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Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey

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Abstract

Irrigation water quality has important implications on salinity, ion toxicity, production cost, and crop failures. There is a need for a comprehensive analysis of spatial and temporal dynamics in parameters at a watershed scale. This information is critical for irrigation management in agricultural production. The Lake Uluabat watershed is a significant agricultural area of Turkey, which is studied using monitored water data. Multivariate assessment is performed using cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) to evaluate temporal and spatial variations in water quality in the watershed. The data is processed by clustering, reducing data dimensionality, delineating indicator parameters, assessing source identification, and evaluating temporal changes and spatial patterns. The results show that the most representative discriminant parameters had more than 90.98% validity in both temporal and spatial analyses. Runoff rate (Q) and water temperature (WT) were identified in the temporal study, while spatial analysis showed bicarbonate (HCO3−), sulfate (SO42−), and boron (B3+) as indicators. Salinity, sodicity, boron hazard, and alkalinity affect both spatial and temporal water quality patterns in the watershed. It is observed that continued use of poor-quality irrigation water can adversely affect agriculture and soil health in a watershed. Spatio-temporal relationships in parameters will be useful in sustainable irrigation management and farm planning for improving crop productivity and soil health.

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References

  • Abrol, I. P., Yadav, J. S., & Massoud, F. I. (1988). Salt-affected soils and their management, FAO Soils Bulletin 39. Rome: FAO.

    Google Scholar 

  • Akcaman, N., Tas, I., & Coşkun, Y. (2017). Effects of different salinity levels of irrigation water on germination of guar gum (Cyamopsis tetragonoloba). Turkish Journal of Agricultural And Natural Sciences, 4(2), 130–137.

    Google Scholar 

  • Aksoy, E., & Özsoy, G. (2002). Investigation of multi-temporal land use/cover and shoreline changes of the Uluabat Lake Ramsar site using RS and GIS, International Conference On Sustainable Land Use And Management, 10–13 June 2002, Çanakkale, Turkey, 318–325.

  • Almeida, C., Quintar, S., González, P., & Mallea, M. (2008). Assessment of irrigation water quality. A proposal of a quality profile. Environmental Monitoring and Assessment, 142(1–3), 149–152.

    CAS  Google Scholar 

  • Andriesse, J. (1988). Nature and management of tropical peat soils. Rome: FAO FAO Soils Bulletin, 59.

    Google Scholar 

  • Armstrong, J. S., & Soelberg, P. (1968). On the interpretation of factor analysis. Psychological Bulletin, 70(5), 361–364.

    Google Scholar 

  • Aydin, M., & Güngör, Y. (2015). Effects of Lake Uluabat and Gölyazi to human activities and tourism. Academic Journal of Science, 04(02), 89–100.

    Google Scholar 

  • Ayers, R. S., & Westcot, D. W. (1985). Water quality for agriculture (Vol. 29). Rome: Food and Agriculture Organization of the United Nations.

    Google Scholar 

  • Baluch, M. A., & Hashmi, H. N. (2019). Investigating the impact of anthropogenic and natural sources of pollution on quality of water in upper Indus Basin (UIB) by using multivariate statistical analysis. Journal of Chemistry, 00(00), e4307251. https://doi.org/10.1155/2019/4307251.

    Article  CAS  Google Scholar 

  • Batayneh, A. T. (2012). Toxic (aluminum, beryllium, boron, chromium and zinc) in groundwater: health risk assessment. International Journal of Environmental Science and Technology, 9(1), 153–162.

    CAS  Google Scholar 

  • Bauder, T. A., Waskom, R. M., Sutherland, P. L., Davis, J. G., Follett, R. H., & Soltanpour, P. N. (2011). Irrigation water quality criteria. Online document. https://www.mountainscholar.org/bitstream/handle/10217/182905/AEXT_005062014.pdf?sequence=13. Accessed 10/1/2019.

  • Bulut, E. (2005). Assessment of management policies for Lake Uluabat Basin using AVSWAT, (Master’s thesis, Middle East Technical University, The Department of Environmental Engineering, Ankara, Turkey).

  • Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1–27.

    Google Scholar 

  • Castendyk, D. N., & Eary, L. E. (Eds.). (2009). Management Technologies for MetalMining Influenced Water, vol. 3 – mine pit lakes: characteristics, predictive modeling, and sustainability. Littleton: Society for Mining, Metallurgy, and Exploration 304 p.

    Google Scholar 

  • Çelenli, A. (2000). Environmental geochemistry of lake Uluabat (Unpublished doctoral dissertation). Istanbul Technical University, Graduate School of Science Engineering and Technology, Istanbul.

  • Cha, S. H. (2007). A comprehensive survey on distance/similarity measures between probability density functions. City, 1(2), 1.

    Google Scholar 

  • Chaparzadeh, N., D’Amico, M. L., Khavari-Nejad, R. A., Izzo, R., & Navari-Izzo, F. (2004). Antioxidative responses of Calendula officinalis under salinity conditions. Plant Physiology and Biochemistry, 42(9), 695–701.

    CAS  Google Scholar 

  • Chen, H., & Jiang, J. G. (2010). Osmotic adjustment and plant adaptation to environmental changes related to drought and salinity. Environmental Reviews, 18, 309–319.

    Google Scholar 

  • Chen, J., Lyu, Y., Zhao, Z., Liu, H., Zhao, H., & Li, Z. (2019). Using the multidimensional synthesis methods with non-parameter test, multiple time scales analysis to assess water quality trend and its characteristics over the past 25 years in the Fuxian Lake, China. Science of the Total Environment, 655, 242–254.

    CAS  Google Scholar 

  • Chen, P., Li, L., & Zhang, H. (2015). Spatio-temporal variations and source apportionment of water pollution in Danjiangkou Reservoir Basin, Central China. Water, 7(6), 2591–2611.

    CAS  Google Scholar 

  • Diamantini, E., Lutz, S. R., Mallucci, S., Majone, B., Merz, R., & Bellin, A. (2018). Driver detection of water quality trends in three large European river basins. Science of the Total Environment, 612, 49–62.

    CAS  Google Scholar 

  • Duan, W., He, B., Nover, D., Yang, G., Chen, W., Meng, H., Zou, S., & Liu, C. (2016). Water quality assessment and pollution source identification of the eastern Poyang Lake Basin using multivariate statistical methods. Sustainability, 8(2), 133.

    Google Scholar 

  • DSI (2018). DSI 2018 official water resources statistics. General Directorate of the State Hydraulic Works. http://www.dsi.gov.tr/dsi-resmi-istatistikler/resmi-i-statistikler-2018/2018-y%C4%B1l%C4%B1-verileri. Accessed 10/12/2019.

  • Edelstein, M., Ben-Hur, M., Cohen, R., Burger, Y., & Ravina, I. (2005). Boron and salinity effects on grafted and non-grafted melon plants. Plant and Soil, 269(1–2), 273–284.

    CAS  Google Scholar 

  • Egamberdieva, D., Renella, G., Wirth, S., & Islam, R. (2010). Secondary salinity effects on soil microbial biomass. Biology and Fertility of Soils, 46(5), 445–449.

    CAS  Google Scholar 

  • Essa, T. A. (2002). Effect of salinity stress on growth and nutrient composition of three soybean (Glycine max L. Merrill) cultivars. Journal of Agronomy and Crop Science, 188(2), 86–93.

    CAS  Google Scholar 

  • Eynard, A., Lal, R., & Wiebe, K. (2005). Crop response in salt-affected soils. Journal of Sustainable Agriculture, 27(1), 5–50.

    Google Scholar 

  • FAO (2017). The future of food and agriculture - trends and challenges. Food and Agriculture Organization of the United Nations. www.fao.org/3/a-i6583e.pdf. Accessed 9/20/2019.

  • Fischer, G., Tubiello, F. N., van Velthuizen, H., & Wiberg, D. A. (2007). Climate change impacts on irrigation water requirements: effects of mitigation, 1990–2080. Technological Forecasting and Social Change, 74(7), 1083–1107.

