Assessment of surface water quality in Legedadie and

Acta Ecologica Sinica 38 (2018) 81–95

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Assessment of surface water quality in Legedadie and Dire catchments, Central Ethiopia, using multivariate statistical analysis Yilikal Anteneh a,c,⁎, Gete Zeleke b,c, Ephrem Gebremariam c a b c

Department of Geography and Environmental Studies, Arba Minch University, Arba Minch, P.O. Box: 2584, Ethiopia Water and Land Resource Center (WLRC), Addis Ababa, P.O. Box: 3880, Ethiopia Ethiopian Institute of Architecture, Building Construction, and City Development (EiABC), Addis Ababa University (AAU), Addis Ababa, P.O. Box: 518, Ethiopia

a r t i c l e

i n f o

Article history: Received 5 January 2017 Received in revised form 15 May 2017 Accepted 26 May 2017 Available online 9 March 2018

a b s t r a c t The quality of surface water in an area could be determined both by anthropogenic actions and natural processes. The current study assessed surface water quality in Legedadie and Dire catchments that cover a total area of 285 km2 in Central Ethiopia northeast of the nation’s capital Addis Ababa and within close proximity (20-30 m) from this city.. Accordingly, Addis Ababa is, and will continue to be, the major beneficiary of the the catchments which are in fact the primary sources of potable water supply to the city. Despite its potential ecosystem benefits, the catchment area was seriously affected by the rapid socioeconomic development phenomena that had been taking place in and around the city over the previous six decades. As a result, the catchment water resources are characterized by decades of deterioration because of severe pollution problems directly associated with mismanagement of natural resources coupled with other several factors. Hence, the current study was sought to examine the temporal and spatial determinants of catchment water pollutants. To that end, 14 water quality monitoring stations were selected and set up both in upstream and downstream parts of six rivers and two reservoirs. The water samples collected from different sites were monitored for 30 standard water quality parameters including nutrients, inorganic variable, and trace metals. Seasonal data were then measured for the 30 parameters monitored across two seasons (summer and autumn) over a two-year period (June 2014 – November 2015). The complex two-year seasonal data matrix that comprised of 3,660 observations was treated using multivariate statistical techniques: cluster analysis (CA), factor analysis/principal components (FA/PCA), and discriminant analysis (DA). CA successfully classified both the temporal (summer, autumn, and summer predominant) and spatial (natural, agricultural, urban, and mixed) clusters of water quality monitoring sites. Dimension reduction from FA/PCA was not as substantial as expected since it enabled only30% reduction from the original data matrix. On the other hand, DA procedures demonstrated the best result regarding data reduction and pattern recognition in both temporal and spatial analysis. It extracted data significantly and provided 5 parameters (Temp, pH, DO, salinity, and TN) to afford 96.8% right allocations during temporal analysis and only 7 parameters (pH, Turbidity, TN, Total hardness, Pb, Fe, and Cu) to yield 85.2% right allocations during the spatial analysis.Thus, these water quality parameters were most significant for seasonal and spatial water quality variation in the catchment. Therefore, DA enabled a significant reduction of the dimension of the original data matrix into a few significant parameters that affect water quality. In conclusion, the study demonstrated that multivariate statistical techniques are very useful for analysis and interpretation of complex water quality data sets for efficient assessment of water quality and identification of pollution sources as well as an effective understanding of the space and time effects of water quality. © 2017 Ecological Society of China. Published by Elsevier B.V. All rights reserved.

1. Introduction In recent decades, water contamination has become a rising threat to humanity and natural ecosystem, which demands the need to ⁎ Corresponding author at: P.O. Box 2584, Arba Minch, Ethiopia. E-mail addresses: [email protected], [email protected] (Yilikal Anteneh), [email protected] (Gete Zeleke), [email protected] (Ephrem Gebremariam).

http://dx.doi.org/10.1016/j.chnaes.2017.05.005 1872-2032/© 2017 Ecological Society of China. Published by Elsevier B.V. All rights reserved.

investigate and comprehend the space and time variabilities of pollutants [1]. Surface waters are highly exposed to pollution, wastewater discharge, and are most vulnerable to contamination resulting in a general decline in quality [2]. In Ethiopia, realizations of the changing situations of hydrological systems in many areas of the country have increased concerns among the scientific community over wetlands and water quality issues. Most importantly, the rising threat of water contamination appears to be evident and imminent in the Legedadie and Dire catchments in Central Ethiopia northeast of the nation's capital city

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Addis Ababa. Due to its close proximity to both catchments (about 30 km), Addis Ababa is, and will continue to be, the major beneficiary of the catchment water, among other vital ecosystem services. The nearby city is characterized by a very rapid and unbalanced population growth and unplanned built-up expansions leading to fast urbanization processes.1 On the one hand, despite being the primary sources of potable water supply to the city, the catchments are seriously affected by the rapid socioeconomic development phenomena that had been taking place in and around the city over the previous six decades. This has increased vulnerability of the catchment to water pollution. On the other hand, as the city was swiftly growing, its dependence principally on upstream rural areas for potable water supply was increasing like no other time [3]. Because water supplying reservoirs are situated in rural landscapes with an average of 30 km distance from the city.As a result, the catchments water resource are characterized by decades of deterioration emanating from severe pollution problems directly associated with mismanagement of natural resources coupled with many other several factors. As in most areas in developing countries [4] the intensified anthropogenic impact on land resources in the study catchment coincided with very sensitive ecological conditions; its effects may be even more immediate and more severe. Nevertheless, the actual impact of non-point sources and land use practices on water bodies in the study area was largely unrecorded. The lack of information, coupled with the anticipated detrimental effects of development activities, has created increasing concerns. Indecent utilization and poor management of natural resources such as land, rivers, vegetation, and the ecosystem at catchment level have led to environmental problems including sedimentation and siltation to critical water bodies and reservoirs [5].2 Wherein rivers play a significant role in transporting and assimilating the metropolitan and manufacturing wastewater and run-off from agricultural land [2]. In view of the above water pollution issues and vulnerability factors of surface waters in the catchment area, we undertook a related study to investigate the land use and land cover changes over the past three decades in the catchment. According to the study results, the land cover in the area was significantly changing particularly during the past 15 years.3 The population size was ever increasing, and urban centers were rapidly expanding. Generally, water supply in a given area can be influenced not only by human induced factors local to the water supply but also anywhere from within the watershed area [6]. Likewise, upstream activities can affect the quantity and quality of water supply downstream. Thus, the provision of improved water service, among other things, requires proper management of upstream ecosystems [7]. Hence, the rapid socio-economic development phenomena and associated problematic situations noted above provided an opportunity for the current study to assess surface water quality in these important catchments. The study was sought to examine the temporal and spatial determinants of catchment water pollutants using multivariate statistical analysis. Multivariate statistical procedures were found to be suitable to characterize and evaluate naturally and anthropogenically induced temporal and spatial water quality variations as proposed by Helena et al. [8]. 2. Materials and methods 2.1. The study area The current surface water quality assessment study was undertaken in the Legedadie and Dire catchment area that covers a total area of 1

Crampton J. Unpublished Internal Water Aid paper, available at http://www.wateraid. org/documents/plugin_documents/water_quality_for_addis_briefing_note.pdf 2005, accessed 31-01-2017. 2 Julien P, and Shah S. Sedimentation initiatives in developing countries. Draft report presented to UNESCO-ISI. Colorado State University. 2005. Available at: http://citeseerx. ist.psu.edu/viewdoc/download?doi=10.1.1.552.867&rep=rep1&type=pdf 3 Yilikal et al., 2017. Dynamics of Land Change: Insights from three level intensity analysis employed in Legedadie- Dire catchments, Ethiopia. Unpublished.

