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Journal of Environmental Management 91 (2010) 1500e1510

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Journal of Environmental Management

journal homepage: www.elsevier .com/locate/ jenvman

Mapping the vulnerability of groundwater to the contamination of fourcarbonate aquifers in Europe

J. Vías a,*, B. Andreo b, N. Ravbar c, H. Hötzl d

aDepartment of Geography, University of Málaga, E-29071 Málaga, SpainbDepartment of Geology and Centre of Hydrogeology, University of Málaga,, E-29071 Málaga, SpaincKarst Research Institute, Postojna, SloveniadDepartment of Applied Geology, University of Karlsruhe, Kaiserstraße 12, D-76128 Karlsruhe, Germany

a r t i c l e i n f o

Article history:Received 7 October 2008Received in revised form2 February 2010Accepted 15 February 2010Available online 25 March 2010

Keywords:COP methodVulnerability of contaminationStatistical analysisCarbonate aquifer

* Corresponding author. Tel.: þ34 952132172; fax:E-mail addresses: jmvias@uma.es (J. Vías), andre

sa.Ravbar@zrc-sazu.si (N. Ravbar), heinz.hoetzl@agk.u

0301-4797/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.jenvman.2010.02.025

a b s t r a c t

The vulnerability of four European aquifers with different hydrogeological and climatic characteristics wasevaluated using the COP method. The results obtained were statistically analyzed by determinationcoefficients tomeasurewhich factor has greater importance in the vulnerability index. Furthermore, a newparameter has been designed to measure the vulnerability for the whole of the aquifer. The resultsdemonstrate that COP is a useful method to assess the vulnerability of the test sites under consideration.The results obtained are coherent with the conceptual model of each pilot aquifer and the availablehydrogeological information (hydrographs, isotopic data, tracer tests). Fissured carbonate aquifers (diffuseflow systems) are less vulnerable than karst aquifers (conduit flow systems) and the vulnerability index ismore positively correlated with the O factor (unsaturated zone protection capacity) in the first case. Thekarst aquifers aremore vulnerable than fissured aquifers and they showa higher correlation between the Cfactor (karst features) and the vulnerability index. Climatic variation (precipitation for example) influencesthefinal vulnerability indexof the aquifers according to theweight in the index and the spatial distribution.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

In general, the protection of the environment and its resourcesdepend on a risk analysis of human activities to carry outa sustainable management and development. The risk is the resultof the relations between several components (Zwahlen, 2004;Perles et al., 2009): hazard, vulnerability, exposure, mitigation, etc.

In case of groundwater resources, vulnerability maps of theaquifers are an important tool for the groundwater managementand protection (Zwahlen, 2004). The vulnerability is a componentof the groundwater risk to the contamination that doesn't take intoaccount the human behaviour. Therefore, the prevision (nonprevention) of the risk is possible using vulnerability maps whichcan avoid an inadequate land use.

The groundwater protection in karst aquifer has a specialimportance, because the transit time for unsaturated and saturatedzone is so quickly that the attenuation of the pollutant, e.g. faecaland pathogenic microorganisms, is very poor (Zwahlen, 2004). Thisis also important for groundwater management. In karst aquifers

þ34 952131700.o@uma.es (B. Andreo), Nata-ni-karlsruhe.de (H. Hötzl).

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there are special landforms (dolines, poljes, karrenfield, etc) thatreduce the effectivity of the protective cover and consequentlyincrease the vulnerability.

At present, there are many vulnerability methods (Vrba andZaporozec, 1994; and Zwahlen, 2004), but there is not an interna-tional consensus regarding which method should be used to assessthe vulnerability of the different aquifers. When different methodsare applied for vulnerability mapping it can be difficult to deter-mine which is better suited to the characteristics of the aquifer.

To study this problem, several authors performed statisticalanalyses to determinewhich vulnerability indexes aremost suitablefor any given aquifer. For instance, Corniello et al. (1997) performeda statistical correlation analysis using the results obtained by eachmethod and the vulnerability indexes estimated in the same studyarea. Another technique used, in carbonate aquifers, is sensitivityanalysis (Gogu et al., 2003; Petelet-Giraud et al., 2000), to determinethe importance of the factors considered for a given method toobtain a vulnerability index. Vías (2005) and Vías et al. (2006a)performed a statistical analysis of the factors and parameters withthe vulnerability indexes of severalmethods in two pilot sites. It wasfound that factors related to attenuation capacity present thehighest correlationwith vulnerability indexes in fissured carbonateaquifers, while those related to flowconcentration at the surface aremore highly correlated with vulnerability indexes in karst aquifers.

