NON-PARAMETRIC, PARAMETRIC TEST AND LINEAR REGRESSION TO ANALYSE TRENDS OF RAINFALL

Abstract Water availability is mostly determined by the measure of precipitation obtained in a given area. Variations in precipitation patterns have a considerable impact on people's livelihoods, particularly in the region where rainfed cultivation predominates. The present study looked at the temporal variance of rainfall in Panchmahal district of Gujarat, India. Mann–Kendall trend analysis was applied to examine monthly, seasonal, and yearly rainfall series from 1981 to 2017. The trends were also checked using a parametric linear regression test. Using the Wetness Index and standardised rainfall difference, a deficiency in annual total rainfall was detected. To inspect the distribution of rainfall on monthly basis, the Precipitation Concentration Index (PCI) value has been computed. The paper presents, both parametric and non-parametric tests forecast an increased inclination in rainwater from the months of March to September, whereas they predict a decrease trend for all other months. This is reflected in an increase in the study area's annual total rainfall. The average coefficient of variation (CV) for rain water data was 59.25959, indicating that there was a lot of variance within and between years. Rainfall nonuniformity was also highlighted by PCI readings.