Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey

Catena 92 (2012) 11–21

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Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey Memet Varol a,⁎, Bülent Gökot b, Aysel Bekleyen b, Bülent Şen c a b c

Ministry of Agriculture and Rural Affairs, Province Control Laboratory, 21010 Diyarbakır, Turkey Faculty of Art and Science, Biology Department, University of Dicle, Diyarbakır, Turkey Faculty of Aquaculture, Department of Basic Aquatic Sciences, Fırat University, Elazığ, Turkey

a r t i c l e

i n f o

Article history: Received 23 May 2011 Received in revised form 16 November 2011 Accepted 22 November 2011 Keywords: Hydrochemistry Inland water resources Multivariate statistical techniques Water pollution Monitoring

a b s t r a c t Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied to evaluate the temporal/spatial variations of water quality data sets for Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin, obtained during 1 year (2008–2009) of monitoring. This study highlights the usefulness of multivariate statistical techniques for the evaluation and interpretation of complex water quality data sets, apportionment of pollution sources/ factors and the design of a monitoring network for the effective management of water resources. Hierarchical CA grouped 12 months into two clusters (wet and dry seasons) and classified ten monitoring sites into four clusters based on similarities in the water quality characteristics. PCA/FA identified five factors in the data structure that explained 80% of the total variance of the data set. The PCA/FA grouped the selected parameters according to common features to help evaluate the influence of each group on the overall variation in water quality. Discriminant analysis showed better results for data reduction and pattern recognition during both spatial and temporal analysis. Temporal DA revealed nine parameters (water temperature, dissolved oxygen, total alkalinity, total hardness, nitrate nitrogen, ammonia nitrogen, total phosphorus, chloride and calcium), affording 100% correct assignations. Spatial DA revealed eight parameters (water temperature, pH, dissolved oxygen, electrical conductivity, nitrate nitrogen, orthophosphate phosphorus, sodium and total suspended solids), affording 92.5% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters responsible for large variations in water quality that could reduce the number of sampling parameters. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Surface water quality is a matter of serious concern today. Anthropogenic influences (urban, industrial and agricultural activities, increasing exploitation of water resources) and natural processes (changes in precipitation, erosion, weathering of crustal materials) degrade surface waters and impair their use for drinking, industrial, agricultural, recreation or other purposes (Carpenter et al., 1998; Jarvie et al., 1998). Because lakes, reservoirs and rivers constitute the main inland water resources for domestic, industrial and irrigation purposes, it is imperative to prevent and control water pollution and to have reliable information on water quality. In view of the spatial and temporal variations in the hydrochemistry of surface waters, regular monitoring programmes are required for reliable estimates of the water quality (Singh et al., 2004). The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor

⁎ Corresponding author. Tel.: + 90 412 2266046; fax: + 90 412 2266052. E-mail address: [email protected] (M. Varol). 0341-8162/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.catena.2011.11.013

analysis (FA) and discriminant analysis (DA) facilitates the interpretation of complex data matrices to better understand the water quality and ecological status of studied systems. These statistical methods also help with the identification of possible factors/sources that influence water systems and offers a valuable tool for the reliable management of water resources as well as rapid solutions to pollution problems (Shrestha and Kazama, 2007; Simeonova et al., 2003; Varol and Şen, 2009; Varol et al., in press; Vega et al., 1998; Wunderlin et al., 2001). Multivariate statistical techniques have been applied to characterise and evaluate surface freshwater quality, because they are useful in verifying temporal and spatial variations caused by natural and anthropogenic factors linked to seasonality (Helena et al., 2000; Qadir et al., 2007; Shrestha and Kazama, 2007; Singh et al., 2004 and 2005). The evaluation of water quality in most countries has become a critical issue in recent years; especially due to concerns that freshwater will be a scarce resource in the future (Qadir et al., 2007; Simeonov et al., 2003; Singh et al., 2004; Wunderlin et al., 2001). Water quality monitoring is a helpful tool not only to evaluate the impacts of pollution sources but also to ensure an efficient management of water resources and the protection of aquatic life (Strobl and Robillard, 2008). As part of the

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M. Varol et al. / Catena 92 (2012) 11–21

Southeastern Anatolian Project, The Kralkızı, Dicle and Batman dams and hydroelectric power plants were built in the Tigris River basin. Some reports have been published on the water quality of the Tigris River (Varol et al., 2010; Varol et al., in press), but no water quality assessment of the dam reservoirs on the Tigris River and its tributaries has been undertaken. In this paper, we report the first study on the water quality of three major dam reservoirs in the Tigris River basin. The data sets obtained were subjected to different multivariate statistical techniques to obtain information about the similarities or dissimilarities among the monitoring periods or sites, to identify water quality variables responsible for spatial and temporal variations in surface water quality and to determine the influence of the sources (natural and anthropogenic) on the water quality parameters of the dam reservoirs. 2. Materials and methods 2.1. Monitoring area The Kralkızı Dam Reservoir (KDR), Dicle Dam Reservoir (DDR) and Batman Dam Reservoir (BDR) are located in the Tigris River basin of the Southeastern Anatolia region of Turkey (Fig. 1). The KDR is present at the confluence of the Maden Stream. Located between latitudes 38°19′59 and 38°25′20 N, and longitudes 39°47′16 and 40°01′37 E, the KDR has a surface area of 57.5 km2 and a volume of 1919 hm3 at 790 m above sea level. The Kralkızı Dam was erected for hydroelectric power generation in 1997. The DDR is located between latitudes 38°13′49 and 38°25′09 N, and longitudes 40°01′00 and 40°14′16 E with a surface area of 24 km2 and a volume of 595 hm3 at 710 m above sea level. The DDR is fed by the Dipni Stream and the water released from the KDR. The Dicle Dam was erected for hydroelectric power generation, irrigation and drinking water supply in 1997. The BDR is formed at the confluence of the Kulp, Sason and Sorkan streams. Located between latitudes 38°09′34 and 38°17′15 N, and longitudes 41°01′41 and 41°15′30 E, the BDR has a surface area of 49.25 km2 and

Table 1 Locations of sampling sites on the Kralkızı, Dicle and Batman dam reservoirs. Reservoirs

