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Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information

Author

Listed:
  • Mohammad Mehdi Bateni

    (Urmia University)

  • Javad Behmanesh

    (Urmia University)

  • Javad Bazrafshan

    (University of Tehran)

  • Hossein Rezaie

    (Urmia University)

  • Carlo Michele

    (Politecnico di Milano)

Abstract

Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict droughts. Agro-metrological drought index addressing seasonality and autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. Markov chain is adopted to model drought for joint occurrence of different classes of drought severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain model. The proposed model, which benefits suitability of Markov chain models for modeling droughts, showed improvement results in prediction scores relative to classic Markov chain model not including SST-M information, additionally.

Suggested Citation

  • Mohammad Mehdi Bateni & Javad Behmanesh & Javad Bazrafshan & Hossein Rezaie & Carlo Michele, 2018. "Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4345-4358, October.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:13:d:10.1007_s11269-018-2056-8
    DOI: 10.1007/s11269-018-2056-8
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    References listed on IDEAS

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    1. Ana Paulo & Luis Pereira, 2007. "Prediction of SPI Drought Class Transitions Using Markov Chains," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1813-1827, October.
    2. Paulo, A.A. & Ferreira, E. & Coelho, C. & Pereira, L.S., 2005. "Drought class transition analysis through Markov and Loglinear models, an approach to early warning," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 59-81, August.
    3. Joseph Park & Jayantha Obeysekera & Michelle Irizarry & Jenifer Barnes & Paul Trimble & Winifred Park-Said, 2011. "Storm surge projections and implications for water management in South Florida," Climatic Change, Springer, vol. 107(1), pages 109-128, July.
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    Cited by:

    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. Wentao Yang & Min Deng & Jianbo Tang & Rui Jin, 2020. "On the use of Markov chain models for drought class transition analysis while considering spatial effects," 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. 103(3), pages 2945-2959, September.

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