Neshat Et Al 2014 MUY BUENO

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O RI G I N A L A RT I CL E

Estimating groundwater vulnerability to pollution usinga modified DRASTIC model in the Kerman agricultural area, Iran

Aminreza Neshat • Biswajeet Pradhan •

Saied Pirasteh • Helmi Zulhaidi Mohd Shafri

Received: 14 February 2013 / Accepted: 20 July 2013 / Published online: 2 August 2013

Springer-Verlag Berlin Heidelberg 2013

Abstract Groundwater contamination from intensive

fertilizer application affects conservation areas in a plain.The DRASTIC model can be applied in the evaluation of

groundwater vulnerability to such pollution. The main

purpose of using the DRASTIC model is to map ground-

water susceptibility to pollution in different areas. How-

ever, this method has been used in various areas without

modification, thereby disregarding the effects of pollution

types and their characteristics. Thus, this technique must be

standardized and be approved for applications in aquifers

and particular types of pollution. In this study, the potential

for the more accurate assessment of vulnerability to pol-

lution is achieved by correcting the rates of the DRASTIC

parameters. The new rates were calculated by identifying

the relationships among the parameters with respect to the

nitrate concentration in groundwater. The methodology

was implemented in the Kerman plain in the southeastern

region of Iran. The nitrate concentration in water from

underground wells was tested and analyzed in 27 different

locations. The measured nitrate concentrations were used

to associate and correlate the pollution in the aquifer to the

DRASTIC index. The Wilcoxon rank-sum nonparametric

statistical test was applied to determine the relationship

between the index and the measured pollution in Kerman

plain. Also, the weights of the DRASTIC parameters were

modified through the sensitivity analysis. Subsequently, the

rates and weights were computed. The results of the studyrevealed that the modified DRASTIC model performs more

efficiently than the traditional method for nonpoint source

pollution, particularly in agricultural areas. The regression

coefficients showed that the relationship between the vul-

nerability index and the nitrate concentration was 82 %

after modification and 44 % before modification. This

comparison indicated that the results of the modified

DRASTIC of this region are better than those of the ori-

ginal method.

Keywords Modified DRASTIC GIS

Groundwater Hydrogeology Sensitivity analysis Kerman plain Vulnerability

Introduction

Groundwater is an important and prominent resource in

most countries, particularly for those in arid and semi-arid

areas. Water quality has been given more emphasis in

groundwater management (Pradhan 2009; Ayazi et al.

2010; Manap et al. 2012, 2013). Aquifers are usually

unconfined and highly permeable, thereby causing their

high susceptibility to surface contamination (Javadi et al.

2011a, b). The potential groundwater pollution by human

activities at or near the surface has been considered the

primary basis for the management of this major resource by

implementing preventive policies.

The introduction of potential contaminants to a location

on top of an aquifer at a specified position in an under-

ground system is defined as groundwater vulnerability

(National Research Council 1993). Vulnerability assess-

ment must be based on scientific, accurate, and objective

A. Neshat B. Pradhan (&) H. Z. M. Shafri

Department of Civil Engineering, Faculty of Engineering,

University Putra Malaysia, 43400 Serdang, Selangor, Malaysia

e-mail: [email protected]; [email protected];

[email protected]

S. Pirasteh

Department of Geography and Environmental Management,

Faculty of Environment, University of Waterloo, Waterloo,

Canada

1 3

Environ Earth Sci (2014) 71:3119–3131

DOI 10.1007/s12665-013-2690-7



evidence. Various methods have been introduced to esti-

mate groundwater vulnerability with high accuracy (Javadi

et al. 2011a, b). In most cases, these procedures consist of

analytical tools intended to correlate groundwater con-

tamination with land activities. There are three categories

of assessment processes and procedures: the process-based

simulation models, the statistical methods (Harbaugh et al.

2000), and the overlay and index methods (Dixon 2004).Process-based models generally require a large amount

of primary and secondary data to apply the mathematical

models for creating the principal tool. Such methods seem

more complex and difficult to use on a regional scale.

Statistical methods use data on the known areal contami-

nant distribution and describe the contamination potential

for a specified geographical region using the available data

in the regions of interest (National Research Council 1993).

Overlay and index methods emphasize the combination

of different regional maps by allocating a numerical index.

Both methods are easy to apply in geographic information

systems (GIS), particularly on a regional scale. Therefore,these techniques are the most popular methods used in

vulnerability evaluation. The most widely used among

these techniques include GOD (Foster 1987), IRISH (Daly

and Drew 1999), AVI (van Stemproot et al. 1993), and

DRASTIC (Aller et al. 1987). DRASTIC is widely applied

in various countries, including the USA (Plymale and

Angle 2002; Fritch et al. 2000; Shukla et al. 2000), China

(Yuan et al. 2006; Huan et al. 2012; Yin et al. 2012),

Jordan (Naqa et al. 2006), Morocco (Ettazarini 2006), Iran

(Javadi et al. 2011a, b), Palestine (Mimi et al. 2012),

Tunisia (Saidi et al. 2010, 2011), and Portugal (Pacheco

and Sanches Fernandes 2012).

Despite its popularity, the DRASTIC method has some

disadvantages. The DRASTIC model consists of seven

hydrogeologic factors: depth of water, net recharge, aquifer

media, soil media, topography (slope), impact of vadose

zone, and hydraulic conductivity of the aquifer.