Sites

Coordinates

Kralkızı Dam Reservoir

K-1 K-2 K-3 K-4 D-1 D-2 D-3 B-1 B-2 B-3

38°21′ 38°21′ 38°22′ 38°21′ 38°22′ 38°15′ 38°13′ 38°11′ 38°11′ 38°09′

Dicle Dam Reservoir

Batman Dam Reservoir

N–39°52′ E N–39°55′ E N–39°58′ E N–40°00′ E N–40°12′ E N–40°05′ E N–40°10′ E N–41°09′ E N–41°13′ E N–41°12′ E

a volume of 1175 hm3 at 652 m above sea level. The Batman Dam was erected for hydroelectric power generation, irrigation and flood control in 1999. The Tigris Basin has a subtropical plateau continental climate. The continental climate of the basin is similar to those of the Mediterranean region (Anonymous, 2007). The annual mean air temperature varied between 14.6 °C and 21.8 °C with the highest and the lowest temperature of 35.9 °C and 0 °C, respectively. Annual total precipitation ranged from 294.1 mm to 611.1 mm, of which 82% fell from October to April (Anonymous, 2009). In the present study, ten sampling sites were selected on the dam reservoirs as the water quality monitoring network (Table 1). Four sites (K-1, K-2, K-3 and K-4) were located in the KDR, three sites (D-1, D-2 and D-3) were located in the DDR and three sites (B-1, B-2 and B-3) were located in the BDR (Fig. 1). Non-point sources such as agricultural runoff and atmospheric deposition, and point sources such as municipal effluents from the villages and towns around the reservoirs are pollution sources for the dam reservoirs. Manure and chemical fertilisers with nitrogen and phosphorus are commonly used in the basin. Wheat, corn, cotton and lentil are the main crops cultivated in the area. Erosion occurs during cultivation of soil and rainfall events from upland areas. Sheep and cattle grazing are

Fig. 1. Map showing the water quality monitoring sites on the dam reservoirs in the Tigris River Basin.

M. Varol et al. / Catena 92 (2012) 11–21

one of the major agricultural land uses around the reservoirs. In addition, the streams carry nutrients, dissolved salts and suspended solids into the reservoirs during the high flow periods. There are not any industrial pollution sources around the reservoirs. 2.2. Sampling and chemical analysis Water samples were collected at monthly intervals between February 2008 and January 2009. Sampling, preservation and transportation of the water samples to the laboratory were performed according to standard methods (APHA, 1999). The samples were analysed for 18 parameters including water temperature (WT), pH, dissolved oxygen (DO), electrical conductivity (EC), total alkalinity (T-Alk), total hardness (T-Hard), total nitrogen (TN), nitrate nitrogen (NO3–N), nitrite nitrogen (NO2–N), ammonia nitrogen (NH3–N), chemical oxygen demand (COD), total phosphorus (TP), orthophosphate phosphorus (PO4–P), chloride (Cl), sulphate (SO4), sodium (Na), calcium (Ca) and total suspended solids (TSS). Water temperature, pH, dissolved oxygen and electrical conductivity were measured in the field with a portable multimetre. All other parameters were determined in the laboratory following standard protocols (APHA, 1999; ISO, 1986). TN (persulphate digestion followed by 2,6-dimethylphenol method), NO3–N (2,6dimethylphenol method), NO2–N (diazotisation method), NH3–N (phenate method), TP (persulphate digestion followed by ascorbic acid method), PO4–P (ascorbic acid method) and SO4 (turbidimetric method) were analysed by spectrophotometry, whereas Na and Ca were analysed using flame atomic absorption spectrometry (AAS). TSS was determined gravimetrically, total alkalinity by the titration method, total hardness by the EDTA titrimetric method, Cl by the argentometric method and COD by the dichromate reflux method. Each analysis was performed in duplicate and the mean value was taken. All of the water quality parameters were expressed in milligrammes per litre (mg l− 1), except WT (°C), pH and EC (μS cm− 1). The analytical data quality was guaranteed through the implementation of laboratory quality assurance and quality control methods, including the use of standard operating procedures, calibration with standards, analysis of reagent blanks, recovery of known additions and analysis of replicates. The laboratory also participated in regular national programme on analytical quality control. 2.3. Data treatment and multivariate statistical methods Analysis of variance (ANOVA) was performed to analyse the significant spatial and temporal differences (p b 0.05). Relationships among the considered variables were tested using Pearson's coefficient with statistical significance set at p b 0.05. Kaiser–Meyer–Olkin (KMO) and Bartlett's sphericity tests were performed to examine the suitability of the data for PCA/FA (Shrestha and Kazama, 2007; Varol and Şen, 2009). KMO is a measure of sampling adequacy that indicates the proportion of variance that is common, i.e., variance that may be caused by underlying factors. A high value (close to 1) generally indicates that PCA/FA may be useful, as was the case in this study, where KMO = 0.75. Bartlett's test of sphericity indicates whether a correlation matrix is an identity matrix, which would indicate that variables are unrelated. The significance level of 0 in this study (less than 0.05) indicated that there were significant relationships among the variables. The temporal and spatial variations of the water quality parameters (Table 1) were evaluated through season and site–parameter correlation matrix using the Spearman non-parametric correlation coefficient (Spearman's R). The water quality parameters were grouped in four different seasons (winter, spring, summer and autumn) and three sites (Kralkızı, Dicle and Batman), and each assigned a numerical value in the data file, which as a variable corresponding to the season and site were correlated (pair by pair) with all the measured parameters.

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Multivariate analysis of the water quality data set was performed using CA, PCA/FA and DA techniques (Panda et al., 2006; Shrestha and Kazama, 2007; Simeonov et al., 2003; Simeonova et al., 2003; Singh et al., 2004; Varol and Şen, 2009; Varol et al., in press; Vega et al., 1998; Wunderlin et al., 2001). DA was applied to raw data, whereas CA, PCA and FA were applied to experimental data standardised through z-scale transformation to avoid misclassification due to wide differences in data dimensionality (Liu et al., 2003; Shrestha and Kazama, 2007; Singh et al., 2004). Furthermore, the standardisation procedure eliminated the influence of different units of measurements and rendered the data dimensionless. 2.3.1. Cluster analysis (CA) CA, an unsupervised pattern recognition technique, reveals the intrinsic structure of a data set without making a priori assumptions about the data to classify the objects of the system into categories or clusters based on their nearness or similarity (Vega et al., 1998). Hierarchical clustering is the most common approach, where clusters are formed sequentially by starting with the most similar pair of objects and forming higher clusters in a step-by-step fashion. The Euclidean distance usually gives similarities between two samples, and a ‘distance’ can be represented by the ‘difference’ between analytical values from both of the samples (Otto, 1998). Hierarchical agglomerative CA was performed on the normalised data set using Ward's method with Euclidean distances as a measure of similarity. This method uses the analysis of variance approach to evaluate the distances between clusters while attempting to minimise the sum of squares of any two clusters that can be formed at each step. CA was applied to the water quality data set to group the similar sampling sites (spatial variability) and seasonal (temporal) similarity among the variables (samples), resulting in spatial and temporal dendrograms. The linkage distance is reported as Dlink /Dmax, which represents the quotient between the linkage distances for a particular case divided by the maximal distance, multiplied by 100, as a way to standardise the linkage distance represented on the y-axis (Shrestha and Kazama, 2007; Simeonov et al., 2003; Singh et al., 2004). 2.3.2. Discriminant analysis (DA) DA is used to determine the variables, which discriminate between two or more naturally occurring groups. It operates on raw data and the technique constructs a discriminant function for each group (Johnson and Wichern, 1992; Singh et al., 2004; Wunderlin et al., 2001), as in the equation below (Eq. (1)):

f ðGi Þ ¼ ki þ

n X

wij pij

ð1Þ

j¼1

where i is the number of groups (G), ki is the constant inherent to each group, n is the number of parameters used to classify a set of data into a given group, wj is the weight coefficient, assigned by DA to a given selected parameters (pj). In this study, four groups for temporal (four seasons) and three groups for spatial (three sampling regions) evaluations have been selected and the number of analytical parameters used to assign a measure from a monitoring site into a group (season or sampling area) has been taken as n. DA was applied to raw data by using the standard, forward stepwise and backward stepwise modes to construct discriminant functions to evaluate both the spatial and temporal variations in water quality and to identify the most significant discriminating variables. The site (spatial) and the season (temporal) were the grouping (dependent) variables, while all the measured parameters constituted the independent variables. 2.3.3. Principal component analysis/factor analysis (PCA/FA) The PCA technique extracts the eigenvalues and eigenvectors from the covariance matrix of original variables. The PCs are the uncorrelated

