Water Productivity Journal (WPJ) Quarterly Publication

Document Type : Original Research Paper


1 M.Sc., Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran

2 Professor, Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran



Introduction: Iran is one of the countries in the world that needs water resources planning. In this research, the maximum and minimum temperatures in seven meteorological stations are analyzed and predicted. Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time. In time series analysis, analysts record
data points at consistent intervals over a set period of time rather than just recording
the data points intermittently or randomly. The purpose of this study is to use the time series method in predicting the maximum and minimum temperatures in the Zayandehrud basin, Isfahan, Iran.
Materials and Methods: The Zayandeh Rud River is the main supplier for drinking water to a population of over 4.5 million in the three provinces of Isfahan, Yazd, and Charmahal-Bakhtiari. It provides agricultural water for over 200,000 hectares, supplies water to several large industries, and is and the hub of tourism in the central plateau of Iran. This river used to have significant flow all year long, but today runs dry due to water extraction before reaching the city of Isfahan. Isfahan Province is located in the center of the Islamic Republic of Iran. The total area of the Isfahan province is 106179 square kilometers, approximately 6.25 percent of the total Iran area. The city lies in the lush Zayandeh Roud River plain of foothills of the Zagros Mountains Range. Isfahan is about 1580 meters high from the sea level. It has a mild climate. ZayandehRud River is the main source and element of the development and beauty of Isfahan. The river rises from the eastern slopes of the Zagros Mountain range. The city is located in the lush plain of the river ZayandehRud, at the foot of the Zagros mountains. Situated at 1.590 meters above sea level on the eastern side of the Zagros Mountains, Isfahan has a dry climate (Köppen BWk). Record Periods include 7 days, 15 days, 30 days, seasonal and daily time steps. The output data were demonstrated by box Diagram, Normal and Grubs Beck. The modeling was performed by examining the autocorrelation and partial autocorrelation diagrams and Akaike, Schwartz and Hanan Quinn criterias. Then normality was test by Kolmogorov-Smirnov and Jarque Bera tests. Durbin Watson and Pert Manto tests were used to check the accuracy of the model. The trend and homogeneity are analysed using MATLAB and the stationery and modeling are done using Minitab, Eviews software.
Results and Discussion: Maximum temperature (meteorological parameter) intervals of 7 days, 15 days, 30 days, seasonal, and daily, and as the number of data decreases, the amount of error increases, the data interval had an increasing trend in daily period. In the R2 criteria, all of the stations were above 0.7, and in terms of correctness, the model of Kabutarabad station had a better answer than all of the other stations. For this reason, this station was selected. In terms of the test that used for the normality of all of the stations, the results were similar, so that the daily time interval was skewness and kurtosis and the intervals of 7, 15 and 30 days and seasonal were examined for Kolmogorov Smirnov and Jarque bera tests. The percentage error was below than 20%. The models obtained for the intervals were as follows: for the 15 and 30 days intervals of Sarima and for the 7 days, seasonal and daily intervals of Arima.
Conclusions: For validation in the series with daily and 7days time intervals, 5% of data were considered, in 15days and 30days 10% of data, and seasonal 20% of data were considered. Trend was checked by Mann Kendall method and was observed only in daily time interval. The method of estimating the parameters is calculated from the least squares method. Also in modeling the maximum and minimum temperatures, mainly SARIMA model was fitted in 15days and 30days period and ARIMA in 7days and daily period and seasonal period. R2 was higher than 0.7 and the average squares error and error percentage was below 20%.
The limitations of the project were as follows: One of the hypotheses was that the impact of artificial and human factors on the study data was negligible and the length of the period used is considered as a sample of the total statistical population of that station. It is recommended that firstly the effect of climate change on the prediction of results would be studied, secondly using wavelet in time series analysis, thirdly using and comparing ARCH and GARCH time series models.
The minimum and maximum temperature has a large impact on evapotranspiration and farmland water consumption. Using time series analysis, both minimum and maximum temperatures could be predicted. Therefore, water consumption could be estimated for future crop management and having an efficient water productivity.


