A Bayesian peaks-over-threshold analysis of floods in the Itajaí-açu River under stationarity and nonstationarity

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• 作者：Artur Tiago Silva ; Maria Manuela Portela…
• 关键词：Nonstationary flood frequency analysis ; Peaks ; over ; threshold ; Bayesian inference ; Markov chain Monte Carlo ; Predictive distribution
• 刊名：Stochastic Environmental Research and Risk Assessment
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：185-204
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：1436-3259
• 卷排序：31

文摘

In this paper we revisit the case study of Silva et al. (Stoch Env Res Risk A. doi:10.1007/s00477-015-1072-y, 2015), the Itajaí-açu River at Apiúna (Southern Brazil), with an augmented data set and Bayesian inferential techniques. Nonstationary Poisson-GP models are used to study the joint influence of El Niño-Southern Oscillation (ENSO) and of upstream flood control structures on the flood regime at the study site. The Niño3.4 DJF index and a dimensionless reservoir index are used as covariates. Prior belief about the GP shape parameter is elicited by fitting the GEV distribution to AMS samples from 138 sites in Southern Brazil with 40 or more years of data and deriving the distribution from the estimates of that parameter. Following a data-driven exploratory analysis, a Markov chain Monte Carlo (MCMC) procedure is used to sample from the posterior distribution of parameters. Model evaluation and selection used Bayes factors and two information criteria. Results show evidence that, while upstream dams play a significant, though small, role in reducing flood hazard, the influence of the climate covariate is stronger, and an increase in ENSO amplitude in the last decades has led to the occurrence of higher annual maximum floods. MCMC samples are used to derive the Bayesian predictive distribution of annual flood quantiles and design life levels. Uncertainty analyses based on the posterior distribution of parameters and quantiles are presented.