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M. Varol et al. / Catena 92 (2012) 11–21

(orthogonal) variables that are obtained by multiplying the original correlated variables with the eigenvector, which is a list of coefficients (loadings or weightings). Thus, the PCs are weighted linear combinations of the original variables. PC provides information on the most meaningful parameters, which describe the whole data set through data reduction with a minimum loss of original information (Helena et al., 2000; Singh et al., 2004; Vega et al., 1998). It is a powerful technique for pattern recognition that attempts to explain the variance of a large set of inter-correlated variables and transform them into a smaller set of independent (uncorrelated) variables (principal components). Factor analysis further reduces the contribution of less significant variables obtained from PCA, and the new group of variables, known as varifactors (VFs), is extracted by rotating the axis defined by PCA. A varifactor (VF) can include unobservable, hypothetical and latent variables, whereas a PC is a linear combination of observable variables (Helena et al., 2000; Vega et al., 1998). PCA of the normalised variables (water-quality data set) was performed to extract significant PCs and to further reduce the contribution of variables with minor significance; these PCs were then subjected to varimax rotation (raw) to generate VFs. 3. Results and discussion The basic statistics for all of the water quality parameters measured during the sampling period of one year at ten different sites on the dam reservoirs are summarised in Table 2. Most water quality parameters except EC, T-Alk, T-Hard, Cl, SO4, Na and Ca did not show significant spatial variations (p > 0.05). However, all water quality parameters studied showed significant temporal differences (p b 0.05). WT, pH, TN and COD displayed higher values in the dry season, while higher values for DO, EC, T-Alk, T-Hard, NO3–N, NO2–N, NH3–N, TP, PO4–P, Cl, SO4, Na, Ca and TSS in the wet season. The temporal and spatial variations of the water quality parameters (Table 2) were evaluated through season–parameter and site– parameter correlations matrix. Except for NO2–N, Cl and SO4, all the measured parameters were found significantly (p b 0.05) correlated

K-1 K-2

Cluster 1

K-3 K-4 D-2

Cluster 2 Cluster 3

D-3 D-1 B-1

Cluster 4

B-2 B-3

0

20

40

60

80

100

120

(Dlink/Dmax)*100 Fig. 2. Dendrogram showing hierarchical clustering of monitoring sites according to Ward's method with Euclidean distance.

with season. Among these, TN and NO3–N exhibited the highest correlation coefficient (Spearman's R = − 0.70). Other parameters exhibiting high correlation with season were WT (R = 0.65), DO (R = −0.63) and T-Hard (R = − 0.62). The site–parameter correlation matrix indicated that pH, EC, T-Alk, T-Hard, NO3–N, NO2–N, SO4, Ca and TSS showed correlation with site. Among these, T-Alk exhibited the highest correlation coefficient (R = −0.46), followed by EC (R = −0.43) and SO4 (R = − 0.40). The season and site-correlated parameters can be taken as representing the major source of temporal and spatial variations in water quality of the reservoirs. 3.1. Cluster analysis Spatial CA rendered a dendrogram (Fig. 2) where all ten sampling sites on the reservoirs were grouped into four statistically significant clusters at (Dlink / Dmax) × 100 b 50. Cluster 1 consisted of four sites (K-1, K-2, K-3 and K-4), cluster 2 consisted of two sites (D-2 and D-3), cluster 3 consisted of one site (D-1) and cluster 4 consisted of three sites (B-1, B-2 and B-3). The cluster classifications varied with significance level

Table 2 Mean, minimum and maximum values of water quality parameters at different locations of the Kralkızı, Dicle and Batman dam reservoirs. Reservoirs Kralkızı

K-1

K-2

K-3

K-4

Dicle

D-1

D-2

D-3

Batman

B-1

B-2

B3

Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max.

WT

pH

DO

EC

T-Alk

T-Hard

TN

NO3–N

NO2–N

NH3–N

COD

TP

PO4–P

Cl

SO4

Na

Ca

TSS

17.5 4.6 27.2 17.3 5.0 27.0 17.3 5.0 26.7 17.2 4.4 26.7 18.0 4.0 26.6 17.7 4.1 26.0 17.9 4.1 26.5 18.5 5.4 26.7 18.5 5.6 26.8 18.5 5.7 26.7

8.47 8.16 8.64 8.50 8.30 8.66 8.49 8.27 8.70 8.48 8.25 8.67 8.46 8.15 8.94 8.50 8.00 8.83 8.49 7.88 8.80 8.58 8.10 8.94 8.57 8.12 8.90 8.58 8.11 8.92

8.93 7.03 11.39 8.87 6.89 11.40 8.75 6.84 11.29 8.69 7.01 11.27 9.84 8.18 13.25 9.41 8.51 11.81 9.36 8.22 11.59 8.76 6.89 11.98 8.74 6.90 11.43 8.68 6.91 11.03

278 252 308 278 259 308 278 261 308 277 261 308 315 237 353 283 230 324 281 230 326 219 174 280 221 174 280 222 174 280

118 94 138 120 100 142 121 98 144 121 100 150 128 102 156 119 88 148 117 88 150 92 70 122 92 68 124 94 70 126

169 150 200 166 138 200 169 140 200 169 150 200 174 140 204 166 132 200 168 136 200 133 92 192 136 94 194 137 90 194

0.697 0.157 2.260 0.544 0.149 0.868 0.550 0.106 0.798 0.638 0.172 1.330 0.832 0.191 2.730 1.110 0.184 6.570 0.950 0.250 2.620 0.774 0.221 1.730 0.620 0.196 1.060 0.616 0.213 1.160

0.141 0.009 0.483 0.134 0.007 0.361 0.145 0.002 0.391 0.144 0.008 0.397 0.178 0.006 0.886 0.222 0.005 0.536 0.219 0.005 0.520 0.302 0.001 0.719 0.298 0.002 0.684 0.299 0.002 0.789

