Research Journal of Environmental and Earth Sciences 4(2): 177-185, 2012
ISSN: 2041-0492
© Maxwell Scientific Organization, 2012
Submitted: November 01, 2011
Accepted: November 29 , 2011
Published: February 01, 2012
The use of Kriging Techniques with in GIS Environment to Investigate
Groundwater Quality in the Amman-Zarqa Basin/Jordan
1
1
Atef Al-Mashagbah, 1Rida Al-Adamat and 2Elias Salameh
Institute of Earth and Environmental Science, Al al-Bayt University, Mafraq, Jordan
2
Department of Geology, Faculty of Sciences, Jordan University, Amman, Jordan
Abstract: The Kriging techniques are called the best linear unbiased estimator since it tries to have a mean
residual error equal to zero. It aims to minimizing the variance of the errors and hence is a strong advantage
over other estimation methods like inverse distance weighting or moving average. In this study, the ordinary
kriging techniques were used to estimate groundwater quality parameters (Ca2+, Mg2+, Na+, K+, ClG and NO3G).
It was found that the spatial interpolation of groundwater quality of the study area poses various problems due
to the complex impact of cultivation and urbanization systems of the study area. Testing of various methods
and parameters, by comparing and ranking their cross-correlation errors, show that ordinary kriging method
using the different semivariogram models provides the overall good results. Also, most of the groundwater
quality parameters have moderate spatial structure except NO3 and Ca that have a strong spatial structure.
Key words: Amman-zarqa, GIS, groundwater, Jordan, kriging
INTRODUCTION
closest measured values usually have the most influence.
However, the kriging weights for the surrounding
measured points are more sophisticated than those of
IDW. IDW uses a simple algorithm based on distance, but
kriging weights come from a semivariogram that was
developed by viewing the spatial structure of the data
(ArcGIS Tutorial).
Examples on the use of GIS and kriging techniques
for groundwater quality assessment are found in the
literature (Zhang et al., 1996; Van et al., 1999; Gogu
et al., 2001; Liu et al., 2004; Babiker et al., 2004; Nas and
Berktay, 2006; Babiker et al., 2007; Nas and Berktay,
2010).
In this study, Kriging techniques were used to
interpolate groundwater parameters (Ca2+, Mg2+, Na+, K+,
ClG and NO3G) in the study area.
Geostatistics is a technology for estimating the local
values of properties that vary in space (Oliver and
Webster, 1991). The theory is based upon the concept of
a random variable, which expresses a continuous variable
depending on a location (Isaake and Srivastava, 1989). It
is expected that observations close together in space will
be more alike than those further apart (Stein et al., 1997).
Geostatistical techniques are useful in providing
estimates of sample attributes at locations with sparse
information (Burrough and McDonnell, 1998). These
methods work by defining the spatial structure of the
phenomena by autocorrelation such as semi-variograms,
then estimating values between measured points based on
the degree of spatial autocorrelation or covariance found
in the data (Robertson, 1987). Consequently, simpler
alternatives to kriging, such as the Inverse Distance
Weighting (IDW) have been used as interpolation
methods. This technique is easier to implement due to the
fact that the estimation of values does not require any
measure of either spatial autocorrelation or spatial autocovariance (Isaake and Srivastava, 1989).
The Kriging techniques are called the best linear
unbiased estimator since it tries to have a mean residual
error equal to zero (Clark, 1979). It aims to minimizing
the variance of the errors and hence is a strong advantage
over other estimation methods like inverse distance
weighting or moving average. Kriging forms weights
from surrounding measured values to predict values at
unmeasured locations. As with IDW interpolation, the
METHODOLOGY
Study area: The study area is a part of the northeastern
plateau of Jordan within the Amman Zarqa basin,
covering parts of Zarqa and Mafraq governorates. It
covers an area of about 824 km and is defined by the
coordinates: latitudes 32º1! and 32º23! and longitudes
36º6! and 36º39! (Fig. 1). The basalt flows constitutes the
main aquifer in the investigated area. It is a highly porous
and scoriaceous reservoir (Bender, 1974). The basalt
plateau, within which the aquifer is located, covers an
area of 11103 km in the Northern Badia of Jordan.
