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Agricultural Water Management 113 (2012) 57– 63

Contents lists available at SciVerse ScienceDirect

Agricultural Water Management

j ourna l ho me page: www.elsev ier .com/ locate /agwat

patial and temporal mapping of groundwater salinity using ordinary krigingnd indicator kriging: The case of Bafra Plain, Turkey

akan Arslan ∗

ndokuz Mayis University, Faculty of Agriculture, Department of Agricultural Structures and Irrigation, 55139 Samsun, Turkey

r t i c l e i n f o

rticle history:eceived 10 February 2012ccepted 17 June 2012vailable online 12 July 2012

eywords:eostatistics

a b s t r a c t

Groundwater salinity contributes significantly to soil salinization in irrigated areas. In this study, spatialand temporal analyses of groundwater salinity were performed based on data from 97 wells monitoredover a 7-year period. ArcGIS Geostatistical Analyst was used in exploratory data analysis, semivari-ogram model selection, cross-validation and development of a groundwater salinity distribution pattern.Groundwater salinity semivariogram models varied by year and included exponential (2004, 2009),spherical (2005), J-Bessel (2006) and rational (2007, 2008, 2010) models. Ordinary Kriging (OK) was

ISemivariogramrrigated agriculture

used to analyze spatial variability of groundwater salinity, whereas Indicator Kriging (IK) was used toanalyze groundwater salinity in relation to pollution threshold values.

Spatial variability maps show a decrease in groundwater salinity from 2004 to 2010, with salinitylevels in 31% of the study area exceeding 5.0 dS m−1 in 2004, compared to 9% of the study area in 2010.Moreover, probability maps show that 13.60% of the total area had the highest probability (0.8–1.0) of

n 200

exceeding the threshold i

. Introduction

Groundwater is the main source of irrigation water supplyor many settlements. Because poor-quality irrigation water canlter soil physicochemical properties, causing soil salinization andeducing crop productivity (Ramsis et al., 1999), it is important tovaluate the quality of any groundwater that may be potentiallysed for irrigation. Especially in areas with an excessively highater table, soil may suffer from associated problems of salinity

nd waterlogging.Sampling and mapping in the earth sciences are complicated

y spatial and temporal patterns. The discipline of geostatisticsrovides very useful techniques for handling spatially distributedata such as soil and groundwater pollution (Cemek et al., 2007;elgado et al., 2010; Gokalp et al., 2010; Nas and Berktay, 2010). By

dentifying spatial patterns and interpolating values at unsampledocations, geostatistical analysis can play a vital role in the sustain-ble management of groundwater systems by providing estimatednput parameters at regular grid points from measurements takent random locations (Kumar, 2007). Many authors have empha-ized the role of geostatistics in the management and sustainability

f regional water resources (Demir et al., 2009; Baalousha, 2010;ash et al., 2010; Zhou et al., 2011).

∗ Tel.: +90 362 3121919x1271.E-mail address: hakan.arslan@omu.edu.tr

378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agwat.2012.06.015

4, compared to 0% in 2010.© 2012 Elsevier B.V. All rights reserved.

Kriging is a geostatistical interpolation technique that has anumber of variations, including simple kriging, ordinary kriging(OK), co-kriging, stratified kriging and non-linear kriging, withordinary kriging used most frequently (Yimit et al., 2011). Yimitet al. (2011) used ordinary kriging to map the salinity of a ground-water irrigation source in China. Theodossiou and Latinopoulos(2007) used kriging to interpolate groundwater levels in theAnthemountas Basin of northern Greece and cross-validation toestimate the accuracy of the interpolated values. Ahmed (2002)demonstrated the accuracy of kriging methods used to estimatetotal dissolved solids (TDS) in groundwater in India. Hooshmandet al. (2011) used kriging and cokriging to estimate sodiumadsorption ratios and chloride content in an agricultural field inIran.

Another geostatistical variant, indicator kriging (IK) is used toestimate the proportion of values that fall within specific classintervals (Mulla and McBratney, 2001) by incorporating the uncer-tainty of the value of variables at unsampled locations. Kuisi et al.(2009) used both OK and IK to analyze the spatial variability ofgroundwater nitrate and salinity in the Amman-Zarqa Basin; thefindings indicated groundwater nitrate levels in 73% of the studyarea exceeded 50 mg/L. Dash et al. (2010) applied OK and IK toanalyze the spatial variability of groundwater depth and qualityparameters in Delhi; the authors found groundwater chloride lev-

els in 62% of the study area exceeded 250 mg/L and salinity levelsin 69% of the area exceeded 2.5 dS m−1. Gaus et al. (2003) used dis-junctive kriging to estimate concentrations of arsenic in shallowgroundwater in Bangladesh and to map the probability of arsenic

58 H. Arslan / Agricultural Water Man

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Fig. 1. Location of the study area.

evels in drinking water exceeding national limits for most of theountry.

