Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin, China

Abstract This study developed a hierarchical Bayesian (HB) model for local and regional flood frequency analysis in the Dongting Lake Basin, in China. The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model. The generalized extreme value (GEV) distribution was selected as the extreme flood distribution, and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate. The Markov Chain Monte Carlo (MCMC) method with Gibbs sampling was employed to calculate the posterior distribution in the HB model. The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles, while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles, due to the fact that the lower confidence bounds tended to decrease as the return periods increased. Furthermore, the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method, such as the homogeneity region assumption and the scale invariance assumption. The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site, but the index flood method with L-moments does not demonstrate this uncertainty directly. Therefore, the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme, and of quantifying the associated predictive uncertainty.