The topography of the basalt area mainly consists of
a gentle slope plateau with a mean elevation of about
Corresponding Author: Rida Al-Adamat, Institute of Earth and Environmental Science, Al al-Bayt University, Mafraq, Jordan,
Tel.: +96226297000 (2784); Fax: +96226297062
177
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Fig. 1: Location of the study area
The basalt rocks in the north-eastern part of Jordan
belong to the north Arabian volcanic province, extending
to the territories of Saudi Arabia and Syria covering an
area of about 12.000 K min Jordan. The basalt aquifer
represents the main aquifer in the study area, where the
water bearing zone comprises a highly porous and
scoriaceous reservoir (MWI, 2009).
The basalt rocks form an aquifer of high
hydrogeological importance and hydraulic characteristics
with water of very good quality.This aquifer is considered
the major shallow aquifer within the study area. The
basalt aquifer is made up of a series of semi independent
pipes or channels lying side by side to each other that
forms a good conduit aquifer system, but with little
storage capabilities (Mac Donald and Partness., 1965).
750 m above sea level. The predominant topography of
the area is a flat lying terrain, with elevation incline from
northeast to south and west. The south-eastern, western
and some part to the north of the area are topographically
high although occupied by small elongated hills. The
highest elevation in the study area is about 1038 m a.s.l
lying in the northeast corner (near the Syrian border). On
the contrary, the deepest area lies at about 500 m above
sea level, in the southern area. The drainage is of
moderate relief and is affected by morphological rises and
depressions of lava flows. The Wadis drain south and
southwest. Wadis in the low land part have been
straightened for drainage purposes which are mainly used
as discharge of groundwater.
The climate in the study area is cold in winter, while
in summer it is very hot. The mean maximum daily
temperature is above 28ºC. Usually the precipitation
begins in October and reach maximum in January then
begins to decrease until May when the precipitation
season ends. June, July and August are the hottest months
where the temperature can reach 40ºC. Contrary wise,
December, January and February are the rainiest months
and the temperature goes on some days below the freezing
point. Some rainfall is also received in November and
March but it is in general erratic and falls in a few storms.
The study area encompasses a variety in land cover.
Land cover features such as bare land, dry grass land,
vegetables, fruitful trees, agricultural crops are common
in this area. Wheat and barley are among the main crops
species in the basalt area.
The main aquifer in the study area consists of a basalt
aquifer system underlain by Amman -Wadi As Sir aquifer
(B2/A7) which consist of a carbonate rock sequence and
is in turn underlain by the Kurnub sandstone aquifer
system.
GIS methodology: The groundwater parameters maps
were drawn using the most suitable method. For this
purpose, for each groundwater parameters, a table was
constructed consisting of the XY coordinates and the
concentrations of each parameter at that place. By using
this table, a suitable variogram model with respect to
spatial structure of each parameter event was fitted using
GS+ software (for the data which were not normally
distributed, logarithm of data were used). Then by using
variogram models and its parameters (nugget effect, sill,
and the range) interpolation was carried out using the best
kriging method.
The mapping procedure has started by sampling of
pollutants, these samples were indicated by point shape
file (vector), so it was converted to raster through
interpolation using Kriging methods in GS+ and ArcGIS
Software because kriging techniques are the most
appropriate methods comparing to other interpolation
178
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Histogram of NO 3
Histogram of log NO3
10
8
Frequency
11
Frequency
13
7
3
6
3
0
0
0.13
0.34
0.56
NO3
0.77
0.98
-0.89
-0.67
-0.45
Log-NO 3
-0.23
-0.01
33.068
43.790
Fig. 2: Frequency histogram of the NO3 and Log NO3
Fig. 3: Spatial prediction map for the ordinary kriging interpolation of log NO3
Histogram of log-CI
Histogram of CI
8
9
Frequency
12
Frequency
10
5
3
6
3
0
0
-0.05
0.373
0.795
1.218
1.640
0.900
Log-CI
11.623
22.345
CI
Fig. 4: Frequency distribution of the Cl and Log Cl
techniques. The raster files that represent the hydrogeochemical data created within the GS+ and GIS include,
Ca2+, Mg2+, Na+, K+, ClG and NO3G parameters.
normal distribution. The graphical histogram test was
chosen to test the normality of data (Fig. 2). The ArcGIS
Geostatistical Analyst in addition to excel software
provides log transformations for converting skewed
distributions into normal distributions.
