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AGIS/RemoteSensing-basedmethodologyforgroundwaterpotentialityassessmentinTirnavosarea,Greece

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DimitriosOikonomidis

AristotleUniversityofThessaloniki

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N.Kazakis

AristotleUniversityofThessaloniki

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K.Voudouris

AristotleUniversityofThessaloniki

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Availablefrom:N.Kazakis

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Journal of Hydrology 525 (2015) 197–208

Contents lists available at ScienceDirect

Journal of Hydrology

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

A GIS/Remote Sensing-based methodology for groundwater potentialityassessment in Tirnavos area, Greece

http://dx.doi.org/10.1016/j.jhydrol.2015.03.0560022-1694/� 2015 Elsevier B.V. All rights reserved.

⇑ Corresponding author.E-mail addresses: oikonomi@geo.auth.gr (D. Oikonomidis), sdimogianni@gmail.

com (S. Dimogianni), kazanera@yahoo.com (N. Kazakis), kvoudour@geo.auth.gr (K.Voudouris).

D. Oikonomidis a,⇑, S. Dimogianni a, N. Kazakis b, K. Voudouris b

a Aristotle University of Thessaloniki, School of Geology, Laboratory of Remote Sensing and GIS Applications, Greeceb Aristotle University of Thessaloniki, School of Geology, Laboratory of Engineering Geology & Hydrogeology, Thessaloniki, Greece

a r t i c l e i n f o s u m m a r y

Article history:Received 20 January 2015Received in revised form 25 February 2015Accepted 29 March 2015Available online 2 April 2015This manuscript was handled by Peter K.Kitanidis, Editor-in-Chief, with theassistance of Magdeline Laba, AssociateEditor

Keywords:Groundwater explorationGISRemote sensingThessalyWater suitability mapping

The aim of this paper is to assess the groundwater potentiality combining Geographic InformationSystems and Remote Sensing with data obtained from the field, as an additional tool to the hydrogeologi-cal research. The present study was elaborated in the broader area of Tirnavos, covering 419.4 km2. Thestudy area is located in Thessaly (central Greece) and is crossed by two rivers, Pinios and Titarisios.Agriculture is one of the main elements of Thessaly’s economy resulting in intense agricultural activityand consequently increased exploitation of groundwater resources. Geographic Information Systems(GIS) and Remote Sensing (RS) were used in order to create a map that depicts the likelihood of existenceof groundwater, consisting of five classes, showing the groundwater potentiality and ranging from veryhigh to very low. The extraction of this map is based on the study of input data such as: rainfall, potentialrecharge, lithology, lineament density, slope, drainage density and depth to groundwater. Weights wereassigned to all these factors according to their relevance to groundwater potential and eventually a mapbased on weighted spatial modeling system was created. Furthermore, a groundwater quality suitabilitymap was illustrated by overlaying the groundwater potentiality map with the map showing the potentialzones for drinking groundwater in the study area. The results provide significant information and themaps could be used from local authorities for groundwater exploitation and management.

� 2015 Elsevier B.V. All rights reserved.

1. Introduction been found that remote sensing, besides helping in targeting

Groundwater varies spatially and temporally and since it is themost valuable source of water, it should be studied more thor-oughly concerning its’ evaluation and potentiality. For sustainabledevelopment of water resources it is necessary to identify areaswhere groundwater replenishment is performed (Evaggelopoulos,2005). Remote Sensing (RS) and Geographic Information Systems(GIS) can prove useful tools in groundwater exploration mapping.A number of papers have been published concerning applicationsin Hydrogeology, among them: Tweed et al. (2007) and Leblancet al. (2007) described how RS data and GIS can be used to estimaterecharge and discharge areas and surface and groundwater interac-tion. Entekhabi and Moghaddam (2007) presented the estimationof groundwater recharge using RS observations of soil moisture.

Remote sensing (RS) through the delineation of lineaments hasan immense importance in hard rock hydrogeology as it can iden-tify rock fractures that localize groundwater (Das, 1990). It has also

potential zones for groundwater exploration, provides inputstoward estimation of the total groundwater resources in the areaand the selection of appropriate sites for drilling or artificialrecharge. RS is a useful tool in areas where the coverage of detailedgeological and hydrogeological maps and field data is insufficient(Hoffmann and Sander, 2007; Srivastava and Bhattacharya, 2006).Sander (2007) gave a general overview of lineament mappingand interpretation using RS data for groundwater explorationreferring the limitations in semi-arid hard rock areas.

