SARIMA Modeling of Seasonal Rainfalls(Case Study: Khorasan Province, Iran)

Document Type : Original Article

Authors

1 Irrigation Specialist, Toos-Ab Consulting Eng., Mashad, Iran

2 Professor of irrigation, Ferdowsi. Univ.of Mashad, Iran

3 Agronomist, Agricultural Engineering Licensing Organization, Mashad, Iran

Abstract

Khorasan province bieng located in an arid and semi-arid part of Iran, has often experienced drought during the recent years. Occurrence of consequent droughts during the last few years, has shown that drought prediction is a subject that deserve more attention. One way to achive such goal is modeling the rainfall. In this research, annual rainfall data of the eleven synoptic stations of Khorasan province from 1970 to 2002 have been used. Seasonal autoregressive integrated moving average method (SARMA) was used for modeling the seasonal rainfals of these stations. Based on this model, the amount of rainfall for spring, fall and winter was predicted. Anomally zoning was also prepared for Khorasan province.
 

Keywords

References

عساکره, ح و م, خردمندنیا (1381), ”مدل‌سازی SARIMA برای متوسط درجه حرارت ماهانة جاسک“, نیوار, شمارة 46 و 47, صفحة 41-54
محمد‌ نیا قرایی, س. ن, جاودانی. س, جوانمرد. ل, خزانه‌داری و م, خسروی (1379), ”بررسی شاخص ارزیابی و امکان سنجی شاخص شدت خشکسالی پالمر در ایران“, مجموعه ‌مقالات کنفرانس خشکسالی کرمان.
نیرومند, ح (1376), ”تحلیل سری‌های زمانی (روش‌های یک‌متغیره و چند متغیره)“, انتشارات دانشگاه فردوسی مشهد.
Box,G. E. P and G. M. Jenkins. (1967) Time series analysis forecasting and control, Holden-Day, San Francisco.
Burlando P, A.Montanari and R.Ranzi. (1996) “Forecasting of storm rainfall by combined use of radar, rain gages and linear models”, J. Hydrology, 42, pp. 199-216.
Haltiner, J.P. and J.D. Salas. (1988) “Development and testing of a multivariate, Seasonal ARIMA(1,1) model”, J. Hydrology, 104,  pp. 247-272.
Hisdal, H.,and Lena M. Tallaksen.(2003), “Estimation of regional meteorological and hydrological drought characteristics: a case study for Denmark”, J. Hydrology, 281, pp. 230-247.
Kendall ,D.R. and J.A. Dracup. (1991) “A comparision of index-sequential and AR(1) generated hydrologic sequences” J. Hydrology. 122. pp. 335-352.
Maidment, D.R.(1993), Handbook of Hydrology. Chapter 19 in: Analysis and state modeling of Hydrologic time series. Salas, J. D, Engineering research center, Colorado State University, Fortcollins, Colorado, pp. 19.36-19.39.
Noakes,D.J., McLeod, A.I. and Hipel, W., (1985), “Forecasting monthly riverflow time series”. International Journal of Forecasting, 1, pp. 179-190.
Noakes, D.J., Hipel, K.W., McLeod, A.L., Jimenz, C.,and Yakowitz, S. (1988), “Forecasting annual geophysical time series”. International Journal of Forecasting, 4, pp. 103-115.
Ooms, M. , P. H. Franses, (2001). “A seasonal periodic long memory model for monthly river flows”. Environmental Modeling and Software, 16, pp. 559-569.
Rao, R. and G.Padmanabhan, (1984), “Analysis and modeling of palmers drought index series”, J. Hydrology, 68 , pp. 211-229.
Toth. E, A. Montanari and A. Brath.(1998), “Real-Time flood forecasting via combined use of conceptual and stochastic models”, Phys. Cem. Earth (B), 24(7),  pp. 793-798.
Iran-Water Resources Research
Volume 1, Issue 3
October 2005
Pages 41-53

Files

History

  • Receive Date: 10 September 2004
  • Accept Date: 01 October 2005

Share

How to cite

Statistics