GIS-based DRASTIC method for groundwater vulnerability...

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GIS-based DRASTIC method forgroundwater vulnerability assessment:a reviewS.M. Shirazi a , H.M. Imran a & Shatirah Akib aa Faculty of Engineering, Department of Civil Engineering,University of Malaya, Kuala Lumpur, Malaysia

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To cite this article: S.M. Shirazi, H.M. Imran & Shatirah Akib (2012): GIS-based DRASTIC method forgroundwater vulnerability assessment: a review, Journal of Risk Research, 15:8, 991-1011

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GIS-based DRASTIC method for groundwater vulnerabilityassessment: a review

S.M. Shirazi*, H.M. Imran and Shatirah Akib

Faculty of Engineering, Department of Civil Engineering, University of Malaya, KualaLumpur, Malaysia

(Received 18 January 2012; final version received 29 February 2012)

Groundwater vulnerability is a burning issue all over the world due to thedeterioration of groundwater level and increasing contamination which posesserious detrimental risk to the environment. To identify this risk, extensiveresearch has been carried out to assess the groundwater vulnerability by usingdifferent methods. Generally, the process-based method, statistical method, andoverlay & index methods are used in this regard. DRASTIC method is one typeof overlay & index method for vulnerability assessment. This paper represents acomprehensive review of available literature on the applications of GeographicInformation System (GIS)-based DRASTIC method for groundwater vulnerabil-ity assessment. Also, some other types of overlay & index methods are com-pared with the DRASTIC method. This study discusses the rescaling of ratingranges and modification of DRASTIC parameters, and shows the comparison ofDRASTIC method with other vulnerability assessment methods. In addition, thisstudy identifies some research gaps on the present state of groundwater vulnera-bility assessment and proposes some research needs for further studies. Thefindings of this study indicate that the combination of GIS and DRASTIC aremore viable for groundwater vulnerability assessment. Furthermore, modifiedDRASTIC method can be used for agricultural, arid, semi-arid, and basalticregions.

Keywords: DRASTIC; groundwater; vulnerability; GIS

Introduction

Groundwater vulnerability refers to intrinsic characteristics that determine the sensi-tivity of the water to be adversely affected by an imposed contaminant load. Theanthropogenic and agricultural activities are responsible for deterioration of ground-water level and increasing vulnerability. Due to the deterioration of groundwaterlevel, sustainable development plans are needed to protect these resources (Nagesw-ara and Narendra 2006). Groundwater has a major contribution to agricultural,industrial, drinking, and other municipal uses, particularly in the region where othersources of water are lacking. To get continuous supply of water and mitigateadverse effects, it is urgent to define definite strategies and guidelines for qualitycontrol, monitoring and management of groundwater. An assessment of groundwa-ter vulnerability is the most feasible step regarding these purposes. The main con-

*Corresponding author. Email: [email protected]

Journal of Risk ResearchVol. 15, No. 8, September 2012, 991–1011

ISSN 1366-9877 print/ISSN 1466-4461 online� 2012 Taylor & Francishttp://dx.doi.org/10.1080/13669877.2012.686053http://www.tandfonline.com

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cept of groundwater vulnerability assessment is the areas which are more vulnerableto pollution than others (Piscopo 2001). Process-based method, statistical method,and overlay & index methods are generally used to assess groundwater vulnerabilityin many parts of the world. The limitations of process-based method are availabilityof adequate data and quality for the capture of physical, chemical and biologicalreactions which occur from the surface through the groundwater regimes. The statis-tical method includes uncertainty and tries to minimize the error and used parame-ters coefficient instead of weight. The lack of this method is proper monitoringdata. This method is only applicable to those regions where the groundwater con-tamination is governed by similar factors. Overlay & index methods are the mostsuitable method for groundwater vulnerability assessment overcoming all the limita-tions mentioned above. Some common overlay & index methods are DRASTIC,SEEPAGE, SINTACS, GOD, and EPIK, in which the DRASTIC method is themost recognized worldwide for groundwater vulnerability assessment. This methodis based on the assumption that some known major factors control the groundwatervulnerability and that those can be weighted. It is very costly and time consumingto assess groundwater vulnerability for a specific site, whereas DRASTIC method ismore economic and less time consuming to assess a wide range of regional ground-water vulnerability, overcoming sloppy, uncontrolled development of land, andundesirable activities. This method was first developed (Aller et al. 1985) for theUS Environmental Protection Agency. Then the method has been modified by manyresearchers and scientists based on geological or hydrogeological settings, climateconditions, and other special situations. Geographic Information System (GIS) is avery efficient tool for analyzing, interpreting, manipulating and incorporating thegeological, hydrogeological and geomorphological data (Anbazhagan and Nair2004; Jha and Peiffer 2006; Jha et al. 2007).