285 km2 in Central Ethiopia. Both of the catchments and reservoirs in each catchment are found in Oromia National Regional State along rural landscapes of the region northeast of the nation's capital city Addis Ababa and within a short distance (20–30 km) from the city. The two catchments form the upstream sections of the Big Akaki River flowing from northeast to southwest direction and constitute the drainage system that forms the northwest section of the Awash River basin. Like almost all parts of Ethiopia, the study catchment area in general is characterized by four distinct seasons: summer, June–August; autumn, September–November; spring, December–February; and winter, March–May. Summer is the rainy season and autumn denotes the end of the rainy season. Winter is normally the dry season, which extended to spring, but occasionally raining starts late in spring. While temporal cluster analysis has conducted, a mix of summer and autumn monitored sites were cluster together in one group. This cluster has consisting of twice more summer-monitored sites than autumn sites. Hence, in an effort to make temporal clustering more informative, this cluster was labeled as summer predominant season; hence, it should be understood from the context of clustering procedure. Soil types in the catchment are wide-ranging. Vertisols, Leptosols, and Cambisols are the predominant soil types in the area. Luvisols and Fluvisols are also commonly found in the catchment. Although the catchments are geographically located within Oromia Regional State, they are the primary sources of potable water supply to Addis Ababa city. More specifically, the study was carried out in selected water quality monitoring sites across six rivers and two reservoirs that were sought to gather representative sample data from the surface water systems in the two catchments. Table 1 provides a general description of the study catchment area. 2.2. Sampling monitored parameters and analytical methods The monitoring site selection and sampling strategy were designed to cover a broad range of determinants across the Legedadie and Dire catchment, which could reasonably represent the quality of surface water systems in the catchment accounting for both upstream and downstream parts of the rivers and final receiving reservoirs in the catchments. Accordingly, 30 water quality parameters were found to be relevant variables that were necessary to examine the spatial and temporal variations in the catchment water quality and thereby successfully conduct the study. All the monitored parameters were selected based on the water quality assessment guide of UNESCO, WHO and UNEP [9]. To account for the spatial variability of water quality parameters, a total of 14 monitoring sites (9 in Legadadie and 5 Dire catchments) that were designed to cover both the upstream and downstream parts of the streams and reservoirs as the final recipient of discharges were Table 1 General description of the Legedadie and Dire catchment area. Legedadie catchment

Dire catchment

Area (km2) Location Latitude Longitude

207.3

77.7

09°01′50″N–09°12′56″N 38°56′35″E–39°04′13″ E

Elevation (m) Season

2402–3226 4 (summer, autumn, spring, winter) 16–26

09°08′23″N–09°13′20″N 38°49′44″E–38° 57′52″ E 2502–3240 4 (summer, autumn, spring, winter) 16–26

1000–1250 Vertic Cambisols Legedadie Dam 165,000 m3/day Big Akaki Awash drainage basin 9

1230–1300 Leptosols to Cambisols Dire Dam 42,000 m3/day Big Akaki Awash drainage basin 5

Mean monthly temperature (°C) Mean annual rainfall (mm) Dominant soil type Reservoir Dam Water supply Receiving catchment Basin Monitoring sites under the current study

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selected and set up. The upstream monitoring sites in the rivers were labeled as Yeso (YsU1), Bosena (BoU1), Lege-Beri (LBU1), Sekoru-fule (SfU1), Lege-Bolo-kultibe (BkU1), and Lege-Jila (LJU1). Similarly, the downstream monitoring sites in the same rivers were labeled as Yeso (YsD2), Bosena (BoD2), Lege-Beri (LBD2), Sekoru-fule (SfD2), LegeBolo-kultibe (BkD2), and Lege-Jila (LJD2). The monitoring sites in the two reservoirs were labeled as Legedadie reservoir (LegR) and Dire reservoir (DirR). Fig. 1 depicts a map of the catchment water sampling sites across the six rivers and two reservoirs in the study area. In addition, to account for the temporal variability of the water quality parameters, monitoring was carried out for two consecutive years during 2014 and 2015. The 30 water quality parameters were monitored over two seasons during summer, the rainy season or high-flow period, and autumn/fall, which denotes the end of the rainy season or the low-flow period. Monitoring did not involve the other two seasons since streams are intermittent and become disappear soon after during these periods. Two-year data were then collected from the 14 catchment water quality monitoring stations across six rivers and two reservoirs over the two seasons. Water samples were taken from the selected sampling sites in the study catchment (Fig. 1) using 250, 500, and 1000 mL highdensity polyethylene (HDPE) bottles at approximately mid-depth of the rivers and reservoirs. Duplicate samples were also collected closer to the site where analytical samples were taken. Each HDPE bottles had been carefully rinsed three times with doubly deionized water in the laboratory before the sampling campaign. Until collection, dried sample containers were kept in sealed polyethylene bags. All the sampling, preservation, transportation and analytical protocols were conducted by the standard methods of surface water [10,11]. The water samples were then analyzed for the 30 standard water quality parameters which include nutrients, inorganic variable and trace metals. Samples were transported and analyzed immediately after collection. The water quality parameters monitored for examination of spatio-temporal variabililities in the study catchment along with symbols of the standard units used to measure them are listed as follows. 1. 2. 3. 4.

pH Temperature (Temp), °C Turbidity, NTU Electrical conductivity (EC), μS/cm

5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

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Total suspended solids (TSS), mg/L Total dissolved solids (TDS), mg/L Dissolved oxygen (DO), mg/L Chlorophyll a (Chl-a),mg/L Salinity, 0/00 Nitrite nitrogen (NO2), mg/L Nitrate nitrogen (NO3), mg/L Ammonia nitrogen (NH3), mg/L Total nitrogen (TN), mg/L Phosphate (PO−3 4 ), mg/L Potassium (K), mg/L Total hardness (T-hard), ppm Fluoride (F−), mg/L Iron (Fe), mg/L Lead (Pb), mg/L Cobalt (Co), mg/L Cadmium (Cd), mg/L Chromium (Cr), mg/L Arsenic (As), mg/L Zinc (Zn), mg/L Thallium (Ti), mg/L Tellurium (Te), mg/L Selenium (Se), mg/L Manganese (Mn), mg/L Copper (Cu), mg/L Magnesium (mg), mg/L Eight of the parameters were monitored in-situ among which six (pH, Temp, DO, TDS, Salinity, and EC) were measured with HACH HQ40d multifunctional portable multimeter with ±0.1 accuracy levels and two (Chl-a and turbidity) with portable Aqua Fluor model 8000010. Whereas, the remaining parameters were analyzed in the laboratory. Among these, Fe, Zn, Cu, Cr, Co, As, Mn, Pb, Cd, and Mg were determined by Atomic Absorption Spectrophotometer (Buck scientific model 210); NO2, NO3, and PO–3 4 by colorimetric method (UV–VIS Spectrophotometer); TN by Macro-Kjeldahl Method; TSS by gravimetric method; total hardness by EDTA titration method (0.01 N); and K by flame spectrophotometer (JENWAY) method. Laboratory results of Co, Cd, Te, Se, Cr, and Ti were below detection level and thus these parameters were excluded from further analysis.

Fig. 1. The study catchment area (left) and the catchment water sampling sites of six rivers and two reservoirs.

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The standard methods of surface water protocols were carefully followed as noted above [10,11]. Before the analysis all the glassware's were washed by the same procedure as the sample bottles were washed. Acidified water samples were filtered through Whitman filter paper No. 42. Standard working solutions of all concerned metals were prepared by appropriate dilution form of 1000 mg/L certified standard solutions in deionized water. Calibration curves were regularly generated to evaluate the performances of the analytical methods and instruments. The data quality was checked by careful standardization, procedural blank measurements, spiked and duplicate samples. 2.3. Data processing and analysis Scientific researchers examining the spatiotemporal variability of water quality have testified that issues of water quality such as eutrophication are vastly dependent on patterns of land uses and influences of run-off discharge from watershed [12,13]. Urban area, built up, and industrial wastewater discharge establish the persistent polluting source; together with the seasonal phenomenon of surface run-off, which is primarily affected by climate in the catchment [2]. Seasonal and spatial variants in precipitation, surface run-off, water abstraction, inter-flow, effluent discharge and outflow pumping have all strongly influencing discharge into the waterway and the subsequent concentration of pollutants in water systems [14]. Pursuant to this, meaningful information on water quality status and pollution sources is imperative for the execution of sustainable and more efficient water use management strategies [15,16,17]. Characterization of the physicochemical conditions and spatiotemporal variability of surface water as well as distinguishing the principal sources of pollutants require appropriate analytical techniques and approaches. Catchment joint ecosystem planning, which is supposed to be a subsequent resource management undertaking, requires reliable estimates of water quality from consistent measurements by a composite data matrix. However, rendering relevant information from an extensive water quality data sets can sometimes be cumbersome [18,19]. Since large and complex water quality data matrices comprise of numerous physicochemical parameters from regular monitoring, they are often difficult to interpret and draw meaningful conclusions [9,20]. Thus, significant data reduction and interpretation of measured parameters apparently requiring appropriate analytical tools which makes it imperative for researchers to ascertain and apply suitable statistical techniques. Multivariate analytical procedures are the most commonly used approaches that best fit in extracting vital information from extensive water quality data sets [8,21,22,24]. Therefore, the study applied analysis of multivariate statistical procedures to characterize, evaluate and demonstrate natural and anthropogenic induced temporal and spatial water quality variations as proposed by Reisenhofer et al. [25], Vega et al. [14], and Helena et al. [8]. The two–year data structure consisting of 3660 observations in six rivers and two reservoirs were analyzed using three main multivariate statistical techniques: cluster analysis (CA), factor analysis/principal component analysis (FA/PCA), and determinant analysis (DA). Land use and land cover changes from 2000 to 2015 were assessed in order to see the effects and associations of land use and land cover change on water quality parameters. Landsat scenes Enhanced Thematic Mapper Plus (05 December 2000) and OLI TIRS (23 December 2015) all with 30 m spatial resolution were obtained from the United States Geological Survey (USGS). We used Landsat scene since USGS provides free access to 30 m Landsat image for scientific purposes which was found to be good enough for purpose of the study. Moreover, Landsat provides image that have already georeferenced to projection system either Universal Traverse Mercator (UTM) or coordinate system. Acquisition dates during the mid-dry season were selected in order to get cloud-free images (at least b10%). Moreover, images captured in the same months were selected with a view to reduce the possibility of