Integration with Geographic Information Systems (GIS) allows asynergistic processing of multi-source spatial data. Using of GIS inhydrogeology is only at it is beginning, but there have been suc-cessful applications that started to develop (Sener et al., 2005;Howari et al., 2007; Nagarajan and Singh, 2009). Use of GIS forgroundwater potentiality mapping offers the ability to store,manipulate and analyze data in different formats and at differentscales (Rahman, 2008). Once in the database, it is then possibleto register all data as data layers with a common coordinate sys-tem and manipulate them to produce thematic maps, includingthe overall study area vulnerability map (Voudouris, 2009; Seneret al., 2009; Voudouris et al., 2010).

198 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

In recent years, many techniques have been applied by severalresearchers to map the groundwater potentiality and/or the siteselection for drinking-water boreholes. Gupta and Srivastava(2010) used RS and GIS to identify groundwater potential zones.Al Saud (2010) proposed a method to map potential areas forgroundwater storage using RS and GIS taking into account the fac-tors: rainfall, lithology, rock fractures, slope, drainage and landcover/use. Elewa and Qaddah (2011) developed a method forgroundwater potentiality mapping in the Sinai Peninsula (Egypt),using RS and GIS. They used eight parameters: rainfall, net ground-water recharge, lithology or infiltration, lineament density, slope,drainage density and depth to groundwater. Konkul et al. (2014)applied a similar method to map the hydrogeological characteris-tics and groundwater potentiality of Huay Sai area (Thailand),using potential surface analysis. Antonakos et al. (2014) appliedmulticriteria analysis within GIS environment in order to producea distribution map of site suitability for drilling new productionboreholes in Korinthia Prefecture (Greece). The most importantof these criteria relate to the productivity of the aquifers (hydraulicproperties, recharge, etc.), groundwater quality, and economic andtechnical issues (drilling cost, morphology, site accessibility, etc.).

This paper presents the study of groundwater potentiality map-ping of the broader Tirnavos area (Thessaly region, central Greece)by modification of a previously applied methodology (Elewa andQaddah, 2011) with the contribution of Remote Sensing andGeographic Information Systems, which aims to establish a supple-mentary or amending tool in locating groundwater. In addition athematic map showing the potential zones with drinking ground-water was illustrated. For this reason rainfall data, hydrogeologicaland hydraulic characteristics of aquifers, hydromorphological,lithological and groundwater quality data were used.Furthermore, the reliability of groundwater potentiality mappingwas controlled by using drilling data.

2. Location, physical background and hydrogeology of the studyarea

The study area is located in Central Greece, in the region ofThessaly, occupying an area of 419.4 km2 (Fig. 1). The geomorpho-logical development of the region is characterized by mild to rela-tively high relief with altitudes ranging from 27 m to 886 m. Thearea belongs to the Mediterranean climate type, Csa (Balafoutis,1977) and is characterized by hot and dry summers and mildand wet winters. The mean temperature of the area is 14.2 �C,showing maximum value in July (25.1 �C) and minimum value inJanuary (4.7 �C). Land use is predominantly agricultural in low-lands and the usage of groundwater for irrigation has beenincreased during the last decades.

Pinios river, which gathers almost all the runoff of theThessalian plain and has a total length of 205 km, dominates inthe drainage network of the wider region. The drainage networkof Thessaly is relatively simple because of its morphology. In factit is a deep basin, surrounded by high mountains, which meansthat the rain water cannot be obtained easily. According toEvaggelopoulos (1974), the alluvial basin owes its’ rich ground-water to Titarisios River, which also crosses the area. The alluvialaquifer accepts abundant supply during winter, which is localizedmainly upstream of the river. The central part of the basin ofLarissa has accepted the effect the Pinios river. The northwesternpart of the basin of eastern Thessaly is a creation of Titarisios riverof debris, corrosion products of the basin, which were depositeddownstream of Titarisios, during the exit from the mountain com-plex, to the tectonic draft generated in the wider area of Tirnavos.

