A GIS-based DRASTIC model for assessing aquifer

Science of the Total Environment 345 (2005) 127 – 140
www.elsevier.com/locate/scitotenv
A GIS-based DRASTIC model for assessing aquifer vulnerability
in Kakamigahara Heights, Gifu Prefecture, central Japan
Insaf S. Babiker*, Mohamed A.A. Mohamed, Tetsuya Hiyama, Kikuo Kato
Hydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
Received 6 July 2004; accepted 5 November 2004
Available online 21 January 2005
Abstract
Vulnerability assessment to delineate areas that are more susceptible to contamination from anthropogenic sources has
become an important element for sensible resource management and land use planning. This contribution aims at estimating
aquifer vulnerability by applying the DRASTIC model as well as utilizing sensitivity analyses to evaluate the relative
importance of the model parameters for aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture central Japan. An
additional objective is to demonstrate the combined use of the DRASTIC and geographical information system (GIS) as an
effective method for groundwater pollution risk assessment. The DRASTIC model uses seven environmental parameters (Depth
to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity) to
characterize the hydrogeological setting and evaluate aquifer vulnerability. The western part of the Kakamigahara aquifer was
dominated by bHighQ vulnerability classes while the eastern part was characterized by bModerateQ vulnerability classes. The
elevated north-eastern part of the study area displayed bLowQ aquifer vulnerability. The integrated vulnerability map shows the
high risk imposed on the eastern part of the Kakamigahara aquifer due to the high pollution potential of intensive vegetable
cultivation. The more vulnerable western part of the aquifer is, however, under a lower contamination risk. In Kakamigahara
Heights, land use seems to be a better predictor of groundwater contamination by nitrate. Net recharge parameter inflicted the
largest impact on the intrinsic vulnerability of the aquifer followed by soil media, topography, vadose zone media, and hydraulic
conductivity. Sensitivity analyses indicated that the removal of net recharge, soil media and topography causes large variation in
vulnerability index. Moreover, net recharge and hydraulic conductivity were found to be more effective in assessing aquifer
vulnerability than assumed by the DRASTIC model. The GIS technique has provided efficient environment for analyses and
high capabilities of handling large spatial data.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Aquifer vulnerability; DRASTIC; GIS; Model sensitivity; Kakamigahara; Japan
* Corresponding author. Tel.: +81 52 789 3473; fax: +81 52 789 3436.
E-mail address: s040116dd@mbox.nagoya-u.ac.jp (I.S. Babiker).
0048-9697/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2004.11.005
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I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
1. Introduction
Groundwater has been considered as an important
source of water supply due to its relatively low
susceptibility to pollution in comparison to surface
water, and its large storage capacity (US EPA, 1985).
However, there are significant sources of diffuse and
point pollution of groundwater from land use
activities, particularly agricultural practices. The
intrusion of these pollutants to groundwater alters
the water quality and reduces its value to the
consumer (Melloul and Collin, 1994). Prevention
of contamination is therefore critical for effective
groundwater management. Spatial variability and
data constraints preclude monitoring all groundwater
and make remediation activities expensive and often
impractical. Vulnerability assessment has been recognized for its ability to delineate areas that are more
likely than others to become contaminated as a result
of anthropogenic activities at/or near the earth’s
surface. Once identified, these areas can be targeted
by careful land-use planning, intensive monitoring,
and by contamination prevention of the underlying
groundwater.
The Kakamigahara aquifer, which is located in the
southern part of Gifu Prefecture, central Japan,
provides a source of water for domestic, industrial
and agricultural use. High nitrate levels exceeding
the 13 mg/l concentration (the human affected value)
have been encountered in groundwater of the
Kakamigahara aquifer (Terao et al., 1985, 1993;
Mohamed et al., 2003) while some concentrations
have already exceeded the maximum acceptance
level (44 mg/l NO3) according to Japan regulations
(Babiker et al., 2004). The dominant source of nitrate
contamination has been identified as agricultural land
use, particularly vegetable cultivation with some
possibility of urban sources (residential, commercial,
and industrial; Babiker et al., 2004). To ensure that
this aquifer can remain as a source of water for the
Kakamigahara area, it is necessary to estimate
whether certain locations in this groundwater basin
are more susceptible to receive and transmit pollution. Therefore, the first objective of this study is to
evaluate the Kakamigahara aquifer vulnerability
using Depth to water, net Recharge, Aquifer media,
Soil media, Topography, Impact of vadose zone, and
hydraulic Conductivity (DRASTIC), the empirical
model of the U.S. Environmental Protection Agency
(EPA; US EPA, 1985). The second objective is to
evaluate the relative importance of the DRASTIC
model parameters for assessing aquifer vulnerability
in Kakamigahara Heights through sensitivity analysis. An additional objective is to demonstrate the
combined use of DRASTIC and geographical information system (GIS) as an effective method for
groundwater pollution risk assessment and water
resource management.
2. Background
The concept of groundwater vulnerability was
first introduced in France by the end of the 1960s to
create awareness of groundwater contamination
(Vrba and Zoporozec, 1994). It can be defined as
the possibility of percolation and diffusion of
contaminants from the ground surface into the
groundwater system. Vulnerability is usually considered as an bintrinsicQ property of a groundwater
system that depends on its sensitivity to human and/
or natural impacts. bSpecificQ or bintegratedQ vulnerability, on the other hand, combines intrinsic vulnerability with the risk of the groundwater being
exposed to the loading of pollutants from certain
sources (Vrba and Zoporozec, 1994). Groundwater
vulnerability deals only with the hydrogeological
setting and does not include pollutant attenuation.
The natural hydrogeologic factors affect the different
pollutants in different ways depending on their
interactions and chemical properties.
