A novel method to analyze pattern shifts in rainfall using cluster analysis and probability models

TL;DR

This paper introduces a new approach combining cluster analysis and probability models to detect and analyze rainfall pattern shifts at the district level, aiding localized climate change mitigation strategies.

Contribution

It presents a novel methodology for analyzing micro-level rainfall patterns using multivariate techniques and probability models, addressing a gap in district-level climate change studies.

Findings

Identified climate change patterns over 122 years in Varanasi district.

Successfully fitted probability models to monthly rainfall data.

Revealed significant shifts in rainfall patterns at the district level.

Abstract

: One of the prominent challenges being faced by agricultural sciences is the onset of climate change which is adversely affecting every aspect of cropping. Modelling of climate change at macro level have been carried out at large scale and there is ample amount of research publications available for that. But at micro level like at state level or district level there are lesser studies. District level studies can help in preparing specific plans for the mitigation of adverse effects of climate change at local level. An attempt has been made in this paper to model the monthly rainfall of Varanasi district of the state of Uttar Pradesh with the help of probability models. Firstly, the pattern of the climate change over 122 years has been unveiled by using exploratory analysis and using multivariate techniques like cluster analysis and then probability models have been fitted for selected months