This approach primarily uses the seven parameters to

compute for the vulnerability index. Each parameter is

allocated specific weights and rating values, as shown in

Table 1 (Aller et al. 1987). The DRASTIC model is con-

sidered one of the best index models for vulnerability map-

ping since it was first introduced by Aller et al. (1985, 1987).

This technique disregards the effects of regional character-

istics. Therefore, uniform weightsand rating values are used.

Moreover, this method does not use a standard validationtest

for the aquifer. Thus, several researchers have continued to

develop this model using different methods. Some of the

notable studies include those by Secunda et al. (1998),

Melloul and Collin (1998), Zhou et al. (1999), Thirumalai-

vasan etal. (2003),Dixon (2005), Antonakos and Lambrakis

(2007), Bojorquez-Tapia et al. (2009), Ckakraborty et al.

(2007),Dennyet al. (2007),Hamza et al. (2007), Leone et al.

(2009), Nobre et al. (2007), Pathak and Hiratsuka (2011),

Remesan and Panda (2008), Saidi et al. (2011), and Hailin

et al. (2011). Several groups attempted to correlate the vul-

nerability index using chemical or contaminant parameters

(Kalinski et al. 1994; Rupert 1999; McLay et al. 2001).

Certain index methods modified the DRASTIC model by

varying the factors and respective weights or by incorpo-

rating alternative data on human activities, such as land useand contaminant loading. Al-Hanbali and Kondoh (2008)

combined a human activity impact index derived from land

use or cover data using the DRASTIC model. Their work

demonstrated that human activities affect the groundwater

quality and increase the risk of pollution in the Dead Sea

groundwater basin in Jordan.

The existing literature reports a limited number of studies

on groundwater vulnerability from specific contamination

sources based on GIS and index methods. This lack of

information may be due to the previous definition of

groundwater vulnerability for nonpoint sources. However,

the existing index methods may still be useful in evaluatingthe risk of groundwater pollution in local and larger scales

when it is combined with site-specific information, such as

the hydrogeology and contamination history (Alexander

et al. 1986). Recent studies have attempted to correlate the

vulnerability index with chemical or contaminant parame-

ters (Kalinski et al. 1994; Rupert 1999; McLay et al. 2001),

whereas others correlate the nearby land to its vulnerability.

However, the rates or weights of the DRASTIC model could

not be used efficiently in these investigations.

Nitrateis not naturally found in surface groundwater. This

compound is considered a good indicator of contaminant

movement from the surface to groundwater, particularly in

land allocated for agricultural use (Javadi et al. 2011a, b).

Carvalho (2009) conducted pollution risk assessment by

integrating DRASTIC results with nitrate concentrations.

Subsequently, other researchers used nitrate to modify

DRASTIC (Panagopoulos et al. 2006; Javadi et al. 2011a, b).

Pacheco and Sanches Fernandes (2012) used nitrates to

perform a correspondence analysis. Panagopoulos et al.

(2006) and Javadiet al. (2011a, b) calibrated themodel using

nitrate before a correlation coefficient was obtained to

describe the relationship of the vulnerability index and

nitrate concentration. In the current study, the rate of

DRASTIC parameters was calibrated for the study area by

measuring the nitrate concentration of groundwater. The

relationship between the vulnerability indicators and the

parameters was statistically analyzed to calibrate the rates

using the Wilcoxon rank-sum nonparametric statistical test

(Wilcoxon 1945). In addition, the single-parameter sensi-

tivity analysis wasapplied to compute the effective weight of

each parameter in the Kerman plain.

The main contribution of this research compared with

previously published literature in Javadi et al. (2011a, b) is

3120 Environ Earth Sci (2014) 71:3119–3131

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the use of two nitrate samples in the same month, which

makes more accurate correlation. Apart from that, the area

of interest in this study is the Kerman plain which is

located in the southeastern part of Iran. The Kerman plain

is located in arid and semiarid regions, with groundwater as

its only water source because of the scarcity of surface

water. Moreover, the Kerman plain experiences heavy

pumping of groundwater, which becomes a serious prob-

lem because it continuously lowers the water table. The

majority of the study area is covered with agricultural

lands, and the application of fertilizers is a common

practice. The DRASTIC model can be used to demonstrate

the application of the proposed method and to provide a

basis for its environmental management.

Study area

The Kerman plain is an arid and semi-arid area; the

majority of the study area is composed of agricultural land.

Pistachio is the most economically important product in

this region. The Kerman plain has an area of 978 km2 in

the southeastern part of Iran (Fig. 1). The highest ground

elevation in the area is 1,980 m, with the lowest point

being 1,633 m above sea level. The average annual rainfall

in the study area was 108.3 mm in 2011. The vadose zone

consists of silt and clay, sand and gravel, or gravel and sand

with silt. The aquifer medium is composed of marlstone

and shale, silt and clay, massive sandstone, or sand and

gravel. The total net recharge of the study area is 186.06

million cubic meters per year (MCM per year). The max-

imum electrical conductivity (EC) in this study area is

3,880 lmoh/cm (micromhos per centimeter), and the

average EC is 2,700 lmoh/cm. The minimum EC is

1,100 lmoh/cm. In addition, the southeastern regions of

the plain have the maximum EC. The geology of Kerman

plain contains Cretaceous and Eocene conglomerates (PC),

intrusive rocks (gp), Eocene and Neogene volcanism, and

Neogene, or younger, sediments.