The present paper reviews the concepts, significance, and applicability of GIS-based DRASTIC method for groundwater vulnerability assessment. It enforces themodification of rating ranges of DRASTIC parameters based on geological, hydro-geological, and climatic conditions in the different parts of the world. However, thispaper emphasizes the calibration systems of DRASTIC method. Above all, theexisting gaps in the current state of knowledge on groundwater vulnerability assess-ment were required to identify for future research.

Conventional DRASTIC method

DRASTIC method generally used seven hydrogeological parameters to assessgroundwater vulnerability. The parameters were considered as depth to groundwatertable (D), net recharge (R), aquifer media (A), soil media (S), topography (T),impact of vadose zone (I) and hydraulic conductivity (C). The input informationsuch as borehole data, meteorological data, hydrological data, geology data, soildata, lithology data, contour map, and topography map were used to develop GISdatabase. The method was used to consider various circumstances such as arid orsemi-arid regions, agricultural, industrial, municipal, coastal, septic tank, and landfillareas. The parameters were rated and weighted due to their relative importance tocontamination. Weighting and rating ranges were considered from 1 to 5 and 1 to10, respectively. A multiplier defined as weight was multiplied with each parameterrating for each interval and then the products were summed up to calculate the finalDRASTIC index. This index indicated the relative degree of groundwater

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vulnerability of an area. Higher index value indicated the greater possibility of con-tamination. Final vulnerability map was generated by integrating all the thematicmaps of DRASTIC parameters through the GIS environment. ArcGIS software wasa powerful tool to generate different thematic maps, GIS database, format conver-sion, overlaying maps, integrating maps, and so on. Some extension tools (spatialanalyst, 3D analyst, and geostatistical analyst) of GIS software were extensivelyused in DRASTIC method. Many researchers and scientists assessed groundwatervulnerability based on the above concept and used the Equation (1) (Kim andHamm 1999; Ibe, Nwankwor, and Onyekuru 2001; Withowski et al. 2003; Tovarand Rodriguez 2004; De Silva and Hohne 2005; Jasrotia and Singh 2005; Shahidand Hazarika 2007; Chitsazan and Akhtari 2009; Moghaddam, Fijani, and Nadiri2010).

DRASTIC Index ðDIÞ ¼ DrDw þ RrRw þ ArAw þ SrSw þ TrTw þ IrIw þ CrCw ð1Þ

Groundwater vulnerability assessment in the coastal region was an importantissue. The colluvial–alluvial sediment region was more vulnerable to contamination(Junior Silva and Pizani 2003). The input data sources were used as groundwaterdepth, aquifer recharge, lithology, soil types, topography, and permeability. Anthro-pogenic activities and seawater intrusion were prevailing factors for groundwatervulnerability. Conventional DRASTIC method was used in the arid region of Barkaregion of Oman (Jamrah et al. 2008). The study showed the long-term changes ofvulnerability index for 1995 and 2004. Groundwater samples were analyzed formajor ions, nutrients, COD (Chemical Oxygen Demand), BOD (Biochemical Oxy-gen Demand), and bacteria to cross check the DRASTIC vulnerability index. Majoranions such as NO�

3 , NO�2 , Cl�, SO2�

4 , PO2�4 , F�, and Br� were analyzed and

developed correlations between vulnerability index values and specific groundwaterquality parameters to valid the DRASTIC method.

Sensitivity analysis of DRASTIC parameters

Sensitivity analysis was carried out to show the relationship between the effectiveand theoretical weight of the DRASTIC parameters. The analysis helped to avoidthe subjectivity to nature for vulnerability assessment which provided very impor-tant information to assign the weighting and rating ranges of the parameters. Twotypes of sensitivity analysis – namely map removal sensitivity and single parameterremoval sensitivity analysis – were carried out on this regard. First one representedthe sensitivity of final vulnerability map to remove one or more map layers andworked-out Equation (2). The single parameter removal sensitivity analysis testindicated the influence of each parameter on final vulnerability measurement. Effec-tive weight of each subarea was estimated by the Equation (3). From the sensitivityanalyzed results, researchers understood that their assign weight was perfect or inneed of modification. Both the conventional DRASTIC method and sensitivity anal-ysis were used to groundwater vulnerability assessment by many researchers andscientists (Kwansiririkull et al. 2004; Babiker et al. 2005; El-Naqa, Hammouri, andKuisi 2006; Ckakraborty, Paul, and Sikdar 2007; Bazimenyera and Zhonghua 2008;Rahman 2008; Hasiniaina, Zhou, and Guoyi 2010; Al Hallaq and Elaish 2011;Samake et al. 2011).