0.006 0.000 0.012 0.005 0.000 0.012 0.005 0.000 0.012 0.005 0.000 0.014 0.007 0.000 0.017 0.005 0.000 0.015 0.005 0.000 0.014 0.007 0.000 0.016 0.007 0.000 0.015 0.014 0.000 0.090

0.035 0.001 0.076 0.033 0.001 0.080 0.035 0.001 0.082 0.035 0.001 0.090 0.045 0.008 0.116 0.036 0.002 0.091 0.034 0.002 0.082 0.045 0.002 0.126 0.046 0.005 0.107 0.046 0.005 0.115

5.30 1.82 8.54 5.21 2.05 8.48 5.06 2.23 8.16 4.51 0.96 8.07 6.73 2.89 8.33 6.20 2.69 10.00 7.48 1.12 28.40 4.32 1.43 6.47 4.31 0.59 7.40 4.07 0.45 6.86

0.062 0.031 0.125 0.061 0.033 0.137 0.066 0.031 0.137 0.066 0.031 0.145 0.066 0.030 0.157 0.065 0.034 0.131 0.063 0.032 0.120 0.062 0.013 0.135 0.061 0.018 0.139 0.060 0.017 0.133

0.038 0.020 0.099 0.037 0.020 0.096 0.038 0.019 0.099 0.035 0.017 0.078 0.037 0.016 0.098 0.039 0.010 0.104 0.038 0.020 0.097 0.034 0.005 0.092 0.030 0.006 0.083 0.032 0.005 0.079

19.7 14.7 24.2 19.8 14.6 22.5 19.9 15.0 22.5 19.5 13.9 22.2 26.5 15.8 31.3 22.6 16.1 25.3 22.4 16.0 25.8 19.5 13.7 24.3 19.6 14.1 26.1 19.4 12.8 24.6

20.5 16.6 24.8 20.2 16.9 24.5 19.8 16.7 23.9 19.7 16.9 23.6 20.9 15.2 25.6 19.1 13.9 22.4 18.7 14.2 21.6 16.1 10.7 23.6 16.4 10.9 23.7 16.1 11.2 23.5

4.38 2.00 5.74 4.41 2.28 5.47 4.80 2.34 7.11 4.59 2.01 6.41 7.27 4.28 10.96 5.13 2.48 6.83 5.14 2.90 6.74 4.87 1.83 6.57 4.65 1.86 7.23 4.51 1.93 6.66

37.55 30.12 57.23 37.38 30.44 56.46 38.62 31.59 57.85 38.71 30.16 57.45 44.84 34.89 76.88 39.32 30.20 62.82 38.99 28.97 64.43 32.03 20.58 58.19 33.02 22.21 59.22 33.21 21.67 58.87

4.0 1.2 8.6 3.3 1.0 8.4 3.8 0.9 8.5 3.2 0.8 8.4 4.6 1.0 13.7 2.4 0.5 6.9 2.4 0.5 6.8 2.5 0.7 7.8 2.5 0.7 8.0 2.5 0.8 7.7

M. Varol et al. / Catena 92 (2012) 11–21

because the sites in these clusters had similar characteristic features and natural backgrounds that were affected by similar sources. Each cluster represents geographical location of sampling sites located at each dam reservoir. Cluster 1 corresponded to sampling sites of the KDR, while cluster 4 corresponded to sampling sites of the BDR. Cluster 2 corresponded to two sites (D-2 and D-3) of the DDR, while cluster 3 corresponded to D-1 site of the DDR. Because D-1 site of the DDR, which was located in the confluence of the Dipni Stream, had different water quality characteristics, it was likely to enter into cluster 2. The results from one-way ANOVA showed that EC, Cl, Na and TSS of D-1 site (cluster 3) were significantly different (p b 0.05) from those of D-2 and D-3 sites (cluster 2) (Table 3). Clusters showed significant differences in water quality parameters except WT, pH, NH3–N, TP and PO4–P with significant high levels of NO3–N and NO2–N in cluster 4, DO, EC, T-Alk, THard, Cl, SO4, Na, Ca and TSS in cluster 3 and TN and COD in cluster 2 (Table 3). Compared to their average data and information in clusters with drinking water guidelines (Table 3), we can conclude that all clusters correspond to low polluted regions, confirmed by their spatial characterisation and consistent with the result using water quality parameters. This result implied that for the rapid assessment of water quality, only one site in each cluster could serve as a good spatial assessment of the water quality of the whole group. Temporal CA generated a dendrogram (Fig. 3) that grouped the 12 months into two clusters at (Dlink / Dmax) × 100 b 50. Cluster 1 included February, December, January, October, November, March and April, closely corresponding to the wet season in Turkey. In this study period, about 82% of annual total precipitation fell from October to April in the basin. Cluster 2 included the remaining months (May, June, July, September and August), closely corresponding to the dry season. It suggested that sampling during only two seasons (wet and dry) in a year may suffice for assessment of temporal variations in water quality of the reservoirs. Thus, it was evident that the CA technique was useful in offering a reliable classification of surface waters in the whole region and made it possible to design a future sampling strategy in an optimal manner. In this manner, the sampling frequency and number of sites in the monitoring network would be reduced, thereby lowering the cost without losing any significance of the outcome. There are several reports where this approach has been successfully applied in water quality assessment programmes (Simeonov et al., 2003; Singh et al., 2004; Varol et al., in press; Wunderlin et al., 2001).

15

Feb. Dec. Jan.

Cluster 1

Oct. Nov. Mar. Apr. May Jun.

Cluster 2

Jul. Sep. Aug.

0

20

40 60 80 (Dlink/Dmax)*100

100

120

Fig. 3. Dendrogram showing hierarchical clustering of monitoring periods according to Ward's method with Euclidean distance.

3.2. Correlation analysis The ten sampling stations were combined to calculate the correlation matrix of the 18 analysed variables (Table 4). Because they are affected simultaneously by spatial and temporal variations, the correlation coefficients should be interpreted with caution. Nevertheless, some clear hydrochemical relationships could be readily inferred. High and positive correlations were observed between EC, T-Alk, THard, SO4, Na and Ca (r = 0.547 to 0.886), which are responsible for water mineralisation. NO3–N was positively correlated with TP, Ca and TSS, indicating that these variables are derived from similar sources and also moving together. A significant positive correlation was observed among TN, TP and PO4–P, which are responsible for phytoplankton growth. As expected, DO was negatively correlated with temperature because the solubility of oxygen in water decreases with increasing temperature. 3.3. Discriminant analysis Temporal variations in water quality were further evaluated through DA. Temporal DA was performed on raw data after dividing the whole data set into four seasonal groups (spring, summer, autumn and winter). Discriminant functions (DFs) and classification matrices

Table 3 Mean values with standard errors (S.E.) and ANOVA for water quality parameters in clusters of the sampling sites. Cluster 1

WT pH DO EC T-Alk T-Hard TN NO3–N NO2–N NH3–N COD TP PO4–P Cl SO4 Na Ca TSS

Cluster 2

Cluster 3

Cluster 4

Mean

S.E.

