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.
Due to global warming, there is an increasing concern regarding persistent and severe heat waves. The maximum daily surface air temperature observations show strong non-stationary features, and the increased intensity and persistence of heat wave events have been observed in many regions. The heat wave persistence day frequency (HPF) curve, which correlates the intensity of a heat wave persistence event for days with return periods, can be a useful tool to analyze the frequency of heat wave events. In this study, non-stationary HPF curves are developed to explain the trend in the increase of the surface air temperature due to climate change, and their uncertainty is analyzed. The non-stationary HPF model can be used in climate change adaptation management such as public health, public safety, and energy management.
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