In Turkey, large areas are affected by irrigation-related ground-ater problems. The Bafra Plain Right Bank Irrigation Area, covering

% of the nation’s total irrigated area, is one of the largest irrigationnd drainage projects in Turkey. Excessive use of irrigation water,eepage from canals, inefficient irrigation methods and inade-uate or malfunctioning drainage systems have led to groundwaterrainage and salinity problems in the irrigation area, reducing thefficiency and sustainability of irrigation and drainage infrastruc-ures and threatening soil quality (Cemek et al., 2006).

The present study combined the use of geostatistics and Geo-raphic Information System (GIS) technology to analyze the spatialistribution and seasonal variability of groundwater salinity in theafra Plain between 2004 and 2010.

. Materials and methods

.1. Study area description

The Bafra Plain is located in the Kizilirmak Delta in the provincef Samsun in northern Turkey (Fig. 1) between 41◦30′–41◦45′N lat-tude and 35◦30′–36◦15′E longitude at an average elevation of 5 mbove sea level (Temizel et al., 2011). The region has a semi-humidlimate, with mean temperatures ranging between 6.9 ◦C in Januarynd 22.2 ◦C in July (annual mean temp.: 13.9 ◦C). The annual pre-ipitation is 722.5 mm, most of which falls between September andpril (Anon., 2010).

Formation of the first Kizilirmak Delta plain began approx-mately 300,000 years ago, and formation of the second deltalain began around 100,000 years ago. The third and finallain was formed from the sediments carried by the delta and

agement 113 (2012) 57– 63

parallel streams approximately 10,000 years ago during the Flan-drian transgression of the Holocene (Akkan, 1970). The first twoplains comprise the slopes and higher altitudes of the delta,whereas the third plain, which covers the largest area, has a verygentle gradient (0.008) that extends to the Black Sea and includesnumerous lagoons and wetlands formed by successional dunesand forests (Ozesmi, 1992). Quaternary sediment deposits rangebetween 30 and 130 m in thickness and increase from south tonorth (Alac am: 30–60 m; Karabogaz Lake: 85 m; Bafra and East-ern Delta Region: 90 m; Coastal Region: 130 m) (Karaliaoglu andIslamoglu, 1998).

Various properties of soils in the study area are given in Table 1(Arslan and Demir, 2012). The majority of soil extends beyond1.5 m in depth. Soils are comprised of alluvial materials from dif-ferent elevations and are fine-textured, exhibit moderate hydraulicconductivity and have a high mean pH (approximately 8.2). Soilsublayers tend to be massive in structure, and a high organic mat-ter content has been found in some soils located below an elevationof 2 m, although this varies with drainage and aeration (Ozesmi,1999).

Crop patterns in the study area vary considerably. Wheat, toma-toes, pepper, watermelon, beet, and sweet melon predominate inthe irrigation season, whereas cabbage and leek are grown in therainy season, and corn is grown as both a primary and secondarycrop. With the recent completion of irrigation projects and subse-quent increases in the availability of water, rice production has alsoincreased considerably.

Canal irrigation is the most common irrigation system in thearea and is mostly conducted using border-and-furrow irrigationmethods. Sprinkler irrigation is rare, and drip irrigation is nonexis-tent. Approximately 75% of the area is irrigated with surface waterand the remaining 25% with groundwater (Arslan and Demir, 2011).

2.2. Groundwater sampling and analysis

Groundwater salinity (EC dS m−1) within the 10,350 ha BafraPlain Right Bank Irrigation Area was monitored from 2004 to 2010during the month of September according to C etin and Diker (2003).Measurements were taken using digital meters immediately aftersampling at 89 points in 2004 and 97 points from 2005 to 2010. Ele-vations and coordinates of observation wells were obtained froma 1/5000-scale map, and latitudes and longitudes were confirmedusing Global Positioning Systems (GPS).

In areas where no measurements were taken, groundwatersalinity values were interpolated using ordinary kriging and indi-cator kriging, and the data obtained was used to draw spatialvariability and probability maps of the distribution of groundwa-ter salinity in the research area. Both OK and IK estimations wereobtained using the geostatistical software package ARCGIS 10.0with Geostatistical Analyst Extensions.