Many approaches have been developed to evaluate aquifer vulnerability. They include processbased methods, statistical methods, and overlay and
index methods (Tesoriero et al., 1998). The processbased methods use simulation models to estimate the
contaminant migration but they are constrained by
data shortage and computational difficulties (Barbash
and Resek, 1996). Statistical methods use statistics to
determine associations between spatial variables and
actual occurrence of pollutants in the groundwater.
Their limitations include insufficient water quality
observations, data accuracy and careful selection of
spatial variables. Overlay and index methods combine factors controlling the movement of pollutants
from the ground surface into the saturated zone
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
resulting in vulnerability indices at different locations. Their main advantage is that some of the
factors such as rainfall and depth to groundwater can
be available over large areas, which makes them
suitable for regional scale assessments (Thapinta and
Hudak, 2003). However, their major drawback is the
subjectivity in assigning numerical values to the
descriptive entities and relative weights for the
different attributes.
DRASTIC is an index model designed to produce
vulnerability scores for different locations by combining several thematic layers. It was originally
developed for manual overlay of semiquantitative
data layers however the simple definition of its
vulnerability index as a linear combination of factors
shows the feasibility of the computation using GIS
(Fabbri and Napolitano, 1995). GIS are designed to
collect diverse spatial data to represent spatially
variable phenomena by applying a series of overlay
analysis of data layers that are in spatial register
(Bonham-Carter, 1996).
3. Study area
Kakamigahara Heights is composed of low hills 20
to 60 m above sea level and located on the northern
border of the Nohbi plains in central Japan (Fig. 1).
129
The Kiso River intersects the southern edge of the
area, running from east to west towards the plains.
Kakamigahara City is the third largest city in Gifu
Prefecture having a population over 135,000 inhabitants. The dominant industries are aircraft, transport
equipment and industrial metal manufacturing. Intensive vegetable cultivation, mainly carrots, is practiced
in the eastern side of the Heights, sustained by
nitrogen fertilizer applications. The area is characterized by a warm and mild climate with an average
annual temperature of 15.5 8C and a rainfall of 1915
mm, (mean of 30-year records from Gifu City rainfall
station, Japan Meteorological Agency).
Kuroboku soils, Kisogawa mudflow deposits,
and the Kakamigahara formation constitute the
Kakamigahara Terrace, which is underlain by the
Nohbi Second Gravel Formation, the Tokai Group
and the basement (Yokoyama and Makinouchi,
1991). The Kakamigahara Formation consists of
sands with pumice ranging in age from ca. 0.09 to
0.06 Ma. The Nohbi Second Gravel Formation is
composed of boulder gravels with high permeability
while the Tokai Group consists of weathered clayey
gravels with low permeability. The Kakamigahara
groundwater basin consists of the highly permeable
Nohbi Second Gravel formation and the overlying
coarse sediments. The aquifer forms an east–west
elongated valley-like topography deepening westward. The aquifer thickness ranges from 15 meters
in the east to more than 90 m in the west. Due to
the lack of confining clay layers, the aquifer is
considered typically unconfined. The groundwater
flow is from east to west and the water velocity is
high on average (2103 m/s; Babiker et al.,
2004). The aquifer is characterized by a high
recharge rate from direct infiltration (Mohamed,
2003).
4. Methods
Fig. 1. Location of Kakamigahara Heights.
A DRASTIC model applied in a GIS environment
was used to evaluate the vulnerability of the
Kakamigahara aquifer. The DRASTIC model was
developed by the U.S. Environmental Protection
Agency (EPA) to evaluate groundwater pollution
potential for the entire United States (Aller et al.,
1987). It was based on the concept of the hydro-
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geological setting that is defined as ba composite
description of all the major geologic and hydrologic
factors that affect and control the groundwater
movement into, through and out of an areaQ (Aller
et al., 1987). The acronym DRASTIC stands for the
seven parameters used in the model which are:
Depth to water, net Recharge, Aquifer media, Soil
media, Topography, Impact of vadose zone and
hydraulic Conductivity (Table 1). The model yields a
numerical index that is derived from ratings and
weights assigned to the seven model parameters. The
significant media types or classes of each parameter
represent the ranges, which are rated from 1 to 10
based on their relative effect on the aquifer vulnerability. The seven parameters are then assigned
weights ranging from 1 to 5 reflecting their relative
importance. The DRASTIC Index is then computed
Table 1
The DRASTIC model parameters
Factor
Description
Relative
weight
Depth to water
Represents the depth from the ground
surface to the water table, deeper
water table levels imply lesser chance
for contamination to occur.
Represents the amount of water
which penetrates the ground surface
and reaches the water table, recharge
water represents the vehicle for
transporting pollutants.
Refers to the saturated zone material
properties, which controls the
pollutant attenuation processes.
Represents the uppermost weathered
portion of the unsaturated zone and
controls the amount of recharge that
can infiltrate downward.
Refers to the slope of the land
surface, it dictates whether the runoff
will remain on the surface to allow
contaminant percolation to the
saturated zone.
Is defined as the unsaturated zone
material, it controls the passage and
attenuation of the contaminated
material to the saturated zone.
Indicates the ability of the aquifer to
transmit water, hence determines the
rate of flow of contaminant material
within the groundwater system.
5
Net Recharge
Aquifer media
Soil media
Topography
Impact of
vadose zone
Hydraulic
Conductivity
4
3
2
1
5
3
applying a linear combination of all factors according to the following equation:
DRASTIC Index ¼ Dr Dw þ Rr Rw þ Ar Aw þ Sr Sw
þ Tr Tw þ Ir Iw þ Cr Cw
ð1Þ
where D, R, A, S, T, I, and C are the seven
parameters and the subscripts r and w are the
corresponding rating and weights, respectively.
