Uncertainty Quantification of Future Design Rainfall Depths in Korea

Abstract

One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios. However, the projected future design rainfall intensity varies greatly depending on which climate model is applied. In this study, future rainfall Intensity–Duration–Frequency (IDF) curves are projected using various combinations of climate models. Future Ensemble Average (FEA) is calculated using a total of 16 design rainfall intensity ensembles, and uncertainty of FEA is quantified using the coefficient of variation of ensembles. The FEA and its uncertainty vary widely depending on how the climate model combination is constructed, and the uncertainty of the FEA depends heavily on the inclusion of specific climate model combinations at each site. In other words, we found that unconditionally using many ensemble members did not help to reduce the uncertainty of future IDF curves. Finally, a method for constructing ensemble members that reduces the uncertainty of future IDF curves is proposed, which will contribute to minimizing confusion among policy makers in developing climate change adaptation policies.

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

“…In terms of climate change adaptation policy, the rate of change of the design rainfall depth at a specific duration for a specific return level at a specific site should eventually be given as a single value. This single value will be the ensemble average no matter what method we take [2,11]. However, as shown in previous studies [10,17] and Figure 4a of this study, the future rainfall extremes estimated by the stationary frequency analysis method using future rainfall data showed a lot of variation depending on which climate model combination was applied.…”

“…In most studies, including those of Hosseinzadehtalaei et al [17] and Kim et al [11], the future rainfall extremes taking into account climate change scenarios are estimated based on the ratio of rainfall quantiles under present climate conditions to rainfall quantiles under future climate conditions (i.e., rate of change). When estimating the rainfall quantile in present or future climate conditions, stationary conditions are generally assumed for each period (e.g., 1980-2010, 2021-2050, and 2071-2100).…”

“…However, even recently developed climate models simulate temperature stably [3][4][5] but still expose many limitations to the simulation of rainfall extremes [6][7][8][9][10]. As can be found in the results of Kim et al [11], very different future projection outputs are obtained depending on which RCM-simulated future rainfall data are used to analyze rainfall extremes. Recently, a series of attempts have been made to investigate future rainfall extremes based on the relationship between temperature and rainfall extremes [12][13][14].…”

Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates

“…In this study, individual rainfall events were first separated from the daily rainfall series. The applied interevent time definition (IETD) is 1 d (Kim and Han, 2010). Then, in a rainfall event, it was set to select only one value at most as a POT series.…”

Quantification of Heat Wave and Projection of Future Heat Wave Using H-Index

“…많은 연구결과들에 따르면 미래에 우리나라의 기온은 현재의 기온보다 크게 높아질 것으로 보고되고 있으므로 Sim et al, 2014;Won et al, 2018;Sim et al, 2019;Lee et al, 2020), 폭염으로 인한 피해 또한 증가할 것으로 전망된다 (Kim et al, 2019 (Cannon et al, 2015;Lee et al, 2016;Cha et al, 2017;Lee and Kim, 2018;Choi et al, 2019;Kim et al, 2020).…”