Long Term and Seasonal Rainfall Trend in Bapatla District of Andhra Pradesh Using Advanced Statistical Methods

P. Swarnalatha, R. Srinivas, Yamini Leela A., K. Jeevan Babu,V. Srinivasa Rao,Santosha Rathod, D. Ramesh

International Journal of Environment and Climate Change(2023)

Cited 0|Views3
No score
Abstract
The primary objective of this study is to analyse the annual and seasonal rainfall trends in the Bapatla district of Andhra Pradesh over the long term. The seasonal rainfall data for Bapatla district was collected from the NASA Power website and covering the period from January 1982 to December 2021. To gain insights into the patterns present in the rainfall data for Bapatla district, a combination of parametric methods, including linear regression, and non-parametric tests such as the Mann-Kendall test, Sen’s slope test, Modified-Mann Kendall test, Innovative trend analysis were employed in this approach. The randomness of the rainfall data under investigation was assessed using the Wallis and Moore test. To detect the single change point of the rainfall pattern Pettitt test was employed. The results of linear regression trend method exhibited both increasing and declining trends in rainfall pattern. Notably, the months of April, May and June months exhibited a statistically significant increasing trend other months exhibited no significant trend in Mann-Kendall test, modified Mann-Kendall test, Sen’s slope tests. The monsoon season exhibited a statistically significant trend and pre-monsoon, post-monsoon periods and anuual rainfall exhibited non-significant trends in Mann-Kendall and Sen’s slope tests. Moreover, the pre monsoon and monsoon seasons exhibited significant trend in rainfall pattern of Bapatla district by modified Mann-Kendal test. This study serves to raise awareness among agricultural stakeholders, particularly farmers, regarding the fluctuations in monthly and seasonal rainfall patterns and enables them to more effectively allocate resources and prepare for anticipated water shortages during non-monsoon months.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined