Environ. Eng. Res.

2018; 23(1): 84-91 pISSN 1226-1025

https://doi.org/10.4491/eer.2017.072 eISSN 2005-968X

Groundwater pollution risk mapping using modified

DRASTIC model in parts of Hail region of Saudi Arabia
Izrar Ahmed1, Yousef Nazzal2†, Faisal Zaidi3
1
Environment Department, Dubai Municipality, Dubai, UAE
2
College of Natural & Health Sciences, Zayed University, P.O.Box 144534, Abu Dhabi, UAE
3
Department of Geology & Geophysics, King Saud University, Riyadh, Saudi Arabia

ABSTRACT
The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi
Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes
determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations
in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical
data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible
contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment.
DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use
pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done
with GOD and AVI vulnerability models. Model validation was done with NO3, SO4 and Cl concentrations. These maps help to assess the
zones of potential risk of contamination to the groundwater resources.

Keywords: Al Hail, DRASTIC aquifer vulnerability, Groundwater resources, Landuse pattern, Risk map, Saudi Arabia

1. Introduction component of recharge, nature of vadose zone, aquifer parameters.

The collective impact of these components would define an area
in terms of its susceptibility to contamination from surface. The
Management of water resources is a critical issue in arid countries
combined effect of selected hydrogeological parameters helps in
like Saudi Arabia. Factors like limited rainfall, high evaporation
determining pollution attenuation capacity of an area. The spatial
and lack of surface water have adverse effect on groundwater
variation of parameters further helps in determining relative sus-
resources. With time, groundwater resources get minimal replen-
ceptibility in a way that the area either susceptible or relatively
ishment and restricted into limited geological time and space.
safer from pollution depending upon actual behavior of the
Due to increasing pace of urbanization and agricultural expansions,
parameters. The relationship of each parameter and pollution can
groundwater resources are being over-exploited resulting to an
be estimated through relative rating criteria. Many studies eval-
unsustainable declining trend. These factors can cause severe deg-
uated the potential of a hydrogeological environment to pollution
radation to the quality and quantity of groundwater resources
contamination using different models including DRASTIC [9],
[1, 2]. Studies conducted in different parts of the Saq and overlying
SEEPAGE [10], GOD [11], EPIK [12], and AVI [13]. These methods
aquifers suggested that groundwater resources are quantitavely
integrate various hydrological and hydrogeological parameters
over-exploited [3, 4] and facing quality deterioration through in-
classified by unique ratings and weights.
creased human interaction and rock water interaction [5-8]. For
DRASTIC technique was developed by the US Environmental
sustainable solutions, groundwater resources need to be protected
Protection Agency (EPA) as a technique for assessing groundwater
from over-exploitation and quality deterioration. This in turn would
pollution potential. The product of variable rating classes and
require detailed information of hydrogeological behavior of aquifer
constant weight assigned to each parameter generates weighted
under consideration; which would come after assessment of various

This is an Open Access article distributed under the terms Received June 11, 2017 Accepted October 8, 2017
of the Creative Commons Attribution Non-Commercial License
†
(http://creativecommons.org/licenses/by-nc/3.0/) which per- Corresponding author
mits unrestricted non-commercial use, distribution, and reproduction in any Email: yousef.nazzal@zu.ac.ae
medium, provided the original work is properly cited. Tel: +971-509620945
Copyright © 2018 Korean Society of Environmental Engineers

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 Environmental Engineering Research 23(1) 84-91