Concerning its’ lithology, it is subsumed in the Pelagonian zoneand consists of fluvial terraces, fluvio-terrestrial formations, scree,

schists, gneisses, eluvial mantle, alluvial deposits and marbles(Fig. 1). The geological bedrock consists of crystalline moschoviticschist, which is almost impermeable. Limestones and marbles atthe western margin of the alluvial area are 400–500 m thick andtheir permeability factor is high, due to existence of intense karstphenomena and tectonics. The main aquifer systems are developedwithin alluvial deposits and marbles. The alluvial deposits are pre-sent in the majority of the area. They are divided into two aquifers,the deeper one (300 m thick) and the shallow one (70 m thick),separated by an impermeable layer, 50 m thick. An additionalrecharge of alluvial aquifer from the karstified aquifer takes placeby the NW-lying hydraulic contact, as showed from the hydrogeo-logical cross section (Fig. 1).

3. Materials–methodology

For the implementation of this work, the following data andsoftware were used:

� Geological maps covering the study area (Sheets: Larisa,Gonnoi, Elassona, Farkadona, 1:50,000 scale, source:Institute of Geology and Mineral Exploration/IGME).

� Landsat-7/ETM + satellite image, acquisition date: 28/01/2000 (URL1).

� Digital Elevation Model/DEM from ASTER satellite (ASTER/GDEM), horizontal spatial resolution 30 m (URL2).

� Meteorological, climatological, groundwater level mea-surements and chemical analyzes of groundwater samples.

� Image processing software: ENVI 4.8� GIS software: ArcGIS 10.1.

Geological maps of IGME were scanned, imported into ArcGIS10.1 and georeferenced to the UTM/WGS84 projection system. Byusing ENVI 4.8 software, the bands of the satellite image were ini-tially ‘‘layer stacked’’, georeferenced, then the file was resized sothat only the broader study area is included, then it was radio-metrically corrected (log-residuals option) and finally a properfalse color composite image was created. Then, the creation of the-matic maps took place, using rainfall, recharge, lithology, linea-ment density, slope, drainage network and depth to groundwatertable. A weighted spatial probability modeling was applied foridentifying groundwater potential areas, according to their rele-vance to the existence of groundwater. Eventually, a groundwaterpotentiality map was created, consisting of five gradational poten-tiality classes, ranging from very low to very high (Fig. 2).

The mathematical method of Analytical Hierarchy Process(AHP), which was introduced by Saaty (1980), was used to derivethe final groundwater potentiality map. The AHP method has beenapplied in many hydrogeological studies for site suitability analysis(Banai-Kashani, 1989; Pourghasemi et al., 2012). To do so, theindividual groundwater potentiality factors, were given values(weights) according to their significance. In order to achieve this,all the factors were paired with each other and following that, eachfactor was given an arithmetic value between 1 and 9, according toits’ significance when compared to the other factor, with which itformed the pair (Table 1). In the resulting table, an arithmetic valueof 9 indicates that a row factor is much more significant than thecorresponding column factor with which it has been compared,while an arithmetic value of 1 means that both factors are equallysignificant. Fractional values are also possible, indicating that a fac-tor is less significant when compared to the factor with which ithas been paired. After the completion of Table 1, the arithmeticmean method has been applied to its results (Table 2). For exam-ple, the value of 0.33 (lithology column crossed with rainfall lineof Table 2), resulted after dividing value 1 with value 3 (total) from

Fig. 1. Location and geology of the study area.

D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208 199

the lithology column of Table 1. In that way, the weights for eachpreparatory factor were calculated (mean column of Table 2).

Then, the groundwater potentiality map was produced, inaccordance with the mathematical equation (1):

M ¼ w1X1 þw2X2 þw3X3 þw4X4 þw5X5 þw6X6 þw7X7

þw8X8 ð1Þ

where M is the value for each pixel of the final groundwater poten-tiality map of the study area. Variables w1, w2, w3, w4, w5, w6, w7

and w8 are the weight values for each preparatory factor and vari-ables X1, X2, X3, X4, X5, X6, X7, and X8 are the rating values for eachpixel according to the preparatory factor to which it is referred(Domakinis et al., 2008).