2.3. Statistical analysis

Data was analyzed in four stages. First, a normality(Kolmogorov–Smirnov) test was conducted to test for normaldistribution for each year. Second, descriptive statistics for annualgroundwater salinity levels were generated, including arithmeticmeans, standard deviations, minimums and maximums. Third,trend analysis was used to identify the best predictive model fromamong 11 different semivariogram models tested. Finally, krigingtechniques were used to estimate or predict salinity concentrationsat unsampled locations.

The theoretical basis of geostatistics has been fully describedby several authors (Xie et al., 2011; Mendes and Ribeiro, 2010).The main tool in geostatistics is the variogram, which expressesthe spatial dependence between neighboring observations. The

H. Arslan / Agricultural Water Management 113 (2012) 57– 63 59

Table 1Physical and chemical properties of soils in the study area (Arslan and Demir, 2012).

Soil property Depth (cm) Mean Minimum Maximum S.D. C.V.

Sand (%) 0–30 25.61 4.94 70.42 16.48 64.3530–60 24.04 6.52 68.66 16.63 69.1860–90 24.42 3.85 80.85 18.84 77.1590–120 29.41 6.69 87.12 22.46 76.37

Silt (%) 0–30 26.76 11.53 42.34 9.34 34.9030–60 27.21 12.50 47.91 8.77 32.2360–90 30.62 3.38 53.92 13.67 44.6490–120 30.29 3.45 52.97 15.34 50.64

Clay (%) 0–30 47.63 15.51 83.06 18.75 39.3730–60 48.75 17.99 80.47 18.31 37.5660–90 44.96 7.99 86.44 22.80 50.7190–120 40.30 5.13 80.39 22.13 54.91

EC (dS m−1) 0–30 2.41 1.08 7.01 1.15 47.7230–60 2.42 1.15 4.67 0.89 36.7860–90 2.49 0.96 7.37 1.12 44.9890–120 2.43 1.06 7.02 1.14 46.91

pH 0–30 7.86 7.41 8.75 0.28 3.5630–60 8.06 7.56 9.42 0.41 5.0960–90 8.21 7.88 9.41 0.35 4.2690–120 8.30 7.83 9.42 0.38 4.58

ESP (%) 0–30 10.97 4.72 27.71 5.41 49.325.34.74.9

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30–60 14.26

60–90 15.14

90–120 15.83

ariogram, �h, can be defined as one-half the variance of the dif-erence between the attribute values at all points separated by h asollows:

h = 12n

n∑i=1

[Z(�i) − Z(�i + h)]2 (1)

here �h is the estimated or “experimental” semi-variance valueor all pairs at a lag distance h; Z(�i) is the water quality value atoint i; Z(�i + h) is the water quality value of other points sepa-ated from xi by a discrete distance h; xi are the georeferencedositions where the Z(�i) values were measured; n represents theumber of pairs of observations separated by the distance h (Isaaksnd Srivastava, 1989; Hernández-Stefanoni and Ponce-Hernández,006).

OK was used to generate predictive maps for annual ground-ater salinity and interpolate groundwater salinity values fornsampled locations. IK was used to obtain data to plot seasonalroundwater salinity probability maps.

Prediction performances were assessed by cross-validation. For model to provide accurate predictions, the standardized meanrror should be close to 0, the root mean square error (useful whenomparing models) and average standard error should be as smalls possible, and the root mean square standardized error should belose to 1 (ESRI, 2008). Mean error (ME), root mean square errorRMSE), were estimated using the following formulas:

E =∑

(zi∗ − zi) (2)

MSE =√∑

(zi∗ − zi)2

n(3)

here Zi is the predicted value, Zi∗ is the observed value, and n ishe number of observations.

. Results and discussion

.1. Descriptive statistics

Table 2 provides a summary of groundwater salinity statisticsrom 2004 to 2010. Kriging methods work best if data is normallyistributed and satisfies the assumption of equal variability. In this

9 35.88 8.34 58.492 40.14 9.11 60.178 43.57 9.29 58.69

study, the Kolmogorov–Smirnov test showed EC values during thestudy period were not normally distributed; therefore, values werelog-transformed prior to the calculation of semivariance.

3.2. Ordinary Kriging

Circular, spherical, tetraspherical, pentaspherical, exponential,Gaussian, rational quadratic, hole effect, K-bessel, J-bessel and sta-ble semivariogram models were evaluated in this study (Nas andBerktay, 2010).