This model was selected based on the following
considerations. DRASTIC uses a relatively large
number of parameters (seven parameters) to compute
the vulnerability index, which ensures the best
representation of the hydrogeological setting. The
numerical ratings and weights, which were established
using the Delphi technique (Aller et al., 1987), are
well defined and are used worldwide. This makes the
model suitable for producing comparable vulnerability maps on a regional scale. The necessary information needed to build up the several model parameters
was available in the study area or could easily be
inferred. Data analyses and model implementation
were performed using the GIS software of the
International Institute for Geo-Information Science
and Earth Observation (ITC), Netherlands, bIntegrated
Land and Water Information SystemQ (ILWIS 3.1).
4.1. Preparation of the parameter maps
Several types of data were used to construct
thematic layers of the seven model parameters. A
summary of the data types, sources and usages is
available in Table 2. The location of the 134 water
wells was digitized from the accompanying topographic map and was linked to an attribute table
containing the depth to groundwater table. Essentially,
the elevation of the well and the mean water level
table were provided for the last 6 years (1997–2002).
The depth to water table was obtained by subtracting
the water table level from the elevation of the well and
averaging over a six-year period. In a relatively small
area and a fairly isotropic aquifer, the static water
table level exhibits a smooth and gradual change of
heads. Therefore, an exact interpolation scheme is
appropriate for generating a smooth surface representation for the high degree of spatial continuity of the
groundwater surface in an aquifer. The inverse
distance moving average interpolation technique was
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
131
Table 2
Data used for constructing the seven parameter layers
Data type
Source
Format
Borehole data
(water table level)
Kakamigahara City Hall
Environmental Office, Division
of Human Health and Environment
Japan Meteorological Agency website:
(http://www.data.kishou.go.jp)
Ministry of Land Infrastructure
and Transport website:
(http://tochi.milt.go.jp)
Table
Location map
Annual rainfall
(mean)
Geology map
Geological profiles
Soil map
Yokoyama and Makinouchi
Ministry of Land Infrastructure
and Transport website:
(http://tochi.milt.go.jp)
Japan Geographical Survey Institute
Land use and
topographic map
Hydraulic conductivity
Kakamigahara City Hall
Environmental Office, Division of
Human Health and Environment
Date
Used to produce
D
1:15,000
1998~2002
1997
1971~2000
R
1:50,000
1998
A
Digital
1:50,000
1991
1998
I
A and I
S
Hard copy
1:50,000
1998
T and R
Table
Digital
Table
Map
therefore performed on the point data using a limiting
search distance of 3000 m to ensure including a high
number of input points. In the moving average
algorithm, every pixel in the output map is assigned
a value based on the weighted average of all points at
a distance smaller than the limiting distance. The
weight of an input point is proportional to its distance
from the output pixel. The inverse distance is the
weight function which ensures that relatively larger
weights are assigned to points close to an output pixel.
This better satisfies the interpolation scheme and helps
generate the smooth surface representation suitable for
such type of data. The depth to water table map was
then classified into ranges defined by the DRASTIC
model and assigned rates ranging from 1 (minimum
impact on vulnerability) to 10 (maximum impact on
vulnerability). The deeper the groundwater, the
smaller the rating value.
Because the Kakamigahara aquifer is recharged
mainly by direct infiltration from precipitation
(Mohamed, 2003, Master thesis), the recharge map
was constructed from the rainfall data according to the
following formula:
Net recharge ¼ ðrainfall evapotranspirationÞ
recharge rate
Scale
ð2Þ
Pollution sources
C
~1:70,000
The rainfall map was obtained by interpolating a
thirty years mean of annual precipitation (mm/year)
from seven representative rainfall stations in the south
¯ gaki, Mino,
of Gifu Prefecture (Gifu, Mino Kamo, O
and Inigawa) and the north of Aichi Prefecture (Inabu
and Nagoya). The inverse distance moving average
interpolation technique described above was used to
construct the rainfall map. Evapotranspiration data
from the Kakamigahara was not available, so instead
the evapotranspiration value of 900 mm/year from a
representative area in central Japan (Kondoh and
Nishiyama, 2000) was used. The recharge rate was
assumed to be 20% for the urbanized area and 85%
for the rest of the study area (Babiker, 1998). The
recharge map was then classified into ranges and
assigned ratings from 1 to 10. High recharge rates
were assigned high numerical rates.
The Aquifer media and the Impact of vadose zone
were obtained using a subsurface geology map,
geological sections, and drilling profiles of the
Kakamigahara aquifer (Figs. 4 and 7 in Yokoyama
and Makinouchi, 1991). The subsurface geology map
was imported in digital format from the website of the
Japan Ministry of Land, Infrastructure and Transport
(http://tochi.mlit.go.jp), geo-referenced and on-screen
digitized to create a representation of the different
geological units. The geological sections were then
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used to encode the geological units according to the
DRASTIC model rating system. The coarse (saturated
or unsaturated) media was assigned a high rating
value compared to the fine media types.
The soil map was also imported in digital format
into the ILWIS program, referenced to area coordinates and on-screen digitized to delineate the
different soil units. The texture of the various soil
types in Kakamigahara was obtained from a representative description of World soil types provided by
the United States Department of Agriculture (USDA,
1999). The soil media types were then assigned
ratings from 1 to 10 according to their permeability.
Coarse soil media have high rates in comparison to
fine soil media.
The topography layer was constructed from the
topography and land use map by interpolation. The
topography and land use map was first scanned and
registered. The elevation contour lines were digitized
together with the elevation points to be used in the
interpolation. The contour interpolation process
involves two steps (ITC-ILWIS, 2001): rasterization
and interpolation. Both the segment contour map and
the point elevation map were rasterized and combined.