errors resulting from seasonal differences between the time points. Data for the areal extent (northeast of Addis Ababa) were then extracted from Landsat scenes with Path 168 and Row 054. Image processing was done using the Idrisi software (Clark Labs, Worcester, MA, USA) and ENVI®Classic + IDL 64-bit image processing and analysis software from EXELIS Visual Information Solutions and ArcGIS 10.2. A minor atmospheric correction was carried out using Quick Atmospheric Correction (QUAC) available with ENVI classic + IDL image processing. The images were also processed for radiometric, spatial, and spectral enhancements before classification, which simplified image interpretation process. A radiometric correction was performed in all the Landsat images before classification. Since the standard deviation and histogram specification under the interactive stretch type alternatives allows comparison of various bands interactively on a histogram and make a range of each band values more similar, it was applied for radiometric enhancement. If there existed wide variation in the value range of different bands, the classified image output would become more sensitive to errors [26]. Thus, band stretching was performed to minimize the sensitivity of different bands to a range of values during classification and to consider each band's attribute more equally [27]. Spatial Enhancement of Landsat imagery was performed using high pass filter by traversing a three by three filter over the raster to enhance the edges passive features in the image. The resampling technique was frequently changed to get a more realistic image interpretation in the image analysis window. The linear interpolation options create a smooth looking result, and cubic convolution creates a sharper looking image, while a majority technique creates specific filter windows by assigning the most common value to a pixel within specific filter window. All Landsat images are in integer formats and should be converted into float data before performing spectral enhancement using the flow tool GIS algorithm. Single band rationing, square root index and NDVI were performed between bands so as to enhance the image spectrally. Image composites were obtained using near-infrared, red, and green bands of the imagery. The land categories were generated using a hybrid method of unsupervised and supervised approaches. Initially, the method classified the categories into 148 different clusters/colors of qualitative data relationship in the Terr Set Cluster module. The clusters were subsequently classified into 6 dominant categories based on information from aerial photographs, Google Earth images, previous maps, and field surveys that were conducted during December through April 2014 and 2015. From the hard classifier options in TerrSet (version 18.2), a maximum likelihood classification scheme that combines previous information after Bayes theory [28] was applied. This classification scheme is based on the probability density function associated with a particular training site signature. Pixels are assigned to the most likely class based on a comparison of the posterior probability that belongs to each of the signatures being considered. Taking into account the variability of the classes by means of covariance matrix is the major advantage of this classification scheme [29]. Besides, it is the most accurate algorithm that can provide the best result over the other methods if properly applied [29,30]. Six land cover categories and changes were identified after allotting each pixel to the most probable group based on a comparison of the succeeding likelihood that it belonged to each of the signatures that were being assumed (Fig. 4). The categories include Agriculture, built up, grazing land, forests, bare lands, and waterbodies. The spatial variations of catchment water quality under the effects of the different land cover categories were evaluated through Pearson's rank order correlation coefficient. 2.4. Data treatment and test of normality distribution Multivariate statistical methods employed for water quality study are highly susceptible to outliers and non-normal distributions, due to the

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Table 2 Statistical descriptives of physicochemical concentrations of Legedadie and Dire reservoirs and major streams (June 2014–November 2015). Monitored values Parametera Temp pH Turbidity EC TDS TSS DO Chl-a Salinity NO2 NO3 NH3 TN PO4–3 K T-Hard F− Pb Fe Zn As Mn Cu Mg N (list wise) a b c d e f

Min 15.20 5.70 14.00 4.00 15.90 31.00 3.10 29.76 0.10 0.02 0.17 0.01 0.24 0.13 11.00 70.30 0.08 0.70 8.00 0.76 0.02 9.00 0.62 1.01 96

International standards Max 27.70 8.20 299.13 395.10 198.02 178.00 14.40 592.20 40.70 0.94 2.99 0.62 3.99 1.40 27.00 274.00 0.34 1.24 22.12 1.11 0.07 15.27 0.84 1.90

X̄ ± S.E. 20.98 ± 0.34 7.54 ± 0.06 90.85 ± 8.73 139.09 ± 12.71 74.26 ± 6.06 107.82 ± 2.99 7.47 ± 0.36 220.19 ± 14.85 12.85 ± 1.36 0.34 ± 0.02 1.19 ± 0.10 0.19 ± 0.01 1.66 ± 0.11 0.62 ± 0.03 18.73 ± 0.38 150.16 ± 5.75 0.18 ± 0.01 0.95 ± 0.01 14.99 ± 0.37 0.95 ± 0.01 0.05 ± 0.00 11.29 ± 0.10 0.70 ± 0.00 1.39 ± 0.02

SD 3.31 0.60 85.52 124.50 59.41 29.27 3.58 145.46 13.33 0.19 1.01 0.14 1.10 0.26 3.73 56.37 0.07 0.12 3.68 0.11 0.01 0.99 0.05 0.25

WHOb nlf 6.5–8.5 ≤5 400 500 ≤30 nl nl nl 3 11 0.5 nl nl 10 100 1.5 0.01 0.3 3.0 0.01 0.05 2.0 30

EUc nl 6.5–8.5 4 nl 500 nl nl nl nl 0.1 11 0.5 nl 5 10 50 1.5 0.01 0.2 nl 0.01 0.05 2.0 nl

USEPAd ≤15 6.5–8.5 0.5–1.0 nl 500 nl nl nl nl 1 10 nl nl nl 10 b200 4 0.015 0.3 5 0.01 0.05 1.0 2

Canadae ≤15 6.5–8.5 0.3 nl b500 nl nl nl nl 3.2 10 nl nl nl 10 150 1.5 0.010 0.3 b5 0.01 0.05 1.0 nl

All parameters are in mg/L except for temp (°C), turbidity (NTU), EC (ms/cm) and salinity (0/00). World Health Organization Guidelines for drinking water quality, 4thed. European Union drinking water standards, directive 98/83/EC. United States Environmental Protection Agency: National Primary Drinking Water Regulations. Canadian Drinking Water Quality, EU's Drinking water standards nl means no limit listed.

influence of particularly rare extreme events. Therefore, appropriate data pretreatment was taken into consideration. To avoid the influence of rare extreme pollution events during the study period, outliers were screened by making box plots. The distribution of data was assessed by Z-values of skewness and kurtosis and the Shapiro-Wilk test of p-value Because some variables such as Chl-a, EC and NO3 were not within the range, the data were transformed in arithmetic base-10 logarithm. The skewness and kurtosis Z-values were well within the range of −1.96–1.96. Thus, the data were approximately normally distributed regarding skewness and kurtosis Z-values. The distribution of the data was also assessed by Shapiro-Wilk test p-values. For this test of normality, the null hypothesis usually positions that the data are normally distributed. Therefore, if the p-value is lower than 0.05, then the H0would be rejected. For most of the variables, the p-values were above 0.05, which dictates to keep the null hypothesis since the data were approximately normally distributed. In this study, varimax rotation of the principal components (PCs) derived from the original standardized variables was performed to reduce the contribution of variables with minor significance during the computation process of FA/PCA, which simplified the data structure perfectly. Factors with eigenvalues higher than 1were considered for each situation. Test of the scree plot, a graphical method that plots the eigenvalues until their gradual reduction appears to stabilize to the right of the plot, can also be used to decide the number of varifactors (VFs) considered [31]. DA builds discriminant functions (DFs) to evaluate water quality variations by seasons and monitoring sites based on three different modes: standard, forward stepwise and backward stepwise. The standard DA forms DFs encompassing all parameters. In the forward stepwise approach, parameters are added one after another, starting with the most significant until no significant variations are found. In the backward stepwise mode, contrary to the forward stepwise procedure, variables are removed one by one, starting with the least significant until no

significant changes occur. According to Johnson and Wichern [32], the discriminant function can be computed as follows: n

f ðGi Þ ¼ K i þ ∑ W ij ∙P ij j¼1

where, • i, represents the number of groups (G). In the case of the current study, the number of groups can be different depending on whether we evaluate temporal (3 groups) or spatial variations (4 groups), • ki, represents the constant inherent to each group, • n, is the number of variables used to categorize a set of data into a given class. In this case, n denotes the number of analytical variables from a monitoring station into a group of season or catchment area, • wj, is the weight coefficient, assigned by DA to a specified selected parameter pj, and • pj, is the analytical value of the selected variable. For effective discriminant analysis, the classification table of correct and incorrect predicts can yield a high accurate percentage. In the case of the current study, the efficiency of discriminant functions can be tested through cross-validation. It was done only for those cases in the analysis, and in cross validation, each variables is classified by the computation resulting from all variables other than that parameter as proposed by Johnson and Wichern [32]. 3. Results and discussion The results of the monitored water quality variables summarized in Table 2 were compared with various international standards of surface water quality. Most of the measured water quality parameters were