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e1510 1501

Neukum and Hötzl (2007) analyzed the vulnerability maps for theBauschlotter Platte aquifer by means of quantiles (QQ plots),observing the statistical distribution of vulnerability values withrespect to a reference distribution. Specifically, they found thatdifferences in vulnerability maps are not important when anequivalent classification is used; the large differences found forvulnerability depend on the type of classification used within eachmethod. Other attempts to validate vulnerability maps were madeby Leone et al. (2009), Tolga et al. (2009) and Almasri (2008) bycorrelating the vulnerability maps with the maps of the ground-water quality.

In the present study, the statistical and spatial analysis has beendone for the vulnerability maps using the COP method, (Vías et al.,2006b) in four European test sites. Other applications of the COPmethod have been carried out by Kiros and Zhou (2006) in Ethiopia,Ducci (2007) in Italy, Ravbar (2007) in Slovenia, Dimitriou et al.(2008) in Greece, Leyland (2008) in South Africa and Plan et al.(2008) in Austria. In the majority of the cases, the results areconsistent with the conceptual model of the aquifers and thehydrogeological data. The COP method was developed within theframework of the European COST Action 620 (Zwahlen, 2004) andwas tested in two carbonate aquifers in southern Spain withdifferent climatic and hydrogeological characteristics. The resultsderived from the application of the COP method at these sites arecoherent with the specific hydrogeological characteristics of thesite, and they were checked using different techniques for valida-tion (Andreo et al., 2006; Vías et al., 2006b). Nevertheless, it shouldbe taken into account that the criteria used to check the vulnera-bility maps were based on hydrodynamic, hydrochemical, isotopicand artificial tracer parameters, and that these types of data are notalways available for all aquifers. Moreover, the variety of hydro-geological and climatic contexts at the two southern Spanish pilotsites is not sufficiently representative for a Europe-wide scale.These considerations explain the need for the application of theCOP method to other European regions, to provide a broader viewand to improve the method with regards to the implementation ona European scale, in accordance with the criteria of the COST Action620 on “Vulnerability and Risk Mapping for the Protection ofCarbonate (Karst) Aquifers” (Daly et al., 2002; Zwahlen, 2004). TheEuropean COST Action 620 (Zwahlen, 2004) was carried out todevelop a method for karst vulnerability mapping on a Europeanscale. The comparison done in present is the first comparativeanalysis of several European carbonate aquifers done by one givenmethod for vulnerability mapping.

The aim of the present work is to apply the COP method indifferent European carbonate aquifers and to analyze the vulnera-bility results using statistical and spatial analysis. The study wasapplied to the following carbonate aquifers (Fig. 1): Sierra de Líbar(Spain), Sierra de Mijas (Spain), Bauschlotter Platte (Germany) andthe catchment area of the Podstenj�sek springs (Slovenia).

The test sites differ in geological, hydrogeological and climatecharacteristics, as well as in the degree of karstification (Table 1).Sierra de Mijas has fissured flow behaviour and is located in a semi-aridMediterranean climate domain. The Podstenj�sek catchment andSierra de Líbar are typical karst aquifers where the climate is humidcontinentaleoceanic and semi-humid Mediterranean, respectively.The Bauschlotter Platte aquifer presents hydrogeological character-istics that are in between the previous two, and is located in a humidcontinental climate domain.

2. Characteristics of the test sites

Sierra de Líbar covers a surface area of 90 km2 in a mountainousregion in the south of Spain with abundant rainfall, over 1500 mmper year, which seasonally precipitates during winter time. The

aquifer is formed by Jurassic dolostones and limestones overlain byCretaceous marls and marly-limestones with a thickness of morethan 400 m. The geological structure comprises a major anticlinefold (with a coffer shape) in a N40E direction containing Jurassiclimestones at the core and a central minor syncline occupied byCretaceous marls. The lithological and geological structures deter-mine the existence of a relief characterised by steep slopes at theborders and a plateau in the central sector with abundant karstlandforms (Delannoy, 1987) including karren, sinkholes and poljeswith swallow holes. Predominantly in the northern and central partintermittent/temporary streams infiltrate directly into thelimestones.Most discharge takes place by four springs located alongthe southern border. The available spring hydrographs indicaterapid responses to precipitation with sharp changes in flow(Benavente and Mangin, 1984; Sánchez et al., 1998; Andreo et al.,2006) typical for karst aquifers with conduit flow behaviour. InSierra de Líbar soils overlying the limestones are leptosols(thickness < 30 cm), whereas vertisols have developed over themarls (thickness z 1 m).

