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Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach

Author

Listed:
  • Saeed Azimi

    (University of Sistan and Baluchestan)

  • Erfan Hassannayebi

    (Sharif university of Technology)

  • Morteza Boroun

    (University of Texas at Arlington)

  • Mohammad Tahmoures

    (Zanjan Agricultural and Natural Resources Research Center, AREEO)

Abstract

This study presents a steady-state Markov chain model to predict the long-term probability of drought conditions. The research aims to propose a rigorous framework for statistical analysis of drought characteristics and its trends over time for a large area of aquifers and plains in Iran. For this purpose, two meteorological indicators called the Standardized Precipitation Index (SPI), and the Groundwater Resource Index (GRI) are examined. The groundwater drought study includes more than 26,000 wells in about 600 meteorological stations over 20 years being surveyed daily. This study discusses the spatial interpolation of drought steady-state probabilities based on recorded SPI and GRI data at three intervals, i.e., 1994 to 2004, 2005–2015, and 1994 to 2015. The final zoning of the system results in an average increase in the steady-state constant of the SPI index in the first half of the whole study period to approximately 62%. While in the second period of study, the average percentage of the steady-state climatic drought was calculated to be 75%. The average amount of drought in the extended study area of the country was found to be up to 46%.

Suggested Citation

  • Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02683-5
    DOI: 10.1007/s11269-020-02683-5
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    References listed on IDEAS

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    1. Zhenya Li & Zulfiqar Ali & Tong Cui & Sadia Qamar & Muhammad Ismail & Amna Nazeer & Muhammad Faisal, 2022. "A comparative analysis of pre- and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities of Markov Chain," 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. 113(1), pages 547-576, August.
    2. Marzieh Mokarram & Tam Minh Pham, 2023. "Prediction of drought-driven land use/land cover changes in the Bakhtegan Lake watershed of Iran using Markov chain cellular automata model and remote sensing data," 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. 116(1), pages 1291-1314, March.
    3. Joanna Wicher-Dysarz & Tomasz Dysarz & Joanna Jaskuła, 2022. "Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland," IJERPH, MDPI, vol. 19(23), pages 1-18, November.

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