A Statistical Dual Approach of Rainfall Trend Analysis A Review

This study aims to analyze rainfall trends and assess their temporal variability, which are important for understanding regional water patterns and their impacts on the management of water resources. This research uses non-parametric statistical methods, including the Mann–Kendall test, Sen’s slope estimator, and Pettitt’s test, to evaluate trends and sudden changes in rainfall data over long periods. By looking at spatial and seasonal variability, the research highlights the significant contributions of winter, as well as the rainfall totals during the pre-monsoon, monsoon, and post-monsoon seasons. By using the proposed implementation strategy, rainfall trend analysis can be improved by using new technologies called innovative pivot trend analysis and innovative polygonal trend analysis (IPTA). This will provide a graphical approach to trend analysis and trend detection, and we can compare with other non-parametric rainfall trend analysis methods, and this also provides useful insights for climate-sensitive sectors. Combining spatial and temporal variability with extreme event analysis, climate change attribution, and hydrological modelling will support strong decision-making for sustainable agriculture and the management of water resources.