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well within the range of the limits set by international standards (Table 2). The Pb, Fe, K, and turbidity were detected to be in higher ranges than the water quality standards. 3.1. Temporal variations in catchment water quality The study examined temporal variations in catchment water quality through correlation matrix of seasonal factors. The findings revealed that all the 24 measured parameters except F−, Zn, and As were significantly (p b 0.01) correlated with the season (Table 3). Among these, salinity exhibited the highest correlation coefficient (Pearson's r = − 0.95) followed by TN (r = − 0.85), hardness (r = 0.79), NO3 (r = −0.79), TDS (r = 0.63) and EC (r = 0.62). The results revealed that seasonal factors were the major sources of temporal variations in water quality. Seasonal variations in river discharge associated with high eroding capacity of rivers mainly during high flow summer season could be attributed to the high seasonality of several water quality parameters. This was found to be consistent with other reports [2,33]. 3.1.1. Temporal cluster analysis Pattern recognition techniques were employed to capture and document more evidence on the parameters associated with temporal variation [18]. The first exploratory approach was the use of cluster analysis (CA) in the standardized data matrix in Z score, sorted first by season and then by specific sampling sites. CA extracted a dendrogram (Fig. 2) showing the 96 cases studied divided into three broad clusters at (Dlink/Dmax) × 100 b 25 (Fig. 5). Cluster One accounted for 42 cases and all cases (100% of the cluster) belonged to the regular autumn season that covers the months from September to November. Cluster Two accounted for 18 cases, of which 12 cases (67% of the cluster) belonged to the summer season that covers the months from June to August. This cluster also had a clear predominance of summer cases. Cluster Three contained 36 cases all belonging to the wetter summer season covering the months from June to August. Cluster One comprised of ten monitoring sites sampled entirely during autumn: LBU1, LBD2, LegR, DirR, SfU1, SfD2, BkU1, BkD2, LJU1, and LJD2. Urban discharge recipient rivers were predominant in these

Table 3 Parameters bivariate correlation with seasonal factors. Parameters

Seasonal factors

Temp pH Turbidity EC TDS TSS DO Chl-a Salinity NO2 NO3 NH3 TN PO4–3 K T-hard F− Pb Fe Zn As Mn Cu Mg

−0.03⁎ −0.07 0.48⁎⁎ 0.62⁎⁎ 0.63⁎⁎ 0.44⁎⁎

⁎⁎ Significant at the 0.01. ⁎ Significant at the 0.05.

0.06 0.48⁎⁎ −0.95⁎⁎ 0.13 −0.79⁎⁎ 0.53⁎⁎ −0.85⁎⁎ −0.32⁎⁎ −0.11 0.79⁎⁎ −0.07 0.27⁎⁎

−0.32⁎⁎ 0.17 0.11 0.31⁎⁎ 0.57⁎⁎ 0.39⁎⁎

Fig. 2. Dendrogram showing temporal analysis of water quality monitoring sites in Legedadie and Dire catchment.

sites. During this season, streams were characterized by steadily decreasing violent flows, which led to associated diminishing of discharge, eroding capacity, and pollutant contributions. Furthermore, improperly discharged solid and liquid municipal wastes were mostly wear down by the first turbulent incident of the streams from June to August. Cluster One, therefore, contained cases with medium mean − loadings of turbidity, total hardness, TSS, Chl-a, NO2, PO–3 4 , F , Pb, Zn, Cu, and Mg. Cluster Two consisted of four water quality monitoring sites: YsU1, YsD2, BoU1, and BoD2. About 67% of the sites in this cluster were monitored during summer, while the remaining 33% were observed in autumn season. Hence, we referred this cluster as summer predominant cluster to convey that the cluster is consisting of mixed sites which were monitored in autumn as well as in summer. These sites were receiving pollutants totally from non-urbanized landscapes relatively dominated by Eucalyptus tree (Eucalyptus globulus and E. camaldulensis) forest covers and bare lands. Due to the effects of the contributing landscapes, the potential of top soil erosion was relatively low; and so was its pollutant contributions. Thus, Cluster Two was contained cases of lower mean lodgings in turbidity, total hardness, TSS, Chl-a, NO2, K, F−, Zn, Cu, and Mg. Cluster Three consisted of 12 sampling sites which were monitored entirely in the summer season: LBU1, LBD2, BoU1, BoD2, LegR, DirR, SfU1, SfD2, BkU1, BkD2, LJU1, and LJD2. This cluster entails sites which were receiving contaminants from both urban and rural landscapes. With summer being the rainy season in the study area like in most parts of Ethiopia, sheet erosion mainly from pre-prepared agricultural plots was very common during this monitoring season. Besides, rivers were mostly aggressive and carried everything found along their course,most importantly all sorts of wastes thrown everywhere and left open. Cluster Three, therefore, encompassed monitoring sites of higher contaminant effects as evidenced by cases with higher mean loadings of turbidity, total hardness, TDS, TSS, EC, Chl-a, NO2, NH3, F−, Pb, Zn, Cu, and Mg.

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3.1.2. Temporal discriminant analysis Temporal variations in water quality were also assessed using discriminant analysis (DA). Season-based DA was performed on standardized data after clustering the whole data set into three groups based on the periods when sampling sites were monitored: summer cluster, autumn cluster, and summer-predominant cluster. The discriminant function (DF) presented in Table 4 and the classification matrices in Table 5 were obtained as a result of the standard, forward stepwise, and backward stepwise procedures of DA. During the forward stepwise technique, parameters were built systematically starting from the more to the less significant changes until no significant changes were obtained. In the backward stepwise mode, variables were removed from the least to the most important changes achieved procedurally [33]. About 24 and 14 discriminant variables were used by the DFs in both the standard and forward stepwise approaches, respectively (Table 4), and yielded the corresponding CMs correct allocation of 98.9% cases (Table 5). However, DA offered CMs with 96.8% correct allocation with only five discriminant parameters during the backward stepwise procedure (Table 4). These included temperature, pH, salinity, dissolved oxygen, and total nitrogen. Thus, the temporal DA results indicated that these five parameters were the most significant variables to discriminate the three seasons. That is, the variables represented most of the likely seasonal changes in the catchment water quality. Temperature showed significant seasonal variation. The temperature values were significantly highest in the low-flow season and lowest during the high-flow season. The higher temperature recorded in the dryer months was expected since the heat from sunlight could increase the temperature of surface water due to minimal cloud cover. Similarly, the decreasing water temperature during the rain months was attributable to the heavy rainfall conditions, coupled with the formation of thicker cloud cover, during this period. Higher salinity values were recorded during the low-flow than the high-flow season. The high-flow period coincided with the rainy season, and large volumes of freshwater were discharged into the water systems of streams and reservoirs that lowered salinity level by dilution of the water. Consistent with this,

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Table 5 Classification matrix for discriminant analysis of temporal variation in water quality. Monitoring sites by season

Standard DA mode Autumn Summer dominated Summer Total Forward stepwise DA mode Autumn Summer dominated Summer Total Backward stepwise DA mode Autumn Summer dominated Summer Total