The inclusion of the elevation points in the contour
interpolation assist to avoid the creation of flat areas
on hilltops or bottom of depressions as a result of
being enclosed by the same contour line. The
interpolation process was then performed on the
combined map using the Borgefors distance method
(ITC-ILWIS, 2001) and yielded a digital elevation
map (DEM). The height value for each undefined
pixel between two contours was found by calculating
the shortest distance towards the two isolines. The
vertical and horizontal filters bDyQ and bDxQ were
applied on the DEM in order to calculate the
horizontal and vertical gradients. The two filter maps
were used to obtain the slope percentage according to
the following formula:
Slope ð%Þ ¼ ðHYPðDx; DyÞ=pixel sizeÞ 100
ð3Þ
where HYP is a map calculation function of ILWIS to
calculate the positive root of the sum of square Dx
plus square Dy (Pythagoras rule) where Dx and Dy
are the horizontal and vertical gradients, respectively.
The slope map was then sliced into ranges and
assigned ratings ranging from 1 to 10. Flat areas
obtain high rates because they slow down the runoff
allowing more time for the contaminant to percolate
down to reach the groundwater, while steep areas
increase the runoff washing out the contaminant hence
are assigned low rates.
The hydraulic conductivity map was also scanned
and spatially registered. The different hydraulic
conductivity zones in the area were defined and
assigned ratings according to DRASTIC.
4.2. Aquifer vulnerability assessment
The DRASTIC vulnerability index was computed
according to Eq. (1). In order to understand the
vulnerability index, it is necessary to choose a
representation method which can expose the aquifer
vulnerability in an appropriate fashion and simultaneously allows comparability between different areas.
Basically, Aller et al. (1987) introduced a national
colour coding of the vulnerability maps. The DRASTIC indices are first classified into ranges by
imposing arbitrary thresholds. Those ranges are then
assigned the colours of the pseudo colour look-up
table (ranging from violet to red). Here, the vulnerability scores are presented based on the classification
scheme introduced by Chung and Fabbri (2001). In
this method, the vulnerability indices are classified
based on a fixed interval of area percentage in the
study area. The vulnerability index values were first
sorted in a descending form and then the vulnerability
indices corresponding to each 5% of the total number
of pixels in the study area were taken as thresholds for
the classification. Colours were then assigned to the
ranges of the subsequent percentages of pixels. The
cool colours (shades of blue) indicate bLowQ vulnerability, the shades of green indicates bModerateQ
vulnerability while the warm colours (shades of red)
indicate bHighQ vulnerability. Because this representation demonstrates the results without imposing
arbitrary thresholds, it is considered free of subjectivity and useful in comparing results from different
areas or vulnerability models.
According to Civita (1994), the integrated vulnerability can be obtained by overlying a representation
of the actual pollution sources, which are subdivided
on the basis of their pollution potential (e.g., urban
areas, cultivated areas, waste dumps, industrial complexes, and the like), on the intrinsic vulnerability
map. The integrated vulnerability map was obtained
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
by combining the vulnerability map and the potential
pollution sources extracted from the land use map. In
Kakamigahara Heights, nitrate contamination of
groundwater was found to closely associate mainly
with vegetable cultivation and to a lesser extent with
urban land (Babiker et al., 2004). Therefore, the two
land use types were considered as potential pollution
sources of groundwater of different degrees. To obtain
the integrated or specific vulnerability, we assumed
that there is no risk of contamination from land use/
cover types rather than vegetable fields and urban
land. Vegetable fields are expected to impose a high
risk of contaminant loading onto the ground surface
(e.g., nitrogen fertilizers) compared to urban land. It
has been suggested that the vulnerability classes
should be critically considered only where a groundwater contamination risk exists. The vulnerability
classes are not considered where there is no risk of
contamination.
4.3. Sensitivity analysis
One of the major advantages of the DRASTIC
model is the implementation of assessment using a
high number of input data layers (Evans and Myers,
1990) which is believed to limit the impacts of errors
or uncertainties of the individual parameters on the
final output (Rosen, 1994). Some authors (e.g.,
Barber et al., 1993; Merchant, 1994) however, have
argued that a DRASTIC-equivalent result can be
obtained using a lower number of input parameters
and can achieve a better accuracy at less cost. Some
studies employed the DRASTIC model for aquifer
vulnerability using a lower number of parameters
(McLay et al., 2001) or assuming the constancy of
the missing parameter (Secunda et al., 1998). Moreover, the unavoidable subjectivity associated with the
selection of the seven parameters, the ratings, and
the weights used to compute the vulnerability index
has also been criticized (Napolitano and Fabbri,
1996). Here, we attempt to evaluate whether it was
really necessary to use all of the seven DRASTIC
parameters to assess the Kakamigahara aquifer
vulnerability by performing model sensitivity analysis. The rated DRASTIC parameters were first
evaluated for interdependence and variability.
According to Rosen (1994), the independency of
DRASTIC parameters decreases the probability of
133
misjudgment. In fact, most of the DRASTIC
parameters are naturally closely related.
Two sensitivity tests were performed; the map
removal sensitivity analyses introduced by Lodwick
et al. (1990) and the single-parameter sensitivity
analysis introduced by Napolitano and Fabbri (1996).
The map removal sensitivity measure identifies the
sensitivity of the suitability map (vulnerability map)
towards removing one or more maps from the
suitability analysis and is computed in the following
way:
S ¼ ðjV =N V V=nj=V Þ 100
ð4Þ
where S is the sensitivity measure expressed in terms
of variation index, V and VV are the unperturbed and
the perturbed vulnerability indices respectively, and N
and n are the number of data layers used to compute V
and V V. The actual vulnerability index obtained using
all seven parameters was considered as an unperturbed vulnerability while the vulnerability computed
using a lower number of data layers was considered as
a perturbed one.
The single-parameter sensitivity measure was
developed to evaluate the impact of each of the
DRASTIC parameters on the vulnerability index. It
has been made to compare the beffectiveQ or brealQ
weight of each input parameter in each polygon with
the btheoreticalQ weight assigned by the analytical
model. The beffectiveQ weight of each polygon is
obtained using the following formula:
W ¼ ðPr Pw =V Þ 100
ð5Þ
where W refers to the beffectiveQ weight of each
parameter, P r and P w are the rating value and weight
of each parameter, and V is the overall vulnerability
index.
