Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach
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Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach Saeed Azimi 1 & Erfan Hassannayebi 2 & Morteza Boroun 3 & Mohammad Tahmoures 4 Received: 7 February 2020 / Accepted: 21 September 2020/ # Springer Nature B.V. 2020
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
* Erfan Hassannayebi [email protected] Saeed Azimi [email protected] Morteza Boroun [email protected] Mohammad Tahmoures [email protected] Extended author information available on the last page of the article
Azimi S. et al.
1 Introduction Drought refers to the lack of rainfall in the long-term in a way that causes a lack of soil moisture and reduced surface and groundwater reserves. Drought is seriously affecting human and environmental activities (Ferral et al. 2017). Today, reliable forecasting of drought conditions is of great importance for future planning in the environment, energy (Gholizad et al. 2017), urban, and economic development (Haile et al. 2020). Precipitation is one of the most critical factors in defining drought. A significant lack of rainfall indicates the threshold for future drought (Tsakiris et al. 2007). Meteorological drought is primarily due to a lack of water, leading to drought hydrology if it continues (Bucior-Kwaczyńska 2018). Precipitation is the most relevant parameter used in the concept of drought, and the level of drought occurrence is the scarcity or lack thereof. The lack of rainfall can decrease other water resources such as underground aquifers (Jia et al. 2017). Many natural phenomena, such as drought, contain factors that cannot be accurately predicted or measured (Rolim and de Souza Filho 2020). To some extent, this prediction is possible if there is adequate knowledge about their history. Many natural abnormalities are likely to be caused by
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