% correct

Seasons assigned by DA Autumn Summer predominant

Summer

97.2 100 100 98.9

35 0 0 35

1 18 0 19

0 0 42 42

97.2 100 100 98.9

35 0 0 35

1 18 0 19

0 0 42 42

94.6 94.4 100 96.8

35 1 0 36

1 17 0 18

1 0 41 42

McLusky [34] stated that rainfall could cause dilution of water bodies and hence cause a reduction in salinity.Whereas, in dry season months, heat generated by sunlight could increase evaporation from surface water and making it more saline. The dissolved oxygen (DO) values were higher at the summer than the autumn sampling stations which was attributed to the effects of higher temperature. The result was consistent with the reports of Chapelle et al. [35] and Buentello et al. [36] who indicated that the solubility of oxygen decreases in the dry season due to high temperature while it increases at a lower temperature during the wet season. The inverse relation between temperature and DO were also recorded with the findings of Shrestha et al. [33]. The level of DO was lowered severely in autumn sampling station, which might be a result of oxidization of wastes. Nutrients discharged into a water body during high-flow season helps the plants and algae to grow more rapidly than normal. As this

Table 4 Classification function coefficients for discriminant analysis of temporal variations in water quality (Fisher's linear discriminant functions). Parametera Standard Mode

Temp pH Turbidity TDS DO Chl-a Salinity TN PO–3 4 T hard – F Pb As Mg EC TSS NO2 NO3 NH3 K Fe Zn Mn Cu (Constant)

Forward stepwise mode

Backward stepwise mode

Autumn Coef.b

Summer dominated Coefb

Summer Coefb

Autumn Coef.b

Summer dominated Coef.b

Summer Coef.b

Autumn Coef.b

Summer dominated Coef.b

Summer Coef.b

10.190 8.803 −1.912 5.178 −10.054 −5.352 26.975 −11.667 −6.669 5.894 2.479 3.427 −3.236 0.458 −2.561 −1.738 −1.075 2.867 2.907 −3.999 −0.281 0.319 −0.512 −2.220 −25.217

−4.614 0.050 6.157 −0.548 0.889 1.045 −12.249 −2.689 6.539 −14.143 −2.933 −3.674 4.299 −5.072 −3.809 −1.463 −0.725 2.682 −1.707 3.087 −3.503 −2.018 1.276 0.074 −29.189

−6.757 −3.816 −1.000 −4.203 18.035 4.140 −17.872 8.971 2.913 1.009 −0.868 −1.363 0.931 1.781 3.828 2.116 1.232 −3.607 −1.760 2.105 1.742 0.591 −0.108 1.871 −14.541

8.871 9.425 −3.210 −1.014 −9.739 −2.880 −8.639 −3.302 −4.860 3.218 2.382 3.113 −2.988 1.359

−4.945 −0.756 4.227 −2.672 −0.496 1.427 18.436 −5.550 5.929 −14.277 −3.138 −3.742 4.417 −5.591

−5.485 −3.527 0.940 2.014 16.613 1.857 −0.496 8.473 1.624 3.360 −0.697 −1.065 0.669 1.231

8.871 10.142

−5.485 −0.956

−4.945 −3.527

−8.639

4.496

18.436

18.799 −3.465

−10.956 −8.625

−12.035 8.653

−20.568

−25.501

−11.138

−10.533

−4.161

−6.130

Significance level at 0.01. a All parameters are in mg/L except for temp (°C), turbidity (NTU), EC (ms/cm) and salinity (0/00) b Discriminant function coefficient for winter, summer predominant and summer seasons correspond to wij as defined in Eq. (1)

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happens, there is also an excess die-off of the plants and algae, which would later decompose in water and add to the DO depletion [37] during the low flow period.

Higher pH values at the low-flow than the high-flow season was similar to that reported by Olıas et al. [38], which might had resulted from decaying of domestic and industrial wastes as well as litter when

Fig. 3. Clustered box and whiskers plots of major temporal water quality discriminant variables.

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there was a reduced dilution and rainfall run-off discharge. It could also come from Vertisols that covered most parts of the catchment with pH values ranging from slightly to strongly alkaline.4 Thus, the higher pH values in the water might be due to the high run-off effects of strongly alkaline soil in the catchment. That is, the surface water was changing from acidic to alkaline due to the influx of bicarbonate (HCO3) ions as a result of soil erosion caused by run-off through rainwater in the rainy season. TN was higher in the summer and lower in the autumn monitoring stations. That could happen because of the fact that typically during summer rainfall run-off nutrients could be discharged from agricultural management systems to the rivers [39]. Erosion and soil losses by surface runoff were usually considered to be the predominant sources of nutrient pollution from croplands [40]. Furthermore, the seasonal variation of water temperature has a pronounced effect on the rates of biochemical decay [41]. The rates of nitrification and denitrification decrease with decrease in temperature. Because of that, during summer, NO3 and NH4 are less efficiently removed from the water so that their concentrations remain relatively high. When the water temperature rises, the nitrification and denitrification processes resume, at increasing rates with increase in temperature, thereby lowering the concentrations in the water. 3.1.3. Box and whisker plots Box and whiskers plots were employed to explore and interpret the temporal variations of water quality graphically based on the most significant variables that discriminate the three seasons. Total nitrogen and dissolved oxygen were higher during the high-flow summer season (Fig. 3). Whereas, high levels of Temp, pH, and salinity were observed during the low-flow season in autumn in both rivers and reservoirs across the two catchments. pH was higher in streams during fall and in reservoirs during summer. 3.2. Spatial variations in catchment water quality Spatial variations in catchment water quality were initially assessed through Pearson's rank–order correlations (Table 6). Firstly, the predominant land covers in the catchment were identified through LULCC analysis (2000 to 2015). The classes included agriculture, built up, grazing land, forests, bare lands, and water bodies (Fig. 4). The spatial variations of water quality under the effects of the different land cover categories were then evaluated through Pearson's rank order correlation coefficient. Land change among the six land categories (Fig. 4) were statistically assessed by change analysis tool that runs individual land categories statistically. Changes among the categories between two points in time were examined by means of transition matrix. The transition matrix was generated from maps overlaid between the two time intervals. There were significant changes5in all land cover and land use categories between the two points in time (2000–2015) except for water, which possess subtle change. The total area of land exhibiting change was higher than the total area of persistence across the landscape in the time intervals. The total area persistence in its original land category was found to be 49% of the general landscape domain from 2000 to 2015. In other words, 51% of the total area of the catchment landscape was exhibiting change between two points in time. An assessment of land change at the general landscape domain level showed that agriculture was the mainland gaining category, while grazing land followed by bare land was the main losing category. Agriculture gained 7229.7 ha between the years 2000 and 2015 (Fig. 4) 4 Oromia Water Works Design and Supervision Enterprise (OWWDSE), 2011. Finfinne surrounding special zone of Oromia integrated land use planning study project, section I: main report. Unpublished. Finfinne/Addis Ababa 5 Significant land change process in the catchment has identified from the results of the research we have been undertaking in the same catchment on LULCC and SWAT simulation modeling in parallel with this study

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Table 6 Correlation matrix of water quality parameters and land covers in the catchment (Pearson's R coefficient). Parametera

Tamp pH Turbidity EC TDS TSS DO Chl-a Salinity NO2 NO3 NH3 TN PO–3 4 K T hard F– Pb Fe Zn As Mn Cu Mg

Land cover categoryb Agriculture

Bare land

Built up

Forest

Grassland

0.13 0.03 0.44⁎⁎ 0.17 0.09 0.05 −0.22⁎ 0.33⁎⁎ 0.01 0.49⁎⁎ 0.27⁎⁎

−0.21⁎ 0.34⁎⁎ 0.33⁎⁎

0.27⁎⁎

0.11 −0.06 0.27 −0.06 −0.12 −0.02 −0.17 0.27⁎⁎ −0.07 0.12 −0.03 0.14 −0.05 0.12 −0.49⁎⁎ −0.05 −0.01 0.17 −0.13 −0.01 0.09 0.19 −0.17 0.21⁎

0.30⁎⁎ −0.19 0.37 0.16 0.12 0.17 −0.37⁎⁎ 0.51⁎⁎ 0.14 0.27⁎⁎ 0.19 −0.18 0.19 0.04 0.04 0.23⁎ 0.17 −0.11 0.53⁎⁎ 0.35⁎⁎

0.13 0.13 0.02 −0.13 0.07 0.29⁎⁎ 0.32⁎⁎ 0.21⁎ 0.32⁎⁎ −0.09 −0.07 0.08 0.63⁎⁎

−0.14 −0.21⁎ 0.11 −0.37⁎⁎ 0.03 −0.12 −0.09 −0.19 0.33⁎⁎ −0.15 0.30⁎⁎

−0.31⁎⁎ −0.02 −0.06 0.24⁎ −0.29⁎⁎ −0.02 0.16 0.52⁎⁎ −0.12 0.28⁎⁎

0.02 0.52⁎⁎ 0.15 0.09 0.24⁎

−0.52⁎⁎ 0.49⁎⁎ 0.13 0.43⁎⁎ 0.19 −0.10 0.12 0.14 0.12 0.23⁎ 0.10 0.36⁎⁎ 0.53⁎⁎ 0.37⁎⁎ 0.00 −0.41⁎⁎ 0.32⁎⁎ 0.07