The quantitative parameters were classified according to thegrading method of equal intervals of ArcGIS 10.1. This methodhas been used in the classification of hydrogeological studies and

the classification of vulnerability degrees (Huan et al., 2012;Kazakis, 2013; Kazakis and Voudouris, 2015).

4. Results

The eight factors for groundwater potentiality mapping (rain-fall, potential recharge, lithology, lineament density, slope, drai-nage density and depth to groundwater are examined separatelyin the following paragraphs. The thematic maps portray the eightfactors that are extracted for the calculation of the final map. Thevalues’ range was reclassified into five classes, based on theweighted spatial probability modeling, with equal intervals(Table 3). The reclassification was performed based on the poten-tiality of groundwater existence. The factors’ weights used arerelated to the participation of each factor on the groundwaterentrapment. All maps use the same classification, however theydon’t contribute to the same extent. The weights were adopted

Fig. 2. Flow-chart of operations performed.

Table 1Pair wise comparison of the preparatory factors that affect groundwater potentiality.

Rainfall Lithology Potential recharge Slope Density of lineaments Density of drainage network Depth to groundwater

Rainfall 1 1 3 3 9 9 9Lithology 1 1 3 3 9 9 9Potential recharge 1/3 1/3 1 1 7 7 7Slope 1/3 1/3 1 1 7 7 7Density of lineaments 1/9 1/9 1/7 1/7 1 1 1Density of drainage network 1/9 1/9 1/7 1/7 1 1 1Depth to groundwater 1/9 1/9 1/7 1/7 1 1 1Total 3 3 8.4 8.4 35 35 35

Table 2Calculation of factor weights (in combination with Table 1).

Rainfall Lithology Potentialrecharge

Slope Density oflineaments

Density of drainagenetwork

Depth togroundwater

Mean(weight)

Rainfall 0.33 0.33 0.36 0.36 0.26 0.26 0.26 0.30 (30%)Lithology 0.33 0.33 0.36 0.36 0.26 0.26 0.26 0.30 (30%)Potential recharge 0.11 0.11 0.12 0.12 0.20 0.20 0.20 0.15 (15%)Slope 0.11 0.11 0.12 0.12 0.20 0.20 0.20 0.15 (15%)Density of lineaments 0.04 0.04 0.02 0.02 0.03 0.03 0.03 0.04 (4%)Density of drainage

network0.04 0.04 0.02 0.02 0.03 0.03 0.03 0.04 (4%)

Depth to groundwater 0.04 0.04 0.02 0.02 0.03 0.03 0.03 0.04 (4%)

200 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

and optimized from the results of experience or judgments ofexperts in previous similar works on groundwater potentialitymapping (Elewa and Qaddah, 2011). The effectiveness (last columnof Table 3) of each factor-parameter is calculated by multiplying itsweight by the rate (Al Saud, 2010). Factors with equal value ofeffectiveness have the same effect on groundwater potentiality.

In previous applications of the above method, an extra factor,the total dissolved solids (TDS), has been used for the assessmentof groundwater potentiality (Elewa and Qaddah, 2011; Konkulet al., 2014). TDS represents the summation of the ions in ground-water and is considered as a groundwater quality aspect. TDS val-ues range from 205 to 1100 mg/L in the study area. The lowerconcentrations are observed in the western part of the study area,in marbles, while in contrast with the higher values, which areobserved in the sedimentary formation in the southern and in a

small part in the center of the area. In the present application thisfactor is omitted, assuming that the groundwater quality is irrele-vant factor to assess the groundwater potentiality. Furthermore,the complexity of the method is decreased by the smaller numberof the factors.

4.1. Rainfall

This factor is one of the most important and was assigned aweight of 30% (0.30) in the final groundwater potentiality value.The higher the rainfall, the higher the groundwater potentiality.During the present study, monthly rainfall data were collectedfrom 4 stations of the wider area in combination with the DigitalElevation Model (DEM). The mean annual rainfall ranges from434 to 499 mm in the lowland areas, while in the mountainous

Table 3Ranks and weights for factors used for groundwater potentiality mapping.