The Sierra de Mijas aquifer extends over 100 km2 to the west ofthe city of Málaga, in the south of Spain. The topography is verypronounced except at the northern and eastern borders whereslopes are relatively plane. The average rainfall is 600 mm per year.The aquifer is formed by Triassic marbles highly fractured witha thickness of over 600m overlying Palaeozoic metapelites (Andreoet al., 1997). The geological structure of these marbles is formed byESE-WNW isoclinal folds, which have subsequently been affectedby faults. Karst features are very scarce. At the northern and easternborders of the system Neogene-Quaternary marly and detritalsediments cover the marbles. Two main soil types can be distin-guished: the Pliocene marls are covered by soil with a thicknessamong 70 cm and 1 m and its texture is siltyeclayey, whereas themarbles are covered by leptosol less than 30 cm thick and animportant lateral discontinuity. Groundwater flow is from thecentral part towards the borders, where the main points ofdischarge are located. Hydrograph analyses and chemical data ofthese springs show that the system has diffuse flow behaviour(Andreo, 1997; Andreo et al., 1997).

The Bauschlotter Platte test area is part of the cuesta landscapeof southern Germany and is located between Karlsruhe and Stutt-gart, encompassing approximately 135 km2. The geologicalsequence of the test area consists of Triassic sedimentary rocks(Muschelkalk and Keuper). Limestone occupies most of the area,together with marls and dolostones of the Upper Muschelkalk witha low to moderate karstification. Accordingly, only a few karstfeatures are developed at the surface, and cave systems are notknown in the area. Quaternary loess and alluvial sediments witha highly variable thickness cover the Triassic rocks in a sporadicmanner. In addition, the whole area is covered by soils, witha thickness varying between 0.3 and 1 m. The recharge is mainly bydiffuse infiltration due to precipitation. Concentrated infiltrationpoints are restricted to a few dolines or sinking streams temporarilyfeeding the aquifer, with flow rate of 1e2 L/s (Neukum et al., 2008).The average rainfall is 880 mm per year with a range between1300 mm and 800 mm per year.

The catchment area of the Podstenj�sek springs extends overabout 9 km2 in the south-western part of the Upper Pivka valley insouth-western Slovenia. The catchment area occupies moderatelykarstified Cenomanian limestone and limestone breccias, as well asPalaeocene limestone that are over thrusted over impermeableEocene flysch layers, thus forming a shallow karst aquifer. Due tothe strong and fast hydrologic variations as a result of precipitationevents or snowmelt, the groundwater table fluctuates over severaltens of metres. No permanent surface stream recharges the aquifer.As a result of groundwater fluctuations and weak connections

Fig. 1. Location of the test sites and their general hydrogeological characteristics.

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e15101502

between different karst conduits, two intermittent lakes appearwhenever the groundwater level is sufficiently high (Ravbar, 2007).There are alluvial deposits in the area of the intermittent lakes andperiglacial material is deposited in the dolines of the uplifted dryvalley at the outskirts of the catchment. Shallow chromic Cambisolintertwine with Rendzina layers. The thickest layers of soil (morethan 1 m) are found at the bottom of the concave relief shapes (i.e.dolines, karst depressions), while a patchy soil cover dominates atthe rest of the surface. A large part of the area is overgrown with

forest and meadows and low-intensity pasturing prevails. Precipi-tation is distributed relatively equally throughout the year, andranges between 1500 and 1600 mm per year.

3. Methodology

In the present study, the COP method (Vías et al., 2006b) forvulnerability mapping was applied to the previously mentionedtest sites. The conceptual basis of this method is an integral

Table 1Summary of the main characteristics of the test sites.

Sierra de Líbar Sierra de Mijas Bauschlotter Platte Podstenj�sek

Location South Spain South Spain South west Germany South west SloveniaExtension 90 km2 100 km2 135 km2 100 km2

Mean annual precipitation 1500 mm 600 mm 1100 mm 1550 mmLithology Limestones Marbles Limestones and dolostone LimestonesAge Jurassic Triassic Triassic CretaceousType of Aquifer Karstic Fissured Fissured-karstic KarsticMain karst forms Poljes and karrenfield No karstic form Only one swallow hole Dolines and karrenfielsRecharge Autogenic and allogenic Autogenic Autogenic and allogenic Autogenic and allogenicType of soils Leptosol and vertisol Leptosol and regosol Loess ClayeyThickness of soils <0.5e1 m <0.25 m and <0.5 m >1 m 0e1 mMain land use Forest (quercus)

and scrublandForest (coniferous) andscrubland

Agriculture, meadowsand forest (deciduous)

Forest (coniferous)and meadows

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e1510 1503

interpretation from the European Approach to intrinsic vulnera-bility mapping in karst aquifers (Daly et al., 2002; Goldscheider andPopescu, 2004; Andreo et al., 2006). This European Approach wasdeveloped in the framework of COST Action 620 (Zwahlen, 2004).