Materials and methods

Data and DRASTIC method

The data used to obtain the hydrogeological parameters of

the DRASTIC model are listed in Table 2. This method

was established by the United States Environmental Pro-

tection Agency (USEPA) to classify the pollution potential

Table 1 Original DRASTIC weights and rating systems

D epth to wa te r ( m) R ec ha rge ( mm) Top og ra ph y

(slope %)

Conductivity (m/

day)

A qu if er media V ad os e z on e ma te rial So il med ia

Range Rating Range Rating Range Rating Range Rating Range Rating Range Rating Range Rating

(0–1.5) 10 (0–50.8) 1 (0–2) 10 (0.04–4.1) 1 Massive shale 2 Confining

layer

1 Thin or

absent

10

(1.5–4.6) 9 (50.8–101.6) 3 (2–6) 9 (4.1–12.3) 2 Metamorphic/

igneous

3 Silt/clay 3 Gravel 10

(4.6–9.1) 7 (101.6–177.8) 6 (6–12) 5 (12.3–28.7) 4 Weathered

metamorphic

igneous

4 Shale 3 Sand 9

(9.1–15.2) 5 (177.8–254) 8 (12–18) 3 (28.7–41) 6 Limestone 3 Peat 8

(15.2–22.8) 3 ([254) 9 ([18) 1 (41–82) 8 Glacial till 5 Sandstone 6 Shrinking

clay

7

(22.8–30.4) 2 ([82) 10 Bedded

sandstone,

limestone

6 Bedded

limestone,

sandstone

6 Sandy loam 6

([30.4) 1 Loam 5

Massive

sandstone

6 Sand and

gravel

6 Silty loam 4

Massive

limestone

8 W. silt Clay loam 3

Sand and gravel 8 Sand and

gravel

8 Muck 2

Basalt 9 Basalt 9 Noshrinking

clay

1

Karsts

limestone

10 Karsts

limestone

10

DRASTIC weight: 5 DRASTIC weight: 4 DRASTIC

weight: 1

DRASTIC weight: 3 DRASTIC weight: 3 DRASTIC weight: 5 DRASTIC weight: 2

Source: Aller et al. (1987)

Environ Earth Sci (2014) 71:3119–3131 3121

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of aquifers (Aller et al. 1987). Vulnerability to contami-

nation is defined as a dimensionless index function of

hydrogeological factors, contamination sources, and

anthropogenic effects in any specific area (Plymale and

Angle 2002). Groundwater vulnerability commonly refers

to the potential contamination from nonpoint sources or

distributed point sources of pollution, such as pesticides or

nitrates from fertilizers in agricultural practices. Since the

development of the DRASTIC model (Aller et al. 1987) for

groundwater vulnerability assessment by USEPA in the

late 1980s, this type of indexing method has become

popular, and widely used in the US, and worldwide

(Sinkevich et al. 2005; Werz and Hotzl 2007). The index

consists of seven parameters with different weighting fac-

tors and is calculated by

V ¼X7

i¼1

W i Rið Þ; ð1Þ

where V is the index value and W i is the weighted coeffi-

cient for parameter i, with an associated rating value of Ri.

The following physical parameters are included in theDRASTIC method:

D Depth to water table from the soil surface

R net recharge

A Aquifer media

S Soil media

T Topography

I Impact of the vadose zone media

C Conductivity (hydraulics) of the aquifer

Each of these hydrogeological factors is given a rating

from ‘‘1’’ to ‘‘10’’, and the DRASTIC parameters are

weighted from ‘‘1’’ to ‘‘5’’ according to their relative

contribution to the potential contamination (Aller et al.

1987). The resulting index is a relative measure of vul-

nerability to contamination. Areas with a higher index

value are more vulnerable, as compared with those with a

lower index. The rates and weights of the original

DRASTIC model parameters are presented by Aller et al.

(1987). The seven layers of the DRASTIC model are pre-

sented in Fig. 2.

Fig. 1 Study area

Table 2 Sources of data used for creation of hydro-geological

parameter for DRASTIC method

No data type Sources

1. Hydrogelical data Meteorological Organization of

Kerman

2. Geology map Geolo gical su rvey of IRAN

3. So il map Soil and water research Institu te of

Kerman

4. Topography Water organizations of Kerman

5. Wells Water organizations of Kerman

6. Hydraulic

conductivity

Water organization of Kerman

7. Geological profile Water organization of Kerman

8. Groundwater balance

of Kerman plain

Water organization of Kerman

9. Sample wells Surveyed in my study area and took two

times samples using GSP technique


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Fig. 2 Seven layers of DRASTIC model (a depth of water, b recharge, c aquifer media, d soil, e topography (slope %), f impact of vadose zone,

g hydraulic conductivity)


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DRASTIC data layers

The assigned layers for the seven DRASTIC parameters

were constructed in raster GIS, based on Table 1.

Depth of water

The water table depths were measured from 28 observationwells. The ArcGIS Geostatistical Analyst extension by

Krigging interpolation was applied to interpolate the points

and to develop the raster map with a pixel size of 100 m.

Krigging was previously used to obtain significant results

in groundwater level analysis (Kumar 2007; Gundogdu and

Guney 2007; Theodossiou 1999). The methodology for

groundwater vulnerability measures the water table depth

from the surface as the parameter of interest. This param-

eter represents the distance that a contaminant must travel

from the surface to reach the groundwater. A deeper water

level indicates a longer time for contamination (Aller et al.