index data layer. Several researchers have addressed the necessity Precambrian elevated complex of igneous and metamorphic rock
to modify the DRASTIC method integrating landuse pattern [14, units known as the Arabian Shield. The northern part of the prov-
15], sensitivity analysis [13], and to validate with groundwater ince is covered by the Great Nafud desert area. The southern
quality parameter. Some worker modified DRASTIC by adding side is underlain by resistant rugged igneous and metamorphic
parameters like land use pattern [16-18]. mountain chains. Between these two geomorphologic landscapes,
The present study outcome would provide information which there are plain expanses hosting the most urban and agricultural
can be put together for decision making tool for groundwater re- land of the region.
source protection and management of Hail region.
2.3. Geology and Hydrogeology
The Saq formation rests directly on the crystalline rocks of the
2. Study Area Precambrian basement, and overlain by the lower part of the Qassim
Formation. Formations overly Saq formation and deposited towards
2.1. Location SE direction. Towards south-eastern direction from Saq formation,
The Hail region lies between approx. 40oE to 42o30//E and 26o50//N a number of geological formations like Hanadir, Kahfa, Raan,
to 28o33//N. This is predominately flat region having no drainage Quwara-Sarah, Qusaiba, Sharawra, Tawil, and Jauf formations fall
network with general slope towards the South-East. The north with variable thickness.
part of Hail region is covered by great Nafud Sand while the At these locations the Saq aquifer is predominately tapped at
southern part is characterized by rugged rocks of variable origin. variable depths. The aquifer has extensive outcrop areas along
The south-east part of the study area provides good cultivated the boundary with the Arabian Shield in the west, where it receives
tracks (Fig. 1). Groundwater forms main source of irrigation, where some recharge which is less than the volumes abstracted from
shallow and deep wells extract water from the Saq and overlying the aquifer. Besides Saq aquifer, other important water supply
aquifer. The main townships of the study area are Tayma, Jubbah aquifers are Kahfah, Quwarah-Sarah. Most water-supply wells are
and Baqa. located within the irrigated areas. The main aquifers of the study
area are Saq, Kahfah and Sarah quwara, although wadi alluviums
are also serving as source of groundwater at places. The Hanadir
and Raan formations are also act as aquitard. The Saq, Kahfah
and Sarah-Quwara have averaged hydraulic conductivity 5 m/d,
10 m/d and 1 m/d, respectively [20].

3. Methodology
The DRASTIC vulnerability index method is based combined im-
pact of weighted index data layers. Each weighted index data
layer is made up after assigning suitable ratings to spatial data
variations and later on multiplying by constant weight factor des-
ignated with each layer. The DRASTIC parameters are depth to
groundwater (D) net recharge (R), aquifer media (A), soil media
(S), topography (T), impact of vadose zone (I) and hydraulic con-
ductivity (C). Each parameter is assigned a unique weight (wi)
and rating (ri). The weight factors range from 1 to 5 showing
significance scale from least to highest. The highest weight is
assigned to parameter layer which allows pollution passage at
Fig. 1. Base map of the study area. comparatively higher rate. The linear additive combination of the
above parameters with weights and ratings was used to calculate
2.2. Climate and Physiography the DRASTIC vulnerability index (DVI) as given below;
The weather in Hail city is generally arid to extremely arid with
summer day time temperatures typically rise to 50°C with a diurnal                       (1)
variation of about 25°C. Climatic data of Hail as provided by the
Saudi Ministry of Defense and Aviation reveal an arid to extremely Where suffixes r and w represents rating and weight assigned
arid pattern. Precipitation approaches 110 mm with two peaks to each parameter. A land-use map was generated using MODIS
of rainfall at March and November. Annual evaporation rates at data with 5 land-use classes to account their role in groundwater
the central part of Saudi Arabia, including Hail, approach 3,480 contamination and land-use classes.
mm [19]. Comparison of DRASTIC model was done with GOD and AVI
Hail region is characterized by its variation in topography and (Aquifer vulnerability Index) models. The GOD vulnerability model
geomorphology. Hail region forms a wide plateau overlying stands for Groundwater occurrence (G), Overall lithology of aquifer

