Bayesian change point analysis for extreme daily precipitation

Abstract

ABSTRACTChange point (CP) analysis of extreme precipitation plays a key role to incorporate non‐stationarity in flood predictions under climate change. This article provides a Bayesian method to detect theCPfrequently appearing in extreme precipitation data. Unlike most published work based on a normal distribution, we allow for the model to follow a generalized Pareto distribution to fit extreme precipitation over a high threshold with aCP, which can effectively utilize tail behaviour of the distribution. The BayesianCPdetection is investigated on four models: a no change model, a shape change model, a scale change model, and both a shape and scale change model. Model selection is performed using the Bayes factor and model posterior probability; the posterior means of the unknownCPand the model parameters before and after theCPcan be obtained based on the selectedCPmodel. Simulation studies and a real data example are provided to demonstrate the proposed methodologies. Finally, model uncertainty issues in the frequency analysis are extensively discussed. It is found that considering the abrupt and sustainedCPin extreme precipitation is important when performing hydraulic or hydrologic design.