Non-Stationary Frequency Analysis of Future Extreme Rainfall using CMIP5 GCMs over the Korean Peninsula

Jeong, Min-Su; Yoon, Sun‐Kwon; Park, Seoyeon; Jung, Jae-Wook; Lee, Joo-Heon

doi:10.9798/kosham.2018.18.3.73

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Recent researches on the estimation of future rainfall extremes in Korea are as follows: Choi et al [20] derived future IDF curves from future precipitation data obtained from RCMs using a scale-invariance technique, and Jeong et al [50] performed frequency analysis of future rainfall extremes using annual maximum rainfall time series using 8 GCMs. Kim et al [51] used the RCM precipitation data to estimate the future design rainfall depth and used it as input data for the runoff model, and Kim et al [52] estimated the future rainfall extremes using 13 GCMs and 6 RCMs, and then evaluated the simulation performance of each model.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Future Design Rainfall Depths in Korea

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Future Design Rainfall Depths in Korea

et al. 2019

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Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

Jeong¹,

Lee²,

Lee³

et al. 2020

J. Korean Soc. Hazard Mitig

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In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena. 요 지 본 연구는 서울지역을 대상으로 하여 기후변화에 따른 정상성 및 비정상성을 가정하여 빈도분석을 수행하였고, 관측 및 예측 자료에 따른 현재와 미래의 지속시간별 확률강우량 산정 및 비교⋅검토를 수행하고자 하였다. 현재 상태의 확률강우량 산정을 위해 기상청에서 제공되는 지속시간별 강우자료를 수집하였고, 미래 입력 자료 구축을 위해 29개 GCMs 시나리오별 RCP 8.5에 대한 일별 강우량 자료를 이용하여 시간적 상세화(Downscaling) 및 편의보정(Bias correction) 등의 과정을 통해 지속시간별 1, 2, 3, 6, 12, 24, 48시간에 대한 예측자료를 생성하였다. 지속시간별 강우자료를 바탕으로 연최대치 계열을 추출하여 관측치는 정상성을 가정한 빈도해석을 수행하였으며, 최적확률분포형 적용을 통한 확률강우량 산정으로 미래 기간별 강우강도-지속시간-빈도(Intensity-Duration-Frequency, IDF) 곡선을 작성하였다. 또한, RCP 8.5에 대한 비정상성(Non-stationary) 빈도분석을 위해서는 베이지안을 기반으로 샘플링이 이루어지는 DE-MC 기법을 적용하였다. 예측에 활용된 29개 기후변화 시나리오 자료에 대한 각각의 산정 결과는 분위추정을 통해 결과를 제시하였다. 서울지점을 대상으로 비정상정 빈도해석 결과 재현기간 30년은 94.4mm이고, 50년은 101.7 mm, 100년은 111.5 mm로 분석되었으며, 정상성 빈도해석 결과와 비교하였을 때 앙상블 평균값은 약 5mm/hr 정도 높게 산정되었다. 서울지역의 기후변화에 따른 수문특성의 변화를 고려하는 경우 지속시간이 커질수록 경향성 및 변동폭이 증가를 보임에 따라 방재성능목표강우량에 대한 보다 세밀한 검토가 필요한 것으로 판단된다.

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Non-Stationary Frequency Analysis of Future Extreme Rainfall using CMIP5 GCMs over the Korean Peninsula

Cited by 2 publications

References 17 publications

Uncertainty Quantification of Future Design Rainfall Depths in Korea

Uncertainty Quantification of Future Design Rainfall Depths in Korea

Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

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