    Google Scholar 

  • Flowers, T. J., Hajibagherp, M. A., & Yeo, A. R. (1991). Ion accumulation in the cell walls of rice plants growing under saline conditions: evidence for the Oertli hypothesis. Plant, Cell & Environment, 14(3), 319–325.

    Google Scholar 

  • Gemici, Ü., & Tarcan, G. (2002). Distribution of boron in thermal waters of western Anatolia, Turkey, and examples of their environmental impacts. Environmental Geology, 43(1–2), 87–98.

    CAS  Google Scholar 

  • Gemici, Ü., Tarcan, G., Helvacı, C., & Somay, A. M. (2008). High arsenic and boron concentrations in groundwaters related to mining activity in the Bigadiç borate deposits (Western Turkey). Applied Geochemistry, 23(8), 2462–2476.

    CAS  Google Scholar 

  • Ghassemi, F., Jakeman, A. J., & Nix, H. A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies. Wallingford: CABI Publishing.

    Google Scholar 

  • Gilbert, R. O. (1987). Statistical methods for environmental pollution monitoring. New York: Wiley.

    Google Scholar 

  • Gude, G. (2018). Emerging technologies for sustainable desalination handbook. London: Elsevier.

    Google Scholar 

  • Gupta, U. C. (2016). Boron. In A. V. Barker & D. J. Pilbeam (Eds.), Handbook of plant nutrition (pp. 241–277). Boca Raton: CRC Press.

    Google Scholar 

  • Gupta, U. C., & Gupta, S. C. (1998). Trace element toxicity relationships to crop production and livestock and human health: implications for management. Communications in Soil Science and Plant Analysis, 29(11–14), 1491–1522.

    CAS  Google Scholar 

  • Hall, S. M. (2018). Rules of thumb for chemical engineers (Sixth ed.). Amsterdam: Elsevier.

    Google Scholar 

  • Helena, B., Pardo, R., Vega, M., Barrado, E., Ferna’ndez, J. M., & Ferna’ndez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Resources, 34, 807–816.

    CAS  Google Scholar 

  • Hirsch, R. M., Alexander, R. B., & Smith, R. A. (1991). Selection of methods for the detection and estimation of trends in water quality. Water Resources Research, 27(5), 803–813.

    Google Scholar 

  • Hollander, M., & Wolfe, D. A. (1973). Nonparametric statistical methods. New York: Wiley.

    Google Scholar 

  • Huang, F., Wang, X., Lou, L., Zhou, Z., & Wu, J. (2010). Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Research, 44(5), 1562–1572.

    CAS  Google Scholar 

  • IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, The National Greenhouse Gas Inventories Programme. https://www.ipcc-nggip.iges.or.jp/public/2006gl/. Accessed 10/05/2019.

  • Iscen, C. F., Emiroglu, Ö., Ilhan, S., Arslan, N., Yilmaz, V., & Ahiska, S. (2008). Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey. Environmental Monitoring and Assessment, 144(1–3), 269–276.

    CAS  Google Scholar 

  • Ismail, A. S., Abdullah, A. M., & Samah, M. A. A. (2017). Environmetric study on air quality pattern for assessment in northern region of peninsular Malaysia. Journal of Environmental Science and Technology, 10, 186–196.

    CAS  Google Scholar 

  • Jianqin, M., Jingjing, G., & Xiaojie, L. (2010). Water quality evaluation model based on principal component analysis and information entropy: application in Jinshui River. Journal of Resouces and Ecology, 1(3), 249–252.

    Google Scholar 

  • Jung, K. Y., Lee, K. L., Im, T. H., Lee, I. J., Kim, S., Han, K. Y., & Ahn, J. M. (2016). Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environmental Technology and Innovation, 5, 67–82.

    Google Scholar 

  • Kaiser, M. O. (1974). Kaiser-Meyer-Olkin measure for identity correlation matrix. Journal of the Royal Statistical Society, 52, 296–298.

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. New York: Wiley.