Main Subjects

Alsharif, M.H., Younes, M.K. & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: Tcastud Seo. Symmetry, 11: 240-257.
Aqalpour, P. & Nadi, M. (2018). Assessing the accuracy of SARIMA model in modeling and long-term forecast of average monthly temperature in different climates of Iran. Climatological Research, 9: 113-126
Asadi Zarch, M.A., Malekinezhad, H., Mobin, M.H., Dastorani, M.T. & Kousari, M.R. (2011). Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran. Water Resources Management, 25: 3485-3504.
Asfaw, A., Simane, B., Hassen, A. & Bantider, A. (2018). Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes, 19: 29-41.
Batty, M. (2008). The size, scale, and shape of cities. Science, 319: 769-771.
De Gois, G., De Oliveira-Júnior, J.F., Da Silva Junior, C.A., Sobral, B.S., De Bodas Terassi, P.M. & Junior, A.H.S.L. (2020). Statistical normality and homogeneity of a 71-year rainfall dataset for the state of Rio De Janeiro Brazil. Theoretical and Applied Climatology, 141: 1573-1591.
Di Persio, L. & Frigo, M. (2016). Gibbs sampling approach to regime switching analysis of financial time series. Journal of Computational and Applied Mathematics, 300: 43-55.
Dudangeh, A., Abedi Koopai, J. & Gohari, J. (2012). Application of time series models to determine the trend of future climatic parameters in order to manage water resources. Water and Soil Science, 16: 59-74.
Eslamian, S. (2014). Handbook of Engineering Hydrology: Engineering. Hydrology and Water Management. CRC Press, USA.
Gardfarstatisticszi, S. & Saberi, Qaisouri, A. (2017). Determining the best time series model in forecasting annual rainfall of selected stations in West Azerbaijan province. Applied Research in Geographical Sciences, 17: 87-105.
Hadizadeh, R., Eslamian, S. & Chinipardaz, R. (2013). Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran, International. International Journal of Hydrology Science and Technology, 3(4): 319-350.
Helmi, M., Bakhtiari, B. & Ghaderi, K. (2020). Modeling and forecasting of meteorological drought using SARIMA time series model in different climatic samples of Iran. Iranian Journal of Irrigation and Drainage, 14: 1090-1079.
Jain, G. & Mallick, B. (2017). A study of time series models ARIMA and ETS. Available at: SSRN.
Kalamaras, N., Philippopoulos, K., Deligiorgi, D., Tzanis, C.G. & Karvounis, G. (2017). Multifractal scaling properties of daily air temperature time series. Chaos, Solitons and Fractals, 98: 38-43.
Kazemzadeh, M. & Malekian, A. (2018). Homogeneity analysis of streamflow records in arid and semi-arid regions of northwestern Iran. Journal of Arid Land, 10:
Khatar, B. & Bahmani, A. (2015). Prediction of soil layer temperature using time series models. Journal of Soil Research, 29(2): 210-199.            
DOI: 10.22092/ijsr.2015.102213
Khazaei, M. & Mirzaei, M. (2014). Prediction of climatic variables using time series analysis of Zohreh watershed. Applied Research in Geographical Sciences, 34: 233-250.
Kocsi,T., Kovács-Székely, I. & Anda, A. (2020). Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary. Theoretical and Applied Climatology, 139 :849-859.
Latif, Y., Yaoming, M., Yaseen, M., Muhammad, S. & Atif Wazir, M. (2020). Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan. Theoretical and Applied Climatology, 139: 741-758.
Little, T.D. (2013). The Oxford Handbook of Quantitative Methods in Psychology, Vol. 1. Oxford University Press, UK.
Modares, R. & Eslamian, S. (2006). Modeling the time series of Zayandehrud river flow. Iranian Journal of Science and Technology, 30: 570-567.
Nury, A.H., Hasan, K. & Alam, M.J.B. (2017). Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University Science, 29: 47-61.
Polwiang, S. (2020). The time series seasonal patterns of dengue fever and associated weather variables in Bangkok. BMC Infectious Diseases, 20: 1-10.
Qin, R.X., Xiao, C., Zhu, Y., Li, J., Yang, J., Gu, S., Xia, J., Su, B., Liu, Q. & Woodward, A. (2017). The interactive effects between high temperature and air pollution on mortality: A time-series analysis in Hefei, China. Science of The Total Environment, 575: 1530-1537.
Shabani, B., Mousavi Baigi, M., Jabbari Noghabi, M. & Hero, M. (2013). Modeling and forecasting the maximum and minimum monthly temperatures of Mashhad plain using time series models. Journal of Water and Soil, 27: 906-896.
Soltani, S., Modarres, R. & Eslamian, S. (2007). The use of time series modeling for the determination of rainfall climates of Iran. International Journal of Climatology, 27: 819-829.
Zhang, J., Zhao, Z., Xue, Y., Chen, Z., Ma, X. & Zhou, Q. (2017). Time series analysis. Handbook of Medical Statistics. Journal of Physics and Chemistry of Solids, 4: 269-285.
Zhongda, T., Shujiang, L., Yanhong, W. & Yi, S.J.C. (2017). A prediction method based on wavelet transform and multiple models’ fusion for chaotic time series. Chaos, Solitons and Fractals, 98: 158-172.
Zhou, Z., Wang, L., Lin, A., Zhang, M. & Niu, Z. (2018). Innovative trend analysis of solar radiation in China during. Renewable Energy, 119: 1962-2015.