Cross-validation was applied to check the best model
that describes the spatial correlation of the nitrate
concentration of the study area. A comparison of RSS and
the R2 for log NO3 show that the spherical model is the
RESULTS AND DISCUSSION
Nitrate (NO3):Data analysis indicates that raw nitrate
values which were used did not follow the normal
distribution, but transformed values have followed the
179
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Fig. 5: Spatial prediction map for the ordinary kriging interpolation of log Cl
Histogram of log_K
11
11
8
Frequency
Frequency
Histogram of K
15
8
4
6
3
0
0
0.100
0.250
0.400
K
0.550
0.700
-1.000
-0.788
-0.575
Log K
-0.363
-0.150
Fig. 6: Frequency distribution of the K+ and Log K+
transformation of data was applied. After the log
transformation, the Cl concentrations are approximately
normally distributed.
The mathematical models (theoretical
semivariograms) adjusted to the experimental
semivariograms, as well as the C0 parameters (nugget
effect), C0+C (sill), a (range), R2 (coefficient of
determination) and SSR (sum of squares of residuals)
related to the log Cl. The nugget is equal 0.1003; the sill
is equal 0.251, while the range is equal to 15120 that
depict the best representation of the spatial distribution
structure of chloride in the study area.
The chloride distribution in the study area has a
moderate spatial dependence according to the nugget to
sill ratio that equal to 40.12%.
The spatial interpolation map of chloride in the study
area, according to the ordinary Kriging method and the
parameter combination, is shown in Fig. 5. In this map the
dark brown color indicate high chloride areas where the
best, which suggests the validity of the spherical model
and its parameters.
An isotropic spherical model with nugget equal
0.0038, a sill equal to the sample variance of 0.0598 and
a range of 7970 km is selected as the best representation
of the spatial structure. The ratio of nugget variance to sill
expressed in percentages equal 6.35%. The ratio is less
than 25%, which indicate that the nitrate distribution in
the study area has a strong spatial dependence.
With the best prediction map determined from the
cross validation process, the nitrate concentration
prediction map that shows the log NO3 distribution in
groundwater can be shown in Fig. 3. In this map, dark
brown color represents high concentrations; where the
light brown color represents low concentrations.
Chloride (Cl): According to the normality test of chloride
data (Fig. 4), Cl concentrations are positively skewed, so
it is not normal distribution, therefore, the log
180
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Fig. 7: Spatial prediction map for the ordinary kriging interpolation of log K+
Histogram of Na
Histogram of log Na
9
8
Frequency
10
Frequency
12
6
3
5
3
0
0
1.400
5.490
9.580
Na
13.670
17.760
0.150
0.883
0.517
1.250
Log Na
Fig. 8: Frequency distribution of the Na and Log Na
The ratio of nugget variance to sill expressed in
percentages equal 33.85%. This value is greater than 25
and less than 75%, thus the potassium distribution in the
study area has a moderate spatial dependence. With the
best prediction map determined from the cross validation
process, the potassium concentration prediction map that
shows the log K+ distribution in groundwater can be
shown in Fig. 7. In this map, the concentrations of K+ is
low in whole the study area except the Khalydia and
Dhuleil areas that have a high concentrations (dark brown
color) comparison to other areas in the study area.
light one shows the minimum chloride areas. In
agreement with this map, the higher concentrations of
chloride are concentrated in the Dhuleil, Halabat areas
and reduce to minimum in the eastern areas.
Potassium (K): The data set of potassium has a high
kurtosis and is positively skewed, so it is not normal
distribution, therefore, the log transformation of data was
applied to be closer from a normal distribution. Histogram
of log-transformed data is presented in Fig. 6. After the
log transformation, the K+ concentrations are
approximately normally distributed.
By Appling the cross-validation methods, the
comparison of the results based on RSS and R2 indicate
that the interpolation technique using kriging (spherical
model) produced the smallest errors.
By using GS+ software, fitting the best model to the
spatial structure of potassium was performed. The
spherical model has the best fit with nugget effect equal
to 0.022, a sill equal to 0.065 and a range of influence
equal to 14970.
Sodium (Na): The data set of Na+ has a high kurtosis and
is positively skewed, so it has been log transformed to be
closer from a normal distribution. Histogram of logtransformed data is presented in Fig. 8. Even if the
distribution obtained can not be considered as normal, it
is much closer than the raw data distribution.
