ASSESSING AND PREDICTING DROUGHT VULNERABILITY OF JESSORE DISTRICT IN BANGLADESH WITH MARKOV CHAIN MODEL

The agricultural sector, which is crucial to the socioeconomic framework of Bangladesh, is particularly exaggerated with different natural calamities such as drought, flood, cyclone etc.Drought primarily arises from insufficient rainfall. Markov Chain model is employed to assess the probability of suffering sequences of wet and dry days in this field.The long-term fluctuations in rainfall and temperature patterns contribute the local and regional drought conditions, leading to failures in productive sectors and a decline in socioeconomic status. This study focused on monitoring and predicting agriculture drought probability by decade of Jessore district from the southwestern part of Bangladesh. The daily rainfall data of Jessore district from January 1961 to December 2020 is considered for investigation. Following the cleansing of the daily rainfall dataset, we computed the data into various time intervals of 5, 7, 10, and 30 days utilizing Microsoft Excel. Subsequently, we employed the R programming language to analyze the drought index using the Markov Chain Model, focusing on a threshold value of 7 mm of rainfall, and to forecast the drought probability for the upcoming decade from 2021 to 2030 based on monthly data. The empirical study showed that the chronic drought prone period is observed for 5 day, 7 day and 10 series at first TPM whereas 30 days series showed occasional drought prone at this TPM but due to global climate change these drought convert to moderate, mild and occasional for most of the series at higher TPM in case of Jessore district and the maximum severity occurred around 7 months during 1961-1970. The forecasted drought probability showed that in Jessore district the next decade will be occasional drought at most of the time.