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SPI-based drought simulation and prediction using ARMA-GARCH model

Author

Listed:
  • liu, Qi
  • Zhang, Guanlan
  • Ali, Shahzad
  • Wang, Xiaopeng
  • Wang, Guodong
  • Pan, Zhenkuan
  • Zhang, Jiahua

Abstract

Drought is one of the most frequent climate-related disasters occurring in North China Plain. The accurate and timely information of drought is vital for crop production and food security. In this study, the monthly precipitation data during 1965–2015 was used to calculate the Standardized Precipitation Index (SPI) with a time scale of 9 months (SPI-9) at five stations in Shandong Province, North China Plain. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model was adopted to eliminate the heteroskedasticity (ARCH effect) in the residuals of ARMA (Autoregressive and Moving Average) model, and the two models were combined into a composite model called ARMA-GARCH model. Both ARMA and ARMA-GARCH models were used to simulate SPI-9 drought index, and the results of comparison between two models showed that the ARMA-GARCH model performed better. Furthermore, the two models were used to predict SPI-9, the result showed that the accuracy of the ARMA-GARCH model is much higher than that of the ARMA model; for maintaining the stability of site-to-site correlation, the ARMA-GARCH model also outperformed the ARMA model. The research indicates that the ARMA-GARCH model could be used to more accurately simulate and predict SPI-9 drought index.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:355:y:2019:i:c:p:96-107
    DOI: 10.1016/j.amc.2019.02.058
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    References listed on IDEAS

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    1. Schwert, G. William, 1987. "Effects of model specification on tests for unit roots in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 20(1), pages 73-103, July.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Zhang, Yongmin & Wang, Ruizhi, 2022. "COVID-19 impact on commodity futures volatilities," Finance Research Letters, Elsevier, vol. 47(PA).
    2. Wilson Kalisa & Tertsea Igbawua & Fanan Ujoh & Igbalumun S. Aondoakaa & Jean Nepomuscene Namugize & Jiahua Zhang, 2021. "Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2047-2076, April.
    3. 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.
    4. Levent Latifoğlu & Mehmet Özger, 2023. "A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction," Sustainability, MDPI, vol. 15(19), pages 1-29, September.

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