Mean

S.E.

Mean

S.E.

Mean

S.E.

17.342 8.485 8.809 (a) 277.500 (a) 119.917 (a) 168.333 (a) 0.607 (a) 0.141 (a) 0.005 (a) 0.034 5.022 (ac) 0.064 0.038 19.694 (a) 20.060 (a) 4.545 (a) 38.065 (a) 3.577 (ab)

1.098 0.019 0.207 2.342 1.919 2.318 0.053 0.018 0.001 0.004 0.287 0.005 0.004 0.368 0.370 0.176 1.056 0.386

17.825 8.494 9.383 (ab) 281.750 (a) 118.167 (a) 166.833 (a) 1.030 (b) 0.220 (ab) 0.005 (ab) 0.035 6.840 (b) 0.064 0.039 22.492 (b) 18.904 (a) 5.137 (a) 39.157 (ab) 2.417 (a)

1.533 0.056 0.224 5.780 4.168 4.153 0.263 0.038 0.001 0.005 1.064 0.008 0.006 0.570 0.463 0.230 1.989 0.476

18.017 8.462 9.842 (b) 315.250 (b) 127.500 (a) 174.333 (a) 0.832 (ab) 0.178 (ab) 0.007 (ab) 0.045 6.728 (bc) 0.067 0.038 26.525 (c) 20.892 (a) 7.272 (b) 44.836 (b) 4.603 (b)

2.305 0.057 0.434 10.812 5.321 4.471 0.213 0.082 0.002 0.009 0.463 0.013 0.008 1.187 0.935 0.610 3.371 1.304

18.486 8.578 8.727 (a) 220.658 (c) 92.778 (b) 135.222(b) 0.670 (a) 0.300 (b) 0.010 (b) 0.046 4.234 (a) 0.062 0.034 19.489 (a) 16.208 (b) 4.675 (a) 32.752 (c) 2.500 (a)

1.203 0.048 0.243 6.695 3.213 5.714 0.058 0.039 0.003 0.006 0.281 0.007 0.004 0.523 0.676 0.261 1.603 0.366

WHO limitsa

6.5–8.5 1500 200 500 50 3 10

250 250 200 100

Cluster 1 (sites K-1, K-2, K-3 and K-4), cluster 2 (sites D-2 and D-3), cluster 3 (site D-1) and cluster 4 (sites B-1, B-2 and B-3). The different letters indicate statistical difference among zones at p b 0.05; LSD test. a WHO (2004).

16

Table 4 Pearson correlation matrix of the 18 physico-chemical parameters determined.

a b

WT

pH

DO

EC

T-Alk

T-Hard

TN

NO3–N

NO2–N

NH3–N

COD

TP

PO4–P

Cl

SO4

Na

Ca

TSS

1 0.573a − 0.600a − 0.559a − 0.604a − 0.638a − 0.128 − 0.549a − 0.286a − 0.147 0.260a − 0.377a − 0.275a − 0.079 − 0.375a − 0.377a − 0.653a − 0.456a

1 0.116 − 0.633a − 0.683a − 0.700a − 0.067 0.035 − 0.349a − 0.002 0.219b 0.122 − 0.161 − 0.283a − 0.604a − 0.471a − 0.425a 0.070

1 0.307a 0.154 0.320a 0.237a 0.624a − 0.040 0.120 − 0.004 0.681a 0.329a 0.023 − 0.044 0.122 0.608a 0.705a

1 0.886a 0.881a 0.039 0.034 0.103 0.046 0.054 0.053 0.063 0.388a 0.763a 0.547a 0.643a 0.227b

1 0.811a − 0.019 − 0.027 0.083 − 0.133 0.000 − 0.075 − 0.021 0.316a 0.761a 0.464a 0.448a 0.063

1 0.144 0.114 0.193b − 0.035 − 0.031 0.093 0.129 0.206b 0.725a 0.446a 0.557a 0.248a

1 0.286a 0.008 − 0.109 − 0.032 0.480a 0.526a 0.189b − 0.180b 0.141 0.237a 0.168

1 0.080 0.012 − 0.108 0.603a 0.235a − 0.166 − 0.240a 0.013 0.401a 0.569a

1 0.217b − 0.226b − 0.118 0.010 0.110 0.200b 0.182b 0.126 − 0.024

1 − 0.246a 0.135 0.152 0.179b 0.015 0.316a 0.372a 0.263a

1 − 0.164 − 0.332a 0.061 0.012 − 0.074 − 0.171 − 0.028

1 0.754a 0.072 − 0.327a 0.143 0.605a 0.725a

1 0.238a − 0.183b 0.252a 0.502a 0.341a

1 0.324a 0.664a 0.279a − 0.053

1 0.500a 0.210b − 0.132

1 0.443a 0.103

1 0.646a

1

Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).

Table 5 Classification functions (Eq. (1)) for discriminant analysis of temporal variations in water quality of the Kralkızı, Dicle and Batman dam reservoirs. Standard mode

Water temperature pH Dissolved oxygen Electrical conductivity Total alkalinity Total hardness Total nitrogen NO3–N NO2–N NH3–N Chemical oxygen demand Total phosphorus PO4–P Cl SO4 Na Ca Total suspended solids Constant

Forward stepwise mode

Backward stepwise mode

Winter coefficient⁎

Spring coefficient⁎

Summer coefficient⁎

Autumn coefficient⁎

Winter coefficient⁎

Spring coefficient⁎

Summer coefficient⁎

Autumn coefficient⁎

Winter coefficient⁎

Spring coefficient⁎

Summer coefficient⁎

Autumn coefficient⁎

− 1.674 1592.224 − 126.509 − 0.513 0.148 4.789 − 0.932 90.704 523.904 − 2034.098 − 2.690 − 2686.034 1626.989 − 0.124 − 4.115 11.696 18.685 − 2.248 − 6626.088

3.496 1618.435 − 121.923 − 0.430 0.551 4.865 1.832 140.672 482.922 − 2243.809 − 2.619 − 2295.249 1425.176 − 3.191 − 4.804 12.708 18.593 − 1.090 − 7010.325

3.160 1633.301 − 125.406 − 0.324 0.166 4.812 0.142 108.876 556.647 − 2121.927 − 3.024 − 2800.516 1531.912 − 2.420 − 5.139 10.260 19.646 − 1.418 − 7073.852

0.859 1627.280 − 129.054 − 0.498 0.476 4.577 − 1.080 88.353 531.121 − 1963.859 − 2.907 − 2812.020 1562.433 − 1.083 − 3.890 12.185 19.568 − 1.841 − 6956.904