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or aquitard (O) and Depth to groundwater (D) (Foster et al. 2006). Table 1. DRASTIC Parameters
Whilst, AVI model considers two key parameters i.e. thickness DRASTIC
of each sedimentary unit above the uppermost aquifer and hydraul- Range Ratings Weight
parameters
ic conductivity of each of these layers. For comparison, vulner-
0-25 10
ability maps were normalized to values from 0 to 1 corresponding
to the ranges of vulnerability classes of the individual methods. 25-50 9
Depth to
50-100 7 5
water
100-150 5
4. Results and Discussion
>150 3
4.1. DRASTIC Model Input Parameters Recharge
3 to 9 4
Data used for various input parameters was procured from different (potential)
techniques, field experiences and sources. DRASTIC model com- Shale 3
bines all data layers representing detailed hydrogeological behavior Wadi beds, mixed lithology 5
of the area of interest. All the data layers should have uniform
Mixed sandstone/limestone 6
grid size and geographical extents. The rating of each parameter Aquifer
depends on the data variation therefore the extents of variation Dolomite/sandstone/evaporite/ 3
media 7
of each parameter encountered in the study area were carefully clastics
accounted to obtain a unique range. In the present study, the Coarse sandstone 8
DRASTIC model was further modified with unique rating classes Sanddunes, alluviums 10
of each parameter relevant to study area (Table 1). In the present
study the DRASTIC methodology with few modifications is adopted Loamy 1
and is described below. Loamy-sand 3
Soil media Loamy-rocky 5 2
4.2. Depth to Groundwater (D) and Net Recharge (R)
Sandy clay 7
The depth to groundwater (D) was obtained measuring piezometers
Sandy 10
available in the study area. The depth to water levels ranges from
50 m to >150 m below ground level (bgl). The depth to water <1 (degree) 10
is assigned highest weight of 5. In general, the deeper water levels 1---2 9
likely to be less vulnerable to contamination than the shallow 2---4 8
water depths. This is due to longer time that the pollutants would
take to reach the ground water table. A weighted index map depth Topography 4----6 7 1
to water level is shown in Fig. 2(a). 6----12 5
Groundwater recharge studies conducted in central Saudi Arabia 12---18 3
show variable values ranging from 4 mm/y [20] to 20 mm/y [21].
>18 1
At the scale of the entire Saq area, groundwater recharge most
probably does not exceed 5 mm/y [20]. The potential recharge Loam 1
variable of R has been calculated from collective ratings of parame- Siltstone 2
ters, like geology, rainfall, drainage class, and slope, which con-
Chert/shale/clay 3
verted to scale of 1 to 10. The variability of each of above parameters Impact of
has been duly accounted to obtain relative potential recharge values Sandstone/ 5
vadose zone 6
(Table 2). In the present study, potential recharge is obtained Limestone
which is subjective assessment and may not be same as calculated Dolomite 8
values but expected to follow similar aerial distribution. As a
Sand 9
result, net recharge in the study area ranges from 3 to 9 dimension-
less values. A weighted index map shows distribution of recharge 0-3 (m/d) 1
zones (Fig. 2(b)). 3-6 3
Conductivity 6-9 5 3
4.3. Aquifer Media (A) and Soil Media (S)
9-12 7
Subsurface material or precisely the aquifer material consists of
sandstone, shale, limestone, alluvium, sand and gravel in varied >12 10
proportions. Aquifer media relates with aquifer vulnerability prin-
ciple in a way that larger the gain size of material, the higher media has been prepared (Fig. 2(c)). Soil type plays an important
its permeability and lower its attenuation capacity. Using relative role providing first interaction of percolated water to an aquifer.
ratings and constant weight of 3, a weighted index map of aquifer The presence of material type, conductivity, water retention ca-

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Table 2. Rating Class Criteria to Different Index Parameter Used to Estimate Groundwater Recharge Potential
Geology Index Drainage Index Slope Index Rainfall Index
Type Rating Type Rating Range Rating Range Rating
Aquifer 10 Large 10 <1 10 150-170 10
Aquiclude 5 Medium 5 1-2 9 130-150 9
Aquitard 1 Small 2 2-4 8 110-130 8
Sabkha 1 No 1 4-6 7 90-110 7
6-12 5 70-90 5
12-18 3 50-70 3
>18 1 0-50 1

a b

c d

Fig. 2. Conceptualization of (a) Depth to water level (b) Recharge potential (c) Aquifer media and (d) Soil media within the study area.

pacity etc. are likely to affect infiltration and also to pollution slope is assigned least weight of 1 implying that rating map would
potential. The value of variable S (Soil type) was obtained from be same as that of weighted index map (Fig. 3(a)).
soil classification maps produced by the geological survey depart- Vadose zone is the unsaturated zone, lying above the water
ment of Saudi Arabia. The predominant soil types were assigned table, which plays an important role in pollution attenuation and
relative ratings and designated by constant weight of 2. The weighted groundwater recharge. The vadose zone in the study area is com-
index map of soil media shows the spatial distribution (Fig. 2(d)). posed of gravel, variable sand, coarse sandstone and clay materials.
The impact of vadose zone has been evaluated through comparative
4.4. Topography (T) and Impact of Vadose Zone (I) analysis of material favoring to water percolation downwards using
The variation in topography was obtained through elevation points harmonic mean approach [22]. A relative rating data layer was
using the triangulation method in the ARC/INFO system. The obtained and multiplied by weight of 5 to produce weighted index
change in topography i.e. the slope controls the likelihood of a vadose zone layer (Fig. 3(b)).
pollutant to be transported by runoff or to remain on the ground
where it may infiltrate the surface. The topography of the study 4.5. Hydraulic Conductivity (C)
area in the form of slope degrees range < 1º to > 18º. The topographic Hydraulic conductivity is controlled by the properties of the aquifer

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a b

c d

Fig. 3. Conceptualization of (a) Topographic slope (b) Impact of vadose zone (c) Hydraulic conductivity and (d) Land use pattern within the
study area.