Journal of Risk Research 993

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Sensitivity; Sv ¼ V=N � V 0=N 0

V

� �� 100 ð2Þ

The differences of theoretical weight and effective weight also calculated by theEquation (3):

We ¼ PrPw

Vp

� �� 100 ð3Þ

A GIS-based groundwater vulnerability assessment carried out in the Russeifaarea of Jordan (El-Naqa, Hammouri, and Kuisi 2006). The relevant data such astopography, geology, land use, hydrology, rainfall, existing aerial photographs, andsatellite imagery were used. ArcGIS software was used to compile all the geospatialdata and generate groundwater vulnerability map. The impact of vadose zone wascalculated due to soil permeability and depth to water table parameters. There wasa signficant concern that the study area was situated at the landfill site. DRASTICindex was calculated due to pesticide effect and included the map removal sensitiv-ity by statistical analysis. The study indicated that the groundwater was highly vul-nerable due to the landfill of the surrounding study area.

Different equations for net recharge calculation of DRASTIC method

Different types of equations were used to calculate the net recharge of DRASTICmethod in many parts of the world based on the variation of geology, hydrogeol-ogy, lithology, land use categories, topography, and climatic and other conditions.The following Equation (4) was used for net recharge calculation by Bazimenyeraand Zhonghua (2008).

Nn ¼ ðPp � EpÞ � Rr ð4Þ

Net recharge was calculated using other Equations (5) and (6) considering gravel,sand, sandy loam, and loamy sand geology, respectively by (Al Hallaq and Elaish2011).

PI ¼ Pp � 10:28� �Pp þ 15:43

2

ð5Þ

PI ¼ Pp � 15:05� �Pp þ 22:57

2

ð6Þ

Recharge was estimated considering the summation of rainfall and irrigationreturn flow, and subtracting the evapotranspiration (Jayasekera, Kaluarachchi, andVillholth 2011). Soil moisture content was accounted to calculate the irrigationreturn flow. The volume of storage water available for plants was calculated usingEquation (7):


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Sw ¼ pDb2

4� AWHC

100�MAD� Z ð7Þ

The assumptions were Z = 0.5m; AWHC=8% and MAD=1.0 for desert plantsand 0.5 for others plants. It was used for the approximate infiltration fraction as 0.4based on rainfall (Kuruppuarachchi 1995). The calculated fraction of irrigationwater recharge to groundwater table was 0.63 over the area. Fault system, fault den-sity, the distance between fault system intersection and drainage system intersection,rainfall amount, slope of the area, and soil permeability were greatly considered(Al-Hanbali and Kondoh 2008) to estimate the net recharge using Equation (8).

RV ¼ RFþ Sp þ SPþ F � FD ð8Þ

A study was carried out by greatly considered the net recharge calculationmethod and its rating system by Kim and Hamm (1999). In this case, Soil Conser-vation Service (SCS) method was used (Morel-Seytoux and Verdin 1981) to definethe net recharge rate. Cumulative direct runoff (Tq) was calculated by the Equations(9)–(11):

Tq ¼Pp � Ia� �

Pp � Ia þ Ssw

2

ð9Þ

Again; Ia ¼ 0:2Ssw ð10Þ

Ssw ¼ 25; 400

CN� 254 ð11Þ

CN value depended on watershed soil types and land use categories. The soilwas classified according to SCS classification and land use was classified accord-ing to US geological survey. Under SCS method, runoff potential was deter-mined based on antecedent moisture conditions (AMC). CN and Sp values weretaken with respect to AMC classification which taken from SCS chart. Finally,cumulative direct runoff (Tq) was calculated for each landuse category using theEquation (12):

Tq ¼Pp � 0:2Ssw� �Pp þ 0:8Ssw

2

ð12Þ

Table 1. Recharge rating systems.

RPR (%) Runoff Land use Rating

0–15 Low Forest and agricultural land 515–25 Moderate Barren land and alluvium 425–30 High Residential area and channel deposit 2130 Very high Water 1


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The net recharge rating ranges (Table 1) were developed based on Runoff Poten-tial Ratio (RPR) which calculated on each land use category and by the followingEquation (13):

RPR ¼ Tq

Ppð13Þ

To evaluate the relative weight of RPR value, the actual runoff (Q) was calcu-lated using Equation (14):

Q ¼ Pa

100� Tq ð14Þ

The new rating ranges of net recharge were selected based on (RPR) whereasRPR mainly depended on land use categories. The study showed that shallowaquifers were more vulnerable due to higher recharge and hydraulic conductivity,and coarse soil. The domestic and industrial waste water were the main sources ofpollution.

A recession curve displacement method was used to estimate the net recharge.Stream flow data within the study area were used for recession curve displacementmethod (Fritch et al. 2000) and suggested the three concepts for vadose zone ratingranges. (a) If overlaying material’s thickness of the aquifer was less or equal to thethickness of weathered zone, then vadose zone media was considered as materialsof the aquifer media. (b) If overlaying material’s thickness of aquifer was greaterthan the weathered zone, but less or equal to vadose zone, then the vadose zonecould be adequately described as a weighted average: ([the aquifer material mediarating × its thickness] + [the overlying material media rating × its thickness])/totalthickness of the vadose zone. (c) If overlaying material’s thickness of the aquiferwas greater than the weathered zone and vadose zone, then vadose zone should berated according to the overlaying material’s characteristics.

