver University, USA) for his valuable suggestions and explanations that helped to elaborate this paper. REFERENCES Aller, L., Bennet, T., Lehr, J. H. & Petty, R. J. (1987) DRASTIC: A standardized system for evaluating groundwater pollution potential using hydrogeologic settings. US Environ. Protection Agency EPA/600/2-85-018. Al-Adamat, R. A. N., Foster, I. D. L. & Baban, S. N. J. (2003) Groundwater vulnerability and risk mapping for the Basaltic aquifer of the Azraq basin of Jordan using GIS, remote sensing and DRASTIC. Appl. Geogr. 23, 303 324. Babiker, I. S., Mohammed, M. A. A., Hiyama, T. & Kato, K. (2005) A GIS-based DRATIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Sci. Total Environ. 345, 127 140. Barber, C., Bates, L. E., Barron, R. & Allison, H. (1993) Assessment of the relative vulnerability of groundwater to pollution: a review and background paper for the conference workshop on vulnerability assessment. J. Austr. Geol. Geophys. 14(2/3), 1147 1154. Chen, S-Y. & Fu, G.-T. (2003) A DRASTIC-based fuzzy pattern recognition methodology for groundwater vulnerability evaluation. Hydrol. Sci. J. 48(2), 211 220.

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