−0.07 −0.49⁎⁎ 0.27⁎⁎ −0.02

a All parameters are in mg/L except for temp (°C), turbidity (NTU), EC (ms/cm) and salinity (0/00) b Land cover in sub-watersheds in ha. ⁎ Significant at the 0.05 level (2-tailed). ⁎⁎ Significant at the 0.01 level.

while also losing about 4005 ha of land to other land classes in the same time interval. Thus, agriculture possessed a net gain of 3225 ha in the second time point, i.e., 2015.The forest land cover category gained 719 ha and lost 1907 ha while Built up gained 1738 ha and lost 1, 418 ha between the two points in time (Fig. 4). Thus, while forest category exhibited a net loss of 1188 ha, built up category had a net gain of 319 ha from other categories. Whereas, the gain and loss for grazing land were 1959 and 4, 019 ha and for bare land 2546 and 2, 795 ha, respectively. Therefore, grazing land experienced a net loss of 2060 ha and bare land 249 ha. Land use is the good predictors of water quality. A small percentage of forestland is usually related to higher concentrations of water pollutants [42,43] and thus negative relationships are found between the percentage of forest land and concentrations of water pollutants [43]. The spatial correlation matrix of water quality parameters across monitoring sites against land cover factors at each sub-watershed showed that all of the measured water quality parameters except As, EC, salinity, and TN were found to be significantly (p b 0.01) correlated with one of the predominant land cover categories. Among these, built up and agriculture land categories exhibited the highest correlation coefficient with turbidity (Pearson's r = 0.52 and r = 0.44, respectively). Built up category has significant positively correlations with Fe (r = 0.53), Zn (r = 0.37), Pb (r = 0.36) and significant negative correlations with Do (r = − 0.52). Previous studies have shown that Watershed that contains higher percentages of urban lands, including residential, commercial, and industrial lands might contribute water pollutants to the environment [44,45]. Positive significant correlations were observed between agricultural land use category and Chl-a (r = 0.33), NO2 (r = 0.49), NO3 (r = 0.27) and Mg (r = 0.62). Carpenter [46] has pointed out that excess fertilization and manure production on agricultural lands create surplus nutrients, which is mobile in many soils and often leaches to downstream aquatic ecosystems. Moreover, [47] has reported that greater amount of soil disturbance and erosion associated

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Fig. 4. Map of land use and cover changes in 2000 (top) and 2015 (bottom) in the Legedadie and Dire catchments.

with more intensive practice of agriculture has significant impact on erosion and water quality. BarelandsshowedsignificantpositivecorrelationswithpH,NH3, PO–3 4 , and Mn. According to Li et al. [48], bare lands had a significant influence on nitrogen concentration in the riverine network. Strong positive correlation of bare land with K and Na, and a major contribution to nitrogen, Cl−, SO2− 4 , Ca, and Mg were also reported by Li et al. [13]. About 46 significant correlations were noticed among land cover categories with measured parameters. Of which, sub-watersheds predominantly covered with built up as well as bare land were found to be significantly correlated with most of the measured variables (12 parameters each) followed by prime agricultural land (10) and grazing land sub-watersheds (9). Whereas, the minimum number of associations was between principally plantation forested sub-watersheds and some (three) of the measured parameters. The result is consistent with the findings from the previous studies [43–45]. Therefore, it is not surprising that most contaminants polluting catchment waters were sourced more from anthropogenic factors of urban areas than natural factors. The relationship between detrimental water quality and urbanization is not new. Wang [49] provided a detailed analysis of the spatial variation in water quality across an entire watershed and

revealed a strong relationship between the degradation of water quality and urban land use. Ren et al. [50] have also reported that rapid urbanization corresponds with rapid degradation of water quality. 3.2.1. Spatial cluster analysis More convincing results of spatial variation associated with catchment landscape configuration and water quality parameters were evident through CA. CA was performed with the standardized data to detect the spatial similarity of groups among the water monitoring sites. Accordingly, all the 14 monitoring sites of the catchment were grouped into four statistically significant clusters at (Dlink/Dmax) × 100 b 12 as shown by the dendrogram presented in Fig. 5. Hierarchical agglomerative cluster analysis was applied. Hence, the number of cluster solutions were decided by considering the practicality of the results given the landscape pattern in the catchments such as the dominant land cover type in each sub-watershed, the location of monitoring sites being either downstream or upstream, and proximity and association to pollutant transferring landscapes which might be either urban or rural. A total 96 cases were equally distributed to the 4 clusters. Cluster One contained 24 cases that corresponded to LBU1, LBD2, BkU1, and

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signified that the CA procedure is most successful and very useful in providing a reliable grouping of surface waters and that CA has the potential to sketch a suitable and optimal spatial sampling strategy which can reduce not only the number of monitoring stations but also the associated costs and time.

Fig. 5. Dendrogram showing spatial analysis of water quality monitoring sites In Legedadie and Dire catchments.

BkD2 monitoring sites. The cluster was situated in the western and middle eastern plain of the catchment adjacent to an urban area and various development activities. Due to their location and proximity to the urban area, monitoring sites in Cluster One were receiving water mainly from urban discharges, domestic sources, and flower farms; thus, the cluster was vulnerable to higher pollution. This cluster was referred to as an urban cluster. Cluster Two encompassed other 24 cases that matched YsU1, YsD2, SfU1, and SfD2 monitoring sites. The cluster was situated in an intermediate area that stretched from the upland parts of Dire catchment to the plain sections of Legedadie catchment. Comparatively, this cluster corresponded to monitoring sites that were receiving water and pollutions from non-point sources, mostly from agricultural landscapes, and thus it was referred to as an agricultural cluster. Cluster Three comprisedof 24 cases that corresponded to LJU1, LJD2, and LegR monitoring sites. The cluster was situated at the most downstream areas of the catchment, and thus it was getting cumulative water and pollutant loads from both point and non-point sources. As the cluster received contaminants from mixed landscape patterns such as agriculture, urban, and grazing lands, it was referred to as a mixed cluster. Cluster Four matched with BoU1, BoD2, and DirR monitoring sites. The cluster was situated at the most upstream areas of the catchment that were covered partly by plantation forest and partly by rocky bare land. This cluster was referred to as a natural cluster. The results of spatial clustering indicated that there was a significant difference among the clusters. Urban and natural clusters corresponded to most and least polluted parts of the catchment, respectively. CA successfully classified water quality monitoring sites based on their spatial characteristics. The CA results agreed with previous water quality studies that effectively applied the same method [24,51,52]. Thus, the results

