Dry and Wet Spells Probabilities Analysis Using Markov Chain Model: A CaseStudy of Lamphelpat, Imphal (West), Manipur
DOI:
https://doi.org/10.53550/Keywords:
Dry and wet spell probability, Markov chain model, Lamphelpat, Imphal (West), ManipurAbstract
In the present study ,weekly rainfall data of Lamphelpat, Imphal (West), Manipur for the year 2011-2024 were used to forecast dry and wet spells probabilities using Markov Chain Model and to find out the time of onset and termination of monsoon. The average annual rainfall was found to be 1522 mm, with the monsoon season contributing the highest proportion (68.60%), followed by pre-monsoon (25.93%) and post-monsoon (5.51%) rainfall. The coefficient of variation indicated relatively stable rainfall during the monsoon and pre-monsoon seasons, while the post-monsoon period exhibited high variability. The analysis revealed that dry weeks were predominant during the 1st to 15th and 41st to 52nd standard meteorological weeks, whereas wet weeks were more frequent during the monsoon period. The probability of consecutive dry and wet weeks highlighted critical periods of moisture stress and adequate water availability. Forward and backward accumulation analysis indicated that the effective onset of the pre-monsoon and monsoon occurred during the 17th and 24th weeks, respectively, with the monsoon extending up to the 40th week, providing an active rainy period of approximately 17 weeks. The study provides useful insights for optimizing sowing time, irrigation scheduling, and crop planning in the study area
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