Bayesian Update of Hydrometeorological Probable Maximum Precipitation

Abstract

The hydrometeorological method to estimate probable maximum precipitation (PMP) has several problems, one of which is related to the update of PMP. This study proposes using the Bayesian method to overcome this problem of updating the PMP. Because this is an amendment to the hydrometeorological method of estimating PMP, other procedures like moisture maximization and storm transposition remain unchanged. This study considers the Seoul rain gauge station in Korea as an application example. A total of 110 major storms that occurred in Korea between 1969 and 2017 are transposed to Seoul to be used in this study. The results are that first, reasonable PMPs are derived only when the likelihood function is based on a step increase function from zero to one at the transposed precipitation. Second, PMPs are updated only when a new, higher transposed precipitation than the previous transposed storms is considered. Third, the PMPs estimated in this study are found to be very similar to the PMPs reported by the Korean government in 1987, 2000, and 2004. However, the behavior of the PMPs in this study, i.e., the duration-PMP relation, is smoother than the PMPs reported by the Korean government. Based on these findings, it is concluded that the Bayesian method can be easily and effectively used for the update of PMPs. Also, an important advantage of using the Bayesian method is that it may substitute for the envelopment procedure in PMP estimation.