Analysis of Rainfall Using Family of Innovative Trend Methods for Climate Change Detection

Trend analysis of hydro-meteorological data is one of the essential procedures in climate change detection studies. Innovative Trend Analysis (ITA) methods are relatively new graphical procedure for determining the trends of time series datasets. This paper proposes the applications of two recent variants of ITA method namely Innovative Polygonal Trend Analysis (IPTA) and Innovative Trend Pivot Analysis (ITPA) method for analysing the temporal trend along with the trend propagation of monthly and seasonal rainfall data of (1871–2016) period of All India main land and North East (NE) region, to capture the signature of climatic changes. The mean-based and standard deviation-based IPTA could capture the progression in the trend. More complex polygons and presence of two or more cycles indicating difference in climatic conditions are obtained for standard deviation in comparison with mean-based analysis. The trend length and slope are computed which depicted the amount as well as transition associated with the rainfall in between the months. A decreasing trend is obtained for the series of months as well as seasons from standard deviation analysis and the highest magnitude of rainfall transition is observed from May to June. To examine the presence of sub trends and risks, ITPA method is applied. Positive trend slope indicates an increase in rainfall from month to month and season to season. From IPTA analysis there is no month or season in which there is a significant change between the first and second half of the datasets of the study period. Thus, the risk factor is low for All India region, which conclude that the climatic change impacts have more visible changes at local spatial scales than very large spatial domain like All India.