Bayesian model for hydrological processes with jumping location and varying dispersion

This study proposes a Bayesian model for the nonstationary generalized extreme value (GEV) distributions with abrupt changes of location parameters and smooth change of scale parameters. Our motivation is that the quantiles of hydrological process depend on scale parameter as well as location parameter in the GEV distribution. The proposed model extends the nonstationary Bayesian model with jumping location parameters on the time domain, and it provides a wider class of models to explain abrupt location changes and smooth dispersion changes simultaneously as well as separately in the hydrological processes. This study also suggests the use of the Bayesian model selection procedure by logarithm of the pseudo marginal likelihood (LPML). Numerical study reveals that the proposed method can provide viable estimates of return levels through model selection. We apply the proposed method to analyze annual maximum precipitation acquired from the Korea Meteorological Administration.