Assessment of Nonstationarity and Uncertainty in Precipitation Extremes of a River Basin Under Climate Change

In this study, an uncertainty analysis of extreme precipitation return levels was performed for the Chaliyar river basin, India, under representative concentration pathways (RCPs) 4.5 and 8.5. Weighted average projections of various climate models (for RCPs 4.5 and 8.5) using reliability ensemble averaging were used in the analysis for projecting the future extremes. To start with, the presence of nonstationarity in the observed annual maximum precipitation (AMP) series and the future ensemble averaged AMP projections were investigated. For this purpose, three generalized extreme value (GEV) models—one stationary model with constant parameters and two nonstationary models with trends in location and scale parameters—were applied to assess the goodness of fit using Akaike information criterion and likelihood ratio test. The best fit model was used in the uncertainty analysis, and the confidence bounds of extreme precipitation return levels were estimated. A nonparametric bootstrapping approach was followed in the uncertainty analysis. Results of the study suggest that a nonstationary GEV distribution with linear trend in location parameter and constant scale and shape parameters are the best fit distribution for the AMP series under the RCP scenarios, whereas the stationary GEV distribution fits the observed AMP series the best. The expected values and confidence bounds of return levels obtained from the uncertainty analysis reveal that precipitation extremes in the river basin would intensify under the projected climate change scenarios. Compared with the RCP4.5 scenario, the confidence intervals of return levels under the RCP8.5 scenario were wider, implying that uncertainty in the latter scenario is higher.