    Google Scholar 

  • Kaya, C., Kirnak, H., Higgs, D., & Saltali, K. (2002). Supplementary calcium enhances plant growth and fruit yield in strawberry cultivars grown at high (NaCl) salinity. Scientia Horticulturae, 93(1), 65–74.

    CAS  Google Scholar 

  • Kazancı, N., Toprak, Ö., Leroy, S. A. G., Öncel, S., Ileri, Ö., Emre, Ö., Costa, P., Erturaç, K., & McGee, E. (2006). Boron content of Lake Ulubat sediment: a key to interpret the morphological history of NW Anatolia, Turkey. Applied Geochemistry, 21(1), 134–151.

    Google Scholar 

  • Kendall, M. G. (1975). Rank correlation methods. London: Charles Grifin.

    Google Scholar 

  • Kim, J. J., Atique, U., & An, K. G. (2019). Long-term ecological health assessment of a restored urban stream based on chemical water quality, physical habitat conditions and biological integrity. Water, 11(1), 114.

    CAS  Google Scholar 

  • Kisi, O., & Ay, M. (2014). Comparison of Mann–Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. Journal of Hydrology, 513, 362–375.

    CAS  Google Scholar 

  • Koklu, R., Sengorur, B., & Topal, B. (2010). Water quality assessment using multivariate statistical methods—a case study: Melen River System (Turkey). Water Resources Management, 24(5), 959–978.

    Google Scholar 

  • Levy, Y., & Syvertsen, J. P. (2004). Irrigation water quality and salinity effects in citrus trees. Horticultural Reviews, 30, 37–82.

    Google Scholar 

  • Li, Y., Xu, L., & Li, S. (2009). Water quality analysis of the Songhua River Basin using multivariate techniques. Journal of Water Resource and Protection, 1(2), 110–121.

    CAS  Google Scholar 

  • Krantzberg, G., Tanik, A., do Carmo, J. S. A., Indarto, A., & Ekdal, A. (2010). Advances in water quality control. USA: Scientific Research Publishing.

    Google Scholar 

  • Mallick, J. (2017). Hydrogeochemical characteristics and assessment of water quality in the Al-Saad Lake, Abha Saudi Arabia. Applied Water Science, 7(6), 2869–2882.

    CAS  Google Scholar 

  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259.

    Google Scholar 

  • Meybeck, M. (2003). Global occurrence of major elements in rivers. Treatise on geochemistry, 5(1), 207–223.

    Google Scholar 

  • Molden, D. (2013). Water for food water for life: a comprehensive assessment of water management in agriculture. London: International Water Management Institute.

    Google Scholar 

  • Munsuz, N., Ünver, İ., & Çaycı, G. (1999). The waters of Turkey. Ankara: Ankara University.

    Google Scholar 

  • Mustapha, A. (2013). Detecting surface water quality trends using Mann-Kendall tests and Sen’s slope estimates. International Journal of Agriculture Innovations and Research, 1, 108–114.

    Google Scholar 

  • Mustapha, A., Aris, A. Z., Juahir, H., & Ramli, M. F. (2013). Surface water quality contamination source apportionment and physicochemical characterization at the upper section of the Jakara Basin, Nigeria. Arabian Journal of Geosciences, 6(12), 4903–4915.

    CAS  Google Scholar 

  • Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The Computer Journal, 26(4), 354–359.

    Google Scholar 

  • Nable, R. O., Bañuelos, G. S., & Paull, J. G. (1997). Boron toxicity. Plant and Soil, 193(1–2), 181–198.

    CAS  Google Scholar 

  • Omwene, P. I., Öncel, M. S., Çelen, M., & Kobya, M. (2018). Heavy metal pollution and spatial distribution in surface sediments of Mustafakemalpaşa stream located in the world’s largest borate basin (Turkey). Chemosphere, 208, 782–792.

    CAS  Google Scholar 

  • Ouyang, Y., Nkedi-Kizza, P., Wu, Q. T., Shinde, D., & Huang, C. H. (2006). Assessment of seasonal variations in surface water quality. Water Research, 40, 3800–3810.