According to the aforementioned approach, the COP methodconsiders three factors to assess the resource vulnerability (Fig. 2):Overlying layers (O), Concentration of flow (C) and Precipitationregime (P). The O factor describes the protection provided to thegroundwater by the layers above the saturated zone. Thickness,porosity and permeability properties of the soil and each litho-logical layer of the unsaturated zone are considered. The C factordistinguishes areas of different infiltration conditions, particularly

Fig. 2. General outline of the COP

where infiltrating water bypasses protective layers (Daly et al.,2002). Thus, two scenarios can be distinguished (Fig. 2): Scenario1 considers allogenic recharge from surface runoff catchment area,through swallow holes. Scenario 2 involves the situation whereautogenic recharge occurs. The P factor includes the quantity ofprecipitation and its temporal distribution (intensity). These factorsdetermine the ability of water to transport contaminants from thesurface to the groundwater.

The relation between the factors and the index of vulnerability,for each aquifer, can be obtained statistically using the correlationcoefficients (Kufs, 1992). In the present study, determination coef-ficient (square of Pearson's correlation coefficient) was used to

method (Vías et al., 2006b).

Table 2Values for COP vulnerability classes.

COP vulnerability classes Class values (cl)

Very high 5High 4Moderate 3Low 2Very low 1

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e15101504

measure the variability in the distribution of COP index data, withrespect to the distribution of C, O and P factors score.

The spatial analysis has been carried out by introducing a newGlobal vulnerability (Gv) parameter. This parameter allows tocompare the vulnerability among different test sites and to deter-mine which is most vulnerable. The Gv parameter evaluates theimportance of each vulnerability class on the map by a weightedaverage. In this way, it is possible to determine which aquifer ismost vulnerable with respect to the others and to decide whether itis coherent with the available hydrogeological information of eachaquifer.

The global vulnerability Gv parameter is defined as:

Gv ¼P

cl�w100

where cl is the vulnerability class value and w is the percentage ofsurface for each vulnerability class. COP vulnerability classes havebeen rated according to the values of Table 2. The range of thisparameter varies between 5 (maximum value) and 1 (minimumvalue) and it allows the comparison of the vulnerability betweenseveral aquifers or several methods. The only one restriction whencomparing several methods is that five vulnerability classes mustbe used, (usually, most methods consider five classes).

Fig. 3. Maps of COP index and COP fac

4. Results

4.1. Vulnerability maps

The vulnerability maps of the four test sites show a high vari-ability according to the different environmental characteristics (e.g.geological, hydrogeological, morphological, climatic and othersettings).

The vulnerability of Sierra de Líbar aquifer (Fig. 3) is mainlyclassified as High or Very High, due to the highly developed epi-karst, which minimizes the protective function of the unsaturatedzone. There are many karst landforms such as dolines and karrenthat are highly vulnerable. Poljes of the central part of the Sierra arecovered by low permeable marly limestone and soil that enablesurface runoff to flow towards swallow holes and thus bypassingthe protective layers. At the western edge of the aquifer theprotective soil cover is not developed and the piezometric level isnot too deep (lower than 150m)which consequently results in highlevels of vulnerability. Lower degrees of vulnerability are assignedto areas with outcropping materials of low permeability (marly-limestones and flysch) that do not provide effective drainagetowards swallow holes.

The zonification of the vulnerability classes by COP index inSierra de Líbar ismainly as a consequence of the C factormap (Fig. 3)and secondly, it depends of the O factor map (Fig. 3). The P factormap doesn't contribute to the vulnerability zoning in Sierra de Líbar.

The vulnerability of Sierra de Mijas (Fig. 4) varies fromModerateto Very High where there are outcroppings of carbonated rocks.These variations are due to the differences of the unsaturated zonethickness. Vulnerability is highest where the unsaturated zone is nothicker than about 100 m. Where the unsaturated zone exceeds300 m thick, the vulnerability degree is the lowest. For thicknessbetween 100 and 300m the vulnerability isModerate. Generally, thevulnerability decreases towards the interior of the Sierra where the

tors in the Sierra de Líbar aquifer.

Fig. 4. Maps of COP index and COP factors in the Sierra de Mijas aquifer.

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e1510 1505

unsaturated zone becomes thicker. Low and Very Low vulnerabilityclasses are found at the northern and eastern borders where the lowpermeable Pliocene marls and soils protect the groundwater.

