Assessment and forecast of water quality in the River Ingulets irrigation system

P. V. Lykhovyd, Ye. V. Kozlenko

Abstract


Scarcity of water is one of the most important problems of irrigated farming. Safe use of contaminated water and waste-water needs continuous monitoring of the water quality and its influence on the irrigated lands and cultivated crops. The Ingulets irrigation system is one of the main systems, which supplies with water fields of Kherson and Mykolaiv regions of Ukraine. The water is contaminated by the effluent disposals and wastes of the metallurgic factories. The new water quality improvement technique was introduced in the Ingulets irrigation system in 2010. The study is dedicated to agricultural assessment of the Ingulets irrigation system water quality with the new amelioration technique by using the FAO and DSTU 2730-94 criteria. It was established, that water quality in the Ingulets irrigation system is still poor, though it becomes better each year since 2010 till nowadays. Total dissoluble salts content in the water is 1489-2280 mg/L, toxic ions content in eCl- is 10.49-21.63 me/L, sodium adsorption ratio is 4.33-7.94 me/L, sodium percentage is 46.4-58.9%, magnesium to calcium ratio is 1.03-1.68, power of hydrogen is 7.31-8.72 in the period from 2007 to 2017. So, the Ingulets irrigation system water requires further amelioration to become safe and suitable for irrigation without any restrictions. Short-term forecast of the water quality by using the triple exponential smoothing with handling of the seasonal effects with multiplicative method of the Holt-Winters algorithm showed that significant improvement of the water quality by some criteria should be achieved till 2025: total dissoluble salts content in the water should be 1212 mg/L, toxic ions content in eCl- should be 6.61 me/L, sodium adsorption ratio should be 4.31 me/L, sodium percentage should be 49.3%, magnesium to calcium ratio should be 1.05, power of hydrogen should be 8.05 in 2025. 


Keywords


water quality; triple exponential smoothing; the Holt-Winters algorithm; forecast; the River Ingulets; irrigation system

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References


APHA. (1995). Standard methods for the examination of water and waste water. American Public Health Association, Washington DC.

Ayers, R.S., Westcott, D.W. (1985). Water quality for agriculture.

FAO irrigation and drainage paper 29. Rev. 1. Food and Agriculture Organization of the United Nations, Rome.

Banjaw, D.T., Megersa, H.G., Lemma, D.T. (2017). Effect of water quality and deficit irrigation on tomatoes yield and quality: a review. Advanced Crop Science and Technology, 5, 295.

Beck, M.B. (1987). Water quality modeling: a review of the analysis of uncertainty. Water Resources Research, 23(8), 1393-1442.

Billah, B., King, M.L., Snyder, R.D., Koehler, A.B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, 239-247.

De Livera, A.M., Hyndman, R.J., Snyder, R.D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.

Everitt, B., & Skrondal, A. (2002). The Cambridge dictionary of statistics (Vol. 106). Cambridge: Cambridge University Press.

Feizi, M., Hajabbasi, M.A., Mostafazadehfard B. (2010). Saline irrigation water management strategies for better yield of safflower (Carthamus tinctorius L.) in an arid region. Australian Journal of Crop Science, 4, 408-414.

Furness, R.W., Bryant, D.M. (1996). Effect of wind on field metabolic rates of breeding northern fulmars. Ecology, 77(4), 1181-1188.

Gardner, E.S. (2006). Exponential smoothing: The state of the art – Part II. International journal of forecasting, 22(4), 637-666.

Gelper, S., Fried, R., Croux, C. (2010). Robust forecasting with exponential and Holt-Winters smoothing. Journal of forecasting, 29(3), 285-300.

Hyndman, R., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.

Kelly, W. P. (1963). Use of saline irrigation water. Soil Science 95(4), 355–391.

Kim, H., Jeong, H., Jeon, J., Bae, S. (2016). Effects of irrigation with saline water on crop growth and yield in greenhouse cultivation. Water, 8(4), 127.

Lewis, C.D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.

Likhovid, P.V. (2015). Analysis of the Ingulets irrigation water quality by agronomical criteria. Success of Modern Science and Education, 5, 10-12.

Liu, S., Tai, H., Ding, Q., Li, D., Xu, L., Wei, Y. (2013). A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Mathematical and Computer Modelling, 58(3), 458-465.

Logan, M. (2011). Biostatistical design and analysis using R: a practical guide. John Wiley & Sons.

Lozovitsii, P.S. (2012). Monitoring of the humus status of soils of the Ingulets irrigation system. Eurasian Soil Science, 45(3), 336-349.

Lykhovyd, P.V., Lavrenko, S.O. (2017). Influence of tillage and mineral fertilizers on soil biological activity under sweet corn crops. Ukrainian Journal of Ecology, 7(4), 18-24.

Ould Ahmed, B.A., Yamamoto, T., Inoue, M. (2007). Response of drip irrigated sorghum varieties growing in dune sand to salinity levels in irrigation water. Journal of Applied Sciences, 7, 1061-1066.

Palani, S., Liong, S.Y., Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597.

Pereira, L.S., Oweis, T., Zairi, A. (2002). Irrigation management under water scarcity. Agricultural water management, 57(3), 175-206.

Reckhow, K.H. (1999). Water quality prediction and probability network models. Canadian Journal of Fisheries and Aquatic Sciences, 56(7), 1150-1158.

Seckler, D., Barker, R., Amarasinghe, U. (1999). Water scarcity in the twenty-first century. International Journal of Water Resources Development, 15(1-2), 29-42.

Shakhman, I. A., Bystriantseva, A. N. (2017). Assessment of Ecological State and Ecological Reliability of the Lower Section of the Ingulets River. Hydrobiological Journal, 53(5).

Shang, X.S., Lin, W.D., Tang, Y.K. (2011). Development and application of a combined water quality prediction model based on exponential smoothing and GM (1, 1). A case study of iron and manganese levels in Yongjiang River. Huanjing Kexue yu Jishu, 34(1), 191-195.

Singh, K.P., Basant, A., Malik, A., Jain, G. (2009). Artificial neural network modeling of the river water quality - a case study. Ecological Modelling, 220(6), 888-895.

Todd, D.K. (1980). Groundwater hydrology. John Wiley and Sons Publications, New York.

Water quality for irrigation. Agronomical criteria: DSTU 2730-94. (1994). Kyiv: Derzhstandart Ukrajiny (in Ukrainian).

Wilcox, L.V. (1955). Classification and use of irrigation water. Circular No. 969. US Department of Agriculture. p. 19. USDA, Washington.




DOI: http://dx.doi.org/10.15421/2018_221

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