Abstract
Abstract In this study, non-stationary frequency analysis was carried out to apply non-stationarity of extreme rainfall driven by climate change using the scale parameter of two parameters of the Gumbel distribution (GUM) as a co-variate function. The surface air temperature (SAT) or dew-point temperature (DPT) is applied as the co-variate. The optimal model was selected by comparing AICs, and 17 of 60 sites were found to be suitable for the non-stationary GUM model. In addition, SAT was chosen as the more appropriate co-variate among 13 of the 17 sites. As a result of estimating changes in design rainfall depth with future SAT rises at 13 sites, it is likely to increase by 10% in 2040 and 18% in 2070.
Highlights
The rise in temperature changes the distribution of extreme rainfall from the physical relationship between temperature and the saturated vapor pressure of water vapor in the atmosphere (O’Gorman & Schneider 2009)
The co-variate Gumbel distributions using surface air temperature (SAT) or dewpoint temperature (DPT) were fitted for annual maximum daily rainfall time series (AMR)
It can be seen that the optimal model of AMR at the Namhae site is a model using the SAT two days before the AMR event as a covariates were fitted to AMR
Summary
The rise in temperature changes the distribution of extreme rainfall from the physical relationship between temperature and the saturated vapor pressure of water vapor in the atmosphere (O’Gorman & Schneider 2009). Global warming in recent decades has produced unprecedented extreme rainfall events around the world (Min et al 2011). Future climate simulation results presented by many GCMs in the IPCC AR5 climate change scenarios predict an increase in extreme rainfall and flooding in a wide range of regions. The spatial resolution of GCMs, which is about 100-km, is not sufficient to quantify accurately extreme rainfall events at the regional level (Kunkel et al 1999). The current climate model is reported to have many problems in terms of stably simulating extreme rainfall, large-scale patterns of temperature changes are reliably simulated (Romps 2011). Looking at the response of extreme rainfall to global warming from appropriate analysis of observed temperature and rainfall data would be one of the rational approaches (O’Gorman 2012)
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