1987). The depths to the water levels for the Kerman plainare classified into three classes: 15–23 m, 23–30 m, and

[30 m, with depth to water rates (Dr) of 3, 2, and 1,

respectively. Given that the study area is located in arid and

semi arid regions, two classes less than 30 m were defined

to represent the area of irrigation return flow.

Net recharge (R)

The net recharge is considered the result of rainfall infil-

tration, irrigation return flow, and absorption wells in the

study area. The total net recharge was computed by the

Kerrman Water Authorities.

Aquifer media (A)

Classification of this area was based on the drilling logs for

each well. Two sections of the aquifer rock are basically

composed of marl and conglomerate rocks in the southern

regions and a small area in the northwestern region. The

extensive sand deposits with a very low percentage of fine-

grained material were identifiedas gravelandsand.Depositsof

fine to coarse sand (fine-medium sand) extended across the

northern andnortheastern regions of the study area. Deposits of

silt and clay were located exclusively in the middle region of

the study area. According to Aller et al. (1987) and Rahman

(2008), glacial till is a mixture of gravel, sand, silt, and clay.

Soil media (S)

The soil map of the Soil and Water Institute of Kerman was

used. The soil media layer indicated the recharge rate that

could infiltrate into the pollution. The soil map consists of

clay loam, gravel, loam, non-shrinking and non-aggregated

clay, sand, sandy loam, and silty loam. Aller et al. (1987)

stated that the maximum rate belonged to gravel, sand, and

sandy loam. Based on the observed soil media layer, sand

with high permeability was located in the northern and

southern regions of the study area.

Topography (T)

Topography in the DRASTIC model displays the slope of

the land surface. The topography was derived from the

digital elevation model using a topographic map

(1:25,000). The topography of the area was divided into

five classes (Aller et al. 1987), which were mostly found in

areas with slopes ranging from 0 to 2 % and from 2 to 6 %.

Impact of the vadose zone (I)

The impact of the vadose zone was classified based on the

drilling logs for each well. The most significant part of thearea included gravel and sand with silt and clay (15–30 %)

in the western region. A small section located in the

northeastern region of the study area exclusively contains

gravel and sand.

Hydraulic conductivity (H)

The hydraulic conductivity of the aquifer was computed

according to the following equation: k ¼ T b

, where k is the

hydraulic conductivity of the aquifer (m/s), T is the trans-

missivity (m2 /s), and b is the thickness of the aquifer (m).

The hydraulic conductivity distribution map was generatedusing pumping test results and a geoelectrical study of the

area. Regions with maximum hydraulic conductivity

exhibited higher chances of distribution contamination.

Hydraulic conductivity was derived by measurement, and

the GIS-ArcView was applied to interpolate the hydraulic

conductivity and create the raster layer. Hydraulic con-

ductivity could be divided into three classes.

All required layers were created; each layer was classified

using the different rating scales The DRASTIC index wasthen

determined by multiplying the obtained values with the weight

factor. The obtained index was divided into seven groups

(Aller et al. 1987). The vulnerability indices and the corre-sponding area percentages are presented in Figs. 3 and 4.

Nitrate measurements

The nitrate concentration was selected as the main

parameter of the initial contamination to calibrate the

DRASTIC model. A total of 27 agricultural wells were

chosen for the analysis and sampling, with two nitrate

samples obtained from each well. The first nitrate sample


1 3



was obtained in May 2010 to calibrate the model, and the

second nitrate sample was obtained in May 2011 to

determine the correlation coefficient between the nitrate

concentration and groundwater vulnerability. The May

2011 samples were normalized before they were used. The

exact location of each well was determined using global

positioning system techniques.

Calibration method

The rates of DRASTIC were initially modified using the Wil-

coxon rank-sum nonparametric statistical test. Subsequently,

the modified DRASTIC was applied for sensitivity analysis.

Nitrate was selected as the primary control parameter in the

study. This parameter was used to change the DRASTIC rates.

Nitrate is not naturally found in groundwater, but it usually

enters via the surface. The Kerman plain is situated in an

agricultural area where fertilizer use is common. Therefore, the

nitrate concentration can be used as an indicator of the vul-

nerability index to reflect the actual situation in the study area.

The following conditions must be met when using nitrate tooptimize the weights and rates: (1) the mean nitrate concen-

tration should be an effect of the agricultural activities on the

surface. (2) The distribution area should be relatively uniform.

(3) Leaching of nitrate occurs because of recharges from the

surface over a long period of time. To ensure the correlation

between contamination and human activities, the vulnerability

index would increase with the increasing nitrate concentration.

Finally, the correlation between nitrate concentration and vul-

nerability would illustrate the higher means vulnerabilityindex.

Agriculture is the primary activity in the selected study area,

thereby ensuring that these basic conditions are satisfied (Pan-

agopoulos et al. 2006; Javadi et al. 2011a, b). The Wilcoxonrank-sum nonparametric statistical test was used to modify the

rates of the DRASTIC model. Sensitivity analysis was then

applied to optimize the weights of the DRASTIC model.

