Non-stationarity of extreme rainfall and its impact assessment in the north-eastern part of India

Abstract

This study investigates the variability in extreme rainfall in northeast India using stationary and non-stationary frequency analysis. Study identified non-stationarity in the annual maximum daily rainfall time series in northeast India, and it was shown that the non-stationary models exhibited more accurate results compared to modelling the series on the assumption of stationarity. Genetic algorithm is employed as optimization algorithm for parameter estimation in the method of maximum likelihood estimation of parameters hereby referred as MLE-GA. The results demonstrate better non-stationary parameter fitting using MLE-GA approach for GEV and PE3 distributions. Results obtained found that all the twelve rainfall stations considered in the region show significant amount of non-stationarity with values ranging from negative impact of -31.97% to positive impact of 38.6% for return period of 100 years. Overall, the research findings indicate non-stationarity in the frequency analysis of extreme rainfall series to have a significant impact on the northeastern region as a whole, and non-stationary modelling is more beneficial and reliable for any water management studies under climate change in the region.