Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River

Document Type : Research Paper

Author

birjand university

Abstract

For over three decades, hydrologists were recommended multivariate models to describe and modeling complex hydrology data. While recently the multivariate models in hydrology is discussed. In multivariate models, the modeling and predicting various parameters can improve by involving other factors. In this study, using both univariate and multivariate periodic ARMA models for modeling monthly discharge in Nazloochaei River in West Azerbaijan Province during the period of 1962-2011 were compared .The results of evaluation and verification models showed that the multivariate periodic ARMA models by involving the climatic parameters such as temperature and precipitation of the basin, the more accurately than univariate periodic ARMA models. Also the result showed that the selected models, maximum and minimum points of discharge to the appropriate model.

Keywords


[1] R.H. Jones, and W. Brelsford, "Time series with periodic structure", Biometrika, Vol. 54, NO. 3-4, December 1967, pp. 403 – 408.
[2] M. Pagano, "On periodic and multiple autoregressions", The Annals of Statistics, Vol. 6, NO. 6, 1978, pp. 1310 – 1317.
[3] B.M. Troutman, "Some results in periodic autoregression", Biometrika, Vol. 66, NO. 2, August 1979, pp. 216 –228.
[4] A.T. Ula, "Periodic covariance stationarity of multivariate periodic autoregressive moving average processes", Water Resources Research, Vol. 26, NO. 5, May 1990, pp. 855 – 861.
[5] A.T. Ula, "Forecasting of multivariate periodic autoregressive moving-average processes", Journal of Time Series Analysis, Vol. 14, NO. 6, November 1993, pp. 645 – 657.
[6] P.H. Franses, and R. Paap, "Periodic Time Series Models", New York: Oxford University Press, 2004.
[7] H. Lütkepohl , "New Introduction to Multiple Time Series Analysis", Springer Science & Business Media, 2005.
[8] M.B. Fiering, "Multivariate techniques for synthetic hydrology", Journal of the Hydraulics Division, Vol. 90, NO. 5, September 1964, pp. 43 – 60.
[9] N.C. Matalas, "Mathematical assessment of synthetic hydrology", Journal of Water Resource, Vol. x3 NO. 4, December 1967, pp. 937 – 945.
[10] N.C. Matalas, and J.R. Wallis, "Statistical properties of multivariate fractional noise processes", Journal of water resource, Vol. 3, NO. 4, 1971, pp. 1460 – 1468.
[11] J.M. Mejia, "On the generation of multivariate sequences exhibiting the Hurst phenomenon and some state university", Fort Colins, Colorado, 1971.
[12] D. Valencia, and J.C. Schaake, "Disaggregation processes in stochastic hydrology", Journal of water resource, Vol. 9, NO. 3, 1973, pp. 580 – 585.
[13] P.E. O'Connel, "Stochastic modeling of long-term persistence in stream flow sequences", Ph.D, Thesis. Imperial College, University of London, 1974.
[14] G.D. Young, and W.C. Pisano, "Operational hydrology using residuals", Journal of the Hydraulics Division, Vol. 94, NO. 4, July 1968, pp. 909 – 923.
[ 15 [ محمد ناظری تهرودی، کیوان خلیلی، فرشاد احمدی و زهرا نظری تهرودی، "مدلسازی دما با استفاده از سریهای زمانی پریودیک آرما
مطالعه موردی ایستگاه سینوپتیک شهر کرمان"، اولین کنفرانس ملی راهکارهای دستیابی به توسعه پایدار، تهران، 1391 .
[16] F. Wilcoxon, "Individual comparison by ranking methods", Biometrics, Vol. 1, NO. 6, December 1945, pp. 363 – 367.
[ 16 [ محمدناظری تهرودی و کیوان خلیلی، " معرفی روش گشتاورهای پیشرفته SAM در برآورد دوره بازگشت خشکی رودخانه )مطالعه
موردی: حوضههای دریاچه ارومیه("، اولین همایش ملی تاثیر پسروی دریاچه ارومیه بر منابع خاک و آب، تبریز، 1392 .
[18] W. Mendenhall, and J. Reinmuth, "Statistics for Management and Economics", Fourth Edition, Duxbury Press, 1982.