In Sierra de Mijas, the distribution of the vulnerability classes byCOP method depends on the variations in the O factor map (Fig. 4).For both the O map and the COP index map, the vulnerability

Fig. 5. Maps of COP index and COP facto

increases from the centre to the borders of the aquifer rocks. Otherminor variations of the vulnerability classes occur due to the C mapin the permeable layers and to the P map where impermeablelayers outcrop, in the borders of the aquifer (Fig. 4).

In the Bauschlotter Platte aquifer (Fig. 5) vulnerability rangesfrom Moderate to Low where loess sediments and soils cover the

rs in the Bauschlotter Platte aquifer.

51.22

2.70 5.403.9310.50

35.06

50.07 45.1831.68

49.0329.00

2.0712.58

53.69

5.13 0.391.436.52 4.41 0.00

0%

25%

50%

75%

100%

Sierra de Líbar(4.19)

Sierra de Mijas(3.37)

Bauschlotter Platte(2.59)

Podstenjšek (3.56)

Very High High Moderate Low Very Low

Fig. 7. Percent distribution of vulnerability classes on total aquifer surface and globalvulnerability value (Gv) among brackets.

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e15101506

aquifer. These layers increase the protective capacity of the unsat-urated zone. The rest of the area ranges between Moderate andVery High vulnerability, which is mainly due to the lesser thicknessof the unsaturated zone, the absence of protective layers and thepresence of swallow holes and connected sinking streams.

The vulnerability map of the Bauschlotter Platte aquifer dependsmainly on the O map (Fig. 5). Variations are similar both in O andCOP index maps and only in small zones the vulnerability is a resultof the Cmap. The Pmap doesn't establish variations in the classes ofthe vulnerability map.

In case of the Podstenj�sek catchment (Fig. 6) the vulnerabilityranges from Moderate to Very High. This aquifer is moderatelykarstified and the protective capacity of the unsaturated zone isreduced by the bypass effect of karst landforms. Vulnerability isVery High in the central part of the aquifer, where surface drainagefrom intermittent lakes occurs. Vulnerability is also Very Highwhere other exokarstic features that are not covered with soil, suchas karren, and fractured areas in the southern part of the aquifer arepresent. In the rest of the area, where no karst landforms areexposed, vulnerability decreases to High or Moderate (dependingon the vegetation cover). Only a small area of low vulnerability ispresent, where soil and low permeable material increase theprotective capacity.

In this aquifer, the distribution of vulnerability classes dependsmainly on the Cmap (Fig. 6) and, in small areas, on the Omap. The Pfactor is a constant and it is impossible that it shows any variationin the final vulnerability.

4.2. Spatial and statistical analysis of vulnerability maps

To compare the final maps for each aquifer in order to decidewhich of them is more vulnerable is a difficult task due to thesubjective nature of a visual analysis. For this reason, the Gvparameter is proposed to compare the vulnerability of the aquifers.

Fig. 6. Maps of COP index and COP fac

According to the Global vulnerability (Gv) parameter, Sierra deLíbar is the most vulnerable among the investigated test sites. TheGlobal vulnerability value is 4.19 and approximately 50% of thesurface is classified as having a Very High vulnerability level (Fig. 7).The other aquifers only have 5% or less of the same category. On thecontrary, the least vulnerable aquifer is the Bauschlotter Platte,(Gv¼ 2.59), where more than 50% of the surface area is classified asLow and Very Low vulnerability. The other aquifers have less areaclassified as Low or Very Low vulnerability. The aquifers of Sierra deMijas and Podstenj�sek have an intermediate vulnerability level,although the latter test site is more vulnerable because there are noareas classified as Low or Very Low vulnerability.

The most vulnerable aquifers, Líbar and the Podstenj�sek catch-ment (Fig. 7) are highly karstified at the surface, and show conduitflow behaviour. This kind of behaviour implies fast groundwaterflow and short transit time of groundwater and subsequently of

tors in the Podstenj�sek catchment.

Table 3Statistical correlation (coefficient of determination) among vulnerability factors andCOP index.

Correlation between Sierra deLíbar

Sierra deMijas

BauschlotterPlatte

Podstenj�sek

COP index e C factor 0.52 0.30 0.26 0.88COP index e O factor 0.30 0.92 0.74 0.16COP index e P factor 0.00 0.00 0.00 0.00

Table 4Percentage of surface where P factor modifies the vulnerability map.

Sierrade Líbar

Sierrade Mijas

BauschlotterPlatte

Podstenj�sek

Surface without variation (%) 81.10 6.22 77.15 54.61Surface with variation (%) 18.90 93.78 22.85 45.39

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e1510 1507

pollutants. The vulnerability in Podstenj�sek is lower because theaquifer is covered by soils, except in a few exokarstic areas,increasing the transit time with respect to Sierra de Líbar.