The experimental semivariogram model for log Na
resulted that the nugget is equal 0.0.044; the sill is equal
0.0.097, while the range is equal to 18250 that depict the
181
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Fig. 9: Spatial prediction map for the OK interpolation of log Na
Histogram of log Ca
14
14
11
Frequency
Frequency
Histogram of Ca
18
9
5
7
4
0
0
0.610
5.208
9.805
14.403
-0.210
19.0
0.163
0.535
Log Ca
Ca
Fig. 10: Frequency distribution of the Ca and Log Ca
Fig. 11: Spatial prediction map for the OK interpolation of log Ca
182
0.908
1.280
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Histogram of log_Mg
Histogram of Mg
16
12
9
Frequency
Frequency
12
8
6
3
4
0
0
0.590
5.293
9.995
Mg
14.698
19.400
-0.230
0.277
0.783
1.290
Log_Mg
Fig. 12: Frequency distribution of the Mg and Log Mg
Fig. 13: Spatial prediction map for the OK interpolation of log Mg
observations of the property is classified as strong for
calcium distribution.
The Kriging interpolated map of calcium
concentrations that cover the study area is shown in
Fig. 11. The ordinary kriging algorithm was applied to
interpolate the Ca2+ data using ArcGIS program. In this
map high concentrations of calcium are localized in
Dhuleil, Halabat areas (dark brown color) and it decrease
towards the east border of the study area (light color).
best representation of the spatial distribution structure of
sodium in the study area. The sodium distribution in the
study area has a moderate spatial dependence according
to the nugget to sill ratio that equal to 45.36 %. The
Kriging interpolated map of Sodium that cover the study
area is shown in Fig. 9.
Calcium (Ca): The data set of Ca2+ has a high kurtosis
and is positively skewed, so it has been log transformed
to be closer from a normal distribution. Histogram of logtransformed data is presented in Fig. 10.
Even if the distribution obtained can not be
considered as normal, it is much closer than the raw data
distribution. By Appling the cross-validation methods, the
comparison of the results based on RSS and R2 indicate
that the interpolation technique using kriging (spherical
model) produced the smallest errors.
An isotropic exponential model with nugget equal
0.08, a sill equal to 0.480 and a range of 51100 is selected
as the best representation of the spatial structure. The ratio
of nugget variance to sill expressed in percentages equal
16.67%. This value is less than 25%, thus the degree of
spatial dependence that establishes the classification of
the degree of spatial dependence between adjacent
Magnesium (Mg): The data set of magnesium has a high
kurtosis and is positively skewed, so it is not normal
distribution, therefore, the log transformation of data was
applied to be closer from a normal distribution. Histogram
of log-transformed data is presented in Fig. (12). After the
log transformation, the Mg concentrations are
approximately normally distributed.
By Appling the cross-validation methods, the
comparison of the results based on RSS and R2 indicate
that the interpolation technique using kriging (spherical
model) produced the smallest errors. The isotropic
variogram of magnesium shows a spherical model with
nugget equal 0.097, a sill of 0.225 and a fitted range of
16040. The ratio of nugget variance to sill expressed in
183
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
percentages equal 43.11%. This value is between 25%
and 75%, thus the magnesium distribution in the study
area has a moderate spatial dependence.
With the best prediction map determined from the
cross validation process, the magnesium concentration
prediction map that shows the log Mg distribution in
groundwater can be shown in Fig. 13. In this map dark
brown color represents high concentrations; where the
light color represents low concentrations of magnesium.
According to this map, the higher concentrations of
magnesium are concentrated in the Dhuleil, Halabat areas
and reduce to minimum in the eastern parts of the study
area.
C
SUMMARY AND CONCLUSION
The study described in this paper utilizes the GS+ and
ESRI ArcGIS Geostatistical Analyst to analyze the spatial
distribution patterns of contaminations in groundwater. In
this study, the ordinary kriging techniques were used to
estimate groundwater quality parameters in the basalt area
of Amman Zarqa basin (Jordan). The outcomes of this
study concluded that:
C
C
C
C
C
C
Prediction of parameter distributions in groundwater
using log transformation to convert the skewed
datasets into morally distributed datasets could be not
accurate.