Parameters Class Average rating (R) Weight (W) Effectiveness

Rainfall Very high 639–678 90 30% (0.30) 27High 598–638 70 21Moderate 560–598 50 15Low 520–559 30 9very low 480–519 10 3

Lithology Very high Unit E 90 30% (0.30) 27High Unit D 70 21Moderate Unit C 50 15Low Unit B 30 9Very low Unit A 10 3

Potential recharge Very high 186–214 90 15% (0.15) 13.5High 159–186 70 10.5Moderate 132–159 50 7.5Low 105–132 30 4.5Very low 77–105 10 1.5

Slope Very high <2 90 15% (0.15) 13.5High 2–5 70 10.5Moderate 5–15 50 7.5Low 15–35 30 4.5very low >35 10 1.5

Lineament density Very high 6.7–8.4 90 4% (0.04) 3.6High 5.1–6.6 70 2.8Moderate 3–3.5 50 2.0Low 1.6–3.3 30 1.2Very low 0–1.6 10 0.4

Depth to groundwater Very high >165.4 90 4% (0.04) 3.6High 124.3–165.4 70 2.8Moderate 82.9–124.2 50 2.0Low 41.8–82.8 30 1.2Very low <41.7 10 0.4

Drainage density Very high <2.8 90 4% (0.04) 3.6High 2.9–5.8 70 2.8Moderate 5.9–8.8 50 2.0Low 8.9–11.7 30 1.2Very low 11.8–14.6 10 0.4

D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208 201

areas ranges from 619 and 749 mm. Thus, an increase of annualrainfall with the altitude was identified. A regression line indicatesthe following relationship between (P in mm) and the altitude (h inm): P = 0.29 h + 474. The above relationship was used to illustratethe rainfall map. The resulting map was classified into five majorclasses (Table 3 and Fig. 3): 639–678 mm/yr (Very High), 599–638 mm/yr (High), 560–598 mm/yr (Moderate), 520–559 mm/yr(Low), and 480–519 mm/yr (Very Low). From the map of rainfall,it can be observed that in areas with higher altitude and rainfall,greater potentiality of obtaining water exists, in comparison toareas of lower altitude. About 67–78% of annual rainfall occurs in5–6 months (October–April next year), while summers are usuallydry. It is estimated that about of 63% of annual rainfall come backto the atmosphere via the process of evapotranspiration.

4.2. Potential recharge

Potential recharge indicates the amount of water that can bereached the water table, adding to groundwater storage.Groundwater recharge represents a significant factor on ground-water potentiality mapping and occurs mainly via the followingmechanisms: direct infiltration of rainfall and infiltration throughriver beds. The distribution of recharge areas are related toincreased rainfall, increased infiltration capacity of the soil, veg-etation type and the presence of surface-water bodies (rivers andtorrents). Potential groundwater recharge factor was assigned aweight of 15% (0.15) in this analysis and can be estimated as theresidual term from the total rainfall minus total evapotranspiration(Konkul et al., 2014):

R ¼ P � ET ð2Þ

where R is the potential groundwater recharge, P is the averageannual rainfall and ET is the actual evapotranspiration (all termsin m3/yr).

For the study of this factor the rainfall and the actual evapotran-spiration estimated by Thornthwaite method were used. Thismethod estimates the actual evapotranspiration in respect to theseasonal variation of temperature and rainfall. The spatial dis-tribution of rainfall and actual evapotranspiration was mappedusing raster calculator (GIS) and the relationship between theseparameters and the elevation. The illustrated map (Fig. 3) indicatesthe potential amount of water that can be added to groundwaterresources. Therefore, it is observed that in the area consisting ofmarbles, the recharge rate is higher than the recharge rate of thearea consisting of gneisses.

4.3. Lithology

The lithology factor (weight 30% or 0.30) is associated with thewater permeability and the ability of the formations to hostgroundwater. The fracture systems, joins, dykes and porosity areinfluence the capacity and specific storage of groundwater amongthe various rock types. The sedimentary aquifers, with primaryporosity, have higher capacity and specific storage of groundwaterthan the karst and fissured rock aquifers in which the groundwaterinteresting is locally and predominately in faults and fractures. Thegeological map was derived from the available geological maps

Fig. 3. Distribution of groundwater potentiality (first pair of numbers in legend), based on rainfall (left) and recharge of lithological formations (right), divided into classes.

