Nonstationary regional flood frequency analysis based on the Bayesian method

Abstract

Most researches on regional flood frequency analysis (RFFA) have proved that the incorporation of hydrologic information (e.g., catchment attributes and flood records) from different sites in a region can provide more accurate flood estimation than using only the observed flood series at the site of concern. One kind of RFFA is based on the Bayesian method with prior information inferred from regional regression by using the generalized least squares (GLS) model, which is more flexible than other RFFA methods. However, the GLS model for regional regression is a stationary method and not suitable for coping with nonstationary prior information. In this study, in nonstationary condition, the Bayesian RFFA with the prior information inferred from regional regression by using the linear mixed effect (LME) model (i.e. a model that adds random effects to the GLS model) is investigated. Both the GLS-based and LME-based Bayesian RFFA methods have been applied to four hydrological stations within the Dongting Lake basin for comparison, and the results show that the performance of nonstationary LME-based Bayesian RFFA method is better than that of stationary GLS-based Bayesian RFFA method according to the deviance information criterion (DIC). Compared with the stationary GLS-based Bayesian RFFA method, changes in uncertainty of regression coefficients estimation of at-site flood distribution parameters are different from site to site by using the nonstationary LME-based Bayesian RFFA method. The use of nonstationary LME-based Bayesian RFFA method reduces design flood uncertainty, especially for the very small exceedance probability at the tail. This study extends the application of the Bayesian RFFA method to the nonstationary condition, which is helpful for nonstationary flood frequency analysis of ungauged sites.