Detection of spatiotemporal patterns of rainfall trends, using non-parametric statistical techniques, in Karnataka state, India.

The unpredictability of the climate has drawn a lot of interest worldwide, especially that of the annual mean temperatures and rainfall. In this study, non-parametric tests such as the LOWESS curve method, Mann-Kendall (MK), SNHT test, Pettitt's test (PT), and Buishand range test (BRT) were used to evaluate long-term (2000-2020) rainfall data series to examine rainfall variability. The Dakshina Kannada district has the highest average rainfall is 3495.6 mm with a magnitude change% of about 26.2, while the Koppala district has the lowest average rainfall roughly about 530.4 mm, with a magnitude change % of about 11.49 mm in a year. The statistics from the fitted prediction line were utilized to determine the maximum coefficient determination (R ²  = 0.8808) in the Uttara Kannada region. Because of the commencement of the present rising era, 2015 is the shift year in rainfall with the highest potential of being a change point in the state's Western Ghats region. It was also revealed that the majority of the districts exhibit positive trends before the change point and vice versa. The current research can be used to plan for and minimize the agricultural and water resource challenges in the state of Karnataka. To link observable patterns to climate variability, the next inquiry must identify the source of these changes. Overall, the study's findings will help organize and improve drought, flood, and water resource management techniques in the state.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)