Table 3 shows the variogram models and groundwater salin-ity parameters for the years 2004–2010. As the table indicates,different models were selected as best-fits for different years.These included exponential (2004, 2009), spherical (2005), J-Bessel(2006) and rational quadratic (2007, 2008, 2010) models. While thedifferent best-fit variograms were used to obtain the most accu-rate estimations, it should be noted that the differences betweenvariogram results may be attributed to differences in climactic con-ditions, drainage and irrigation regimes.

Spatial dependence of groundwater salinity can be classifiedaccording to nugget-to-sill ratio (%), with a ratio of <25% indicatinga strong spatial dependence, a ratio of 25–75% indicating moderatespatial dependence and a ratio of >75% indicating a weak spatialdependence (Cambardella et al., 1994). Findings for nugget-to-sillratios in the present study indicated groundwater salinity to have amoderate spatial structure for all years tested, with similar annualvalues ranging from 6748 m to 12,682 m (Table 3). Cross-validation(Table 4) found groundwater salinity ME to be close to 0 (between−0.0054 and −0.0810) and RMSE to range from 1.205 to 3.033,indicating an accuracy of predictions (Sun et al., 2009).

Nearly all irrigation waters that have shown long-term successhave conductivity values of less than 2.50 dS m−1, and althoughwaters with higher conductivity are used occasionally, the result-ing crop production has been less than satisfactory, except inunusual circumstances (Richards, 1954). In the present study, spa-tial variability of salinity, as represented by EC, was distributed

over five thematic classes indicating non-saline (<2.25 dS m−1),moderate saline (2.25–5.0 dS m−1), saline (5.0–7.5 dS m−1), verysaline (7.50–10.0 dS m−1) and very high saline >10.0 dS m−1 regions(Table 5).

60 H. Arslan / Agricultural Water Management 113 (2012) 57– 63

Table 2Statistical analysis of groundwater salinity.

Years n Min Max Mean Median S.D. Skewness Kurtosis Transformation

2004 89 0.98 24.10 4.95 3.81 3.80 2.76 12.60 Lognormal2005 97 0.36 15.12 3.46 2.76 2.26 2.02 9.23 Lognormal2006 97 1.20 15.62 3.75 2.95 2.40 2.43 10.20 Lognormal2007 97 1.30 11.70 3.45 2.70 1.95 1.86 6.62 Lognormal2008 97 0.60 11.40 3.37 2.64 1.83 1.80 6.76 Lognormal2009 97 0.24 9.96 2.92 2.60 1.47 1.97 8.46 Lognormal2010 97 0.48 12.24 3.04 2.40 1.94 1.96 7.97 Lognormal

Table 3Semivariogram model parameters for the years 2004–2010 (OK).

Years Models Nugget (C0) Sill (C0 + C) Range (m) Nugget ratio

2004 Exponential 0.124 0.454 9965 27.312005 Spherical 0.187 0.422 6748 44.312006 J Bessel 0.158 0.262 12,143 60.312007 Rational Quadratic 0.088 0.280 12,981 31.432008 Rational Quadratic 0.122 0.279 12,682 43.732009 Exponential 0.142 0.278 12,682 51.082010 Rational Quadratic 0.160 0.404 10,691 39.60

Table 4Cross-validation between measured and estimated values for groundwater salinity (OK).

Years Prediction errors

Mean Root mean square Average standard Mean standardized Root mean square standardized

2004 −0.0810 3.033 3.203 −0.0086 0.9102005 −0.0180 1.701 2.430 0.0323 0.6492006 −0.0554 2.122 1.859 −0.0010 1.0202007 −0.0409 1.439 1.480 −0.0060 0.895

yfp

dndroiEpanAcpE7

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2008 −0.0372 1.472 1.638

2009 −0.0054 1.205 1.524

2010 −0.0609 1.555 1.877

Fig. 2 shows the groundwater salinity maps drawn for 2004, theear in which the greatest area suffered from salinity problems, andor 2009, the year in which the smallest area suffered from salinityroblems.

In general, groundwater salinity values in the study areaecreased significantly between 2004 and 2010 (Table 5). In 2004,early all of the study area was found to be suffering from someegree of salinity. Salinity was mapped into 5 categories, with ECanging from 0.98 to 24.10 dS m−1 (mean: 4.95 dS m−1). Only 1%f the area showed an EC in the range of 0–2.25 dS m−1, whichs considered to be non-saline, whereas 68% of the area had anC ranging from 2.25 to 5.00 dS m−1, which could cause severeroblems for many agricultural crops, and 31% had an EC valuebove 5.00 dS m−1, a critical threshold value in drainage engi-eering (C etin and Diker, 2003; DSI, 2005; Kaman et al., 2011).ccording to Kotuby et al. (1997), EC levels over 2.25–5.00 dS m−1

an result in yield reductions of about 50% for rice, tomato, pep-ers, spinach and corn. Of the 31% over 5.00 dS m−1, 16% had anC ranging from 5.00 to 7.50 dS m−1, 8% had an EC ranging from.50 to 10.00 dS m−1, and 7% had an EC in excess of 10.00 dS m−1.

able 5ifferences in groundwater salinity values within the study area, 2004–2010 (ha,%).

Years 0–2.25 dS m−1 2.25–5.00 dS m−1 5.00–

Area (ha) (%) Area (ha) (%) Area (

2004 66 1 7004 68 1707

2005 1622 15 6713 65 1625

2006 – – 8602 83 1748

2007 807 8 8318 80 840

2008 260 3 8820 85 1230

2009 795 8 9071 87 484

2010 2035 20 7408 71 880

0.0141 0.8390.0019 0.734

−0.0246 0.859

In general, EC values increased towards the northeast part of thestudy area (Fig. 2a).

In 2009, salinity was mapped into 3 categories, with ground-water EC ranging from 0.24 to 9.94 dS m−1 (mean: 2.92 dS m−1).EC levels were below 2.25 dS m−1 in 8% of the study area(795 ha), whereas 87% of the study area had EC levels between2.25–5.00 dS m−1 and 5% (488 ha) had EC levels above the criti-cal threshold of 5.00 dS m−1. Of all the years tested, 2009 had thesmallest area affected by excessive salinity (Fig. 2b). By 2010, EC inthe study area exceeded 7.5 dS m−1 in only 15 ha; however, EC val-ues ranging between 5.00–7.50 dS m−1 and 2.25–5.00 dS m−1 werestill observed in 9% and 71% of the area, respectively, indicating anongoing potential risk from salinity.

As Figs. 1 and 2 show, salinity in the region increased withdecreases in height, which can be explained by the leaching of saltsthrough precipitation and irrigation. In 2004, the General Direc-

torate of State Hydraulic Works (DSI) partially completed workon an irrigation and drainage network, with 175 km of drainagecanals opened in a 6000 ha area in the southern part of the BafraPlain. The decrease in salinity observed between 2004 and 2010

7.50 dS m−1 7.50–10.00 dS m−1 10.00 < dS m−1

ha) (%) Area (ha) (%) Area (ha) (%)

16 864 8 709 716 390 4 0 017 0 0 0 0

8 380 4 5 012 40 0 0 0

5 4 0 0 09 27 0 0 0

H. Arslan / Agricultural Water Management 113 (2012) 57– 63 61

Fs

catafo

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northeastern part of the area. From 2005 to 2010, although none

ig. 2. Spatial variability maps of highest and lowest groundwater salinity in thetudy area (OK) (a-2004, b-2009).

an be attributed mainly to the near completion of this irrigationnd drainage system during this time period. With the construc-ion of 220 km of drainage canals, the system was 90% completes of 2010; however, considering that 72%ofthe area was stillound to be under threat from salinity, the area requires continuingbservation.

.3. Indicator Kriging method

IK was also used to generate groundwater salinity probabilityaps for the years 2004–2010. At each sampling site, measure-ents are taken using a continuous scale and converted to discrete

ndicator variables given a value of either ‘1’or ‘0’, with ‘1’ indi-ating a value below the threshold level (in this case, 5.00 dS m−1

or groundwater electrical conductivity) (C etin and Diker, 2003).alues are estimated for unsampled locations in the study area.robability maps for 2004 and 2009 are shown in Fig. 3a and b.,

nd best-fit variogram models for 2004–2010, cross-validation, andnnual predictions of the extent of the study area likely to be threat-ned by excessive salinity are given in Tables 6–8, respectively.

Fig. 3. Spatial distribution of groundwater salinity probabilities in the study area(IK) (a-2004, b-2009).

Findings for nugget-to-sill ratios in the present study indicatedgroundwater salinity to have a moderate spatial structure for allyears tested, with similar annual values ranging from 9651 m to12,682 m (Table 6). Cross validation (Table 7) found groundwatersalinity ME to be close to 0 (between −0.0047 and 0.0046) and RMSEto range from 0.8391 to 1.2150.

Best-fit models varied by year and included Exponential (2004,2010), J-Bessel (2005), Gaussian (2006, 2008), Rational Quadratic(2007) and Hole Effect (2009) models.

Despite a trend of decreasing EC from 2004 to 2010, in 2004,13.60% of the area showed the highest probability (0.8–1.0) ofexceeding the threshold value for EC and more than 11% of thearea showed a strong probability (0.6–0.8) of exceeding the thresh-old, with the problem of excess salinity concentrated mainly in the

of the area showed the highest probability (0.8–1.0) of exceedingthe threshold value (with the exception of 2007, when 1.8% of thearea fell in this range), parts of the area continued to show a strong

62 H. Arslan / Agricultural Water Management 113 (2012) 57– 63

Table 6Characteristics of semivariogram models (IK).

Years Models Nugget (C0) Sill (C0 + C) Range (m) Nugget ratio

2004 Exponential 0.100 0.283 10,988 35.342005 J Bessel 0.104 0.166 12,150 62.652006 Gaussian 0.143 0.288 9651 49.652007 Rational Quadratic 0.080 0.168 12,682 47.622008 Gaussian 0.084 0.187 12,681 44.922009 Hole Effect 0.063 0.084 12,269 75.002010 Exponential 0.051 0.154 12,681 33.12

Table 7Cross-validation between measured and estimated values for groundwater salinity (IK).

Years Prediction errors

Mean Root mean square Average standard Mean standardized Root mean square standardized

2004 0.0012 0.3852 0.4087 0.0030 0.94932005 0.0046 0.3982 0.3383 0.0124 1.17902006 −0.0013 0.3275 0.3710 −0.0027 0.88302007 −0.0016 0.3776 0.3135 −0.0023 1.21502008 0.0014 0.3185 0.3050 0.0076 1.04402009 −0.0013 0.2286 0.1996 −0.0028 1.13402010 −0.0047 0.2384 0.2799 −0.0110 0.8391

Table 8Probability ranges of areas exceeding groundwater salinity thresholds, by year (IK).

Probability range Area (%)

2004 2005 2006 2007 2008 2009 2010

0.0–0.2 49.7 66.9 62.9 67.8 74.7 82.6 75.90.2–0.4 15.8 13.1 19.0 14.4 14.3 14.6 12.80.4–0.6 9.1 13.0 17.8 9.5 8.9 2.8 6.10.6–0.8 11.8 7.0 0.3 6.5 2.1 0.0 5.2

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0.8–1.0 13.6 0.0 0.0

robability (0.6–0.8) of exceeding the threshold (11.8%, 7%, 0.3%,.5%, 2.1% and 5.2%. for 2004, 2005, 2006, 2007, 2008 and 2010,espectively).

. Conclusions

Kriging is considered to be a useful technique for the monitor-ng, evaluation and management of groundwater resources. Thistudy used ordinary kriging and indicator kriging to map the spatialnd temporal variability and probability of excessive groundwateralinity. Data analysis was performed using the geostatistical soft-are package ARCGIS 10.1 with Geostatistical Analyst Extensions.

Spatially, groundwater salinity showed a tendency to increaseowards the north of the Bafra Plain, whereas temporally, ground-ater salinity decreased from 2004 to 2010. Ordinary kriging

howed that in 2004, 31% of the area had non-acceptable levels ofalinity and the remaining 68% was potentially at risk of excessivealinity; moreover, although salinity was found to have decreasedignificantly by 2010, the area remained problematic, with 9% of therea above acceptable levels of salinity and 71% at risk of excessivealinity.

Indicator kriging showed that in 2004, 13.6% of the area, mainlyn the northern part of the plain, had the highest probability0.8–1.0) of exceeding the threshold for acceptable salinity levels.

hile salinity levels had decreased so that none of the area hadhe highest probability of exceeding the threshold in 2010, the facthat 6% of the area still showed a strong probability (0.6–0.8) of

xceeding acceptable salinity levels remains very alarming.

The decrease in groundwater salinity on the Bafra Plain between004 and 2010 can be attributed mainly to the completion of an

rrigation and drainage system and salt leaching from upland areas.

1.8 0.0 0.0 0.0

Acknowledgments

The author is grateful to the technical staff of the Bafra PlainBranch Office at the Seventh Regional Directorate of State HydraulicWorks (DSI), Samsun, Turkey, for their help with field work; toAssoc. Prof. Dr. Bilal CEMEK, Ondokuz Mayıs University, Samsun,for his suggestions on mathematical and statistical calculations.

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