Predicting the probability of transforming different classes of monthly droughts in Iran
Abstract The main objective of this study was to predict the transition probability of different drought classes by applying Homogenous and non- Homogenous Markov chain models. The daily precipitation data of 40 synoptic stations in Iran, for a period of 35 years (1983–2018), was used to access the study objectives. The Effective Drought Index (EDI) was applied to categorize Iran’s droughts. With the implementation of cluster analysis on the daily values of effective drought index (EDI), it was observed that Iran can be divided into five separate regions based on the behavior of the time series of the studied stations. The spatial mean of the effective drought index (EDI) of each region was also calculated. After forming the transition frequency matrix, the dependent and correlated test of data was conducted via chi-square test. The results of this test confirmed the assumption that the various drought classes are correlated in five studied regions. Eventually, after adjusting the transition probability matrix for the studied regions, the homogenous and non-homogenous Markov chains were modeled and Markov characteristics of droughts were extracted including various class probabilities of drought severity, the average expected residence time in each drought class, the expected first passage time from various classes of droughts to the wet classes, and the short-term prediction of various drought classes. Regarding these climate areas, the results showed that the probability of each category is reduced as the severity of drought increases from poor drought category to severe and very severe drought. In the non-homogeneous Markov chain, the probability of each category of drought for winter, spring, and fall indicated that the probability of weak drought category is more than other categories. Since the obtained anticipating results are dependent on the early months, they were more accurate than those of the homogeneous Markov chain. In general, both Markov chains showed favorable results that can be very useful for water resource planners.