The implementation of the sensitivity analysis
requires a well-structured database and a GIS capable
of manipulating large tables. To avoid analysing the
large number of individual pixels in the study area
(639,794), the idea of bunique condition subareasQ
introduced by Napolitano and Fabbri (1996) was used.
It is defined as one or more polygons (area) consisting
of pixels with a unique combination of D i , R i , A i , S i ,
Ti , I i , and C i where D i , R i , A i , S i , Ti , I i , and C i are the
rating values of the seven parameters used to compute
the vulnerability index and 1ViV10. The crossoperation of ILWIS 3.1 was used to obtain the
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subareas. The cross-operation performs an overlay of
two raster maps by combining pixels at the same
locations in both maps and tracking all the combinations that occur between the different values or classes
in both maps (ITC-ILWIS, 2001). All the unique
combinations between the different parameters were
traced through multiple crossing. A total of 818
unique condition subareas have been found in the
study area; however, only 663 subareas that are larger
than 10 pixels in size (1000 m2) were considered in
the statistical analysis of the results.
5. Results and discussion
5.1. DRASTIC parameters and aquifer vulnerability
Fig. 2 shows examples of the rated parameter maps
used to obtain the DRASTIC aquifer vulnerability
index. The depth to water table in Kakamigahara
Heights is shallow (average is 26 m), decreasing
gradually from east to west. This makes the western
part of the Heights more susceptible to contamination
according to DRASTIC assumptions. The rating
scores ranges between 1 and 7 where the highest
scores were assigned to the far eastern part of the area
(depth to water is b9.1 m).
Although the study area is characterized by a high
annual rainfall (N1900 mm/year), the net recharge to
the groundwater aquifer is mainly controlled by the
type of land use/cover on the surface. The lowest
recharge rate (mean, 203 mm/year) was associated
with the urban land use because roofs and pavements
prevent the penetration of rainwater downward. The
rest of the study area had relatively higher recharge
rates (mean, 860 mm/year). The Kakamigahara
aquifer has generally high net recharge (N200 mm/
year) which was assigned high rating scores (8 and 9).
The gravel-rich alluvium of the Second Gravel
Formation mostly constitutes the Kakamigahara aquifer and was assigned a high rating score (8). The
Mesozoic consolidated sediments and the tuff and
volcanic ash deposits were assigned low rating scores
ranging from 2 to 4.
Fig. 2. Examples of the rated maps used to compute the DRASTIC vulnerability index. The rating score 1 implies a minimum impact on
vulnerability while the score 10 indicates the maximum impact.
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
The soil media is generally variable. The major soil
types are the Andosols, the Gleysols and the
Fluvisols. The different soil types were assigned rates
according to their permeability (depending on the
texture). A high score (10) was assigned to the
immature soil and the lowest score (1) was assigned
to the soil in the built-up areas. The loamy Andosols
and Gleysols were assigned moderate rating scores
(3~6).
The topography layer displayed a gentle slope
(0~6%) over most of the study area which has been
assigned the DRASTIC rating scores of 9 and 10. The
slope percentage increases northeast and northwest of
the study area associated with the mountain range.
These areas with steep slopes (N18%) were typically
assigned a low rating score (1) indicating their
minimal effect on the aquifer vulnerability.
In the impact of vadose zone layer, the gravel/sandrich alluvial deposits were assigned a high rating
value (8), the clayey–gravel deposits and the Mesozoic sandstone were assigned the moderate score 6,
while the low rating values 2, 3, and 4 were assigned
to the Mesozoic chert, mud-rich alluvium, and the tuff
and volcanic ash deposits, respectively.
Generally, the Kakamigahara aquifer is characterized by high hydraulic conductivity (N9.4104 m/
s), therefore, was assigned the maximum rating score
of 10. The northern part of the aquifer has a
relatively lower conductivity (4.7105~4.7104
m/s) hence was assigned low rating values ranging
from 1 to 6.
135
The DRASTIC aquifer vulnerability map clearly
shows the dominance of bHighQ vulnerability classes
(shades of red) in the western part of Kakamigahara
Heights while the eastern part is characterized by
bModerateQ vulnerability (shades of green; Fig. 3).
This pattern is mainly dictated by the variation in
depth to water from east to west. The elevated
northeastern part of the study area displays bLowQ
aquifer vulnerability. This is due to the combination of
deep water table, less-porous vadose and aquifer
media and steep topography. Ten vulnerability classes
are identified in the study area at a 5% interval. The
classification scale reflects the same level of detail for
the nine highest classes corresponding to 45% of the
total number of pixels in the study area. The least
vulnerable 55% of the study area is considered less
important and is assigned a one-class interval (N45%).
The integrated vulnerability map shows that the
eastern part of the Kakamigahara aquifer is under a
higher risk of contamination despite its moderate
intrinsic vulnerability (Fig. 4). The more vulnerable
western part of the aquifer is, however, under a lower
contamination risk. This is mainly due to the high
pollution risk associated with vegetable cultivation.
Recently, the analysis of nitrate concentration in 57
water samples from private, farm, monitoring, and
public water supply wells in Kakamigahara Heights
indicated that 90% of the water samples have shown
concentrations above the human affected value (13
mg/l NO3) while 30% exceeded the permissible
concentration (44 mg/l NO3) according to Japan
Fig. 3. Aquifer vulnerability map of Kakamigahara Heights.
136
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
Fig. 4. Integrated vulnerability map combining the aquifer vulnerability with the potential pollution sources in Kakamigahara Heights.
regulation (Babiker et al., 2004). More than 70% of
those wells were associated with intensive vegetable
cultivation fields mainly on the eastern side of the
Heights.