The aquifer of Sierra de Mijas is less vulnerable. It is a poorlykarstified aquifer which is mainly fissured. The transit time fromthe surface to the water table is higher and the behaviour is typicalof a diffuse flow aquifer. Therefore, the hydrogeological conditionspresent more attenuation capacity of the potential pollutants andthe vulnerability index is lower than Sierra de Líbar test site.

The behaviour of the Bauschlotter Platte aquifer is mainly ofa fissured flow type, although there are a few areas which havea high velocity of groundwater flow and high vulnerability asa result, but in general it is less vulnerable because the soil and loessprovide important protective cover.

The COP indexof vulnerability has a higher correlationwith the Cfactor in the aquifers of Sierra de Líbar and Podstenj�sek catchment(Table 3). Both aquifers have hydrogeological behaviour similar tokarst aquifers. The fast response of the springs to a rainy event isa consequence of the direct connection (bypass) between topo-graphic surface and springs. These two aquifers have a high degreeof karstification, with abundant exokarstic landforms connectinginputs and outputs of resources in the aquifer and diminishing theprotective capacity of the unsaturated zone. Therefore, if the kar-stification is a significant process for the vulnerability of the aqui-fers, the factors related to karstification are also important. Thecorrelation between the C factor and the COP index shows where(which aquifer) the karstification is an important factor for thevulnerability.

On the maps of Sierra de Líbar (Fig. 3) and the Podstenj�sekcatchment (Fig. 6) there is a visible resemblance between the Cfactor map and the COP index map, primarily in the Podstenj�sekcatchment. In Sierra de Líbar the distribution of the Cmap is similarto the COPmap but changes the values of the classes. Therefore, thecorrelation between the C factor and the COP index is lower inSierra de Líbar with respect to the Podstenj�sek catchment buthigher between the O factor and the COP index (Table 3).

In Sierra deMijas (Fig. 4) and Bauschlotter Platte (Fig. 5), the COPindex is highly correlated with the O factor (Table 3). This factreflects the importance of the protective function of the overlayinglayers and the predominance of diffuse recharge. In Sierra de Mijasthe protective function is provided by the considerably thickunsaturated zone and in the Bauschlotter Platte by the presence ofloess and soil covers. The O factor map and COP index map arehighly similar in these test sites, mainly so in Sierra de Mijas wherethe correlation coefficient between the O factor and the COP indexis higher (0.9).

The correlation between the P factor and the COP index is low inall investigated test sites. This does not imply that the precipitationregime has no influence on the vulnerability of the aquifers. Due tothe very low spatial variation of the P factor, it behaves likea constant value and rarely has correlates with the vulnerabilityindex. However, the P factor influences the assessed vulnerability ofall aquifers (Table 4, Fig. 8).

The P factor depends on two subfactors: the quantity and theintensity of precipitation. The variations of rainfall levels depend on

the climatic context of the aquifer. The intensity subfactor showsmore clearly the variations between aquifers of diverse climaticcontexts, as is the case in the present study. Therefore, the values ofthe Pi subfactor allow us to test the relative importance of the Pfactor in the vulnerability of an aquifer. Thus, in Sierra de Líbar andSierra de Mijas, the value of the Pi factor ranges from 0.5 to 0.7,while in Bauschlotter Platte and in Podstenj�sek catchment, the Pivalues vary between 0.8 and 1.0. These values indicate that the Pfactor has a greater influence on the COP index in Sierra de Líbarand Sierra de Mijas than in Bauschlotter Platte and Podstenj�sekcatchment. In other words, all else being equal, the vulnerability ofthe aquifers, according to the COP method, is greater in a Mediter-ranean climate domain than in a continental or oceanic one, due tothe stormy conditions of rainfall.

5. Attempt to validate vulnerability maps

Vulnerability maps should be validated in order to serve asa basis for land use management and protection of groundwater.Several tools can be used for the validation of vulnerabilityassessments (Zwahlen, 2004), these include; hydrographs, che-mographs, tracers (natural or artificial) and numerical modelling.Some of these techniques have been used to validate the vulnera-bility maps of the pilot sites.