Sensitivity analysis

According to Babiker et al. (2005), Saidi et al. (2011), the

applied weights for calculating the vulnerability index

Fig. 3 Original vulnerability

map

Fig. 4 The percentage of DRASTIC result


1 3



could differ, depending on the study area. The impact of

the weights of each parameter with their theoretical

weights was compared using the single-parameter sensi-

tivity analysis. In this study, the vulnerability index was

calibrated using rate modification. The influence of the

parameters in the index computation was evaluated using

sensitivity analysis. To investigate for any improvement in

the newly developed DRASTIC map, the nitrate distribu-

tion was compared. The effective weight of each polygon

is defined as

W ¼ Pr Pwð Þ V ð Þ 100; ð2Þ

where W is the effective weight of each parameter and V is

the overall vulnerability index. Pr and Pw are the rating

value and weight of each parameter, respectively. The

ArcGIS software was used to calculate all combinations of

parameters and their weights. A total of 526 unique suba-

reas were found in the study area; these subareas were

considered in the statistical analysis of the results. The

effective weight derived from the single-parameter sensi-

tivity analysis is shown in Table 5. The depth to water

displays the lowest effective weights (mean effective

weight, 4.12 wt%) compared with the theoretical weights

(21.74 wt%). The net recharge, aquifer media, and

hydraulic conductivity had higher effective weights than

the theoretical weights assigned by DRASTIC.

Fig. 5 Original vulnerability

map and nitrate (NO3)

concentration for study area

Table 3 Correlation factors between nitrate concentration and ori-

ginal vulnerability index

Pearson’s correlation

coefficient (%)

Number of

data

Factor

100 27 Nitrate concentration

44 DRASTIC index

Fig. 6 Relationship of DRASTIC intrinsic vulnerability index and

modified DRASTIC to groundwater nitrates concentration for the

Kerman plain


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Fig. 7 Modified DRASTIC

map

Table 4 Original and modified

DRASTIC weighting rates

based on nitrate concentration

Depth to water table: nitrate

concentration divided to six

classes, not used for depth of

water

Factor Range Original rating Mean NO3 Modified rating

Depth to groundwater 15–23 3 No data 3

23–30 2 No data 2

[30 1 No data 1

Recharge (mm) 0–50.8 1 13.4 7.12

50.8–101.6 3 12.8 6.8

101.6–177.8 6 18.8 10

Soil type Clay loam 3 15.13 8.1

Gravel 10 No data 10

Silty loam 4 13.34 7

Loam 5 10 5.3

Sandy loam 6 8.3 4.4

Sand 9 18.83 10

Non-shrinking 1 12 6.4

Topography 0–2 10 13.13 2.5

2–6 9 14.58 2.8

6–12 5 11.59 2

12–18 3 53.2 10

[18 1 No data 1

Impact of vadose

zone

Silt/clay 3 19.6 10

Sand and gravel 8 7.4 3.8

Sand and gravel with silt and

clay

6 13 6.7

Aquifer media Marlstone 2 15.4 9.1

Silt and clay 5 17 10

Sandstone 6 No data 6

Gravel sand 8 13.6 7.9


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Results and discussion

Result of index calibration and evaluation

The difference between the original DRASTIC values and

the nitrate concentration using a sample of 27 wells in May

2010 is presented in Fig. 5. Pearson’s correlation factor

(Table 3) was applied to determine the correlation between

the nitrate concentration and the DRASTIC values, as

illustrated in Fig. 6. Pearson’s correlation value was calcu-

lated at 44 %. This value is relatively low, thereby indicating

that the original vulnerability index must be changed to

obtain a realistic assessment of the potential contamination

in the study area. The highest nitrate concentration was

correlated with the highest rate. Other weighting rates were

linearly changed according to this relation. In the current

method, the rates of the net recharge, aquifer media,

hydraulic conductivity, impact of vadose zone, soil media,

and topography were modified based on the mean nitrate

concentration. The highest rate was assigned to the highest

mean nitrate concentration, the lowest rate was assigned to

the lowest mean concentration, and the remaining rates were

linearly modified. This approach was applied to all layers, as

shown in Table 4, and to the new weight of the modified

DRASTIC model with sensitivity analysis. The new

DRASTIC map was computed by applying the modified

system, as shown in Fig. 7. The modified DRASTIC model

concentration and the percentage of its results are shown in

Fig. 8. The Pearson’s correlation factor was computed forthe modified DRASTIC model; its value was increased to

82 % in the new model, as indicated in Table 5 and Fig. 6.

The calibration results suggested that the modified

DRASTIC model significantly affects the study area.

Nitrate is important to obtain better results in the vulner-

ability map, considering that most of the lands in the

Kerman plain are agricultural. The new rates and weights

of the modified DRASTIC map indicated that 41.34 % of

the area belonged to the very high and high vulnerability

class. The percentage for this class was 50.09 % before the

modification. The percentages for the moderate class

before and after modification were 30.81 and 39.85 %,respectively. The low and very low classes covered 19.09

and 18.81 %, before and after the application of the new

rates, respectively. These results clarified the effect of

modification. In addition, maps were compared to show the

spatial distribution of the index before and after modifi-

cation, as shown in Fig. 9. The result indicated that

45.72 % of the results had a similar class, but 54.28 %

belonged to a different class, thereby verifying the effec-

tiveness of the proposed method. The first nitrate test in

2010 was applied to calibrate the DRASTIC model and to

create the modified DRASTIC model. The second nitrate

test was used to compute for the correlation factor. The

application of the new rates and weights to the layers

clarified the effect of the modification. The region of higher

vulnerability in both maps is situated around the boundary

of the study area, which is mainly composed of highly

permeable sand and gravel. In the modified DRASTIC

model, the vulnerability increases in the center of the

Kerman plain because of agricultural activities (Table 6).

