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

Saeed Azimi (), Erfan Hassannayebi (), Morteza Boroun () and Mohammad Tahmoures ()
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 15, No 10, 4703-4724

Abstract: 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%.

Keywords: Markov chain; Probabilistic analysis; Climate drought; Standardized precipitation index (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11269-020-02683-5

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