− 1.981 1585.018 − 127.613 − 0.641 0.230 4.774

3.051 1612.065 − 123.157 − 0.565 0.606 4.903

2.681 1625.856 − 126.696 − 0.473 0.238 4.829

0.474 1619.836 − 130.195 − 0.636 0.559 4.569

13.415

18.587

18.602

16.168

26.974

35.159

33.267

28.456

1.741 0.359

2.228 0.390

2.025 0.273

2.140 0.094

103.699

152.413

121.938

101.205

169.560

227.055

196.352

171.447

− 2013.574

− 2209.939

− 2084.017

− 1933.579

− 35.057

− 162.438

− 42.319

103.551

− 2780.253 1845.705 − 0.317 − 3.390 12.043 18.491

− 2344.430 1622.121 − 3.215 − 4.263 12.975 18.494

− 2856.810 1742.073 − 2.569 − 4.411 10.555 19.521

− 2883.536 1770.896 − 1.308 − 3.122 12.500 19.407

577.353

863.282

471.694

442.037

− 4.483

− 7.245

− 7.064

− 5.492

− 1.012

− 1.181

− 0.259

− 0.518

− 6587.711

− 6978.438

− 7031.530

− 6916.072

− 302.961

− 489.144

− 437.492

− 356.486

⁎ Discriminant function coefficient for winter, spring, summer and autumn seasons corresponds to wij as defined in Eq. (1).

M. Varol et al. / Catena 92 (2012) 11–21

WT pH DO EC T-Alk T-Hard TN NO3–N NO2–N NH3–N COD TP PO4–P Cl SO4 Na Ca TSS

M. Varol et al. / Catena 92 (2012) 11–21

(CMs) obtained from the standard, forward stepwise and backward stepwise modes of DA are shown in Tables 5 and 6. In forward stepwise mode, variables are included step-by-step beginning with the more significant until no significant changes are obtained, whereas, in backward stepwise mode, variables are removed step-by-step beginning with the less significant until no significant changes are obtained. The standard DA mode constructed DFs including 18 parameters (Table 5). Both the standard and forward stepwise mode DFs using 18 and 14 discriminant variables, respectively, yielded the corresponding CMs assigning 100% of the cases correctly (Tables 5 and 6). However, in backward stepwise mode, DA gave CMs with 100% correct assignations using only nine discriminant parameters (Tables 3 and 4). Thus, the temporal DA results suggest that WT, DO, T-Alk, T-Hard, NO3–N, NH3–N, TP, Cl and Ca are the most significant parameters to discriminate between the four seasons, which means that these nine parameters account for most of the expected temporal variations in the water quality of the reservoirs (Table 5). As identified by DA, box and whisker plots of the selected parameters showing seasonal trends are given in Fig. 4. The average temperature was highest in summer and lowest in winter and showed a clear-cut seasonal effect. A clear inverse relationship between temperature and dissolved oxygen was observed, which is attributed to the seasonality effect. The inverse relationship between temperature and dissolved oxygen is a natural process because warmer water becomes more easily saturated with oxygen and it can hold less dissolved oxygen (Varol and Şen, 2009). A decrease in the average concentrations of total hardness, total alkalinity, chloride and calcium from winter to summer followed by an increase in autumn was observed. This is due to accumulation of major ions (such as bicarbonate, chloride, sulphate, calcium and sodium) in the deeper water during the summer stagnation (Winner et al., 1962). The average concentrations of nitrate nitrogen and total phosphorus were higher in winter and spring compared to summer and autumn. This may be due to stream inputs and surface runoff, which carry more nutrients into the reservoirs during the rainy season (winter and spring). In addition, these nutrients may be used by planktonic blue-green algae which were abundant in the reservoirs in both summer and autumn. The average concentration of ammonia nitrogen was highest in autumn and lowest in spring. Spatial DA was performed with the same raw data set comprising 18 parameters after grouping into three major classes of KDR, DDR and BDR. The sites were the grouping (dependent) variable, while all the measured parameters constituted the independent variables.

17

Discriminant functions (DFs) and classification matrices (CMs), obtained from the standard, forward stepwise and backward stepwise modes of DA, are shown in Tables 7 and 8. Similarly to temporal DA, the standard DA mode constructed DFs including 18 parameters (Table 7). Both the standard and forward stepwise mode DFs using 18 and 16 discriminant parameters, respectively, rendered the corresponding CMs assigning 98% cases correctly (Tables 7 and 8), whereas the backward stepwise mode DA gave CMs with 93% correct assignations using only eight discriminant parameters (Tables 7 and 8). Backward stepwise DA shows that WT, pH, DO, EC, NO3–N, PO4–P, Na and TSS are the discriminating parameters in space (Table 8). Box and whisker plots of discriminating parameters identified by spatial DA (backward stepwise mode) were constructed to evaluate different patterns associated with spatial variations in water quality (Fig. 5). Water temperature did not change much at these sites because they are in the same climatic zone. The average pH value was slightly higher in BDR compared to KDR and DDR. The average concentrations of dissolved oxygen were similar in KDR and BDR, while DDR had a slightly higher average dissolved oxygen concentration. The average value of electrical conductivity was quite lower in BDR compared to KDR and DDR. This may be due to the inflows of the BDR, which have lower concentrations of dissolved ions. The average concentration of nitrate nitrogen was higher in BDR, while the average concentration of orthophosphate phosphorus was lower in BDR. DDR had higher average concentration of sodium due to the Dipni Stream, one of tributaries of the DDR, which has high sodium concentration. The average concentration of total suspended solids was the highest in KDR and the lowest in BDR.

3.4. Principal component analysis/factor analysis (PCA/FA) PCA/FA was performed on the normalised data to compare the compositional pattern between the water samples and to identify the factors influencing each one. PCA of the entire data set (Table 2) revealed five PCs with eigenvalues of >1 that explained about 79.5% of the total variance in the water quality data set. The first PC accounting for 32.9% of the total variance was correlated (loading > 0.70) with WT, EC, T-Alk, T-Hard and Ca. The second PC accounting for 21.5% of total variance was correlated with TP and SO4. Whereas, the third, fourth and fifth PCs, although, accounted for the total variance of 9.7%, 8.3% and 7.1%, respectively, correlated (loading > 0.70) with none of the parameters.