Fig. 4. Distribution of the groundwater potentiality (first pair of numbers in legend), based on lithology (left) and lineaments density (right), divided into classes.

202 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208 203

with scale 1:50,000 from IGME. The different rocks were digitizedas polygons and thus the thematic map was produced.

Five hydrolithological types were detected according to infiltra-tion coefficient and their hydrogeological characteristics of theequivalent rock of the area in combination with lithological mapof Fig. 1. Consequently, the map of Fig. 4 was created and dividedinto classes. Schists, amphiboles and gneisses are impervious geo-logical formations and create a barrier to groundwater infiltrationwhile they have low storativity capacity of groundwater. Therefore,a rating value of 20 was adjusted to these formations (lithologicalunit A). Lithological unit B includes fluvio-lacustrine formationswith a rating of 40. Despite the fact that marbles of the area havehigh infiltration coefficient, a rating of 60 was given owing to theirlocal interest of groundwater (lithological unit C). This lithologicalunit C is supplemented with the recent fluvial terraces and scree-talus cones. The lithological unit D was rated with 80 and com-prised of conglomerates, elouvial mantle, and fluvio-terrestial for-mations. The formation (lithological unit E) with the highest rating(100) is alluvial deposits due to the high infiltration coefficient andthe high storativity capacity of groundwater.

Fig. 5. Landsat-7/ETM+

4.4. Lineament density

A lineament is a linear feature in a landscape which is anexpression of an underlying geological structure such as a fault.For the extraction of lineaments’ density map, a satellite imageLandsat-7/ETM+ was initially processed in ENVI 4.7, in order todetermine the most appropriate False Color Composite/FCC imagefor lineaments’ delineation. Combination of bands 753: RGB (Red/Green/Blue) proved to be the most suitable for this purpose (Fig. 5).After the lineaments’ (satellite image) and faults’ (geological map)digitization, their density was calculated in ArcMap10.1 (LineDensity command). Next, a reclassification of the raster linea-ment-density file followed, into 5 classes, from very low to veryhigh, according to the class boundaries of Table 3, yielding Fig. 4map. The lineament density raster file was assigned a weight of4% (0.04) in the calculation of the groundwater potentiality finalmap, according to AHP methodology. It can be noticed that in thearea of red color, where no faults and lineaments exist, the poten-tiality of the presence of groundwater is low, in contrast with thegreen - colored area, where the probability reaches the maximumlevel.

image 7, 5, 3: RGB.

Fig. 6. Distribution of the groundwater potentiality (first pair of numbers in legend) based on slope (left) and drainage network density (right), divided into classes.

204 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

4.5. Slope

Slopes were produced after the ASTER/GDEM in ArcMap 10.1.The values (in degrees) were reclassified into classes accordingDemek’s classification (1972) as shown in Table 3. In general,slopes rule the ability of surface water to remain on the surfacelong enough to infiltrate or will continue to flow. Usually, the steepslopes indicate greater water velocity. Therefore, it is observed thatin the areas of steeper relief the runoff is increased. This in turnminimizes the degree of groundwater recharge (Doll et al., 2002).On the contrary, on the relatively gentle sloping terrains, thegroundwater potentiality increases due to greater infiltration, thatis the lower the slope, the greater the recharge (Fig. 6). The slopefactor was assigned a weight of 15% (0.15) in the calculation ofthe groundwater potentiality final map, according to AHPmethodology.

4.6. Drainage density

Drainage density is the total length of all the streams and riversin a drainage basin divided by the total area of the drainage basin.It is a measure of how well or how poorly a watershed is drainedby stream channels. The drainage network of the study area wascreated from the ASTER/GDEM through a commands’ sequence inArcMap 10.1 and its density (Fig. 6) was calculated using the‘‘Line Density’’ command. Next, a reclassification of the raster drai-nage network-density file followed, into 5 classes, from very low tovery high, according to the class boundaries of Table 3, yieldingFig. 6 map.

