Rainfall distribution has major impacts on agriculture, affecting soil, hydrology and crop health in agricultural systems(Dourte et al., 2012). Changes in rainfall patterns as a result of climate change are currently associated with problems for water resource managers and hydrologists Gajbhiye et al., 2015). In India a lot of research has been done to investigate the significance of rainfall events with particular concern for the monsoon changes and global warming (Balachandran et al., 2006). During the Indian summer rains, there has been a change in the recent dry season (27% peak in 1981–2011 compared to 1951–1980) and more intense spells(https://reliefweb.int/report/india/assessment-climate-change-over-indian-region-report-ministry-earth-sciences-moes).
Given India's history of recurring droughts and rainfall variability, the use of robust methods to conduct long-term trend and variability studies to obtain important information on changes that have occurred over the past few decades is a crucial contribution (Murugan et al., 2008). As a result, an accurate assessment of the spatial and temporal distribution of precipitation, and monitoring its trends are critical inputs to sustainable agricultural production(Dereje et al., 2012). In an effort to explain the temporal aspects of rainfall distribution, many meteorologists have proposed ways in which seasonal rainfall intensity can be measured. Stabilization of rainfall — that is, the rate of temporal rainfall distribution in the area — can be seen in different models such as Gini (GI) indicator, concentration index (CI) (Monjo and Martin-Vide, 2016 and Yin et al., 2016) and the precipitation concentration index (PCI)( Oliver, 1980). The precipitation concentration index (PCI) is used to measure the temporal precipitation distribution and seasonal precipitation distribution(Kexin Zhang et sl., 2019).
A trend is a substantial change in a random variable over time that may be detected using statistical parametric and non-parametric approaches. The statistical analysis of long-term climate data can also be used to investigate the temporal and spatial variability of climatic characteristics(Patle et al., 2013). Parametric and non-parametric statistical methods can be used to analyse climate variability in time series data. The parametric method assumes data is regularly distributed and free of outliers, whereas non-parametric methods make no such assumption. When the probability distribution is skewed, the parametric t-test has less power than the non-parametric Mann–Kendall test(Hamed and Rao, 1998). Furthermore, at a given confidence level, the Mann–Kendall non-parametric test would establish the existence of a positive or negative trend.
Spatio-temporal precipitation changeability in Tamil Nadu was investigated using monthly precipitation data over a long period of time, with the conclusion that global warming-induced weakening of monsoon circulation characteristics appears to be the main driver of current changes. One of the most crucial indications in determining the reliability of rainfall is inter-annual variability. As a result, understanding the regional level of rainfall trend based on historical data is critical for agriculture. Crop success or failure is intimately tied to rainfall pattern in a rainfed environment.
Therefore, the aim of this study was to distinguish time and location behavior and seasonal rainfall focus using seasonal precipitation concentration index for both south-west and northeast monsoon of Tamil Nadu. A non-parametric statistical technique called the Mann Kendall test is being used to examine the rainfall trend for Tamil Nadu, which will aid the farmers in implementing appropriate agricultural practises, soil and water conservation, and flood/drought management.