Modified DRASTIC approach

Land use had a potential impact on groundwater vulnerability and risk mappingwhich were produced as a consequence of groundwater contamination. ModifiedDRASTIC method was used to assess the groundwater vulnerability and risk map-ping including land use (Secunda, Collin, and Melloul 1998). Al-Adamat, Foster,and Baban (2003) considered D, R, A, S, T, and I parameters because of lacking thehydraulic conductivity data and also, a fixed value of 68 assumed instead of(DrDw +ArAw + IrIw) index value. Since the possible minimum and maximumDRASTIC index were 24 and 220, respectively. The vulnerability classes weredivided into four categories namely: (a) 24–71 (No risk), (b) 72–121 (Low), (c)122–170 (moderate) and, (d) 171–220 (High). Final modified DRASTIC index(MDi) was calculated using the following Equation (15):

MDðiÞ ¼ DIþ LrLw ð15Þ

Khan, Umar, and Lateh (2010) focused on the land use and impact of vadosezone effect on groundwater vulnerability and risk with DRASTIC method. Landuse weight was considered as 5 and hydraulic mean approach (Hussain et al. 2005)


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was used to calculate the impact of vadose zone parameter. The following Equation(16) was used to achieve the approach and final vulnerability index calculated usingthe Equation (1):

Ir ¼ TvPni¼1

TiIri

� ð16Þ

Al-Hanbali and Kondoh (2008) also used the Equation (1) to assess groundwatervulnerability. Modified DRASTIC parameters and rating ranges were used in mostcases of arid and semi-arid regions. Weight and rating ranges were changed due tohydrogeologic settings, land use categories, rainfall, and climatic and other condi-tions. In some cases, some parameters of DRASTIC were removed or added todevelop the modified DRASTIC method by many researchers. Modified equations,weight and rating ranges were given satisfactory result for groundwater vulnerabilityassessment in different regions. The new weight values were considered as 5, 4, 3,5, 3, 3, and 2 for D, R, A, S, T, I, and C factor respectively, based on pesticide con-tamination (Al-Zabet 2002). A fixed index value of 10 was assumed instead of theparameters depth to water table and impact of vadose zone to calculate DRASTICindex (Hasiniaina, Zhou, and Guoyi 2010). The study area belonged to oil field andminerals region. The conductivity map generated by two components (aquifer thick-ness and conductivity) and greatly considered the relation T=Kb, where T= trans-missivity, K= hydraulic conductivity, and b= the thickness of the aquifer. ModifiedDRASTIC method was applied considering the land use parameter and excepthydraulic conductivity in Azraq basin (Jasem and Alraggad 2010). The new weight-ing and rating ranges were used for each DRASTIC parameter (Table 2).

A case study was carried out on aquifer vulnerability assessment to Arsenic pol-lution using DRASTIC and GIS of North Bengal plain in West Bengal of India(Ckakraborty, Paul, and Sikdar 2007). The assumption was that the contaminantsmoved vertically downwards with water and reached groundwater table. The newrating ranges were proposed for D, R, T, and I parameters of DRASTIC method(Table 2).

DRASTIC-Fm method was applied to assess the groundwater vulnerability forthe structural characteristics of fractured-bedrock aquifers (Denny, Allen, and Jour-neay 2007). The fractured media was strictly considered identifying its effect ongroundwater vulnerability. The fractured media was classified as three categories(Fracture orientation, Fracture length, and Fracture density) and also the ratingranges were assigned for those categories. The final Fm factor was rated accordingto the average value of each category rate (Table 3). The weight of Fm factor wasconsidered as 3.

Calibration of DRASTIC method

Groundwater vulnerability was assessed in many parts of the world consideringnitrate contamination. Nitrogen is the basic need for agricultural plants to insure thehigh production (Lake et al. 2003; Schröder et al. 2004; Shirazi, Sholichin et al.2011). Groundwater greatly affected by nitrate contamination all over the world(Birkinshaw and Ewen 2000; Saâdi and Maslouhi 2003; Kyllmar, Mårtensson, andJohnsson 2005; Liu, Ming, and Ankumah 2005). Nitrate contamination mainlyoccurred in the agricultural areas due to application of fertilizers. The soil compo-


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Table2.

Modified

weightin

gandratin

gvalues

ofDRASTIC

parameters.