In Sierra de Líbar aquifer, experiments have been done usingartificial tracers (Eosine, Uranine and Sulforhodamine-B) to validatethe intrinsic vulnerability maps (Andreo et al., 2006; Vías et al.,2006b). The results show the travel time of tracers from the injec-tion point (swallow holes) to arrival at a principle spring to bebetween 3 and 5 days and as a result, the travel time through theunsaturated zone can be assumed to be less than 3 days. Flowvelocities calculated were higher than 43 m/h, with a maximumvelocity of 119 m/h. Such data confirms the high vulnerability of theSierra de Líbar aquifer, particularly when surface water infiltratesinto swallow holes (injection points). These points correspond withthe Very High vulnerability class used within the COP method. Onthe other hand, electrical conductivity (EC) and the isotopic signal ofoxygen in rainfall and groundwater, together with spring hydro-graphs, show that recharge events reach the springs very quickly(less than 3e4 days after the rain) producing rapid increase in flow,from almost zero to several cubic metres per second. This suggeststhat transit times are short due to the development of karsticdrainage (conduit flow) within the calcareous rocks of the Sierra deLíbar aquifer. Although natural tracers provide information on theentire system and artificial tracers provide information only onselected points, the high vulnerability of the Sierra de Líbar aquifer,as assessedbyCOPmethod, is consistentwith actual knownepisodesof groundwater contamination. The contamination is mainly frommicrobiological origin and provokes some punctual diseases such asdiarrhoea, gastroenteritis (Andreo et al., 2006). The variations ofchemical components are low during the episodes of contamination,only Cl� andNO3

� increase a little. These episodes are normally shortin time and are detected where water is abstracted from the aquiferto supply the village of Benaoján (see Fig. 1).

In the Sierra de Mijas aquifer, no tracer tests have been carriedout, although data from natural tracers are available. Historical

Fig. 8. Areas where the vulnerability obtained by COP method varies due to the P factor in the aquifers of sierra de Líbar (A), Sierra de Mijas (B), Bauschlotter Platte (C) andPodstenj�sek catchment (D).

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e15101508

discharge data from the springs shows that infiltration occurs slowlyand that the springs may remain in a state of depletion for severalyears. Correlatory and spectral analysis corroborates the observationthat the system possesses a high inertia. Electrical conductivity datafrom the springs showanarrow range of variation. Tritiumdata fromthe springs indicate that some of the groundwater abstracted insomewells during 1994 originated from recharge which occurred in1986 (Andreo,1997). Groundwaterwithin the Sierra deMijas aquifershows no evidence of contamination, despite extensive threat fromhuman activity across the whole of the aquifer outcrop (e.g. landfill,waste pipe lines in urban areas and petrol stations). The assessmentof moderate vulnerability for the aquifer outcrop, arising from use ofthe COP method, is coherent with former data (Vías et al., 2006b).This moderate vulnerability is a result of: a thick unsaturated zone,relatively low hydraulic conductivity, strong hydraulic inertia andtherefore diffuse flow behaviour.

In Bauschlotter Platte, Neukum et al. (2008) considered for vali-dation field techniques including hand-auger drillings, interpreta-tion of archived borehole data, geoelectrical soundings and soilthickness estimation by geostatistical analysis. In addition, resultson the hydraulic properties were considered such as tracer experi-ments both in the unsaturated and saturated zone, analysis of springhydrographs and pumping tests, analysis of stable isotopes andtritium measurements. Thus, for example, the pumping tests giveaveraged transmissivities of 4 � 10�4 m2/s, which means that karstconduits are uncommon. However, tracer tests at one of the sinkingstreams have revealed very fast subsurface connections over largedistances with velocities between 120 and 200 m/h (Tenhäff andKäss, 1987). The stable isotope (d18O and d2H) investigation on thespring water shows no seasonal variations, due to low karstificationdegree at global scale. The calculation of tritium content in thespring yields a mean transit time between 5 and 6 years, althoughthe uncertainty of the calculation is relatively high since the actualtritium content in rainfall is approaching the natural background

level and the measurement accuracy in the groundwater samplesdecreases with time. Neukum et al., 2008 integrated all this infor-mation in a numerical modelling of flow transport to validatevulnerability mapping. The results obtained by COP method inpresent work are compatible with the hydrogeological data andmodelling performed by the aforementioned authors. Thus, thevulnerability is higher in the catchment area of sinking stream andover limestone outcrops where the unsaturated zone has lowthickness, which is consistent with the higher velocity of flowdeduced from tracer test. In the rest of the area, the loess and soilscover the aquifer increasing the protective capacity of unsaturatedzone and decreasing vulnerability to lower classes, which isconsistent with the transmissivities values deduced form pumpingtest and stable isotope (d18O and d2H) and tritium.