The drainage network that was produced is of dendritic pattern,resultant by water flowing in a homogenous soils’ surface, withoutabrupt changes in geological conditions. According to geomorpho-logical knowledge, the denser the drainage is, the less is the

recharge rate and vice versa. Hence, in the green-colored areasthe groundwater potentiality is higher than in the areas of highdrainage density. The raster drainage network density file wasassigned a weight of 4% (0.04) in the calculation of the ground-water potentiality final map, according to AHP methodology.

4.7. Depth to groundwater

The depth to groundwater is defined as the distance from theground surface to the water table and determines the cost of waterconsumption and abstraction. The water table is highest in April–May and lowest in early October depending on meteorological con-ditions. In general, the deeper the water level is, the greater thecost takes to abstract and exploit the groundwater. This factorwas given a weight of 4% (0.04) in this analysis. Water level mea-surements from 58 boreholes during the period of May 2011 wereused to illustrate the piezometric map of the study area. The depthto groundwater in the alluvial aquifer ranges from less than 3 tomore than 25 m below ground surface, while the depth in the mar-bles aquifer ranges between 15 and 206 m. The resulting map wasclassified into five major classes (Table 3 and Fig. 7): >165.4 m(very high), 124.3–165.3 m (high), 82.9–124.2 m (moderate),41.8–82.8 m (low), and <41.7 m (very low). In the green-coloredareas the level is lower, therefore there is a higher potentiality ofwater in shallower depths. In the red-colored areas, the levelreaches great depth leading to greater difficulty in extractinggroundwater.

5. Final maps – discussion

After the procession of all aforementioned factors, the final mapof potentiality of groundwater existence in Larisa – Tirnavos areawas constructed. The procedure followed is based on multiplying

Fig. 7. Distribution of the groundwater potentiality (first pair of numbers in legend) based on depth to groundwater, divided into classes.

D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208 205

the groundwater potentiality raster file of each factor (Rfi) on theweight (Wfi) assigned to each factor, according to the followingequation (Elewa and Qaddah, 2011); E being the final groundwaterpotentiality value:

E ¼X8

i¼1

ðWfi � RfiÞ ! E

¼ 0:30� Rainfallþ 0:30� Lithologyþ 0:15� Recharge

þ 0:15� Slopeþ 0:04� Lineaments and faultsþ 0:04

� Drainage networkþ 0:04� Depth of groundwater ð3Þ

The resulting values were reclassified into five classes withgroundwater potentiality from very low to very high owing tothe grading method of equal intervals (Fig. 8). This is attributedas: <24–38% (very low), 38–50% (low), 50–62% (moderate), 62–

74% (high) and 74–86% (very high). According to the final map,the area covered by the above classes was calculated (Fig. 9). Itappears that areas of high potentiality occupy an area of46.5 km2, while very low potentiality occurs in an area of0.6 km2. Moderate groundwater likelihood occurs in an area of195.4 km2, which covers the largest part of the study area.

The lithological formations with the highest potentiality forgroundwater are the alluvial deposits and a part of the marblesin the northwest part of the mountainous part. The high potential-ity for groundwater in the marbles is attributed to the highamounts of rainfall and potential recharge, in contrast with thealluvial formation which are rendered to high groundwater poten-tiality owing to their hydrogeological and morphological charac-teristics (low slopes, high infiltration coefficient, etc.). Thegroundwater potentiality map can be a useful tool in order to iden-tify new supply sources for water. The proposed method is suitable

Fig. 8. Groundwater potentiality map of Larisa – Tirnavos.

Fig. 9. Distribution of groundwater potentiality classes in the study area.

206 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

for areas in which carbonate rocks are characterized by high degreeof karstification. However, the flexibility of this method allows therevision of the weights and rating of parameters in order to be suit-able for other regions according to their specific characteristics.

5.1. Suitability map for groundwater quality

Even if the results of the groundwater potentiality maps areverified from the construction of boreholes, it is possible thatgroundwater from a number of boreholes will be excluded fromdomestic uses due to high nitrates concentration which is a char-acteristic of the study area due to the intense agricultural activities(over-fertilization). According to the chemical analyzes of watersamples of May 2011, nitrate values range from 9 to 85 mg/L,exceeding locally the upper limit of 50 mg/L for potable use.Specifically, in the karst area the nitrate values range between 16

Fig. 10. Distribution of nitrates (left) and the final map showing potential zones for drinking groundwater in the study area (right).