References/

parameters

D

R

AS

TI

C

LU

Range

(m)

Rating

Aquifer

media

types

Rating

Soil

type

Rating

Range

(Slope)

Rating

Vadose

zone

Rating

Landuse

types

Rating

Jasem

and

Alraggad

(2010)

0–2

10–

Lim

estone

1Clay

1>2%

7Uncovered

4–

Rainfed

agricultu

re1

2–10

8Sandstone

bed

2Silty

loam

22–

6%6

Soil

3Irrigated

agricultu

re3

10–20

6Sands

&gravels

3Loam

36–

10%

5Uncon

fined

unit

2Urban

areas

5

20–40

4Basalt

5Sand

410

–16%

3Con

fining

unit

1Barred

rocks

1

40–60

2Lim

estone

chalk

416

–25%

2–

–WWTP

4

>60

1–

––

–>25%

1–

–Streams

&WWTP

6

––

––

––

––

––

Streams

3–

––

––

––

––

–Dam

s5

Modified

weight

98

54

15

–7

Stig

teret

al.

(2006)

Pesticide

weight

54

35

34

2–

SIweight

0.186

0.212

0.259

–0.121

––

0.222

References/

parameters

DR

AS

TI

C

Range

(m)

Rating

Range

(inch/h)

Rating

–Soiltype

Rating

Range

(Slope)

Rating

Vadosezone

Rating

Range

(m/d)

Rating

Ckakraborty

etal.

(2007)

<3

10<0.2

2–

––

0–2%

10Clayand

silt

3–

–

3–4

90.2–0.6

4–

––

2–4%

9Sandy

clay

4–

–

(Contin

ued)


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4–5

80.6–2.0

6–

––

4–6%

8Claysand

6–

–5–

67

2.0–6.0

8–

––

6–8%

7Sandand

gravel

8–

–

6–7

6>6.0

10–

––

8–10%

6–

––

7–8

5–

––

––

10–12%

5–

––

8–9

4–

––

––

12–14%

4–

––

9–10

3–

––

––

14–16%

3–

––

10–11

2–

––

––

16–18%

2–

––

11–12

1–

––

––

>18%

1–

––

>12

1–

––

––

––

––

–Javadi

etal.(2011)

0–1.5

100–

22.54

–Clay

loam

3–

–Siltysand

3.3

0.4–4.1

1.67

1.5–4.6

2.3

2–4

3–

Silty

loam

4.2

––

Clay

5.3

4.1–2.3

3.3

––

4–7

6.8

–Loam

1.1

––

Sandstone

1.7

12.3–2

86.7

––

7–10

7.9

–Sandy

loam

6–

–Sand&

Gravel

3.1

28.7–4

110

––

>10

10–

Shrink

Clay

2.5

––

CoarseSand

10–

–

––

––

–Peat

10–

––

––

–


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Table3.

Modified

DRASTIC-Fm

ratin

gvalues.

30°

fault

orientation

classificatio

nand

associated

DRASTIC-Fm

ratin

gsLengthclassificatio

nsand

associated

DRASTIC-Fm

ratin

gsFracturedensity

classificatio

nsand

associated

DRASTIC-Fm

ratin

gs

Min

Max

Rating

Fracturelength

(m)

Rating

Fracturedensity

(fractures/m

)Rating

Extension

285

315

720,000–2

5,000

100–

22

315

345

1015,000–2

0,000

82–

44

345

157

10,000–1

5,000

64–

66

105

135

75000

–10,000

46–

88

135

165

100–5000

2>8

10165

195

7–

––

–Contractio

n195

225

4–

––

–225

255

2–

––

–255

285

4–

––

–15

454

––

––

4575

2–

––

–75

105

4–

––

–


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nent variations (soil leaching potential) had a great effect on decision support sys-tem (DSS) to minimize the agrochemical pollution of groundwater (Brown et al.2003; Holman, Dubus, and Hollis 2004; Shirazi et al. 2010; Shirazi, Ismail et al.2011). The nitrate concentration in groundwater depends on soil nitrate levels, andthe timing and amount of surface loading (Di and Cameron 2002). One of the non-point source pollution of groundwater is caused by nitrate in the agricultural areas(Hubbard and Sheridan 1994; McLay et al. 2001; Shamrukh et al. 2001; Harteret al. 2002; Almasri and Kaluarachchi 2004; Chowdhuary et al. 2005). On-groundnitrogen concentration was considered to assess the groundwater vulnerability.Nitrogen database was very effective to validate the intrinsic vulnerability (Holmanet al. 2005). The on-ground nitrogen loading was rated and weighted, and thenadded with DRASTIC index. Finally, the composite DRASTIC index (CDI) wascalculated by the following Equation (17):

