Abstract
Presence of long memory in climatic variables is frequently observed. The trend assessment becomes difficult in the presence of long-memory as the usual methods are not capable to take care of this property during trend estimation. In order to estimate the trend in presence of long memory, the non-parametric wavelet method has become popular in the recent time. The discrete wavelet transformation (DWT) re-expresses a time-series in terms of coefficients that are associated with a particular time and a particular scale. In the present study, DWT has been applied to estimate the monthly rainfall trend for the monsoon months: June-September in ten selected sub-divisions of India using “Haar” wavelet filter. The results from DWT were cross checked with the non-parametric Mann-Kendall (M-K) test. The investigation reveals that the monthly rainfall trend for the monsoon months of different sub-divisions in India are significantly decreasing over the years. However, in some of the sub-divisions, rainfall trend is increasing. DWT reveals significant trend in most of the sub-divisions whereas M-K test reveals that most of the trends are not significant at 5% level.
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