Table 6 Classification matrix for discriminant analysis of temporal variations in water quality of the Kralkızı, Dicle and Batman dam reservoirs. Monitoring seasons

% Correct

Seasons assigned by DA Winter

Spring

Summer

Autumn

Standard DA mode Winter Spring Summer Autumn Total

100 100 100 100 100

30 0 0 0 30

0 30 0 0 30

0 0 30 0 30

0 0 0 30 30

Forward stepwise mode Winter Spring Summer Autumn Total

100 100 100 100 100

30 0 0 0 30

0 30 0 0 30

0 0 30 0 30

0 0 0 30 30

Backward stepwise mode Winter Spring Summer Autumn Total

100 100 100 100 100

30 0 0 0 30

0 30 0 0 30

0 0 30 0 30

0 0 0 30 30

18

M. Varol et al. / Catena 92 (2012) 11–21

b

28 26 24 22 20 18 16 14 12 10 8 6 4 2

DO (mg l-1)

WT (oC)

a

Winter

c

Spring

Summer Autumn

d

150

T-Hard (mg l-1)

T-Alk (mg l-1)

140 130 120 110 100 90 80 70 Winter

e

f

0,7

NH3-N (mg l-1)

NO3-N (mg l-1)

0,5 0,4 0,3 0,2 0,1

Spring Summer Autumn

Winter

Spring Summer Autumn

Winter

Spring Summer Autumn

Winter

Spring Summer Autumn

0,10 0,08 0,06 0,04 0,02 0,00

0,0 -0,1

-0,02 Winter

Spring Summer Autumn

h

0,14 0,12

26 24

0,10

Cl (mg l-1)

TP (mg l-1)

Winter

210 200 190 180 170 160 150 140 130 120 110

Spring Summer Autumn

0,6

g

12,0 11,5 11,0 10,5 10,0 9,5 9,0 8,5 8,0 7,5 7,0 6,5

0,08 0,06 0,04

22 20 18 16

0,02 0,00 Winter

Spring Summer Autumn

i

14

60

Ca (mg l-1)

55 50 45 40 35 30 25 20 Winter

Spring Summer Autumn

Fig. 4. Temporal variations: (a) WT (water temperature), (b) DO (dissolved oxygen), (c) T-Alk (total alkalinity), (d) T-Hard (total hardness), (e) NO3–N (nitrate nitrogen), (f) NH3–N (ammonia nitrogen), (g) TP (total phosphorus), (h) Cl (chloride) and (i) Ca (calcium).

A Scree plot (Fig. 6) was used to identify the number of PCs to be retained to comprehend the underlying data structure (Vega et al., 1998). Five VFs were obtained through FA performed on the PCs.

The corresponding VFs, variable loadings and the explained variance are presented in Table 9. VF coefficients having a correlation greater than 0.70 were considered significant (strong).

M. Varol et al. / Catena 92 (2012) 11–21

19

Table 7 Classification functions (Eq. (1)) for discriminant analysis of spatial variations of water quality in water quality of the Kralkızı, Dicle and Batman dam reservoirs. Standard mode

Water temperature pH Dissolved oxygen Electrical conductivity Total alkalinity Total hardness Total nitrogen NO3–N NO2–N NH3–N Chemical oxygen demand Total phosphorus PO4–P Cl SO4 Na Ca Total suspended solids Constant a

Forward stepwise mode

Backward stepwise mode

Kralkızı coefficienta

Dicle coefficienta

Batman coefficienta

Kralkızı coefficienta

Dicle coefficienta

Batman coefficienta

Kralkızı coefficienta

Dicle coefficienta

Batman coefficienta

− 27.110 1781.096 − 178.077 3.132 − 1.180 5.968 − 0.635 − 377.837 − 814.712 − 2375.610 0.406 − 2347.306 4491.318 10.139 2.552 − 38.510 12.892 13.113 − 7650.344

− 26.174 1761.656 − 172.482 3.140 − 1.079 5.915 0.224 − 358.342 − 771.476 − 2333.458 0.726 − 2468.435 4515.508 10.654 1.478 − 36.849 12.799 12.363 − 7552.033

− 26.837 1743.242 − 173.328 2.713 − 1.272 5.927 − 1.318 − 340.374 − 673.538 − 2329.847 0.125 − 2392.685 4257.759 10.104 2.062 − 33.734 13.145 11.135 − 7267.736

− 23.528 1419.376 − 140.338 5.875 − 1.228

− 22.611 1402.427 − 135.047 5.858 − 1.128

− 23.291 1384.607 − 135.874 5.437 − 1.319

− 18.190 1143.637 − 117.697 6.132

− 17.554 1130.722 − 114.367 6.100

− 17.998 1112.368 − 114.128 5.747

− 344.465 384.987 − 2231.004 1.125 − 1926.646 4919.810 2.961 8.137 − 37.037 6.746 17.472 − 6099.138

− 324.541 424.083 − 2191.613 1.449 − 2045.148 4946.354 3.545 6.988 − 35.361 6.695 16.638 − 6021.769

− 307.817 512.609 − 2185.051 0.829 − 1980.065 4678.322 2.972 7.628 − 32.294 7.052 15.501 − 5743.009

− 289.193

− 278.879

− 262.284

3348.367

3306.897

3166.498

− 40.578

− 39.044

− 36.692

4.857 − 4985.545

4.077 − 4914.467

3.774 − 4672.218

Coefficients for different monitoring regions correspond to wij as defined in Eq. (1).

Varifactor 1, which explained 33.81% of the total variance, had strong positive loadings (>0.70) on EC, T-Alk, T-Hard, SO4 and Na, a strong negative loading on pH and a moderately negative loading on WT, which can be interpreted as a mineral component of the surface water of the reservoirs. This VF points to natural sources of the ionic groups of salts in the basin from inflows, soil weathering and runoff. Results indicated that EC, T-Alk, T-Hard, SO4 and Na were highest in winter and lowest in summer, while pH and WT were highest in summer and lowest in winter. There is a decrease in concentrations of EC, T-Alk, T-Hard, SO4 and Na in surface water due to accumulation of major ions (bicarbonate, calcium, magnesium) in the deeper water during the summer stagnation. The concentration of free CO2 in surface water is low during the summer. This is presumably due to its use for photosynthetic activity and the considerable accumulation of free CO2 in the deeper water with the development of thermal stratification (Winner et al., 1962). Thus, pH of surface water becomes maximum in summer. VF2, which accounted for 21.68% of the total variance, had strong positive loadings on DO, NO3–N, TP and TSS, a moderately positive loading on Ca and a moderately

Table 8 Classification matrix for discriminant analysis of spatial variations in water quality of the Kralkızı, Dicle and Batman dam reservoirs. Monitoring regions

