Analysis of Temporal and Periodic Changes of Groundwater Depth and Nitrate Concentration Using Time Series Modeling (Case Study: Kabudarahang Plain)

Document Type : Research Article


1 Department of Environmental Engineering Collage, K.N. Toosi University of Technology, Tehran, Iran

2 Civil and Environmental Engineering Collage, Sharif University of Technology, Tehran, Iran



In this study, ground water level fluctuations and Nitrate concentrations of kabudarahang aquifer were investigated with application of time series models for modeling of ground water quantity and quality parameters. For data regarding the status of groundwater level and Nitrate concentration fluctuations in project area time series models were used to forecast the groundwater level and Nitrate concentration. Residual error analysis, comparison of observed and calculated ground water levels and Nitrate concentrations performed and finally a prediction model for ground water conditions in Kabudarahang aquifer developed. Predicted values were calibrated by the Box-Jenkins, Holt Winters and extrapolation axes models. A residual error analysis, based upon calculated and observed groundwater level and Nitrate concentration performed as a model verification tool and finally the Box Jenkins models were evaluated through portmanteau method and Akaike information criterion. The model verification results showed that the SARIMA model is the optimum algorithm to simulate seasonal input data variables. Model results showed that the groundwater level in this aquifer will endure a 6 meter decline in four upcoming years and indicated that the maximum Nitrate concentration would reach 50 mg/l in Bahman and shahrivar of 1390.


