Environment and Water Engineering

Advanced Search

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Indexing and Abstracting

Journal Metrics

Peer Review Process

FAQ

Related Links

News

Paper Format

Forms and Format Template

Publication Fee

Publication Ethics

Open Access Policy

Self-Archiving Policy

Prediction and Analysis of Meteorological Drought Based on Time Series(Case Study: SALMAS Watershed)

Document Type : Research Paper

Authors

  • Motaleb Byzedi 1

  • Maroof Siosemardeh 2

  • Azita Asragah 3

  1. 1 Department of Water Engineering, Faculty of Agriculture, Islamic Azad University, Sanandaj, Iran
  2. 2 Department of Water Engineering, Faculty of Agriculture, Islamic Azad University, Mahabad, Iran
  3. 3 Department of Civil Engineering, Faculty of Civil Engineering, Islamic Azad University, Mahabad, Iran

Abstract

Time series analyses is a base method for more decisions about hydrological process and water operation. In Iran, drought is a continues and normal condition happening frequently and can be predicted by statistical and mathematical methods and models. In this research, meteorological drought was analyzed in 5 stations of Salmas region (Nazarabad, Salmas, Yalgozagaj, Chahrygeolya and Urban) located in West Azarbijan Province, Iran. For this purpose, the monthly rainfall time series, 30 years lengths (1979-2008) for these stations were simulated by times series models as: AR, MA, ARMA and ARIMA. Then the best model was used for forecasting the drought. Then the monthly rainfalls were predicted for next 7 years using the chosen model. In the following, the intensity of droughts was calculated by standard precipitation index (SPI). The final model chosen for the stations of Nazarabad, Salmas, Yalgozagaj, Chahrygeolya and Urban were the ARMA(1,1), ARMA(3,2), ARMA(1,1), ARMA(2,3) and ARMA(4,2) models, respectively. The results showed that for 30 years (1978-2008), drought had increasing trend just in Nazarabad station, while in other stations such as Salmas, Yalgozagaj, Chahrygeolya had decreasing trend.

Keywords

Subjects

 Hydrology

References
Abdollanezhad K. (2015). Random models predict monthly rainfall time series (Case Study: Hashim station, Gorgan). J. Geo-Spatial Plan., 5(17), 15-25 [In Persian].
Aguilera A. M., Escabias M. and Valderrama M. J. (2008). Forecasting binary longitudinal data by a functional PC-ARIMA model. Comput. Statis. Data Anal., 52(6), 3187-3197.
Akbari M. and Bashiri M. (2015). Modeling and forecasting of monthly precipitation using time series analysis. 1st National Conference on Agricultural and Environmental Sciences, Iran [In Persian].
Bashiri A. Moemenpor A. Bashiri J. and Mohamadi N. (2016). River flow modeling using linear models for time series Case Study: River Zarineh. 6th Water Resources Management Conference [In Persian].
Box G. E. P. and Jenkins G. M. (1976). Time series analysis: forecasting and control. Oakland, CA: Holden-day, 2nd Edition, 2-4pp. Bozorgnia A. (1987). Time series analysis and forecasting, Astan Quds Razavi Publisher, Mashhad. 55pp [In Persian].
Chow W. T. and Karelioti S. J. (1970). Analysis of stochastic hydrologic systems. Water Resour. Res., 16, 1569-1582.
Ghorbanpoor M. A., Abbaspoor K., Jalalvand G. and Ashtiani Moghadam G. (2010). Stochastic modeling of surface stream flow at different time scales: Sangsoorakh karst basin, Iran. J. Cave Karst Stud., 72, 1-10.
Jabari I. (2006). Statistical methods in environmental sciences and geography, Kermanshah, Razi University. 25pp [In Persian].
Karamoz M. and Araqinezhad Sh. (2005). Advanced hydrology. Industrial University Amirkabir, 480pp [In Persian].
Khalili K. (2009). Application of nonlinear time series models for short-term modeling of the river (case study: Shahrchay Urmia). PhD Thesis, Faculty of Agricultural Sciences Tabriz University, Tabriz. Iran, 35pp [In Persian].
Khiri H. and Moqim Gh. R. (2014). Annual precipitation forecast Bojnoord using ARIMA model. 4th Conference on Environment, Energy and Bio-defense, Iran [In Persian].
Komomik J., Komomik M., Mesiar R., Szokeova D. and Szolgay J. (2006). Comparison of forecasting performance of nonlinear models of hydrological time series. Phys. Chem. Earth, 31, 1127–1145.
Marofi S., Khatar B., Sadeqifar M., Parsafar N. and Eldormi A.R. (2014). Using time-series prediction of drought SARIMA index SPI, in the central region of the province. J. Water Agri., 28(1), 213-225 [In Persian].
Mckee, T. B., Doesken, N. J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Preprints 8th Conference on Applied Climatology, 179-184
Mirmousavi S. H., Jalali M., Bkhty Grossi H. and Khaef N. (2014). Time series analysis of rainfall patterns in Khoy weather station. Geogr. Space, 14(47), 1-17 [In Persian].
Nasaji M.) 2001(. Determine the drought periods of drought indices using SPI, the first national conference examining ways of dealing with the water crisis, Zabol University, Zabol, Iran. 65pp [In Persian].
Niromand H. and Bozorgnia A. (1993). A review of time series analysis, University of Mashhad. 77pp[In Persian].
Rossi G., Bendini M., Tsakiris G. and Giakoumakis S. (1992). On regional drought estimation and analysis. Water Resour. Manag., 6(4), 249-277.
Thomas H. A. and Fiering M. B. (1962). Mathematical synthesis of stream flow sequences for the analysis of river basins by simulation. In design of water resources systems, ed. A. Mass, et al. Cambridge. Water Resour., 5, 228-267. 
Environment and Water Engineering

Volume 2, Issue 4
September 2016

Pages 346 - 359

Files
History
  • Receive Date: 24 July 2016
  • Revise Date: 20 November 2016
  • Accept Date: 06 December 2016
  • Publish Date: 21 December 2016
Share
How to cite
Statistics