Rainfall Trend analysis using Mann-Kendall and Sen’s slope test estimation - A case study

Abstract

In order to analyze the changing pattern of rainfall in one of the important district in Kerala, this study concentrated on a crucial meteorological variable: precipitation. Agriculture in this region depends on rain, but because it occurs erratically and without warning, it has an adverse impact on the cropping system and the availability of natural water resources. Non-parametric analysis was used to determine the monthly, seasonal, and annual rainfall variability (trend and slope magnitude) from rainfall data spanning 30 years (1989-2018) on a monthly basis. To determine the strength of a trend for time series data, the Mann-Kendall (MK) Test for monotonic analysis of trend and non-parametric Sen's Slope Estimator were utilized. The past 30 years have been used to create monthly, seasonal, and yearly precipitation trends. The 30-year monthly data was used to generate monthly, seasonal, and yearly precipitation trends. The results of the statistical analysis of the entire reference time series data shows that the trend appears to be primarily positive (growing), both on an annual and seasonal scale. There was a decreasing tendency in the spring, pre-monsoon, and post-monsoon seasons. Individually, the seven months of January through February show a growing tendency, whereas the remaining five months of March through December show a declining trend with 99% and 95% confidence levels, respectively, the annual total rainfall and monsoon seasons demonstrated a positive significant increasing trend.