Fig. 8 Percentage of modified DRASTIC result

Table 5 Single-parameter

sensitivity analysis on modified

DRASTIC

Parameter DRASTIC weight Theoretical weight (%) Effective weight (%)

Minimum Mean Maximum SD

D 5 21.74 2.91 4.12 10.49 1.47

R 4 17.39 14.20 22.47 31.01 4.54

A 3 13.04 12.33 17.52 26.09 2.99

S 2 8.70 4.73 9.49 18.35 3.05

T 1 4.35 0.61 2.34 8.93 2.14

I 5 21.74 10.71 23.37 35.71 6.40

C 3 13.04 10.47 15.83 23.81 3.42


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Conclusion

The Kerman plain is located in an arid and semi-arid

region. With groundwater as the only water source in the

area, the evaluation of groundwater quality is crucial. The

increased pumping rates from the water table and the

decreased rainfall in the area accounted for the assessed

groundwater vulnerability in the area.

Applying the DRASTIC model in this region usually

provides a satisfactory assessment of the intrinsic vulner-

ability of groundwater to pollution. In addition, the

majority of the area consists of agricultural land where

inorganic fertilizers are commonly used. Thus, the nitrate

concentrations in the groundwater are primarily due to the

leaching of nitrate from the soil surface layers to the

groundwater. Given the abovementioned considerations,

the original DRASTIC algorithm required calibration and

modification to obtain more accurate results. Thus, we

developed and applied the modified DRASTIC model. Thecorrelation factor between the nitrate concentrations and

the original vulnerability index was evaluated at 44 %,

whereas the correlation factor between the nitrate con-

centrations and the modified DRASTIC model was calcu-

lated at 82 %. These results indicated that the modified

DRASTIC model could provide better results, as compared

with the original DRASTIC model. An advantage of the

modified DRASTIC model is its flexibility for adjusting the

rates and weights of the said model.

The modified DRASTIC model in this study is recom-

mended for evaluating groundwater vulnerability to pol-

lution in agricultural lands with extensive use of nitrates.

The conditions of a specific area significantly influence the

type of modifications to be applied in the DRASTIC model.

The DRASTIC weights can be varied to improve the model

in this study.

Acknowledgments The authors would like to thank Saman Javadi

and Mohsen Dadras for their valuable contribution in this manuscript.

Thanks to three anonymous reviewers for their helpful reviews which

helped us to improve the quality of the earlier version of the

manuscript.

References

Alexander WJ, Liddle SK, Mason RE, Yeager WB (1986) Ground-

water vulnerability assessment in support of the first stage of the

National Pesticide Survey. Research triangle Park, Research

Triangle Institute, Contract No. EPA 68-01 6646, US Environ-

mental Protection Agency, Washington, DCAl-Hanbali A, Kondoh A (2008) Groundwater vulnerability assess-

ment and evaluation of human activity impact (HAI) within the

Dead Sea groundwater basin, Jordan. Hydrogeol J 16(3):

499–510

Aller L, Bennet T, Lehr JH, Petty RJ, Hacket G (1985) DRASTIC: A

standardized system for evaluating groundwater pollution using

hydrological settings. Prepared by the National water Well

Association for the US EPA Office of Research and Develop-

ment, Ada

Aller L, Bennet T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: a

standardized system for evaluating groundwater pollution

potential using hydrogeological settings. EPA/600/2–87/035.

US Environmental Protection Agency, Ada

Antonakos AK, Lambrakis NL (2007) Development and testing of

three hybrid methods for assessment of aquifer vulnerability tonitrates, based on the DRASTIC model, an example from NE

Korinthia, Greece. J Hydrol 333(2–4):288–304

Ayazi MH, Pirasteh S, Arvin AKP, Pradhan B, Nikouravan B, Mansor

S (2010) Disasters and risk reduction in groundwater: Zagros

Mountain Southwest Iran using geoinformatics techniques.

Disaster Adv 3(1):51–57

Babiker I, Mohamed M, Hiyama T, Kato K (2005) A GIS based

DRASTIC model for assessing aquifer vulnerability in Kakami-

gahara Heights, Gifu Prefecture, Central Japan. Sci Total

Environ 345(1–3):127–140

Bojorquez-Tapia LA, Cruz-Bello GM, Luna-Gonzalez L, Juarez L,

Ortiz-Perez MA (2009) V-DRASTIC: using visualization to

engage policymakers in groundwater vulnerability assessment.

J Hydrol 373:242–255

Carvalho GJP (2009) Vulnerabilidade a contaminacao d a s aguassubterraneas na bacia hidrografica do Rio Sordo: comparacao de

modelos baseados no metodo DRASTIC. MSc thesis, Tras-os-

Montes and Alto Douro University, Vila Real

Ckakraborty S, Paul PK, Sikdar PK (2007) Assessing aquifer

vulnerability to arsenic pollution using DRASTIC and GIS of

North Bengal Plain: a case study of English Bazar Block, Malda

District, West Bengal, lndia. JOSH 7(1):101–121

Daly D, Drew D (1999) Irish methodologies for karst aquifer

protection. In: Beek B (ed) Hydrogeology and engineering

geology of sinkholes and karst. Balkema, Rotterdam, pp 267–

327

Table 6 Correlation factors between nitrate concentration and

modified vulnerability index

Pearson’s correlation

coefficient (%)

