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On the use of Markov chain models for drought class transition analysis while considering spatial effects

Author

Listed:
  • Wentao Yang

    (Hunan University of Science and Technology)

  • Min Deng

    (Central South University)

  • Jianbo Tang

    (Central South University)

  • Rui Jin

    (Central South University)

Abstract

Prediction of drought class transitions has been received increasing interest in the field of water resource management. Markov chain models are effective prediction tools that are widely used to analyse drought class transitions by describing the temporal dependency of drought events. However, geophysical events or phenomena (such as drought events) can exhibit spatial effects resulting from spatial heterogeneity and/or dependency. This means that on the one hand the drought processes may vary over space, and on the other hand the state change of a drought event may not only depend on its previous state but also on the previous states of its neighbours, and it is thus unreasonable to directly apply Markov chain models without considering spatial effects. Therefore, this paper proposes a framework that considers spatial effects when employing drought class transition analysis. Three types of Markov chain models are introduced (traditional, local and spatial). To test for the existence of spatial effects, spatial clustering technology is selected to identify spatial heterogeneity, and a Q statistic is used to determine the existence of spatial dependency. Based on the results of these tests, a corresponding type of Markov chain models is then selected to analyse drought class transitions. Monthly rainfall time series data for Southwest China from 1951 to 2010 are employed in a case study, and the results show that spatial heterogeneity exists for both the 3- and 9-month SPI time series; however, the existence of spatial dependency is not confirmed. Forward and backward estimation rules are also obtained for drought class transitions using local Markov chain models.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04113-6
    DOI: 10.1007/s11069-020-04113-6
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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. Pabitra Banik & Abhyudy Mandal & M. Sayedur Rahman, 2002. "Markov chain analysis of weekly rainfall data in determining drought-proneness," Discrete Dynamics in Nature and Society, Hindawi, vol. 7, pages 1-9, January.
    3. 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.
    4. 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.
    5. 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.
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    Cited by:

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    2. Zhongxun Zhang & Kaifang Shi & Zhiyong Zhu & Lu Tang & Kangchuan Su & Qingyuan Yang, 2022. "Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China," Land, MDPI, vol. 11(5), pages 1-29, May.
    3. 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.

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