مدل‌سازی و پیش‌‌‌بینی خشکسالی فصلی با استفاده از شاخص RDI و مدل‌های سری زمانی (مطالعۀ موردی: ایستگاه سینوپتیک تهران)

نویسندگان

دانشگاه فسا

چکیده

خشکسالی از مهم‌ترین بلایای طبیعی تأثیرگذار در بخش کشاورزی و منابع آب می‌باشد که فراوانی وقوع آن به‌ویژه در مناطق خشک و نیمه‌خشک بسیار زیاد است؛ بنابراین اهمیت توجه به شبیه‌سازی و پیش‌بینی خشکسالی، بیش از پیش ضروری می‌نماید. در این مطالعه، با استفاده شاخص خشکسالی RDI و مدل‌های سری زمانی اقدام به بررسی و پیش‌بینی خشکسالی فصلی طی 5 سال آینده (زمستان 2015 تا پاییز 2019) برای ایستگاه تهران در نرم‌افزار ITSM شد. در این مطالعه، از داده‌های 48 ساله (7196ـ2014) ایستگاه تهران با متوسط بارندگی سالانه 76/239 میلی‌متر استفاده شد. براساس نتایج به‌دست‌آمده، بهترین مدل برازش‌شده بر داده‌ها، مدل MA(5) برگرفته از روش Hannan-Rissanen بود. براساس نتایج ضرایب Z(t-1) در تأخیر‌های 3 و 4 در سطح 95‌درصد معنی‌دار نیستند که در مدل صفر در نظر گرفته شدند. با توجه به  P-value آزمونLjung - Box در تأخیر‌های مختلف که برابر با 0/894 بود، می‌توان قابل اطمینان بودن پیش‌بینی را استنباط کرد. نتایج نشان داد که خشکسالی فصلی در 50‌ درصد فصول پیش‌بینی‌شده، دارای شرایط نرمال، در 45‌درصد فصول دارای شرایط نسبتاً نرمال و در 5‌درصد فصول شرایط خشکسالی متوسط را خواهد داشت.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling and prediction of seasonal drought, using RDI index and time series models (Case study: Tehran synoptic station)

نویسندگان [English]

  • Abdol Rassoul Zarei
  • Mohammad Mehdi Moghimi
  • Mohamad Reza Mahmodi
چکیده [English]

Drought is one of the most important natural hazards that should affect Agriculture and water resources. The frequency of its occurrence, especially in arid and semi-arid areas of Iran is very high. Therefore simulation and drought forecasting is necessary more than ever. This factor is importance in the planning and management of natural resources and water resources. In this study seasonal drought over the 5 next years (from winter of 2015 to autumn of 2019) in Tehran station was evaluated using RDI drought index, time series models and ITSM software. In this research climate data of Tehran station from 1967 to 2014 was analyzed (with average precipitation of 239.67 mm/year). Based on Hannan-Rissanen MA (5) model method was the best model fitted to the data. According to results coefficients of model (Z(t-1)) at 3 and 4 lags are insignificant (at the 95% level), therefore this coefficients set zero. According to p-value of Ljung - Box test (0.894) in different lags that is significant in 95% level can be said that the prediction is Reliable. Based on results seasonal drought condition in 50% of predicted seasons will be normal, in 45% of predicted seasons will be near normal and in 5% of predicted seasons will be moderately drought.

کلیدواژه‌ها [English]