Number of data Factor

100 27 Nitrate index

82 DRASTIC index

Fig. 9 The percentage of priority between DRASTIC and modified

DRASTIC


1 3



Denny SC, Allen DM, Journea YM (2007) DRASTIC-Fm: a modified

vulnerability mapping method for structurally controlled aquifers

in the southern Gulf lslands, British Columbia, Canada. Hydro-

geol J 15:483–493

Dixon B (2004) Prediction of groundwater vulnerability using an

integrated GIS-based neuro-fuzzy techniques. J Hydrol

4(309):17–38

Dixon B (2005) Groundwater vulnerability mapping: a GIS and fuzzy

rule based integrated tool. Appl Geogr 25(4):327–347

Ettazarini S (2006) Groundwater pollution risk mapping for the

Eocene aquifer of the Oum Er-Rabia basin, Morocco. Environ

Geol 51(3):341–347

Foster SS (1987) Fundamental concepts in aquifer vulnerability,

pollution risk and protection strategy. In: Duijvenbooden W van,

Waegeningh HG van (eds) TNO Committee on Hydrological

Research, The Hague Vulnerability of soil and groundwater to

pollutants. Proceedings and Information, vol 38, pp 69–86

Fritch TG, McKnight CL, Yelderman JC, Arnold JG (2000) An

aquifer vulnerability assessment of the Paluxy aquifer, Central

Texas, USA, using GIS and a modified DRASTIC approach.

Environ Manage 25:337–345

Gundogdu KS, Guney I (2007) Spatial analyses of groundwater level

using universal Kriging. J Earth Syst Sci 116:49–55

Hailin Y, Ligang X, Chang Y, Jiaxing X (2011) Evaluation of

groundwater vulnerability with improved DRASTIC method.

Procedia Environ Sci 10:2690–2695

Hamza MH, Added A, Rodrıguez R, Abdeljaoued S, Mammou AB

(2007) A GIS-based DRASTIC vulnerability and net recharge

reassessment in an aquifer of a semi-arid region (Metline-Ras

Jebel-Raf Raf aquifer, Northern Tunisia). J Environ Manage

84:12–19

Harbaugh AW, Banta ER, Hill MC, Mcdonald MG (2000) MOD-

FLOW-2000, The US Geological Survey Modular Ground-water

Model- Users guide to modularization concepts and the ground-

water flow process. US Geological Survey Open-File Report

00-92, 121

Huan H, Wang J, Teng Y (2012) Assessment and validation of

groundwater vulnerability to nitrate based on a modified

DRASTIC model: a case study in Jilin City of northeast China.

Sci Total Environ (Article online first available). http://dx.doi.

org/10.1016/j.scitotenv.2012.08.037

Javadi S, Kavehkar N, Mousavizadeh MH, Mohammadi K (2011a)

Modification of DRASTIC model to map groundwater vulner-

ability to pollution using nitrate measurements in agricultural

areas. J Agr Sci Tech 13(2):239–249

Javadi S, Kavehkar N, Mohammadi K, Khodadi A, Kahawita K

(2011b) Calibration DRASTIC using field measurements, sen-

sitivity analysis and statistical method to assess groundwater

vulnerability. Water Int 36(6):719–732

Kalinski RJ, Kelly WE, Bogardi I, Ehrman RL, Yamamoto PO (1994)

Correlation between DRASTIC vulnerabilities and incidents of

VOC contamination of municipal wells in Nebraska. Ground

Water 32(1):31–34

Kumar V (2007) Optimal contour mapping of groundwater levelsusing universal Kriging—a case study. Hydrol Sci J

52:1038–1050

Leone A, Ripa MN, Uricchio V, Deak L, Vargay Z (2009)

Vulnerability and risk evaluation of agricultural nitrogen pollu-

tion for Hungary’s main aquifer using DRASTIC and GLEAMS

models. J Environ Manage 90(10):2969–2978

Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli

MF (2012) Application of probabilistic-based frequency ratio

model in groundwater potential mapping using remote sensing

data and GIS. Arab J Geosci. http://dx.doi.org/10.1007/s12517-

012-0795-z

Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N (2013)

A knowledge-driven GIS modelling technique for groundwater

potential mapping at the Upper Langat Basin, Malaysia. Arab J

Geosci 6(5):1621–1637. http://dx.doi.org/10.1007/s12517-011-

0469-2

McLay CDA, Dragden R, Sparling G, Selvarajah N (2001) Predicting

groundwater nitrate concentrations in a region of mixed

agricultural land use: a comparison of three approaches. Environ

Pollut 115:191–204

Melloul AJ, Collin M (1998) A proposed index for aquifer water-

quality assessment: the case of lsrael’s Sharon region. J Environ

Manage 54:131–142

Mimi ZA, Mahmoud N, Madi MA (2012) Modified DRASTIC

assessment for intrinsic vulnerability mapping of karst aquifers:

a case study. Environ Earth Sci 66(2):447–456

Naqa A, Hammouri N, Kuisi M (2006) GIS-based evaluation of

groundwater vulnerability in the Russeifa area, Jordan. Revista

Mexicana de Ciencias Geologicas 23(3):277–287

National Research Council (1993) Groundwater vulnerability assess-

ment: predicting relative contamination potential under condi-

tions of uncertainty. Committee for assessing ground water

vulnerability. National Academy Press, Washington, DC

Nobre RCM, Filho OCR, Mansur WJ, Nobre MMM, Cosenza CAN

(2007) Groundwater vulnerability and risk mapping using GIS,

modeling and a fuzzy logic tool. J Contam Hydrol 94:277–292

Pacheco FAL, Sanches Fernandes LF (2012) The multivariate

structure of DRASTIC model. J Hydrol. doi:http://dx.doi.org/

10.1016/jhydrol.2012.11.20

Panagopoulos GP, Antonakos AK, Lambrakis NJ (2006) Optimiza-

tion of the DRASTIC method for groundwater vulnerability

assessment via the use of simple statistical methods and GIS.