Table 4Values used for the decision of the areas for groundwater potentiality and quality.

Groundwaterpotentiality

Nitrate concentrations (mg/L)

<50% >50% <25 25–50 >500 1 1 �1 0

Suitability map for drinking waterNon acceptable Moderate acceptable Acceptable0 �1 1

D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208 207

and 18 mg/L, while in the alluvial area between 11 and 85 mg/L(Fig. 10). It is pointed out that the wider study area is one of themost vulnerable zone to nitrate pollution in Greece (Daskalakiand Voudouris, 2008).

Therefore, a further combination between the groundwaterpotential zones and the nitrates concentrations was performed inorder to exclude the polluted zones. The potentiality map wasreclassified into two classes: (1) <50% and (2) >50%. In the areasof the first class, value 0 was assigned, whereas in the areas with>50% the value 1 was given. The nitrates concentration was dividedin three classes. The first class has concentrations below the guidelevel (<25 mg/L), the second is between the guide level and themaximum admissible concentration (25–50 mg/L), the third classhas concentrations above the maximum admissible concentration(>50 mg/L), as defined by the former drinking water Directive(EU Water Framework Directives 2000/60/EC and 2006/118/EC).In the first class, value 1 was assigned, in the second -1 and inthe third 0 (Table 4).

A multiplication between the aforementioned reclassified rasterfiles took place using Raster Calculator (within ArcGIS software)and a map with the acceptable areas for groundwater was created(Fig. 10). The non acceptable class (value 0) covers the highest partof the study area with 53.5%. The acceptable areas (value 1) cover30.8% and are located in the eastern and north-western part of the

study area in the alluvial deposits and marbles, respectively. In themoderate acceptable areas (value �1) the monitoring in thegroundwater hydrochemistry should be more regular in the bore-holes which are used for domestic use. On balance, the producedpotentiality map as well as the suitability map for groundwaterquality should be supplemented from detailed field investigationbefore the drilling of new boreholes for drinking water. It ispointed out that RS cannot replace information collected in thefield research.

5.2. Validation

In order to validate the applied method, the density of theoperating boreholes in areas with available data is related to theproduced maps. Thus, the produced maps were compared withthe existing boreholes. The highest density of boreholes (Fig. 1) isrecorded in the areas with very high groundwater potentiality,indicating the reliability of the method. High and very high poten-tiality areas which are placed in the karst aquifer are characterizedby the absence of boreholes due to the high distance from agricul-tural land and the towns. The high cost for the water transfer is themain reason for the absence of the boreholes but could be used as abackup water resource region for future use.

The validation of the applied empirical method using GIS andremote sensing would benefit in future hydrogeological investiga-tions and collection of new field data concerning yield of boreholes,groundwater recharge and balance, etc. Additional uncertainties inmapping the spatial distribution relates to the compatibilitybetween the scale and the resolution of the mapping technique(Tweed et al., 2007).

6. Conclusions

A specific method based on GIS and Remote Sensing wasapplied to assess the groundwater potentiality in the wider area

208 D. Oikonomidis et al. / Journal of Hydrology 525 (2015) 197–208

of Tirnavos, central Greece. Seven parameters with differentweights (rainfall, potential recharge, lithology, lineament density,slope, drainage density and depth to groundwater) were used,resulting in a final map. The alluvial deposits and a part of the mar-bles are the geological formations with the highest potentiality forgroundwater. The validity of the illustrated map was also checkedby using available drilling data. Furthermore, combinationbetween the groundwater potential zones and the nitrates concen-trations was performed in order to exclude the polluted zones. So,this new-modified method, not only shows the areas of ground-water existence potentiality, but indicates also areas of good-qual-ity groundwater.

The maps obtained by this method can be used by local authori-ties and water policy makers as a preliminary reference in selectingsuitable sites for drilling new boreholes. Therefore, the identifica-tion of areas where aquifers are developed can contribute to therational exploitation and sustainable development of waterresources. The flexibility of the method allows the revision of theweights of included parameters, so the method could be appliedin a wider variety of regions.

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