3.2.2. Factor analysis / principal component analysis The factor analysis/principal component analysis (FA/PCA) was performed to compare the significance of compositional patterns of the samples through reductions in dimensionality and identify the factors influencing each cluster. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests of sphericity were used to examine the validity of PCA before using the method. The aggregated KMO results for the four spatial clusters (0.721) and Bartlett's sphericity (276) (p b 0.05) ascertained that significant reductions in dimensionality would be attained successfully from FA/PCA. Variances were explained with five varifactors in all of the spatial clusters except for the natural cluster that was explained with only four varifactors (Table 7). An eigenvalue indicates the degree of importance of the factors, the most significant factors being those with the maximum eigenvalues. Eigenvalues of 1.0 and above are considered significant [53]. PCA with varimax rotation explained 82.4% of the total variance in the natural cluster, 87.8% in the mixed cluster, 85.8% in the agricultural cluster, and 89.4% in the urban cluster (Table 7). Factor loadings higher than 0.75 are said to be strong, in the range of 0.5–0.75 moderate, and 0.3–0.5 weak [23,54,55]. Table 8 summarizes the loadings variables in varifactors by cluster. In the natural cluster, the first varifactor (VF1) that accounted for 34.4.0% of the total variance had robust and positive loadings on turbidity, EC, TDS, NO2, total hardness, and Cu. The same varifactor had strong and negative loadings on DO, salinity, NO3, and TN. Turbidity, TDS, EC, salinity and total hardness appeared to come most likely from various non-point sources including bare land, forest, and agricultural areas. Hardness was associated with the nature of the geology in the area with which the water had been in contact [56]. In this regard, a large part of the natural cluster was geologically covered with the Cheleleka basalt group6 characterized by high degree of fracturing and penetrative and deep weathering with gray appearance. Hence, the strong and negative loadings of hardness could most likely result naturally from the geological process. Pesticides comprise of a variety of organic compounds such as lindane, isoproturon, and atrazine which, in turn, may contain heavy metals such as Cu [57]. Therefore, the strong and positive loadings of Cu might stem from point sources of pollutions such as cut flower farms that were applying considerable proportions of agrochemicals including pesticides. The high loadings of NO2, TN, NO3, and DO represented non-point pollutant sources, which could include water run-off and soil erosion from croplands, plots of livestock confinement, and decomposition of organic matters from forested lands. VF2 accounted for 22.6% of the total variance and had strong positive loadings on As. Natural mineral deposits contain large quantities of arsenic [56] which may elevate levels of inorganic arsenic in soil and water. Thus, sporadic upstream excavation and quarrying activities and pesticide applications were the likely sources of arsenic. In the mixed cluster, VF1 explained 26.2% of the total variance by strong and positive loadings on EC, NH3, total hardness, Cu, and Mg and a strong negative loading on salinity, NO3, and TN. The mixed cluster was receiving the cumulative effects of all the upstream discharges. The strong loadings on TN, NH3, and NO3, in VF1 signified agricultural non-point pollution, which could most likely be sourced from water drained from agricultural areas that contained domestic and biochemical wastewater. Releases from manufacturing sites might also be the likely sources of these contaminants. High levels of NH3 in surface 6 Oromia Water Works Design and Supervision Enterprise (OWWDSE), 2011. Finfinne surrounding special zone of Oromia integrated land use planning study project, section I: main report. Unpublished. Finfinne/Addis Ababa.

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Table 7 Total variance explained by component in four spatial clusters of the Legedadie and Dire catchment. Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

% variance

Cumulative %

Total

% variance

Cumulative %

Total

% variance

Cumulative %

Natural clustera 1 10.053 2 4.731 3 2.715 4 2.271 5 0.932 6 0.811

41.889 19.713 11.313 9.461 3.885 3.38

41.889 61.602 72.916 82.376 86.261 89.641

10.053 4.731 2.715 2.271

41.889 19.713 11.313 9.461

41.889 61.602 72.916 82.376

8.247 5.425 3.386 2.711

34.364 22.606 14.109 11.297

34.364 56.97 71.079 82.376

Mixed clustera 1 9.138 2 4.466 3 3.157 4 2.711 5 1.612 6 0.83 7 0.72

38.073 18.607 13.155 11.295 6.718 3.457 3

38.073 56.68 69.835 81.13 87.848 91.305 94.305

9.138 4.466 3.157 2.711 1.612

38.073 18.607 13.155 11.295 6.718

38.073 56.68 69.835 81.13 87.848

6.287 4.891 4.548 2.915 2.441

26.198 20.38 18.952 12.147 10.171

26.198 46.578 65.53 77.677 87.848

Agricultural clustera 1 7.896 2 4.184 3 3.72 4 3.248 5 1.555 6 0.986 7 0.867

32.898 17.433 15.501 13.535 6.479 4.109 3.613

32.898 50.331 65.833 79.367 85.846 89.955 93.568

7.896 4.184 3.72 3.248 1.555

32.898 17.433 15.501 13.535 6.479

32.898 50.331 65.833 79.367 85.846

7.211 4.18 3.521 3.458 2.234

30.044 17.417 14.672 14.407 9.307

30.044 47.46 62.132 76.539 85.846

Urban clustera 1 9.592 2 5.121 3 4.393 4 1.297 5 1.047 6 0.693 7 0.55

39.966 21.339 18.304 5.404 4.363 2.889 2.292

39.966 61.305 79.609 85.013 89.376 92.265 94.557

9.592 5.121 4.393 1.297 1.047

39.966 21.339 18.304 5.404 4.363

39.966 61.305 79.609 85.013 89.376

7.181 6.568 4.194 2.008 1.5

29.92 27.365 17.474 8.366 6.25

29.92 57.285 74.759 83.126 89.376

Total

a

Numbers 1 to 6 or 7 in the first column represent the components or varifactors in the respective cluster.

waters could also stem from sources like natural organic matters decay, plants, and agrochemical applications [58]. While Mg, which is generally rare could come from anthropogenic sources, hardness could generate mostly from natural sources. EC and salinity potentially might be generated from unmanaged discharges from domestic sources due to the rapid urbanization processes that were taking place in the catchment [15,51]. In the same cluster,VF2 explained 20.4% of the total variance and had strong positive loadings on Fe. The presence of Fe dictated that the pollution could originate from industrial effluents. In addition, VF3 and VF4 explained 18.9% and 12.1% of the variance with strong positive loadings DO and temperature, respectively. For the agricultural cluster, VF1 explained 30% of the total variance, with strong positive loadings on TN, NO3, Salinity, and Fe and strong negative loadings on NH3, total hardness, and Mn. The major physicochemical variables (salinity, T-Hard, TN, NH3, and NO3) and dissolved trace elements (Mn and Fe) represented the total soluble nutrient and salt concentration and provided insight on chemical changes in river flow [59]. TN, NH3, and NO3 coincided strongly with the local agricultural activities implying that agricultural pollution from cultivated areas might be collected in streams due to rainfall run-off erosion on farm plots [60,61]. Nitrogen was the largest component of both synthetic and organic fertilizer. NO3 is a highly soluble compound and thus it is extremely mobile in the environment. After fertilizer application, all nitrogen components of the fertilizer that were not absorbed by the crops could migrate to the water system. The high loadings on Fe were possibly the contributions of water-soluble metals originating from effluents of the soil and industries. VF2 explained 17.4% of the total variance for the agricultural cluster and with strong positive loadings on turbidity, Chl-a, and Cu were. The high turbidity could be attributed to runoffs from fields with a high load

of soil mainly from farming and excavation sites of quarrying activities. The higher loading on Chl-a was an indication of nutrient concentration in surface water as a result of fertilizer run-off from agricultural activities. VF3in the same cluster explained 14.7% of the total variance with strong positive loadings on Mg and strong negative loading on Temp. Whereas, VF4 explained 14.4% of the total variance with strong positive loading on K. The high loading of K suggests pollution from the application of potash fertilizer to the agricultural lands. For the urban cluster, VF1 explained 29.9% of the total variance with strong positive loading on turbidity, TDS, Chl-a, NH3, total hardness, Mg and strong negative loading on salinity. Whereas, VF2 explained 27.3% of the total variance with strong positive loading on DO. VF3 explained 17.4% of the total variance with strong positive loading on Fe and strong negative loading on Mn. FA/PCA did not result in a considerable data reduction since it selected 17 out of the 24 measured parameters, i.e., 30% reduction. However, it could identify factors/variables responsible for the spatial variation in water quality across the four clusters in the catchment defined by CA. Therefore, the spatial variations of water quality needed to be assessed further using discriminant analysis (DA) so as to discriminate the most significant water quality parameters. 3.2.3. Spatial discriminant analysis Given a set of independent variables, discriminant analysis seeks to associate those parameters that best separate the groups linearly. Spatial variation in water quality was evaluated using DA (Table 10) performed on the raw data set comprising 24 parameters grouped into 4 major classes of urban, agricultural, mixed, and natural cluster sites as defined by CA. In order to identify the most significant variables related to differences among the spatial groups, independent variables constituted all the measured parameters and the dependent variable (i.e.,

– – – – – .492 – – – – – – – – – – – –.387 – – –.361 – – – .522 – .517 – –.702 – .683 –.378 – –.303 .449 – –.593 – – – .373

– – – .687 – – –.336 .329 – – – .394 – – – –.330 – – – .675 – .519

All parameters are in mg/L except for temp (oC), turbidity (NTU), EC (ms/cm) and salinity (0/00). A

VF4

– – – – – – – – – – – – – – – – – –.518 – .522 .482 – – – .365 – – – – – – – .301 .635 .685 – .572 –.645 – – –.317 .385 .955 – –.488 –.790 – –.311

FF3 VF2 VF1

.484 –.594 .906 .747 .756 .627 .387 .941 –.805 .539 –.471 .884 –.564 – –.635 .950 .468 .377 – .523 –.389 –.449 .605 .828

VF5

– – – – – – – – – .528 – – – –.487 – – .326 – – .707 – – – –

VF4

.487 –.339 – .600 .618 –.436 .578 – – – – – – –.374 .813 – – .480 .368 .363 – – – – –.770 .718 –.303 .382 .345 – – – – .413 – – – .425 – .365 .699 – – – – – .370 .876