Hydrol J 14:894–911

Pathak DR, Hiratsuka A (2011) An integrated GIS based fuzzy

pattern recognition model to compute groundwater vulnerability

index for decision making. J Hydro-environ Res 5:63–77

Plymale CL, Angle MP (2002) Groundwater pollution potential of

Fulton County, Ohio. Ohio Department of natural resources

division of water, water resources section. Groundwater Pollu-

tion Potential, Report No 45

Pradhan B (2009) Ground water potential zonation for basaltic

watersheds using satellite remote sensing data and GIS tech-

niques. Cent Eur J Geosci 1(1):120–129. http://dx.doi.org/10.

2478/v10085-009-0008-5

Rahman A (2008) A GIS based DRASTIC model for assessing

groundwater vulnerability in shallow aquifer in Aligarh, India.

Appl Geogr 28(2008):32–53

Remesan R, Panda RK (2008) Groundwater vulnerability assessment,

risk mapping, and nitrate evaluation in a small agricultural

watershed: using the DRASTIC model and GIS. Environ Qual

Manage 5(4):53–75

Rupert MG (1999) Improvements to the DRASTIC groundwater

vulnerability mapping method. US Geological Survey Fact Sheet

FS-066-99, USGS, Reston, VA, USA

Saidi S, Bouri S, Dhia HB (2010) Groundwater vulnerability and risk mapping of the Hajeb-jelma aquifer (Central Tunisia) using a

GIS-based DRASTIC model. Environ Earth Sci 59(7):

1579–1588

Saidi S, Bouria S, Dhiaa HB, Anselmeb B (2011) Assessment of

groundwater risk using intrinsic vulnerability and hazard map-

ping: application to Souassi aquifer, Tunisian Sahel. Agr Water

Manage 98:1671–1682

Secunda S, Collin ML, Melloul AL (1998) Groundwater vulnerability

assessment using a composite model combining DRASTIC with

extensive agricultural land use in Israel’s Sharon region.

J Environ Manage 54:39–57


1 3

http://dx.doi.org/10.1016/j.scitotenv.2012.08.037


http://dx.doi.org/10.1007/s12517-012-0795-z

http://dx.doi.org/10.1007/s12517-012-0795-z

http://dx.doi.org/10.1007/s12517-011-0469-2

http://dx.doi.org/10.1007/s12517-011-0469-2

http://dx.doi.org/10.1016/jhydrol.2012.11.20


http://dx.doi.org/10.2478/v10085-009-0008-5

http://dx.doi.org/10.2478/v10085-009-0008-5

http://dx.doi.org/10.2478/v10085-009-0008-5

http://dx.doi.org/10.2478/v10085-009-0008-5



http://dx.doi.org/10.1007/s12517-011-0469-2

http://dx.doi.org/10.1007/s12517-011-0469-2

http://dx.doi.org/10.1007/s12517-012-0795-z

http://dx.doi.org/10.1007/s12517-012-0795-z





Shukla S, Mostaghimi S, Shanholt VO, Collins MC, Ross BB (2000)

A county-level assessment of ground water contamination by

pesticides. Ground Water Monit R 20(1):104–119

Sinkevich MG, Walter MT, Lembo AJ Jr, Richards BK, Peranginan-

gin N, Aburime SA, Steenhuis TS (2005) A GIS-based ground

water contamination risk assessment tool for pesticides. Ground

Water Monit R 25(4):82–91

Theodossiou N (1999) Evaluation of the distribution of hydraulic

head in an aquifer using the Kriging method. Sci J Hellenic

Hydrotech Assoc Hydrotech 9:3–14

Thirumalaivasan D, Karmegam M, Venugopal K (2003) AHP-

DRASTIC: software for specific aquifer vulnerability assessment

using DRASTIC model and GIS. Environ Modell Softw

18:645–656

Van Stemproot D, Evert L, Wassenaar L (1993) Aquifer vulnerability

index: a GIS compatible method for groundwater vulnerability

mapping. Can Water Resour J 18:25–37

Werz H, Hotzl H (2007) Groundwater risk intensity mapping in semi-

arid regions using optical remote sensing data as an additional

tool. Hydrogeol J 15(6):1031–1049

Wilcoxon F (1945) Individual comparisons by ranking methods.

Biometr Bull 1:80–83

Yin L, Zhang E, Wang X, Wenninger J, Dong J, Guo L, Huang J

(2012) A GIS-based DRASTIC model for assessing groundwater

vulnerability in the Ordos Plateau, China. Environ Earth Sci

69(1):171–185

Yuan M, Zhang X, Wang L (2006) Fuzzy pattern recognition method

for assessing groundwater vulnerability to pollution in the

Zhangji area. J Zhejiang Univ Sci A 7(11):1917–1922

Zhou H, Wano O, Yang Q (1999) A multi-objective fuzzy pattern

recognition model for assessing groundwater vulnerability based

on the DRASTIC system. Hydrol Sci J 44:611–618


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