Assessing the Decadal Variations in Rainfall Intensity and Normal Annual Rainfall Period in Somalia using GEE Remote Sensing Data.

Rainfall analysis is crucial to understanding precipitation patterns and their impacts on natural and human systems, such as agriculture, water resources management, and disaster preparedness and response. In this study, decadal rainfall patterns in Somalia were analyzed using the CHIRPS satellite precipitation dataset due to limited ground station data. The CHIRPS dataset provides gauge observations at different temporal resolutions, satellite estimates, and global climatology. Advanced statistical methods, including Excel and R, were used to analyze the precipitation data, and time series plots, box plots, and histograms were generated for daily, monthly, and annual rainfall for the study area and time limit. The Mann-Kendall trend test and Sen's slope were employed to analyze decadal precipitation trends. Results showed significant year-to-year variability in rainfall levels, with the highest precipitation values occurring in 2013 and 2019, and relatively high levels in 2012, 2015, and 2021, while relatively low levels were recorded in 2016 and 2017. The months of April and May had the highest precipitation values, while the months of June, July, and August had the lowest values. The Mann-Kendall trend test showed a weak negative correlation between annual rainfall and time, with a Kendall's tau value of -0.200, and a two-tailed p-value of 0.436, indicating no significant evidence to suggest a trend in the total annual rainfall data over the given 10-year period. The Sen's slope result also suggested a weak negative correlation between total annual rainfall and time, but no significant trend or meaningful inference about the intercept value. Overall, the study's results have important implications for water resources management, disaster preparedness, and agricultural production in the Somalia. However, the limitations of the data should be considered when making predictions or drawing conclusions about longer-term trends.