% Correct

Regions assigned by DA Kralkızı

Dicle

Batman

100 91.7 100 97.5

48 3 0 51

0 33 0 33

0 0 36 36

Forward stepwise mode Kralkızı 100 Dicle 91.7 Batman 100 Total 97.5

48 3 0 51

0 33 0 33

0 0 36 36

Backward stepwise mode Kralkızı 93.8 Dicle 83.3 Batman 100 Total 92.5

45 5 0 50

3 30 0 33

0 1 36 37

Standard DA mode Kralkızı Dicle Batman Total

negative loading on WT. This VF represents the effects of stream input, agricultural runoff and erosion in the basin. Fertilisers and manure which can contribute to high levels of soil nitrate and phosphorus are commonly used in the basin. It is known that suspended particles tend to have adsorbed nutrients (Varol and Şen, 2009). Streams and agricultural runoff carry more suspended solids with nitrate and phosphorus into the reservoirs during wet season. In addition, the erosion effect occurs during cultivation of soil and rainfall events from upland areas. The contribution of Ca to this factor can be considered as a result of cation-exchange processes at soil–water interface (Guo and Wang, 2004) and dissolution of calcium-bearing minerals, which are found in the region (Akbulut et al., 2009). The inverse relationship between temperature and dissolved oxygen is a natural process because the solubility of oxygen in water decreases with increasing temperature (Varol et al., in press). VF3 (9.77% of the total variance) had strong positive loadings on total nitrogen and orthophosphate phosphorus and a moderately positive loading on total phosphorus. This factor points to nutrient sources of the reservoirs. The sources of total nitrogen can be stream inputs, agricultural runoff, municipal effluents and atmospheric deposition. The sources of orthophosphate phosphorus and total phosphorus comprise soil erosion, surface runoff from croplands and stream inputs. VF4, which explained 8.09% of the total variance, had a strong negative loading on nitrite nitrogen and a strong positive loading on chemical oxygen demand, whereas, VF5 (6.88% of the total variance) had a strong positive loading on ammonia nitrogen and a moderately positive loading on chloride. Both these VFs point to sources of nutrient and organic matter from stream inputs, point sources such as municipal effluents, and nonpoint sources such as agricultural runoff. The results from the temporal PCA/FA suggested that most of the variation in water quality was explained by the soluble salts (natural), physical parameters (natural), nutrient group of pollutants (point and non-point) and organic pollutants (anthropogenic). In this study, FA did not lead to significant data reduction, as we still needed 15 parameters (about 83% of the 18 parameters) to explain 80% of the data variance (Table 9). However, FA served as a means to identify those parameters that had the greatest contribution to temporal variation in the water quality of reservoirs and suggested possible sets of pollution sources in the basin. A similar approach has been used based on PCA/ FA for the evaluation of temporal and spatial variations in water quality (Lambrakis et al., 2004; Mendiguchia et al., 2004; Singh et al., 2004 and 2005; Wunderlin et al., 2001).

20

M. Varol et al. / Catena 92 (2012) 11–21

a

b

28 26

8,8

24

8,7 8,6

20

pH

WT (OC)

22 18 16

8,3

12

8,2

10 8

8,1 Kralkızı

Dicle

Batman

Kralkızı

Dicle

Batman

Kralkızı

Dicle

Batman

Dicle

Batman

d 11,0

340

10,5

320

10,0

300

EC (µS cm-1)

DO (mg l-1)

c

9,5 9,0 8,5

280 260 240 220

8,0

200

7,5

180

7,0

160 Kralkızı

Dicle

Batman

e

f 0,6

0,07

0,5

0,06

PO4-P (mg l-1)

NO3-N (mg l-1)

8,5 8,4

14

0,4 0,3 0,2 0,1 0,0

0,05 0,04 0,03 0,02 0,01

-0,1

0,00 Kralkızı

Dicle

Batman

g

Kralkızı

h 7

8

6

TSS (mg l-1)

7

Na (mg l-1)

8,9

6 5 4

5 4 3 2 1

3

0

2

-1 Kralkızı

Dicle

Batman

Kralkızı

Dicle

Batman

Fig. 5. Spatial variations: (a) WT (water temperature), (b) pH, (c)DO (dissolved oxygen), (d) EC (electrical conductivity), (e) NO3–N (nitrate nitrogen), (f) PO4–P (orthophosphate phosphorus), (g) Na (sodium) and (h) TSS (total suspended solids).

4. Conclusions In this study, different multivariate statistical techniques were used to evaluate the spatial and temporal variations in the surface water quality of the dam reservoirs in the Tigris River Basin. Hierarchical CA grouped 12 months into two clusters (wet and dry seasons) and classified ten monitoring sites into four clusters based on similarities in the

water quality characteristics. The temporal and spatial similarities and groupings determined in this study could facilitate the design of an optimal future monitoring strategy that could decrease the monitoring frequency, the number of sampling stations and the corresponding costs. Although PCA/FA did not result in considerable data reduction, it helped extract and identify the factors/sources responsible for variations in water quality. Five VFs obtained from PCs indicated that the

M. Varol et al. / Catena 92 (2012) 11–21

Eigenvalue

7

21

6

his assistance with the fieldwork. We also thank Dr. Lewis A. Owen for his interest in our paper and for his comments.

5

References

4

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Eigenvalue number Fig. 6. Scree plot of eigenvalues.

parameters responsible for water quality variation were mainly related to soluble salts (natural), physical parameters (natural), nutrient group of pollutants (point and non-point) and organic pollutants (anthropogenic). Discriminant analysis gave better results temporally and spatially. DA rendered an important data reduction as it uses only nine parameters (WT, DO, T-Alk, T-Hard, NO3–N, NH3–N, TP, Cl and Ca) to discriminate between the seasons with 100% correct assignations and only eight parameters (WT, pH, DO, EC, NO3–N, PO4–P, Na and TSS) to discriminate between the three spatial regions with 92.5% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters responsible for large variations in water quality that could reduce the number of sampling parameters. As a result, this study illustrates the utility of multivariate statistical techniques for the analysis and interpretation of complex data sets and, in water quality assessment, the identification and apportionment of pollution sources/factors as well as an understanding of the temporal/spatial variations in water quality for effective water quality management. Acknowledgements The present study was part of a research project funded by The Scientific & Technological Research Council of Turkey (TUBITAK, project no. 107Y216). We gratefully acknowledge Ahmet Bezaroğlu for Table 9 Loadings of experimental variables (18) on significant principal components (with Varimax rotation) for the data set. Variables

VF1

VF2

VF3

VF4

VF5

WT pH DO EC T-Alk T-Hard TN NO3–N NO2–N NH3–N COD TP PO4–P Cl SO4 Na Ca TSS Eigenvalue % Total variance Cumulative % variance

− 0.527 − 0.762 0.060 0.918 0.919 0.868 0.058 − 0.005 0.162 − 0.025 0.126 − 0.108 0.000 0.499 0.853 0.766 0.448 0.018 6.09 33.81 33.81

− 0.652 0.114 0.886 0.233 0.088 0.278 0.150 0.808 − 0.030 0.224 − 0.052 0.747 0.307 − 0.198 − 0.179 0.028 0.666 0.873 3.90 21.68 55.49

− 0.057 − 0.176 0.139 − 0.008 − 0.033 0.065 0.803 0.084 − 0.089 − 0.147 − 0.220 0.582 0.816 0.391 − 0.223 0.215 0.271 0.077 1.76 9.77 65.26

0.375 0.433 0.166 0.111 0.052 − 0.037 0.000 − 0.243 − 0.714 − 0.334 0.720 0.033 − 0.174 0.120 0.003 − 0.157 − 0.105 0.070 1.46 8.09 73.34

0.079 0.057 0.075 0.097 − 0.095 − 0.098 − 0.221 − 0.337 0.062 0.806 − 0.152 0.110 0.194 0.508 0.120 0.420 0.324 0.173 1.24 6.88 80.22

Bold and italic values indicate strong and moderate loadings, respectively.