Dry spells and probability of rainfall occurrence over Tanzania, East Africa

Abstract

Abstract Agriculture is the pillar of Tanzania’s economy, employing a large portion (65%) of the population, however, agriculture is affected by probability of rainfall distribution and dry spells occurrence. In this study, the Markov chain approach employed to analyze the probability of rainfall and dry spells occurrence by using daily datasets of varying length from 1981 to 2019. The length of the maximum dry spells was obtained by using the Instat statistics package (v3.36) based on the longest period of consecutive days with less than 1.0mm (R < 1.0mm) and the length of a dry spells is the sum of the number of dry days in a sequence. The Mann-Kendall’s (MK) test employed for analyzing time series data and detecting trends of maximum dry spells and Sen’s slope to estimate the rate of change (Q2) in days per month. MK test results show insignificant decrease in the length of the maximum dry spells in March at 7 stations out of 9. For the month of April and May, the length of a maximum dry spells is observed to be increasing over most stations although not statistically significant at the 5% significance level. The probability of 8-days of dry spells is high across all stations (42.2%-82.0%) in October, November, and December. Climate change is a significant factor contributing to the occurrence of dry spells in Tanzania. Understanding these causes is essential for the development of adaptation and mitigation measures, that could be water conservation and management, climate-resilient agriculture, ecosystem restoration, and policy support.