5.2. Sensitivity of the DRASTIC model
The statistical summary of the seven rated parameter maps used to compute the DRASTIC index is
provided in Table 3. The highest risk of contamination
(mean value is 9) of groundwater in Kakamigahara
Heights originates from the net recharge parameter.
The soil media, topography, impact of vadose zone,
and hydraulic conductivity imply moderate risks of
contamination (5 and 6) while depth to water and
aquifer media impose a low risk of aquifer contamination (4). Aquifer media, topography, soil media,
and hydraulic conductivity are highly variable (CV%
are 75, 66.7, 60 and 60, respectively) while depth to
water table and impact of vadose zone are moderately
variable (CV% are 50 and 40, respectively). Net
recharge is the least variable parameter (CV% is 11.1).
Table 3
A statistical summary of the DRASTIC parameter maps
Minimum
Maximum
Mean
SD
CV (%)
D
R
A
S
T
I
C
1
7
4
2
50
8
9
9
1
11.1
2
8
4
3
75
1
10
5
3
60
1
10
6
4
66.7
2
8
5
2
40
1
10
5
3
60
S.D. stands for standard deviation and CV for coefficient of
variation.
The low variability of the parameter implies a smaller
contribution to the variation of the vulnerability index
across the study area. The rank-order correlation
analysis (a summary of the result is provided in Table
4) between the seven DRASTIC parameters indicated
that a relatively strong relationship exists between
aquifer media and impact of vadose zone (r=0.81),
aquifer media and topography (r=0.73), and impact of
vadose zone and topography (r=0.56). The former
relationship can be attributed to the procedure
followed in order to construct the two layers from
the subsurface geology map, and the uniform and
unconfined nature of the Kakamigahara aquifer. The
latter two relationships can be explained by the fact
that the Kakamigahara aquifer occupies the alluvial
flood plain which has a typically low slope in
comparison to the mountain range in the north and
northeastern part of the area. Moderate correlation
was found between net recharge and soil media
Table 4
Summary of rank-order correlation analysis’ result between seven
DRASTIC parameters
Correlated parameters
Correlation
coefficient, r
Significance
level, p
Aquifer media and impact of vadose
Aquifer media and topography
Impact of vadose and topography
Net recharge and soil media
Aquifer media and hydraulic
conductivity
Depth to water and topography
0.81
0.73
0.56
0.46
0.30
b0.0001
b0.0001
b0.0001
b0.0001
b0.001
0.29
b0.001
Only statistically significant (confidence level at/or more than 95%)
intercorrelations are tabulated.
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
(r=0.46) mainly due to the different recharge rates
assigned to the soil of the urban land cover and the
rest of the area (see Section 4.1). This agrees well with
the basic assumptions of DRASTIC model (see Table
1). As expected, depth to water table was slightly
correlated to the hydraulic conductivity (r=0.3).
Because of the relatively few significant correlations
at 95% confidence level (Table 4), the DRASTIC
parameters in Kakamigahara Heights were generally
considered independent.
5.3. Map removal sensitivity analysis
The results of the map removal sensitivity analysis
computed by removing one or more data layers at a
time are presented in Tables 5 and 6. The statistical
analysis of the variation index (sensitivity measure)
was applied only to those subareas which are 10
pixels or more in size (633 subareas). Table 4 displays
the variation of the vulnerability index as a result of
removing only one layer at a time. It is clear that high
variation of the vulnerability index is expected upon
the removal of the net recharge parameter from the
computation (mean variation index=15.1%). This
could mainly be attributed to the relatively high
theoretical weight assigned to this layer (Table 1) and
the high recharge rate characteristic of the Kakamigahara aquifer (rating score 9). The vulnerability
index seems to be sensitive to the removal of soil
media and topography layers although the two
parameters are considered theoretically less important
(weights 2 and 1, respectively). This also because of
their relatively high contamination risk (mean rating
scores 5 and 6, respectively). In Table 6, the variation
137
Table 6
Statistics of the map removal sensitivity analysis
Parameters used
D, R, S, T, I and C
D, R, S, T, and I
R, S, T and I
R, S and T
R and S
R
Variation index (%)
Mean
Minimum
Maximum
S.D.
4.5
7
10.1
21
18.7
52.4
0
0
0
0
0
12.5
11.3
19.1
33.9
50.2
93.4
69.1
3.3
4.2
8
11
18.5
11.3
One or more parameters are removed at a time. Total of 633
subareas (z10 pixels in size) were considered. S.D. refers to the
standard deviation.
of the vulnerability index due to the removal of one or
more layers at a time from the model computation is
presented. The removal of the map(s) was based on
the previous map removal sensitivity measure (Table
5). The layers, which compel less variation of the final
vulnerability index, were preferentially removed. The
least average variation index resulted after removing
only the aquifer media layer (4.5%). As more data
layers are excluded from the computation of the
vulnerability index, the average variation index
increases. Although the layers assumed to be the
most effective were considered each time, the
interpretation of the growing average is not clear. It
might partly be attributed to one or more of the
following reasons: the internal variation of the
individual parameter, the weights assigned to them,
and their weak representation of the real world.
Conclusively, considerable variation in the vulnerability assessment is expected if a lower number of
data layers have been used.
5.4. Single-parameter sensitivity analysis
Table 5
Statistics of the map removal sensitivity analysis
Parameter
removed
Variation index (%)
Mean
Minimum
Maximum
S.D.
D
R
A
S
T
I
C
7.9
15.1
4.5
11.6
11.2
10.7
7.2
0
4
0
3
2.3
0.5
0
16.3
41.7
11.3
15.4
16
24.3
24
4
7.5
3.3
2.8
3.3
5.2
4.9
One parameter is removed at a time. Total of 633 subareas (z10
pixels in size) were considered. S.D. refers to the standard deviation.