Table 3. Correlations between Various Parameters and DVI

D R A S T I C DVI
D 1
R 0.240 1
A -0.288 -0.592 1
S 0.054 -0.488 0.415 1
T 0.341 0.798 -0.627 -0.477 1
I -0.050 0.254 -0.213 -0.059 0.191 1
C -0.391 -0.568 0.670 0.217 -0.514 -0.328 1
DVI 0.501 0.064 0.379 0.565 0.035 -0.045 0.257 1

material acquired during geological formation. The hydraulic con- have unique relationship with underlying groundwater
ductivity determines the rate of ground water movement in the environment. Bearing this in mind, all land-use categories were
saturated zone. As hydraulic conductivity has strong bearing on assigned different rating classes. The higher rating of a class would
pollution migration within aquifer body. In the present study, mean that this class provides favorable environment for ground-
the hydraulic conductivity values were calculated from pumping water contamination and vice-versa. The land-use data layer is
test data. Each unit is characterized by its averaged hydraulic assigned a high weight of 5 and the weighted index map of land-use
conductivity (Fig. 3(c)) parameter is shown in Fig. 3(d). The SE part is dominated by
agriculture land-use category.
4.6. Land-use Pattern (L)
A land-use map has been generated using MODIS data using GIS 4.7. DRASTIC Vulnerability Index (DVI) and Pollution Risk
software. Five land-use categories in ascending order area-wise Mapping
are Urban area < Barren rocks < Barren soils < Agricultural A correlation between DRASTIC input layers and DVI was made
area < Sand dunes were selected. Each land-use category would to see relationship between different parameters (Table 3). It is

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evident that the parameters do not correlate with each other except
recharge potential and topographic slope. This implies that each
parameter contributed to DVI scores. Although parameters such
aquifer media and vadose zone show moderate correlation. The
parameters like soil media, depth to water and aquifer media show
moderate correlation with DVI, rest of the parameters are weakly
or negatively correlated with DVI. DRASTIC vulnerability map
was obtained by combining data layers in GIS environment using
Eq. (1). Perusals of DVI map of the study area shows that the
vulnerability index varies across the study area. Given that the Fig. 6. Groundwater pollution risk map.
DVI values range were arranged into different classes; low
(100-122), medium (122-144), high (144-166) and very high (>166).
The majority of the study area is characterized by medium and
high vulnerability classes which are associated with high aquifer
media and soil media values. These parameters carry high rating
and weight itself which contributed to high magnitude of DVI
in this part of study area. For this reason, a set of measures
should be applied in order to protect ground water quality with
priority to such areas. High DVI’s are located towards central and
eastern parts of the study area. Low and medium classes of vulner-
ability were present but limited to small areas thus remained
un-mapped in present scale.
A comparison of 3 vulnerability maps was made using DRASTIC,
GOD and AVI vulnerability models (Fig. 7). All the maps were
normalized at a scale of 0 to 1. As a result of the application,
the GOD and EVI models were used to better compare with
DRASTIC vulnerability maps. GOD method is based on integration
of 3 layers that is, GOD = Groundwater occurrence (G), Overall
lithology of aquifer or aquitard (O) and Depth to groundwater

Fig. 4. Aquifer vulnerability map.

Fig. 7. Comparison of DRASTIC, GOD and AVI vulnerability maps.

table (D) (Foster et al. 2006). The determination of the GOD vulner-
ability index is normalized into 0-1 scale. It was observed, through
the GOD method, that the area of the basin has mostly high or
very high vulnerability, related to high depth of the groundwater
level and also to the low degree of confinement. In AVI vulnerability
model, hydraulic conductivity is the prominent feature determin-
ing the vulnerability classes. Moreover, the results of the DRASTIC
method indicate that most of the basin presents high to very high
vulnerability, especially in areas of shallow water levels, soil types
and aquifer media. The high to very high vulnerability classes
are corresponding with sand-dunes, alluviums and sandstone
region.
The concentrations of Nitrate (NO3), Chloride (Cl) and Sulphate
Fig. 5. Validation of aquifer vulnerability with pollution indicator. (SO4) were measured in groundwater samples at 20 locations and

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Izrar Ahmed et al.

were compared with DVI scores. These chemical parameters were karstic aquifers (Apulia Souhern Italy). Environ. Geol.
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