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Probabilistic Prediction of Drought in Iran Using Homogenous and Nonhomogenous Markov Chains by Peyman Mahmoudi; Allahbakhsh Rigi is a Environmental Science article available to read on EtoBox.

What is Probabilistic Prediction of Drought in Iran Using Homogenous and Nonhomogenous Markov Chains about?

The main objective of this study is to predict the transition probability of different classes of droughts in Iran. The daily precipitation data of 40 synoptic stations in Iran, for a period of 35 years were used to access the main objective of this study. The effective drought index (EDI) was used to recognize the abundance of various drought classes in Iran. Using cluster analysis on the monthly values of EDI, Iran was divided into five separate regions. Homogenous and nonhomogenous Markov chains were used to extract four features, including the probability of occurrence of each class of drought severity, the average expected residence time in each class of drought severity, the average time of the first expected passage of different categories from drought to a wet class, and short-term drought prediction for five regions of drought in Iran. Results suggested that the probability of occurrence of various drought classes decreased with the increase of drought severity; therefore, the highest probability of drought pertained to the drought class. In all five regions under consideration, the homogenous Markov chain has demonstrated the continuity of the severe drought class more th

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Author

Peyman Mahmoudi; Allahbakhsh Rigi

Publisher

American Society of Civil Engineers (ASCE)

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)