    CAS  Google Scholar 

  • Özçağlar, A. (1988). The relationship between the natural environment and the geographical distribution of the agricultural areas in Turkey. Annals of the Geographical Research Institute, 11, 131–149.

    Google Scholar 

  • Parinet, B., Lhote, A., & Legube, B. (2004). Principal component analysis: An appropriate tool for water quality evaluation and management-application to a tropical lake system. Ecological Modelling, 178(3–4), 295–311.

    CAS  Google Scholar 

  • Podmore, C. (2009). Irrigation salinity–causes and impacts. Primefact, 937(1), 1–4.

    Google Scholar 

  • Princi, M. P., Lupini, A., Araniti, F., Longo, C., Mauceri, A., Sunseri, F., & Abenavoli, M. R. (2016). Boron toxicity and tolerance in plants: recent advances and future perspectives. In P. Ahmad (Ed.), Plant metal interaction (pp. 115–147). Amsterdam: Elsevier.

    Google Scholar 

  • Rhoades, J. D. (1996). Salinity: electrical conductivity and total dissolved solids. In D. L. Sparks, A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston, & M. E. Sumner (Eds.), Methods of soil analysis: part 3 chemical methods (pp. 417–435). Wisconsin: Soil Science Society of America, Inc., American Society of Agronomy, Inc..

    Google Scholar 

  • Romesburg, C. (2004). Cluster analysis for researchers. North Carolina: Lulu Press.

    Google Scholar 

  • Salihoğlu, G., & Karaer, F. (2004). Ecological risk assessment and problem formulation for Lake Uluabat, a Ramsar State in Turkey. Environmental Management, 33(6), 899–910.

    Google Scholar 

  • Sânchez-Martos, F., Pulido-Bosch, A., & Calaforra, J. M. (1999). Hydrogeochemical processes in an arid region of Europe (Almeria, SE Spain). Applied Geochemistry, 14, 735–774.

    Google Scholar 

  • Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30, 507–512.

    Google Scholar 

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63, 1379–1389.

    Google Scholar 

  • Shahid, S. A., Zaman, M., & Heng, L. (2018). Soil salinity: Historical perspectives and a world overview of the problem. In S. A. Shahid, M. Zaman, & L. Heng (Eds.), Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques (pp. 43–53). Cham: Springer.

    Google Scholar 

  • Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environmental Modelling & Software, 22(4), 464–475.

    Google Scholar 

  • Shrestha, S., Kazama, F., & Nakamura, T. (2008). Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong River. Journal of Hydroinformatics, 10(1), 43–56.

    Google Scholar 

  • Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M., & Kouimtzis, T. H. (2003). Assessment of the surface water quality in Northern Greece. Water Research, 37(17), 4119–4124.

    CAS  Google Scholar 

  • Singh, K. P., Malik, A., Mohan, D., & Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India): a case study. Water Research, 38, 3980–3992.

    CAS  Google Scholar 

  • Sotiropoulos, T. E., Therios, I. N., Dimassi, K. N., Bosabalidis, A., & Kofidis, G. (2002). Nutritional status, growth, CO2 assimilation, and leaf anatomical responses in two kiwifruit species under boron toxicity. Journal of Plant Nutrition, 25(6), 1249–1261.

    CAS  Google Scholar 

  • Sparks, D. L. (2003). Environmental soil chemistry (Second ed.). San Diego: Academic Press.

    Google Scholar 

  • Su, S., Zhi, J., Lou, L., Huang, F., Chen, X., & Wu, J. (2011). Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques. Physics and Chemistry of the Earth, Parts A/B/C, 36(9–11), 379–386.

    Google Scholar 

  • SYGM (2018). General directorate of water management, Susurluk Basin Flood Management Plan. https://www.tarimorman.gov.tr/SYGM/Sayfalar/Detay.aspx?SayfaId=53. Accessed 12/1/2019. 

  • Szabolcs, I. (1989). Salt-affected soils. Boca Raton: CRC Press.

    Google Scholar 

  • Tak, H. I., Inam, A., Bakhtiyar, Y., & Inam, A. (2012). Effluent quality parameters for safe use in agriculture. In T. S. Lee (Ed.), Water quality, soil and managing irrigation of crops. Intech: Rijeka.