Second, contamination concentrations in the
groundwater can be influenced by soil and
hydrologic condition of the area. Contamination,
once released into the ground, might undergo some
physical, chemical and biological reactions that could
possibly alter concentration distribution. Human
errors and laboratory uncertainties during sampling
and data acquisition are also possible factors for
prediction errors. These uncertainties and limitations
should be considered in the assessment of
groundwater contamination.
REFERENCES
Babiker, I., S. Mohamed, A.A. Mohamed, H. Terao,
K. Kato and O. Keiichi, 2004. Assessment of
groundwater contamination by nitrate leaching from
intensive vegetable cultivation using geographical
information system. Environ. Int., 29(8): 1009-1017.
Babiker, I., S. Mohamed, A.A. Mohamed and H. Tetsuya,
2007. Assessing groundwater quality using GIS.
Water Res. Manage., 21: 699-715.
Bender, F., 1974. Geology of Jordan, Contribution to the
Regional Geology of the Earth. Gebruder Bontrager,
Berlin.
Burrough, P.A. and R.A. McDonnell, 1998. Principles of
Geographical Information Systems. Oxford
University Press, New York.
Clark, I., 1979. Practical Geostatistics. Applied Science
Publishers Ltd., London, pp: 129.
Gogu, R.C., C. Guy, H. Vincent, P. Valerie and D. Alain,
2001. GIS-based hydrogeological databases and
groundwater modeling. Hydrogeol. J., 9: 555-569.
Isaake, E. and R. Srivastava, 1989. An Introduction to
Applied Geostatistics. Oxford University Press,
New York.
Liu, C.W., C.S. Jang and C.M. Liao, 2004. Evaluation of
arsenic contamination potential using indicator
kriging in the Yun-Lin aquifer (Taiwan). Sci. Total
Environ., 321(1-3): 173-188.
Mac Donald, S.M. and Partners, 1965. In Cooperation
with Hunting Technical Services. Wadi Dhuleil
Investigation (Stage-II), WAJ, Amman, Jordan.
Ministry of Water and Irrigation MWI, 2009. Water
Information System. Hydrological, Geological and
Hydrogeological Data Bank. MWI, Water Resources
and Planning Directorate, Amman, Jordan.
Nas, B. and B. Ali, 2006. Groundwater contamination by
nitrates in the city of Konya, (Turkey): A GIS
perspective. J. Environ. Manage., 79(1): 30-37.
In kriging method, searching for semivariograms and
determining the best fitted semivariogram model and
preparing the required statistical assumptions is a
time-consuming and trial and error procedure.
Spatial interpolation of groundwater quality of the
basalt area of the Amman Zarqa basin poses various
problems due to the complex impact of cultivation
and urbanization systems of the study area. Testing
of various methods and parameters, by comparing
and ranking their cross-correlation errors, show that
ordinary kriging method using the different
semivariogram models provides the overall good
results.
Most of the groundwater quality parameters have
moderate spatial structure except NO3, and Ca that
have a strong spatial structure.
The prediction maps generated in this study show an
effective way to help investigate and remedy
groundwater contaminations. They provide visual
representations of contaminations distributions in
groundwater and help environmental professionals
prioritize their limited budgets for contamination
control and cleanup, develop field investigation
strategies and design remedial systems.
There are some limitations found in this study in
using the sample data for the determination of
contamination distributions in groundwater for the
study area.
First, the groundwater parameter concentrations were
significantly skewed and non-normally distributed.
184
Res. J. Environ. Earth Sci., 4(2): 177-185, 2012
Van, H., W. Peter, P.S. Paul and B. Aat, 1999. A GISbased plant prediction model for wetland ecosystems.
Landscape Ecol., 14: 253-265.
Zhang, R., D.H. Jeffrey, P.G. Steven and M. Larry, 1996.
Determination of nonpoint-source pollution using gis
and numerical models. J. Environ. Qual., 25(3):
411-418.
Nas, B. and B. Ali, 2010. Groundwater quality mapping
in urban groundwater using GIS. Environ.
Monitoring Assessment, 160: 215-227.
Oliver, M.A. and R. Webster, 1991. How geostatistics can
help you. Soil Use Manage., 7: 206-217.
Robertson, G.P., 1987. Geostatistics in ecology:
interpolation with known variance. Ecology, 68(3):
744-748.
Stein A., J. Brouwer and J. Bouma, 1997. Methods for
comparing spatial variability patterns of millet yield
and soil data. Soil Sci. Soc. Am. J., 61: 861-870.
185