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Markov Chains of Different Orders for Streamflow Drought Analysis

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  • Hossein Tabari
  • Reza Zamani
  • Hossein Rahmati
  • Patrick Willems

Abstract

Drought is a slow and creeping phenomenon that occurs more frequently in arid and semi-arid regions. In recent decades, among natural disasters influencing human societies the number or frequency of drought event has increased more than others. In this context, the prediction of drought intensity, duration and frequency can help to take the necessary precautions and reduce drought risk. This study employs Markov chains of different orders (0, 1, 2 and 3) to analyze hydrological drought characteristics. Hydrological drought is identified based on the streamflow drought index (SDI) at 3-, 6-, 9- and 12-month time scales using data from 21 hydrometric stations located in the Karkheh River Basin in Iran. According to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), first-order Markov chain models are adequate to reproduce the statistical structure of SDI-based hydrological droughts. Moreover, the steady state probabilities and expected residence time of drought severity for all time scales increase as the degree of severity decreases. The results also indicate that the expected frequency of drought occurrence is higher for smaller time scales (i.e., 3-month and 6-month). Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Hossein Tabari & Reza Zamani & Hossein Rahmati & Patrick Willems, 2015. "Markov Chains of Different Orders for Streamflow Drought Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3441-3457, July.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3441-3457
    DOI: 10.1007/s11269-015-1010-2
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    References listed on IDEAS

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    1. Reza Zamani & Hossein Tabari & Patrick Willems, 2015. "Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses," 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. 76(1), pages 327-346, March.
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    Cited by:

    1. Jie Yang & Yimin Wang & Jianxia Chang & Jun Yao & Qiang Huang, 2016. "Integrated assessment for hydrometeorological drought based on Markov chain 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. 84(2), pages 1137-1160, November.
    2. Nadia Shahraki & Safar Marofi & Sadegh Ghazanfari, 2019. "Modeling of Daily Rainfall Extremes, Using a Semi-Parametric Pareto Tail Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 493-508, January.
    3. Hsin-Fu Yeh & Hsin-Li Hsu, 2019. "Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation," Sustainability, MDPI, vol. 11(6), pages 1-18, March.

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