While the map removal sensitivity analysis presented in the previous section has confirmed the
significance of the seven parameters in the assessment
of the DRASTIC vulnerability index in the study area,
the single parameter sensitivity analysis compares
their beffectiveQ weights with their btheoreticalQ
weights. The beffectiveQ weight is a function of the
value of the single parameter with regard to the other
six parameters as well as the weight assigned to it by
the DRASTIC model. The beffectiveQ weights of the
DRASTIC parameters exhibited some deviation from
their btheoreticalQ weights (Table 7). The net recharge
138
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
Table 7
Statistics of the single parameter sensitivity analysis
Parameter
Theoretical
weight
Theoretical
weight (%)
Effective weight (%)
Mean
Minimum
Maximum
S.D.
D
R
A
S
T
I
C
5
4
3
2
1
5
3
21.7
17.4
13.0
8.7
4.3
21.7
13.0
10.1
27.2
12.9
4.3
4.7
20.9
19.7
3.2
17.7
4.6
1.1
0.6
7.6
2.4
28.2
50
21.1
11.7
12.4
35.1
34.9
6.4
6.5
4.6
2.4
2.8
7.8
5.1
S.D. refers to the standard deviation. Total of 633 subareas (z10 pixels in size) were considered.
tends to be the most effective parameter in the
vulnerability assessment (mean effective wt.% is
27.2%) in agreement with the result from the map
removal sensitivity analysis. Its beffectiveQ weight
exceeds the theoretical weight assigned to it by
DRASTIC (17.4%). The hydraulic conductivity also
shows high beffectiveQ weight (19.7) that exceeds the
btheoreticalQ weight assigned by DRASTIC (13%).
The rest of the parameters, excluding topography,
exhibit lower beffectiveQ weights compared to the
btheoreticalQ weights. The significance of net recharge
and hydraulic conductivity layers highlights the
importance of obtaining accurate, detailed, and
representative information about these factors.
6. Conclusion
In this paper, we have attempted to assess the
aquifer vulnerability of the Kakamigahara groundwater basin employing the empirical index DRASTIC
model of the U.S. Environmental Protection Agency
(EPA). Seven environmental parameters were used to
represent the natural hydrogeological setting of the
Kakamigahara aquifer; Depth to water, net Recharge,
Aquifer media, Soil media, Topography, Impact of
vadose zone, and hydraulic Conductivity. The DRASTIC aquifer vulnerability map indicated that the
western part of the Kakamigahara aquifer is dominated by bHighQ vulnerability classes while the eastern
part was characterized by bModerateQ vulnerability
classes. The elevated northeastern part of the study
area displayed bLowQ aquifer vulnerability. The
integrated vulnerability map shows the high risk
imposed on the eastern part of the Kakamigahara
aquifer due to the high pollution potential of intensive
vegetable cultivation. The more vulnerable western
part of the aquifer is, however, under a lower
contamination risk. No contamination risk is expected
from land use/cover types other than vegetable fields
and urban lands in the Kakamigahara. In the particular
case of Kakamigahara Heights, land-use seems to be a
better predictor of groundwater contamination by
nitrate, although assessing natural aquifer vulnerability is still necessary to avoid augmentation of the
problem and introduction of other pollutants.
The net recharge parameter inflicts the largest
impact (9) on the intrinsic vulnerability of the
Kakamigahara aquifer. Soil media, topography,
impact of vadose zone, and hydraulic conductivity
have moderate impacts (5 and 6) on aquifer vulnerability while depth to water table and aquifer media
have low impacts (4). The rank-order correlation
analysis between the seven DRASTIC parameters
indicated that a relatively strong relationship exists
between aquifer media and impact of vadose zone
(r=0.81), aquifer media and topography (r=0.73), and
impact of vadose zone and topography (r=0.56).
However, the relatively few significant correlations
at 95% confidence level between the DRASTIC
parameters in Kakamigahara Heights reveal their
independence. The map removal sensitivity analysis
indicated that the vulnerability index is highly
sensitive to the removal of net recharge, soil media,
and topography layers but is least sensitive to the
removal of the aquifer media layer. As more data
layers are excluded from the vulnerability index
computation, the average variation index increases.
Therefore, considerable variation in the vulnerability
assessment is expected if a lower number of data
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
layers have been used. The single-parameter sensitivity analysis has shown that net recharge and hydraulic
conductivity are the most significant environmental
factors which dictate the high vulnerability of the
Kakamigahara aquifer. This highlights the importance
of obtaining accurate, detailed, and representative
information about these factors.
The GIS technique has provided an efficient
environment for analyses and high capabilities in
handling a large quantity of spatial data. The seven
model parameters were constructed, classified and
encoded employing various map and attribute GIS
functions. The DRASTIC vulnerability index, which
is defined as a linear combination of factors, was
easily computed. Nevertheless, GIS greatly facilitated
the implementation of the sensitivity analysis applied
on the DRASTIC vulnerability index which otherwise
could have been impractical.
Acknowledgment
We thankfully acknowledge the assistance of the
Kakamigahara City Hall officials who have provided
the necessary materials to carry on this study. Thanks
are also due to Japan Meteorological Agency and
Ministry of Land Infrastructure and Transport, Japan
for making data available online to support different
kinds of research.
References
Aller L, Bennet T, Leher JH, Petty RJ, Hackett G, DRASTIC: a
standardized system for evaluating ground water pollution
potential using hydrogeological settings. EPA 600/2-87-035;
1987. 622.
Babiker IS, 1998. GIS for aquifer vulnerability assessment
applying DRASTIC and SINTACS (A case study in the
Ostrava–Karvina region, Czech Republic). MSc thesis, International Institute for Aerospace Survey and Earth Sciences (ITC),
Enschede, The Netherlands, 124 pp.
Babiker IS, Mohamed AAM, Terao H, Kato K, Ohta K. Assessment
of groundwater contamination by nitrate leaching from intensive
vegetable cultivation using geographical information system.