  • time series
  • prediction
  • Drought
  • RDI index
  • Tehran station
1. Allen,R.G and W.D.pruitt. 1986. Rational use of the FAO Blanycriddle Formula. j. Irrigation and Drainage Engineering, ASCE. vol. 112, NO. 2:139-155. 2. Allen, R. G, L. S. Raes and M. Smith. 1998. Crop evapotranspiration Guidelines for computing crop water requirements. FAO Irrigation and Drainage, NO. 56, FAO, Rome, Italy. 301P. 3. Bari Abarghouei, H., Asadi Zarch, M, A., Dastorani, M, T., Kousari, M, R., Safari Zarch, M. 2011. The survey of climatic drought trend in Iran, Stochastic Environmental Research and Risk Assessment. DOI: 10.1007/s00477-011-0491-7. 4. Bazrafshan, J., A. Khalili .2013. Spatial Analysis of Meteorological Drought in Iran from 1965 to 2003. Desert, 18; 63-71. 5. Bloomfield, P., and Nychka, D. 1992. Climate spectra and detecting climate change. Climatic Change, 21(3):275-287. (In Persian) 6. Bowerman, B.L., and O, Connel, R.T. 1979. Time series and forecasting, PWS Publisher.481p. 7. Folland, C.K. 1990. Observed Climatic Variation and Change, Climate Change, Cambridge University Press, 195-238. 8. Hansen, J., and Lebedeff, S. 1988. Global surface air temperatures: Update through 1987. Geophysical Research Letters, 15(4): 323-326. 9. Jahandideh, M., and Shirvani, A. 2011. Forecasting of drought based Standardized index using time series models in Fars province. Journal of Iran Water Research, 5(9): 19-28. (In Persian) 10. Jamshidi, V. 2007. Analysis of temperature and precipitation in Tehran city by time series. M.Sc Thesis of Tarbiat Modares University, 263 pp. (In Persian) 11. Jones, P.D., Raper, S.C.B., Bradley, R.S., Diaz, H.F., Kellyo, P.M., and Wigley, T. M. L. 1986. Northern hemisphere surface air temperature variations: 1851–1984. Journal of Climate and Applied. Meteorology, 25(2): 161-179. 12. McKee, T.B., Doesken, N.J., Kleist, J .1993. the relationship of drought frequency and duration to time scales. In: 8th Conference on Applied Climatology, 17–22 January, Anaheim, California, pp. 179–184. 13. Kamali, A., Mahmoudian Shushtari, M., and Kamali, I.N. 2006. The forecast monthly inpu Abbaspour reservoir using time series Box-Jenkins. 7th International seminar on river engineering. January, Ahvaz, Iran. 14. Kheradmand-Nia, M., and Asakereh, H. 2001. Pattering of ARIMA for annual average temperature in Jask (Iran). 3th Conference of Stochastic Process. Isfehan University. (In Persian) 15. Maleki, M. 1989. Investigation and modeling of temperature and precipitation in Western country. M.Sc.Thesis of Beheshti University, Statistic Dep, 184pp. (In Persian) 16. Mir mosavi. H. Jalali. M. Abakhti Garosi. H. Khaefi. N. 2014. Time Series Analysis of Rainfall in the Khoi Meteorology Station. Geographical Space. Vol 4 (47):1-17. (In Persian). 17. Morid, S., V. Smakhtin, K. Bagherzadeh. 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27; 2103–2111. 18. Nirumand, H.A., and Bozorgnia A. (translator). 1993. Introduction to Time Series Analysis. C. Chetfield,Mashhad, Ferdowsi University, 290 pp. (In Persian) 19. Noakes, D.J., Mcleod, A.I., and Hipel, W. 1985. Forecasting monthly river flow time series. International Journal of Forecasting, 1(2):179–190. 20. Rasuli, A.A. 2002. Modeling of climate parameters in north-west country. Forecasting monthly temperature of Tabriz city (Iran) by ARIMA model. Journal of Sociology Science, (8). 21. Salas, 1996. ” Applied Time Series in Hydrology”, Mc Grew Hill. 22. Tsakiris G, Vangelis H .2005. Establishing a Drought Index incorporating evapotranspiration. European Water 9/10:3–11. 23. Tsakiris G, Pangalou D, Tigkas D, Vangelis H .2007a. Assessing the areal extent of drought. Water resources management: new approaches and technologies, European water resources association, Chania, Crete -Greece, 14–16 June. 24. Tsakiris G, Pangalou D, Vangelis H .2007b. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manag 21(5):821–833. 25. Tsakiris G, Nalbantis I, Cavadias G .2011. Regionalization of low flows based on canonical correlation analysis. Adv Water Resour 34:865–872. 26. Tsakiris G, Nalbantis I, Vangelis H, Verbeiren B, Huysmans M, Tychon B, Jacquemin I, Canters F, Vanderhaegen S, Engelen G, Poelmans L, Becker P, Batelaan O .2013. A system-based paradigm of drought analysis for operational management. Water Resour Manag 27:5281–5297. 27. Vangelis H, Tigkas D, Tsakiris G .2013. The effect of PET method on Reconnaissance Drought Index (RDI) calculation. J Arid Environ 88:130–140. 28. Zehtabian, Gh.R., K. Karimi, S. Nakhaee Nezhadfard, M. Mirdashtvan, H. Khosravi .2013. Comparability Analyses of the SPI and RDI Meteorological Drought Indices in South Khorasan province in Iran. International Journal of Advanced Biological and Biomedical Research, 9: 981-992.