    Google Scholar 

  • Tank, N., & Saraf, M. (2010). Salinity-resistant plant growth-promoting rhizobacteria ameliorates sodium chloride stress on tomato plants. Journal of Plant Interactions, 5(1), 51–58.

    CAS  Google Scholar 

  • Tomaz, A., Palma, P., Fialho, S., Lima, A., Alvarenga, P., Potes, M., & Salgado, R. (2020). Spatial and temporal dynamics of irrigation water quality under drought conditions in a large reservoir in Southern Portugal. Environmental Monitoring and Assessment, 192(2), 1–17.

    Google Scholar 

  • Ulukütük, S. (2009). Potamofauna of catchment basin Lake Uluabat (Chironomidae-Diptera, Oligochaeta-Annelida) and identification of their relation between water quality. (Doctoral dissertations. Eskisehir Osmangazi University, Institute of Science, Eskisehir, Turkey).

  • Uncumusaoğlu, A. A., & Akkan, T. (2017). Assessment of stream water quality using multivariate statistical techniques. Polish Journal of Environmental Studies, 26(4), 1715–1723.

    Google Scholar 

  • US EPA (1986). Quality criteria for water. The United States Environmental Protection Agency. https://www.epa.gov/sites/production/files/2018-10/documents/quality-criteria-water-1986.pdf. Accessed 12/10/2019.

  • Wise, T. A. (2013). Can we feed the world in 2050? A scoping paper to assess the evidence. Global Development and Environment Institute Working Paper No. 13-04. Medford: Tufts University.

    Google Scholar 

  • Wang, X., Cai, Q., Ye, L., & Qu, X. (2012). Evaluation of spatial and temporal variation in stream water quality by multivariate statistical techniques: a case study of the Xiangxi River basin, China. Quaternary International, 282, 137–144.

    Google Scholar 

  • Varol, M., Gökot, B., Bekleyen, A., & Şen, B. (2012). Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey. Catena, 92, 11–21.

    CAS  Google Scholar 

  • Vega, M., Pardo, R., Barrado, E., & Deban, L. (1998). Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Resources, 32, 3581–3592.

    CAS  Google Scholar 

  • Vengosh, A. (2005). Salinization and saline environments. In H. D. Holland, B. S. Lollar, & K. K. Turekian (Eds.), Environmental geochemistry (pp. 333–366). Amsterdam: Elsevier.

    Google Scholar 

  • Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.

    Google Scholar 

  • Xiao, J., Wang, L., Deng, L., & Jin, Z. (2019). Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Science of the Total Environment, 650, 2004–2012.

    CAS  Google Scholar 

  • Xiaoqing, S., Jianmin, B., Chunpeng, Z., Yu, W., Hanli, W., & Zhuo, J. (2019). Hydrochemistry characteristics and water quality assessment for irrigation along the Second Songhua River in the South of the Songnen Plain, Northeast China. Polish Journal of Environmental Studies, 29(1), 371–395.

    Google Scholar 

  • Yang, L. I., Linyu, X. U., & Shun, L. I. (2009). Water quality analysis of the Songhua River Basin using multivariate techniques. Journal of Water Resource and Protection, 2009(2), 110–121.

    Google Scholar 

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Acknowledgments

The dataset of this study is based upon work supported by the General Directorate of State Hydraulic Works. The authors are thankful to the State Hydraulic Works for their cooperation throughout this study.

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  1. Department of Watershed Management, Faculty of Forestry, Istanbul University-Cerrahpasa, 34473, Istanbul, Turkey

    Ibrahim Yurtseven

  2. Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Holdsworth Hall, Amherst, MA, USA

    Timothy O. Randhir

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Appendix

Appendix

Table 8 Descriptive statistics for selected water quality parameters at different sites
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Table 9 Descriptive statistics for selected water quality parameters by months
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Table 10 Correlation matrices
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Yurtseven, I., Randhir, T.O. Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey. Environ Monit Assess 192, 793 (2020). https://doi.org/10.1007/s10661-020-08723-2

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