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.

在这项研究中，对印度Chaliyar流域的极端降水返回水平进行了不确定性分析，采用的是典型的浓度路径（RCP）4.5和8.5。在分析中，使用可靠性集合平均法对各种气候模型（针对RCP 4.5和8.5）的加权平均预测用于预测未来的极端情况。首先，研究了观测到的年度最大降水量（AMP）系列和未来总体平均AMP预测中非平稳性的存在。为此，使用Akaike信息准则和似然比检验，应用了三个广义极值（GEV）模型（一个具有恒定参数的固定模型和两个具有位置和比例参数趋势的非平稳模型）来评估拟合优度。在不确定性分析中使用了最佳拟合模型，并估算了极端降水返回水平的置信区间。不确定性分析采用非参数自举方法。研究结果表明，在RCP情景下，位置参数线性趋势，比例和形状参数恒定的非平稳GEV分布是最佳的拟合分布，而静态GEV分布最适合观测的AMP系列。通过不确定性分析获得的期望值和收益水平的置信界限表明，在预计的气候变化情景下，流域的极端降水将加剧。与RCP4.5方案相比，RCP8.5方案下收益水平的置信区间更宽，