According to the accepted climate change scenarios, the future rainfall in the Korean peninsula is expected to increase by 3–10%. The expected increase in rainfall leads to an increase of runoff that is directly linked to the stability of existing and newly installed hydraulic structures. It is necessary to accurately estimate the future frequency and severity of floods, considering increasing rainfall according to different climate change scenarios. After collecting observed flood data over twenty years in 12 watersheds, we developed a regional frequency analysis (RFA) for ungauged watersheds by adjusting flood quantiles calculated by a design rainfall-runoff analysis (DRRA) using natural flow data as an index flood. The proposed RFA was applied to estimate design floods and flood risks in 113 medium-sized basins in South Korea according to representative concentration pathway (RCP) scenarios. Regarding the future of the Korean peninsula, compared with the present, the flood risks were expected to increase by 24.85% and 20.28% on average for the RCP 8.5 and 4.5 scenarios, respectively.

As the environment changes, the stationarity assumption in hydrological analysis has become questionable. If nonstationarity of an observed time series is not fully considered when handling climate change scenarios, the outcomes of statistical analyses would be invalid in practice. This study established bivariate time-varying copula models for risk analysis based on the generalized additive models in location, scale, and shape (GAMLSS) theory to develop the nonstationary joint drought management index (JDMI). Two kinds of daily streamflow data from the Soyang River basin were used; one is that observed during 1976-2005, and the other is that simulated for the period 2011-2099 from 26 climate change scenarios. The JDMI quantified the multi-index of reliability and vulnerability of hydrological drought, both of which cause damage to the hydrosystem. Hydrological drought was defined as the low-flow events that occur when streamflow is equal to or less than Q80 calculated from observed data, allowing future drought risk to be assessed and compared with the past. Then, reliability and vulnerability were estimated based on the duration and magnitude of the events, respectively. As a result, the JDMI provided the expected duration and magnitude quantities of drought or water deficit.The GAMLSS framework has been applied to hydrological frequency analysis to provide the design criteria for managing future drought risk [11,12]. Bivariate frequency analysis based on the GAMLSS-copula model was also conducted and proved useful in hydrological prediction [13][14][15]. GAMLSS can be useful to estimate nonstationary index. For example, Wang et al. [16] proposed the time-dependent standardized precipitation index and Bazrafshan and Hejabi [17] suggested using the nonstationary reconnaissance drought index.This study performed statistical analyses with 26 climate change scenarios for the Soyang River basin in South Korea to construct the bivariate time-varying copula models based on GAMLSS theory. Copula functions were used to combine the drought information estimated from low-flow events, which were reliability and vulnerability. Then a new drought index, joint drought management index (JDMI), was developed based on the nonstationary copula model. The potential drought or water supply failure events were quantified as durations and magnitudes by JDMI-based risk assessment. Study Area and Data

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A heat wave countermeasure period was selected and operated to minimize the damage from heat waves in Korea. However, to this date, there are no clear ideas and methodology for selecting a heat wave countermeasure period, resulting in continuous budget expenditure incurred for its preparation and response activities. This requires an efficient selection of countermeasure periods. This study analyzed the time series data trend of heat index using Mann-Kendall and Pettitt test for cities and counties in Gyeongsangbuk-do, and it confirmed the current characteristics of increasing heat waves compared to- those in the past. In addition, K-means cluster analysis was applied to derive the city-wise heat wave characteristics. The cities and counties were classified into three based on the heatwave characteristics and starting point and ending point of each heat wave characteristic representing the years 2004 to 2021 between July 21 to August 11, July 16 to August 15, and July 11 to August 19. It was also confirmed that there was a significant difference from the heat wave countermeasure period (May 20~Sep 30) currently selected. Hence, in the future, economic losses may be minimized in terms of disaster preparedness and response through the selection of the heatwave countermeasure period reflecting temporal and spatial characteristics.