Temporal Trails in the Tapestry of Rainfall: A Modern Trend Review

Abstract This study focused on precipitation, a critical meteorological variable, to examine the shifting rainfall patterns in Bengaluru Karnataka. Rainfall is essential to monitor flood surface runoff in this area, but because it happens randomly and unexpectedly, it negatively affects the drainage and Agricultural system and the availability of natural water resources. annual rainfall variability (trend, linear Regression and slope magnitude) was calculated using Parametric and non-parametric analysis from rainfall data collected over 124 years (1901-2024). This study has been carried out by processing annual hydrological data collected from IMD weather stations across Bangalore district from 1901 to 2024The parametric and non-parametric Sen's Slope Estimator and the Mann-Kendall (MK) Test for monotonic trend analysis were used to assess the strength of a trend in time series data. The Mann-Kendall test for cumulative rainfall data shows a significant increasing trend (Z = 2.98, p = 0.0029), indicating that rainfall has been rising over time. The Sen’s slope estimator quantifies this increase at approximately 1.76 units per period, with a 95% confidence interval ranging from 0.62 to 2.88. This confirms a consistent upward trend in rainfall. There is a significant increasing trend in cumulative rainfall at the 5% significance level. On average, cumulative rainfall is increasing by about 1.76 mm (or relevant unit) per period. The linear regression equation indicates a slight increasing trend in cumulative rainfall over time, with an average annual increase of approximately 1.94 mm. However, the low coefficient of determination (R² = 0.0911) shows that only 9.11% of the variation in rainfall is explained by this linear trend. This conclude that rainfall trend is increasing Key words; Rainfall , Linear Regression , Mann Kendall test, Sen’s slope Estimator and Trend Analysis