In the Podstenj�sek catchment area, Ravbar (2007) includedoutflow as well as electrical conductivity records and temperatureof the main spring waters. In response to the rainfall, these datashow high variations in the flow rate of the Podstenj�sek springranging between 6 L/s and 1600 L/s, and also significant variationsin the temperature (9e10.6 �C) and electrical conductivity (366 and487 mS/cm), whichmeans high functional karstification degree. ThePodstenj�sek spring water is satisfactory from hydrochemical pointof view but bacteria (Escherichia coli and Streptococcus faecalis) havebeen detected in the analysis, particularly during flood periods,which are indicators of faecal contamination (Rabvar, 2007).However, Ravbar and Goldscheider (2009) considered the tracertest as the suitable tool for validation of vulnerability mapping.Sulforhodamine-B and Eosine were injected in the Podstenj�sekcatchment area and they were detected in the springs after 68e72and 80e144 h, respectively. The velocity of flow deduced from thisexperiment varies between 25 and 50 m/h as averages values ofreference. The results obtained from tracer experiments arecompatible with the vulnerability map deduced from COP method,although there is another method (Slovene approach) specially

J. Vías et al. / Journal of Environmental Management 91 (2010) 1500e1510 1509

developed for karst aquifer of this country (Ravbar, 2007), whichresults seem more consistent for the instance.

6. Conclusions

COP method has been applied in four karst aquifers underdifferent hydrogeological characteristics and located in diverseEuropean climatic domains, and the obtained results are coherentwith the previous hydrogeological information. The Sierra de Líbaraquifer has a higher level of vulnerability than the other aquifersmainly due to its high karstification degree, which reduces theprotective function of the overlaying layers. Also the effect ofbypassing the more protective layers by swallow holes and relatedkarst features diminish the overall protective function of theunsaturated zone. The vulnerability of the Podstenj�sek catchmentis also High. However, the presence of soil and sediments reducesthe vulnerability compared to Sierra de Líbar. The vulnerability ofthe Sierra de Mijas aquifer is classified mainly as Moderate to Highdue to the protective capacity of the unsaturated zone with theabsence of karstic landforms on the surface. The Bauschlotter Platteaquifer with the presence of soils, alluvial sediments and loessdeposits has a lower vulnerability classification compared to theother investigated areas. In any case, the results obtained by COPmethod (COP maps) are useful for improving the protection ofwater resources and land use planning.

The statistical correlation between the input factors and the COPindex in each aquifer is also coherent with the hydrogeologicalcharacteristics of the aquifers. In Sierra de Líbar and Podstenj�sekcatchment, the presence of karst landforms on the surface meansthat the most important factor in the distribution of the COP indexis the C factor. In Sierra deMijas and Bauschlotter Platte the absenceof karst landforms and the effect of the unsaturated zone forgroundwater protection mean that the O factor presents thehighest degree of statistical correlation. Therefore, in karst aquifersthe spatial distribution of the vulnerability depends to a largeextent on variations in the C factor, while in fissured aquifers, itdepends more on the O factor. For an aquifer between karstic andfissured (for instance Bauschlotter Platte is not really a fissuredaquifer) it is difficult to say, without a previous analysis, whichfactor must be more highly correlated with the vulnerability.

The P factor does not influence the distribution of the vulnera-bility classes although it affects the index and the vulnerabilityclasses and, consequently, this parameter should be considered inthe vulnerability assessment. This factor presents spatial variationsover Europe, but not at a local or regional scale and there is nocorrelation of this factor with the index, according to the availabledata. This means that the higher the rainfall variations the higherare the influence of P factor. Topography provokes significantspatial variations in rainfall amount even at local scales, but thesechanges are not usually captured in measurements due to the lackof an adequate number of rain stations.

The proposed Global vulnerability parameter (Gv) permits tocompare the importance of the vulnerability classes in each aquifer.The vulnerability of every aquifer has been correctly sorted by thisparameter. The quickness and facility to use this kind of analysis(spatial and statistical) was an advantage with respect to othertypes of analyses. However, an important disadvantage of thisanalysis was with regards to the validity of the results obtained. Itcannot be possible to validate the results, only was obtained aboutthe validity of the method and the vulnerability of the aquifers.

Acknowledgments

This work is a contribution to the projects CGL2005-05427 andCGL2008-06158BTEof the SpanishMinistryof Education and Science,

IGCP 513 of UNESCO and P06-RNM-02161 of Junta de Andalucía, andtoResearchGroupsRNM308 andHUM776of JuntadeAndalucía. Oneof the authors (NR) thank to Slovenian Ministry of Higher Education,Science and Technology in the frame of the Young ResearchersProgramme. The authors thank Dr. Christoph Neukum and anony-mous reviewers for their constructive comments and criticisms.

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