Main Subjects

[1] M. Aflatooni, M. Mardaneh, Time series analysis of groundwater table fluctuations due to temperature and rainfall change in Shiraz plain, International Journal of Water Resources and Environmental Engineering, (2011) 3(9), 176–188. [1]
[2] 1S.H. Ahmadi, A. Sedghamiz, Geostatistical analysis of spatial and temporal variations of groundwater level, Environmental monitoring and assessment, 129(1-3) (2007) 277-294.
[3] S.K. Moon, N.C. Woo, K.S. Lee, Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge, Journal of Hydrology, (2004) 292, 198–209.
[4] J. Moustadraf, M. Razack, M. Sinan, Evaluation of the impacts of climate changes on the coastal Chaouia aquifer, Morocco, using numerical modeling,Hydrogeology Journal, (2008) 16, 1411–1426.
[5] R. Hanson, M. Newhouse, M. Dettinger, A methodology to asess relations between climatic variability and variations in hydrologic time series in the southwestern United States, Journal of Hydrology, 287(1) (2004) 252-269.
[6] T.D. Mayer, R.D. Congdon, Evaluating Climate Variability and Pumping Effects in Statistical Analyses,Ground Water, (2008) 46(42), 212-227.
[7] Z. Chen, S. Grasby, K.G. Osadetz, Relation between climate variability and groundwater levels in the upper carbonate aquifer, southern Manitoba,Canada, Journal of Hydrology, (2004) 290, 243–262.
[8] S.J. Kim, Y. Hyun, K.K. Lee, Time series modeling for evaluation of groundwater discharge rates intoan urban subway system, Geosciences Journal, (2005) 15-22.
[9] A. Kurunc, K. Yurekli, O. Cevik, Performance of two stochastic approaches for forecasting water quality and stream flow data from Yesilirmak River,Turkey, Environmental Modelling & Software, 20(9) (2005)1195-1200.
[10] P.J. Brockwell, R.A. Davis, Introduction to time series and forecasting, Taylor & Francis, 2002.
[11] G.E.P. Box, G.M. Jenkins, G.C. Reinsel, Time Series Analysis: Forecasting and Control, 4th Edition, Prentice Hall, Englewood Cliffs, NJ, 2008.
[12] K.W. Hipel, A.I. Mcleod, Time Series Modelling of Water Resources and Environmental Systems, Amsterdam: lsevier Science, (1994).
[13] C.P. Nayak, Y.R. Satyajirao, K.P. Sudheer,Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resources Management, (2006) 20, 77–90.
[14] J.Y. Lee, M.J. Choi, Y.Y. Kim, K.K. Lee, Evaluation of hydrologic data obtained from a local groundwater monitoring network in a metropolitan city, Korea, Hydrological Processes, (2005) 19,2525–2537.
[15] N. Rajmohan, A. Al-Futaisi, A. Jamrah, Evaluation of long-term groundwater level data in regular monitoring wells, Barka, Sultanate of Oman, Hydrological Processes, (2007) 21, 3367–3379.
[16] R. Reghunath, T. Murthy, B. Raghavan, Time series analysis to monitor and assess water resources:a moving average approach., Environmental Monitoring and Assessment, (2005) 65-72.
[17] J. Ganoulis, H. Morel-Seytoux, Application of stochastic methods to the study of aquifer systems,UNESCO echnical Documents in Hydrology,(1985).
[18] H.L. Yu, H.J. Chu, Recharge signal identification based on groundwater level observations,Environmental onitoring and Assessment, (2012)184, 5971-5982.
[19] A. Padilla, A. Pulido-Bosch, M.L. Calvache, A.Vallejos, The ARMA models applied to the flow of karstic springs, JAWRA Journal of the American Water Resources Association, 32(5) (1996) 917-928.
[20] J.D. Salas, J. Obeysekera, ARMA Model Identification of Geophysical Time Series, Water Resources Research, (1982) 18 : 1011-1021.
[21] N. Samani, M. Yakhkeshi, Stochastic analysis of groundwater level fluctuation in response to hydrologic factors in Behshahr-Neka plain, in: CONFERENCE SECRETARIAT, ISFAHAN UNIVERSITY OF TECHNOLOGY, ISFAHAN(IRAN).1995, pp. 67-68.
[22] G. Tularam, H. Keeler, The study of coastal groundwater depth and salinity variation using timeseries analysis, Environmental Impact Assessmentn Review, 26(7) (2006) 633-642.
[23] J.C. García-Díaz, Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study, Stochastic Environmental Research and Risk Assessment, 25(3) (2011) 331-39.
[24] T. Wilson, A. Ogden, H. Mills III, Time-series analysis of groundwater chemistry in the west Tennessee sand aquifers, Journal of the Tennessee Academy of Science;(United States), 67(3)(1992).
[25] J.C. Loftis, Trends in groundwater quality,Hydrological processes, 10(2) (1996) 335-355.
[26] M. Mondal, S. Wasimi, Choice of model type in stochastic river hydrology, in: 1st International Conference on Water & Flood Management (ICWFM-2007), Dhaka, 2007.
[27] H. Wang, C. Wang, X. Lin, J. Kang, An improved ARIMA model for precipitation simulations,Nonlinear Processes in Geophysics, 21(6) (2014)1159-1168.]
[28] M. Mondal, S. Wasimi, Forecasting of seasonal flow of the Ganges River in Bangladesh with SARIMA model, in: Second Annual Paper Meet and International Conference on Civil Engineering,Dhaka, 2003.
[29] Ö.F. Durdu, Stochastic approaches for time series forecasting of boron: a case study of Western Turkey, nvironmental monitoring and assessment, 169(1-4) (2010) 687-701.
[30] A. Gemitzi, K. Stefanopoulos, Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis, Journal of hydrology, 403(1) (2011)130-140.
[31] M. Karamouz, S. Nazif, M. Falahi, Hydrology and Hydroclimatology: Principles and Applications, Talor&Francis CRC Press, 2012.
[32] J.D. Salas, Applied modeling of hydrologic time series, Water Resources Publication, 1980.
[33] J. Durbin, The fitting of time-series models, Revue de l'Institut International de Statistique, (1960) 233-244.
[34] D.C. Montgomery, C.L. Jennings, M. Kulahci, Introduction to Time Series Analysis and Forecasting, 2nd ed., Wiley & Sons, 2008.
[35] C. Chatfield, The analysis of time series: an introduction, CRC press, 2016.