Impact of Threshold Value for Detecting Drought Index: A Case Study from Pabna District of Bangladesh

The climate plays important role in the agriculture production in any country. Rainfall is one of the most important factors which affect non-irrigated crop. Water deficits and excess water are the greatest constraints for rain fed rice yields. Agricultural drought is mainly inadequacy of rainfall. In this study an attempt has been made to apply Markov Chain model techniques for drought index detection with different threshold value and find out the most drought prone decade of Pabna district by Markov Chain model. The daily rainfall data set for 55 years during the period 1961-2015 is considered in this study. The daily rainfall data were reducing 5, 7 and 10 days and considered threshold value 2.5 mm, 5.0 mm and 7.5 mm. We calculate the Drought Index from the first transition probability matrix and then estimate with higher transition probability matrix. When the higher transition probability matrix became stable, then we estimate the DI. The empirical study showed that the decade 1961-1969, 1970-1979, 1980-1989 was more drought prone than the decade 1990-1999, 2000-2009 and 2010-2015. Among these decades, the decade 1961-1969 was maximum drought prone of all of these three threshold value for 5 days, 7 days and 10 days data in case of Pabna district. This study also showed that the following district is affected by chronic drought proneness in the 1st TPM for threshold value 2.5 mm but at higher TPM both stations are affected different drought proneness some are severe, some are moderate, some are mild some are occasional. It indicates that if we calculate the drought index decade wise both stations are affected by chronic drought proneness at the 1st TPM but due to climate change at higher TPM the drought proneness are not chronic at all. For any data point if we consider different threshold value for identifying drought index then we may conclude that if we increase the threshold value than it decrease the DI value. This study will contribute to policy formulat