CDI ¼ DIþ NwNr ð17Þ

The intrinsic vulnerability of groundwater was assessed to nitrate contaminationand considered five parameters for modification of DRASTIC method (Mishima,Takada, and Kitagawa 2011). Only vertical movement of contamination wasconsidered for this modification. In this case, the aquifers were shallow and aquifermedia had narrow ranges. Soil media were governed by the aquifer mediaparameter. Hydraulic conductivity and aquifer media were less effect for contamina-tion. The more recharge value was considered as less rating value and less rechargevalue was considered as high rating value which was opposite of conventionalDRASTIC and the Equation (18) was used to calculate the nitrate concentration:

Ncon ¼ Erate � Fert

Wpercð18Þ

Finally, Modified DRASTIC index was calculated by Equation (19):

Gv ¼ DrDw þ RrRw þ SrSw þ TrTw þ IrIw ð19Þ

The new weighting and rating ranges were proposed for five parameters ofDRASTIC based on agricultural areas (Javadi et al. 2011). The new rating rangesare shown in Table 2.

DRASTIC method was improved by calibrating the point rating scheme to mea-sured nitrate & nitrite concentration in groundwater (Rupert 2001). Statistical corre-lation was developed between the land use, soils, depth to water level, and nitrate& nitrite concentrations. GIS and statistical techniques were applied to enumeratethe correlations. Based on the correlations, the probability map of nitrate & nitritewere generated. Then uncalibrate DRASTIC map and probability map were com-pared with the independent set of nitrate & nitrite data. The comparison showedthat poor correlations were found between the uncalibrated DRASTIC map andnitrate & nitrite concentrations. There was no significance difference of nitrate &nitrite concentration in groundwater between the low, medium, high, and very highvulnerability categories areas. Good correlations were found between the probabilitymap and nitrate & nitrite concentration. The significant difference of nitrate &nitrite concentration in groundwater indicated between the low, medium, high, and


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very high vulnerability categories areas. The study suggested that groundwater vul-nerability and probability maps can be used to develop the prevention guidelinesfor high susceptible to contamination areas. Groundwater vulnerability was assessedconsidering the severe human impact, semi-arid climate, and very little slope varia-tion (Chitsazan and Akhtari 2009). The main geomorphologic features of the areawere hills, plains, sand dunes, sabkha, steams, and brines. The most aquifers systemof the study area was unconfined. DRASTIC method was evaluated by nitrate con-centration of the study area. The correlations were shown between the DRASTICparameters and nitrate concentration using multivariate statistical method.

Comparison of DRASTIC with other methods

A regional scale of groundwater vulnerability assessment was carried out based onnitrate contamination using conventional DRASTIC and System for Early Evalua-tion of Pollution Potential of Agricultural Groundwater Environments (SEEPAGE)method (Navulur 1996). The study results of DRASTIC method indicated that 24%area was highly vulnerable and 28% area was very highly vulnerable according tothe assessment of SEEPAGE method. The Bayesian probability map also developedfor both methods for computing the probabilities of nitrate occurrence. The proba-bility maps using DRASTIC and SEEPAGE factors showed 26% and 21% areawith a probability of nitrate recognition > 50%, respectively. The water quality dataindicated 76% of the nitrate recognitions were within the areas with probability ofrecognition > 50%. The study suggested that statistical techniques can be used togenerate the regional scale risk maps where data availability were limited andDRASTIC performance was better than SEEPAGE.

DRASTIC and Aquifer Vulnerability Index (AVI) methods were used to assessgroundwater vulnerability mapping and checked the validation of DRASTICmethod (Leal and Castillo 2003). To validate the weighting and rating ranges of theparameters, the raw data maps and parameters rating maps were compared. Overlay-ing isoline map pair’s technique was used to compare between different maps. Ifmajor variations were detected then the rating ranges were modified. Depth to watertable parameter was adjusted and proposed for rescaling the rating ranges. The sim-plification was represented by matrix form as Equation (20):

Td½ � Ar½ � ¼ Cp

� ð20Þ

Again, critical parameter C affected by the weight function W and it presentedby Equation (21).

W½ � Cp

� ¼ Vi½ � ð21Þ

Effective weight was calculated (Napolitano and Fabbri 1996; Gogu and Dasar-gues 2000) based on the Equation (22).

We ¼ Xri � Xwi

Vi� 100 ð22Þ


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The vulnerability variation was calculated (Lodwik, Monson, and Svoboda1990) based on Equation (23) and proposed new rating for depth to water tableparameter.