FF3 VF2

– – .799 – – .467 –.417 .897 – – – – – – – .364 – –.489 .379 .443 –.611 –.483 .813 – – .474 –.406 –.557 –.565 –.493 –.643 –.309 .958 – .953 –.935 .972 .465 – –.782 – .334 .810 – – –.806 – –

VF1

4. Summary of key findings and conclusions

– – –.318 – – – – –.374 – .570 – – – .586 – – .473 – – – .419 – – –

VF5

93

the grouping) constituted clustered monitoring sites. As shown in Table 9, the values of Wilk's lambda and theChi-square for each discriminant function (DF) diverged from 0.022 to 0.761 and 24.4 to 307.9 with a pvalue b 0.01, respectively. The results indicated that the spatial DA was dependable and capable. The respective right allocations of the standard and stepwise approaches are presented in Table 10. About 24 discriminant variables in the standard stepwise procedure yielded CMs that correctly assigned 95.8% of the cases. DFs of about 11 discriminant variables in the forward stepwise procedure yielded CMs that properly allocated 87.5% of the cases.Moreover, DFs of the backward stepwise mode produced a similar result of 85.2% correct assignment with only 7 discriminant parameters. Therefore, the results of DA indicated that pH, turbidity, TN, total hardness, Pb, Fe, and Cu were the most significant discriminant parameters among the four spatial clusters, causing most of the predictable changes of water quality.

.754 –.737 – – – – .517 – – .371 – – – – –.543 – – – – – .647 – – –

VF4 VF3

.320 – –.536 .512 .557 –.583 .778 –.408 – – – – – –.347 .556 –.375 – .608 – – – – – – – –.457 .510 .327 – – – .669 .304 .548 .377 – .414 .374 .548 – .568 – .826 .691 – –.597 .483 –

VF2 VF1

–.496 .305 .462 .751 .734 .617 – .445 –.913 – –.865 .965 –.862 –.317 – .874 – .669 –.484 .307 .498 .582 .807 .815

VF4

–

FF3

– .734 – – – – –.477 – – – .526 .378 .397 – – – .688 –.315 – .620

–.660 – – .586 .586 – .319 .615 –.365 – – – – – .690 .396 .624 –.317 –.632 – .829 – – –.561

VF2 VF1

.692 – .944 .789 .777 .313 –.753 .706 –.853 .755 –.774 –.337 –.833 –.734 – .889 – .592 .706 .588 – –.346 .856 .418 Temp pH Turbidity EC TDS TSS DO Chl–a Salinity NO2 NO3 NH3 TN PO4–3 K T/hardness F– Pb Fe Zn As Mn Cu Mg

Urban cluster Agriculture cluster Mixed cluster Natural cluster ParametersA

Table 8 Loadings of variable for the different clusters in the Legedadie and Dire catchment (bold and italic = strong positive loadings; bold = strong negative loadings).

.670 –.693 – –.481 –.589 – .821 – .475

VF5

Yilikal Anteneh et al. / Acta Ecologica Sinica 38 (2018) 81–95

The study applied different multivariate statistical techniques to examine spatial and temporal variations and identify possible pollutant sources of surface water quality in the Legedadie and Dire catchment area. The significant correlation (p b 0.01) between the measured parameters and the seasonality as well as spatial factors of the predominant land cover categories helped to gain initial insights about the spatial and temporal variability of the monitored water quality parameters. The hierarchical CA grouped the 14 sampling sites into 3 seasonal and 4 spatial clusters of identical water quality characteristics successfully. FA/PCA did not generate significant data reduction; but, it helped to extract and recognize the factors responsible for changes in river water quality across the different monitoring sites. Varifactors obtained from FA showed that the variables accountable for water quality variations were mainly related to anthropogenic (agricultural managements systems, industrial effluents, and municipal wastes) but also natural sources through geological processes. DA procedure applications in standard, forward stepwise, and backward stepwise modes yielded a substantial data reduction and gave the best results both temporally and spatially. For all the monitoring sites, DA provided 96.8% correct allocations in the temporal clusters using only five discriminant parameters (temperature, pH, dissolved oxygen, salinity, and total nitrogen), signifying that these five parameters were the most significant water quality variables to discriminate the monitoring stations across the two seasons. For the spatial water quality variations, DA yielded 85.2% correct assignment with only 7 discriminant parameters (pH, Turbidity, TN, Total hardness, Pb, Fe, and Cu). These water quality variables were the most significant parameters for discrimination across the four spatial clusters and accounted for most of the expected variations of water quality. Therefore, in a nutshell, the study demonstrated that the multivariate statistical techniques applied are valuable for analysis and interpretation of complex water quality data sets for a practical and efficient assessment of water quality, identification of pollution sources, and effective understanding of the space and time effects of water quality. Table 9 Results of discriminant analysis for spatial variation in the Legedadie and Dire catchment. Model Standard

Discriminant function Wilks' lambda Chi-square p-Value

1 2 3 Forward stepwise 1 2 3 Backward stepwise 1 2 3

0.022 0.144 0.480 0.086 0.395 0.759 0.116 0.423 0.761

307.9 156.7 59.4 218.4 82.7 24.6 192.8 77.0 24.4

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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Table 10 Classification function coefficients for discriminant analysis of variations in 4 spatial clusters using 3 models. Standard model Variablesa Urban Coef.b Temp 26.99 pH 187.92 Turbidity 0.01 EC −0.12 TDS −0.02 TSS −0.33 DO 18.21 Chl-a 0.46 Salinity 4.67 NO2 −22.97 NO3 9.41 NH3 12.94 TN 32.50 PO–3 35.33 4 K 2.04 T-hard 0.36 F– 94.10 Pb 262.76 Fe −12.90 Zn 101.55 As −1923.4 Mn 76.98 Cu 1577.68 Mg −158.45 Constant −2084.2

Agric. Coef.b 27.87 193.93 0.05 −0.11 −0.04 −0.33 18.47 0.44 4.96 −19.43 12.28 27.99 25.94 47.52 2.46 0.46 73.90 269.27 −11.66 99.78 −1843.7 78.23 1482.76 −162.19 −2142.9

Forward stepwise model Mixed Coef.b 28.11 194.20 0.06 −0.14 0.00 −0.33 18.82 0.43 4.76 −22.83 11.49 11.52 31.30 43.73 2.37 0.44 112.18 271.63 −13.05 101.81 −1906.9 77.62 1603.71 −168.43 −2203.8

NaturalCoef.b UrbanCoef.b Agric. Coef.b 28.62 17.381 18.017 196.94 16.680 20.804 0.09 0.01 0.01 −0.08 – – −0.04 – – −0.33 – – 17.87 – – 0.47 0.164 0.133 5.15 – – −22.61 – – 11.12 – – 55.32 −48.82 −38.31 33.43 21.02 20.06 42.87 12.842 23.726 1.91 – – 0.31 −0.11 −0.07 72.25 – – 307.39 84.83 84.76 −13.54 −5.94 −4.93 100.30 – – −1887.3 – – 81.17 – – 1503.69 742.20 662.15 −161.78 – – −2234.3 −302.3 −295.82

Backward stepwise model MixedCoef.b NaturalCoef.b Urban Coef.b 18.194 18.465 – 18.504 21.459 13.00 0.01 0.08 0.02 – – – – – – – – – – – – 0.140 0.165 – – – – – – – – – – −50.33 −20.17 – 22.92 23.34 20.35 19.715 18.536 – – – – −0.08 −0.16 −0.14 – – – 85.92 117.70 86.81 −6.09 −6.17 −4.87 – – – – – – – – – 751.65 674.09 671.46 – – – −323.8 −326.6 −293.9

Agric. Coef.b – 16.26 0.02 – – – – – – – – – 19.53 – – −0.09 – 86.31 −4.09 – – – 606.63 – −290.6

Mixed Coef.b – 14.17 0.02 – – – – – – – – – 22.23 – – −0.11 – 87.96 −4.98 – – – 678.73 – −314.7

Natural Coef.b – 16.14 0.09 – – – – – – – – – 23.06 – – −0.17 – 118.52 −5.72 – – – 644.87 – −325.1

Significance level 0.01, Right allocation (%)*: 95.8 (standard); 87.5 (forward stepwise); 85.2(backward stepwise) correctly classified. a All variables are in mg/L except for temp (°C), turbidity (NTU), EC (ms/cm) and salinity (0/00). b Discriminant function coefficient for winter, summer predominant and summer seasons correspond to wij as defined in Eq. (1).

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