Environ Int 2004;29(8):1009 – 17.
Barbash JE, Resek EA. Pesticides in ground water: distribution,
trends, and governing factors. Chelsea, MI7 Ann Arbor Press;
1996.
Barber C, Bates LE, Barron R, Allison H. Assessment of the
relative vulnerability of groundwater to pollution: a review
139
and background paper for the conference workshop on
vulnerability assessment. J Austr Geol Geophys 1993;14(2/3):
1147 – 54.
Bonham-Carter GF. Geographic information systems for geoscientists: modelling with GIS. Computer Methods in the Geosciences, vol. 13. Pergamon/Elsevier Sci Pub; 1996. p. 98.
Chung C.F., Fabbri A.G. Prediction models for landslide hazard
using a Fuzzy set Approach. In: Marchetti M., Rivas V., editors.
Geomorphology and environmental impact assessment. A.A.
Balkema; 2001, p. 31 – 47.
Civita M. Le carte della vulnerabilita ddegli acquiferi allT
inquinamento (in Italian). In: Pitagora, editor. Teoria and
Practica, Bologna; 1994. 325 pp.
Evans BM, Myers WL. A GIS-based approach to evaluating
regional groundwater pollution potential with DRASTIC. J Soil
Water Conserv 1990;45:242 – 5.
Fabbri AG, Napolitano P. The use of database management and
geographical information systems for aquifer vulnerability
analysis. Contribution to the International Scientific Conference
on the occasion of the 50th Anniversary of the founding of the
Vysoka Skola Banska, Ostrava, Czech Republic; 1995.
ITC-ILWIS. Ilwis 30 academic user’s guide. The Netherlands7
International Institute for Aerospace Survey and Earth Sciences
(ITC); 2001.
Kondoh A, Nishiyama J. Changes in hydrogeological cycle due to
urbanization in the suburb of Tokyo metropolitan area, Japan.
Adv Space Res 2000;26(7):1173 – 6.
Lodwick WA, Monson W, Svoboda L. Attribute error and
sensitivity analysis of map operations in geographical information systems: suitability analysis. Int J Geogr Inf Syst
1990;4(4):413 – 28.
McLay CDA, Dragten R, Sparling G, Selvarajah N. Predicting
groundwater nitrate concentrations in a region of mixed
agricultural land use: a comparison of three approaches. Environ
Pollut 2001;115:191 – 204.
Melloul A, Collin M. Water quality factor identification by the
dPrincipal ComponentsT statistical method. Water Sci Technol
1994;34:41 – 50.
Merchant JW. GIS-based groundwater pollution hazard assessment:
a critical review of the DRASTIC model. Photogramm Eng
Remote Sensing 1994;60(9):1117 – 27.
Mohamed MA. Dynamics of nitrate contamination in the groundwater system (A case study in the Kakamigahara area, Gifu
Prefecture, central Japan). MSc thesis, Division of Earth and
Environmental Sciences, Graduate School of Environmental
Studies, Nagoya University, Nagoya, Japan; 2003. 63 pp.
Mohamed MA, Terao H, Suzuki R, Babiker IS, Ohta K, Kaori K,
et al. Natural denitrification in the Kakamigahara groundwater
basin, Gifu Prefecture, central Japan. Sci Total Environ 2003;
307(1–3):191 – 201.
Napolitano P, Fabbri AG. Single-parameter sensitivity analysis for
aquifer vulnerability assessment using DRASTIC and SINTACS
HydroGIS 96: application of geographical information systems
in hydrology and water resources management. Proceedings of
Vienna Conference. IAHS Pub, vol. 235, 1996. p. 559 – 66.
Rosen L. A study of the DRASTIC methodology with emphasis on
Swedish conditions. Ground Water 1994;32(2):278 – 85.
140
I.S. Babiker et al. / Science of the Total Environment 345 (2005) 127–140
Secunda S, Collin ML, Melloul AJ. Groundwater vulnerability
assessment using a composite model combining DRASTIC
with extensive agricultural land use in Israel’s Sharon region.
J Environ Manage 1998;54:39 – 57.
Terao H, Kajikawa M, Morishita Y, Kato K. Influence of pesticides
and fertilizers on the groundwater in vegetable field zone.
Geochemistry 1985;19:13 – 38.
Terao H, Yoshioka R, Kato K. Groundwater pollution by nitrate
originating from fertilizer in Kakamigahara Heights, central
Japan. IAH Hydr, vol. 4. Hanover7 Verlag Heinz Heise; 1993.
p. 51 – 62.
Tesoriero AJ, Inkpen E.L, Voss FD. Assessing ground-water
vulnerability using logistic regression. Proceedings for the
Source Water Assessment and Protection 98 Conference, Dallas,
TX; 1998. p. 157 – 65.
Thapinta A, Hudak PF. Use of geographic information systems for
assessing groundwater pollution potential by pesticides in
Central Thailand. Environ Int 2003;29(1):87 – 93.
USDA, (United States Department of Agriculture). Natural
Resource Conservation Service Soil taxonomy, a basic system
of soil classification for making and interpreting soil surveys.
Agriculture Handbook, vol. 436. Washington DC7 U.S. Government Printing Office; 1999. 20402.
US EPA (Environmental Protection Agency). DRASTIC: a standard
system for evaluating groundwater potential using hydrogeological settings, Ada, Oklahoma WA/EPA Series; 1985. 163.
Vrba J, Zoporozec A. Guidebook on mapping groundwater
vulnerability. IAH International Contribution for Hydrogeology,
vol. 16. Hannover7 Heise; 1994. p. 131.
Yokoyama T, Makinouchi T. Geology of the Kakamigahara Terrace
and the underlying groundwater basin, Gifu Prefecture, central
Japan. J Geol Soc Jpn 1991;97(11):887 – 901.