VvXi ¼ ðVi � VXiÞVi

� 100 ð23Þ

The comparison between different vulnerability assessment method such as AVI,GOD (Groundwater occurrence, G; Overall lithology of aquifer, O; and Depth togroundwater level, D), DRASTIC, and EPIK (Epikarst, E; Protective cover, P;Infiltration conditions, I; and Karst network development, K) were conducted fordiffuse flow carbonate aquifers (Vias et al. 2004). The aquifer was highly vulnerableaccording to the AVI method and moderately vulnerable according to the otherthree methods. The vulnerability maps indicated that AVI method was not suitablewhereas GOD method was adequate for vulnerability assessment of diffuse flowcarbonate aquifers. Lithological parameters were the most significant for groundwa-ter pollution potential while depth to groundwater level had minor influence. Highvulnerability area was derived by EPIK method for the fractured zones were con-tradicted with very low karst areas. Among the above methods, EPIK is adequatefor karstification areas and GOD is adequate for poorly karstification carbonateareas. Moreover, DRASTIC and AVI methods are more suitable for land use man-agement.

Susceptibility Index (SI) method and nitrate concentration map were used toevaluate the DRASTIC model for groundwater vulnerability assessment (Stigter,Ribeiro, and Dill 2006). It was considered the weights of the parameters as Table 2.

The DRASTIC index and SI calculated as the following Equations (24) and(25):

DRASTIC Index ðDIÞ ¼ 5Dþ 4Rþ 3Aþ 2S þ T þ 5I þ 3C ð24Þ

SI ¼ 0:186Dþ 0:212Rþ 0:259Aþ 0:121T þ 0:222LU ð25Þ

The DRASTIC vulnerability map, SI index map, and nitrate concentration mapwere compared to each other and large discrepancies were found. To remove thesediscrepancies, a new map was generated by subtracting the assessed vulnerabilityclass from the nitrate concentration vulnerability class at all location. Where theclass differences were minus one or zero or one, the vulnerability was consideredas correct. Where the differences were two or three and four or five above thenitrate concentration class, it was considered that vulnerability assessed by overesti-mated or extremely overestimated. The DRASTIC method was optimized using thestatistical method and GIS (Panagopoulos, Antonakos, and Lambrakis 2006). Tomodify the weight of DRASTIC parameters, the correlations were establishedbetween the DRASTIC parameters and nitrate concentration. Based on correlationvalue, negligible parameter removed from DRASTIC model and developed newEquation (26) for groundwater vulnerability assessment.

VðintrinsicÞ ¼ 3Dþ Rþ 5Aþ 2T þ 2:5I ð26Þ


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The land use weighting and rating ranges were assigned based on nitrate con-centration of the study area. The buffer zone radius of nitrogen was calculatedbased on the Equation (27).

Rc ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiQp � t

p � n � H

rð27Þ

Finally, specific vulnerability of groundwater was calculated considering landuse parameter and by the Equation (28):

Aquifer Pollution Risk,

VðspecificÞ ¼ 3Dþ Rþ 5Aþ 2T þ 2:5I þ 5L ð28Þ

EPIK and DRASTIC methods were used to assess the groundwater vulnerabilityand indicated the protection zone (Hammouri and El-Naqa 2008). The EPIK was amultiattribute method which was mainly used in karst region. The factor E and Kwere determined with respect to geological and morphological informations,whereas the P and I factor were determined from soil and land use/cover maps. Thefinal protection index F was calculated by the Equation (29):

F ¼ aE þ bpþ cI þ dK ð29Þ

The DRASTIC model is a straightforward method and generally it is applicablewhere the hydrological data are available. EPIK is used the region which is sub-jected to karst features (holes, caves, and sinkholes).

Groundwater vulnerability based approach was used to delineate the groundwa-ter protection zones around springs of fracture media (Pochon et al. 2008). Uncon-solidated porous media was used as protective materials. Considering thehydrological diversity, individual solution was applied for each hydrological setting.Distance method and isochrone protection method were applied for low vulnerabil-ity and slightly vulnerability springs which consist of three protection zones such asS1, S2, and S3. Zone S1 suggested that the distance must extended at least 10maround or upstream of the springs which integrated drains, draining trenches, andgalleries. Zone S2 suggested the outer distance of S1 and S2 zones must at least100m and zone S3 suggested that the distance between the external limits of S2 andS3 zones equal to the same distance between the outer limits of S1 and S2 zones.DISCO (Discontinuities and protective Cover parameter) method was applied forhighly vulnerable springs. DISCO method was applied at four stages. Firstly, thediscontinuities and protective cover parameters maps were prepared for wholecatchment area and rated the value of ‘D’ (range 0–3) and ‘P’ (range 0–4) based onhand drilling, on-site soil analysis, geomorphological map, geophysics, and infiltra-tion test. Secondly, intermediate protection factor was calculated by the Equation(30):

Fint ¼ 2Dc þ Pt ð30Þ

Then intermediate protection map was prepared. Thirdly, final protection mapwas modified by updating the intermediate protection map based on runoff parame-ter, slope gradient, and soil permeability. Fourthly, protection map was converted


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into protection zones using some conversion factor. The discontinuity and protectivecover factors were considered to generate the discontinuity map and protection zonemap for the study area. In conclusion, the effectiveness of the study needs to verifyfrom data of long-term groundwater quality monitoring and further case studies.

