Author(s): Yong-Tak Kim; Jin-Guk Kim; Hyun-Han Kwon
Linked Author(s):
Keywords: No Keywords
Abstract: Quantifying future changes in rainfall pattern is of utmost importance for the water resources management. Especially, the estimation of intensity-duration-frequency (IDF) curves for rainfall data is routinely required in urban hydrology and needs to be revised to reflect the future changes in rainfall variability. Since climate change scenarios built on General Circulation Models (GCMs) can offer only rainfall projections at daily scale, a downscaling method is commonly employed to project the changes in rainfall at sub-daily scale to support the construction of IDF curves. In these contexts, this study developed and compared two different downscaling approaches. First, we focus on exploring a Copula function within a Bayesian inference framework to estimate rainfall IDF curves. Second, this study investigates a Local-Regional Scaling-Invariant approach in a Bayesian inference framework to estimate regional IDF curves in a changing climate. Moreover, Multiple CORDEXRCMs based on RCPs 4.5 and 8.5 scenarios was applied to explore changes in extreme rainfalls over South Korea.
Year: 2018