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Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices

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  • Abdol Rassoul Zarei

    (Fasa University)

  • Mohammad Reza Mahmoudi

    (Fasa University
    Duy Tan University)

Abstract

Drought forecasting and monitoring play a significant role in reducing the negative effects of global meteorological droughts caused by different intensities at different temporal and spatial scales in different regions, especially in regions with high dependency on rainwater. The present study tries to compare the accuracy of stationary time series (ST) models including autoregressive moving average (ARMA), moving average (MA) and autoregressive (AR) and cyclostationary time series (CT) models including periodic autoregressive moving average (PARMA), periodic moving average (PMA) and periodic autoregressive (PAR) to predict drought index (i.e. monthly reconnaissance drought index (RDI)) in periodic data series considering that CT models are more powerful and efficient than ST models by using data series of 8 synoptic stations with different climate conditions in Iran from 1967 to 2017. According to the results the monthly RDI was significantly periodic in all selected stations. The PAR (25) model was the best fitted CT model in data series at all stations and on the other hand, the following models were the best-fitted ST models in data series: the AR models at Babolsar and Rasht AR (25) and at Gorgan AR (24) and ARMA models at Tehran ARMA (2, 3), at Zahedan and Shiraz ARMA (2, 4) and at Esfahan and Shahre Kord ARMA (2, 5). Based on the best fitted CT and ST models, the results showed that the correlation coefficients (R) between observed and simulated RDI vary from 0.882 to 0.946 and from 0.693 to 0.874, respectively from January 1967 to December 2017. According to the best fitted CT and ST models, the validation test of the best fitted models indicated that the R between observed and simulated RDI vary from 0.634 to 0.883 and 0.585 to 0.847, respectively from January 2012 to December 2017. In total, it can be concluded that that the accuracy and capability of CT models in predicting the RDI were more than those of the ST models at all stations and the hypothesis of the study was confirmed.

Suggested Citation

  • Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2020. "Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5009-5029, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02710-5
    DOI: 10.1007/s11269-020-02710-5
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    1. liu, Qi & Zhang, Guanlan & Ali, Shahzad & Wang, Xiaopeng & Wang, Guodong & Pan, Zhenkuan & Zhang, Jiahua, 2019. "SPI-based drought simulation and prediction using ARMA-GARCH model," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 96-107.
    2. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    3. Dimitris Tigkas & Harris Vangelis & George Tsakiris, 2020. "Implementing Crop Evapotranspiration in RDI for Farm-Level Drought Evaluation and Adaptation under Climate Change Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4329-4343, November.
    4. G. Tsakiris & D. Pangalou & H. Vangelis, 2007. "Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 821-833, May.
    5. Abdol Rassoul Zarei, 2018. "Evaluation of Drought Condition in Arid and Semi- Arid Regions, Using RDI Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1689-1711, March.
    6. Samaneh Ashraf & Amir AghaKouchak & Ali Nazemi & Ali Mirchi & Mojtaba Sadegh & Hamed R. Moftakhari & Elmira Hassanzadeh & Chi-Yuan Miao & Kaveh Madani & Mohammad Mousavi Baygi & Hassan Anjileli & Davo, 2019. "Compounding effects of human activities and climatic changes on surface water availability in Iran," Climatic Change, Springer, vol. 152(3), pages 379-391, March.
    7. E. Arnone & Marco Cucchi & Sara Dal Gesso & Marcello Petitta & Sandro Calmanti, 2020. "Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4313-4328, November.
    8. Pawan S. Wable & Madan K. Jha & Ankit Shekhar, 2019. "Comparison of Drought Indices in a Semi-Arid River Basin of India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 75-102, January.
    9. U. Surendran & B. Anagha & P. Raja & V. Kumar & K. Rajan & M. Jayakumar, 2019. "Analysis of Drought from Humid, Semi-Arid and Arid Regions of India Using DrinC Model with Different Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1521-1540, March.
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    Keywords

    ST models; CT models; RDI; ARMA; PARMA;
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