Research needs

The concept of groundwater vulnerability mapping is developed mainly based onhydrogeological settings. The vulnerability mapping assumes that the physical envi-ronment (soil–rock–groundwater system) may provide self-purification or naturalattenuation. The following research needs are mentioned for further studies:

(1) Further research can be carried out on the relationship between the soil andprotective cover such as cation-exchange capacity and-anion exchange capac-ity and its characteristics.

(2) Contaminant mobility indices and screening models can be developed forgroundwater vulnerability assessment based on field observation and compar-ison with physics-based simulations of coupled solute transport and fluidflow in saturated or unsaturated systems.

(3) More studies should be done on groundwater protection methods and tech-niques; moreover, some study has been carried out on this regard which isnot adequate and well established.

(4) Developing DSS approach to combine with groundwater vulnerability mapbased on stochastic screening model for groundwater resources managementand monitoring.

Summary and conclusions

Groundwater vulnerability is a widespread problem in worldwide. Two main com-ponents are considered for DRASTIC method; firstly, the map able units whichtermed as hydrogeologic settings and secondly, the application of numerical valuesof relative ranking of hydrogeologic factors. The paper attempts to present theapplication of DRASTIC method for groundwater vulnerability assessment, more-over some comparison between DRASTIC and other related methods are presented.The GIS techniques are provided the great facilities to accomplish and handle thecomplex and extensive databases for groundwater vulnerability assessment. Thesalient conclusions are summarized below:

(1) The modified DRASTIC method is better than conventional DRASTICmethod in the arid, semi-arid, basaltic, and agricultural and land fill regions.

(2) Sensitivity analysis is very helpful for DRASTIC method. It indicates whichparameter has the most significant contribution to groundwater vulnerability.The differences between theoretical and effective weights of DRASTICparameters are demonstrated by sensitivity analysis.

(3) Extensive approaches are established to net recharge calculation based ondifferent geological and hydrogeological conditions.

(4) The DRASTIC method is calibrated by nitrate concentration in groundwateror others-related method. The evaluation system is the comparison between


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Notations

Ar assigned ranges for critical parameterAWHC available water holding capacityCN curve numberCp critical parameterDc discontinuity rangesDb diameter of basinEp evaporationErate elution rateF protection indexFint intermediate protection factorF � FD rate of the average of the distance from the faults system (F) and the distance

from the intersection locations between the faults and the drainage systems (FD)Fert fertilizer inputGv groundwater vulnerabilityH length of the well screen, namely the saturated thickness of the aquifer for full

penetrating wellsIa initial abstractionIr weighted harmonic mean of vadose zoneIri rating of layer i of vadose zoneL contaminant loading per land use categoryLr & Lw land use rating and weightingLU land useMAD management allowable depletion (dimensionless)n porosityN and N′ the number of data layers used to compute the V and V′Nn net rechargeNcon nitrate concentration in percolation waterNw andNr

weight and rating that given the total on-ground nitrogen loading

Pa percentage of the total area covered by each land use categoryPc cumulative amount of rainfallPI percolation indexPp annual average rainfall/PrecipitationPr andPw

weight and rating of individual DRASTIC parameters that used for effectiveweight calculation

Pt protective cover rangesQ actual runoffQp pumping rate of the wellr rating of the parametersRc radius of the circleRr recharge rateRF rainfall factorRPR runoff potential ratioRV recharge valueSsw maximum watershed storageSI susceptibility indexSP soil permeabilitySp slope percentageSv sensitivity analysisSw volumes of storage waterTd applied transformations to a data seriesTq cumulative direct runoffTv total thickness of the vadose zonet travel time for which volume was being calculated

(Continued)


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vulnerability index maps of various methods or correlation between the vul-nerability index values and nitrate concentration values over the study area.

(5) The presence of nitrate in groundwater easily indicates the pollution potenti-ality by the contaminants since nitrate is not generally present in groundwa-ter under natural conditions.

(6) Land use map is very effective to generate groundwater risk map and helpsto explanation the on-ground nitrogen loadings and permits realistic alloca-tions in various nitrogen sources.

(7) In some cases, the DRASTIC parameter’s weighting and rating ranges canbe modified and one or more parameters also can be added or subtractedfrom conventional DRASTIC method based on the geology, hydrogeology,land use categories, and climatic and other conditions.

(8) In agricultural areas, it is better to rescale the weighting and rating ranges ofconventional DRASTIC parameters due to land use and nitrate concentrationresulting from pesticides and fertilizers.

AcknowledgmentFinancial support by the Institute of Research Management and Monitoring (IPPP),University of Malaya (UM), Malaysia under UMRG research grant number RG 092/10SUSis gratefully acknowledged.

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GIS-based DRASTIC method for groundwater vulnerability...

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