Hierarchical Bayesian Model for Assessing Extreme Daily Rainfall Through Combining Climatic and Topographic Information

Abstract

Frequency analysis of the annual maximum rainfall time series is essential for designing infrastructures to provide protection against local floods and related events. However, the results of the frequency analysis obtained are ambiguous. In this study, we aimed to develop a spatial hierarchical Bayesian model framework through combining the climatic and topographic information. To confirm the applicability of the proposed method, the results of at-site frequency analysis and regional frequency analysis using the index flood method were compared in the Busan-Ulsan-Gyeongnam region. Furthermore, a hierarchical Bayesian model was developed, in which the parameters of the generalized logistic distribution comprised relatively simple covariate relationships upon considering the possibility of expansion into various probability distributions and more complex covariate structures. The uncertainty of this model was analyzed using the coefficient of variation of rainfall quantile ensemble. The results confirmed that the regional frequency analysis using the hierarchical Bayesian model combined with the climatic and topographic information could provide an accurate estimate of extreme daily rainfall with relatively good agreement with the estimate at a specific site, but is a more reliable approach.