• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.170-170
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• 2018

In some case studies, the heavy precipitation events and rapid cyclogenesis in the extratropics can be caused by moist and warm tropical air masses. Tropical Moisture Exports (TME) correspond to the meridional transport of moist air masses, primarily born in tropical oceanic areas, to higher latitudes; and are closely related to flood events, especially in the mid-latitudes. The TME for the region of interest is mostly estimated by the back tracking approach using Lagrangian Analysis Tools (LAGRANTO) from ECMWF Re-Analysis (ERA) data. In this study, we aim to estimate the TME that are related to rainfall in Korea. The major moisture sources of the TME that contribute to heavy rainfall and extreme floods in Korea are identified. The TME is found to have significant connection with extreme events in Korea such as heavy rainfall and extreme flood events. The results show the most of the moisture sources comes from the west Pacific during the warm half of the year and it contributes significantly to the annual TME and is linked to the East Asian monsoon.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.857-865
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• 2013

집중 호우로 인한 피해가 증가하면서 다양한 기법들을 이용하여 강우량 예측에 대한 관심이 높아졌다. 최근에는 극단분포를 활용하여 강우량을 예측하려는 시도가 늘고 있다. 본 연구에서는 일반화 극단 분포를 활용하여 실제 서울시의 1973년부터 2010년까지 7월달의 사후예측분포를 생성하고, 수치적인 계산을 위해서 MCMC (Markov chain Monte Carlo)알고리즘을 활용하였다. 이 연구를 통해서 사후예측분포의 점추정값들을 비교하였고 2011년 7월달의 자료와 비교해 봤을 때 집중 호우의 확률이 증가한 것을 알 수 있었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.499-499
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• 2018

Hydro-meteorological extremes are trivial in these days. Therefore, it is important to identify extreme hydrological events in advance to mitigate the damage due to the extreme events. In this context, exploring temporal distribution of sub-daily extreme rainfall at multiple rain gauges would informative to identify different states to describe severity of the disaster. This study proposehidden Markov chain model (HMM) based rainfall analysis tool to understand the temporal sub-daily rainfall patterns over South Korea. Hourly and daily rainfall data between 1961 and 2017 for 92 stations were used for the study. HMM was applied to daily rainfall series to identify an observed hidden state associated with rainfall frequency and intensity, and further utilized the estimated hidden states to derive a temporal distribution of daily extreme rainfall. Transition between states over time was clearly identified, because HMM obviously identifies the temporal dependence in the daily rainfall states. The proposed HMM was very useful tool to derive the temporal attributes of the daily rainfall in South Korea. Further, daily rainfall series were disaggregated into sub-daily rainfall sequences based on the temporal distribution of hourly rainfall data.

• 한국물환경학회지
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• 제33권3호
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• pp.256-272
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• 2017

Extreme rainfall has become more frequent over the Korean peninsula in recent years, causing serious damages. In a changing climate, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to overestimate (or underestimate) the design rainfalls. A main objective of this study is to develop a stochastic disaggregation method of seasonal rainfall to hourly extreme rainfall, and offer a way to derive the nonstationary IDF curves. In this study, we propose a novel approach based on a Four-Parameter Beta (4P-beta) distribution to estimate the nonstationary IDF curves conditioned on the observed (or simulated) seasonal rainfall, which becomes the time-varying upper bound of the 4P beta distribution. Moreover, this study employed a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters. The proposed model showed a comparable design rainfall to that of GEV distribution under the stationary assumption. As a nonstationary rainfall frequency model, the proposed model can effectively translate the seasonal variation into the sub-daily extreme rainfall.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.390-390
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• 2019

Application of Hidden Markov Model (HMM) to the hydrological time series would be an innovative way to identify extreme rainfall events in a series. Even though the optimum number of hidden states can be identify based on maximizing the log-likelihood or minimizing Bayesian information criterion. However, occasionally value for the log-likelihood keep increasing with the state which gives false identification of the optimum hidden state. Therefore, this study attempts to identify optimum number of hidden states for Colombo station, Sri Lanka as fundamental approach to identify frequency and percentage of extreme rainfall events for the station. Colombo station consisted of daily rainfall values between 1961 and 2015. The representative station is located at the wet zone of Sri Lanka where the major rainfall season falls on May to September. Therefore, HMM was ran for the season of May to September between 1961 and 2015. Results showed more or less similar log-likelihood which could be identified as maximum for states between 4 to 7. Therefore, measure of central tendency (i.e. mean, median, mode, standard deviation, variance and auto-correlation) for observed and simulated daily rainfall series was carried to each state to identify optimum state which could give statistically compatible results. Further, the method was applied for the second major rainfall season (i.e. October to February) for the same station as a comparison.

• 한국지역지리학회지
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• 제17권5호
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• pp.505-520
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• 2011

본 연구에서는 호우의 변화경향을 유역별로 분석하였다. 이를 위해 한국을 6개의 유역으로 나누고 호우와 관련된 7개의 극한강수지수를 분석하여 변화지속성을 파악하였다. 호우량은 호우일수보다 증가경향이 더 지속적이다. 일강수량이 50mm 이상 강수일수와 95 퍼센타일 이상 강수량의 증가경향이 가장 지속적이다. 호우관련지수는 분석기간 동안 대부분 증가경향이지만 한강 유역, 낙동강 상류지역, 동해안 지역이 다른 유역에 비해 증가경향이 뚜렷하다. 금강 유역과 섬진강 유역은 호우의 증가경향이 통계적으로 유의하지 않고 변동성이 크다. 호우의 증가경향은 1970년대 중반 이후 한강과 낙동강 유역에서 지속적이지만 2000년대 중반 이후 증가경향이 지속적으로 나타나는 지점들이 감소한다. 이는 최근 호우의 빈도와 강도가 더욱 불규칙해지고 있음을 의미한다.

• 한국지구과학회지
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• 제28권5호
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• pp.585-597
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• 2007

The characteristics of the rainfall events on the Korean peninsula have been investigated by means of regional and global observational data collected from 1954 to 2004 with an emphasis on extreme cases $80\;mm\;day^{-1}$. According to our analysis, long-term annual rainfall anomalies show an increasing trend. This trend is pronounced in the month of August, when both the amount of monthly rainfall and the frequency of extreme events increase significantly. Composite maps on August during the 8 wet years reveal warm SST anomalies over the eastern Philippine Sea which are associated with enhanced convection and vertical motion and intensified positive SLP over central Eurasia during August. The rainfall pattern suggests that the most significant increase in moisture supply over the southern parts of China and Korea in August is associated with positive SLP changes over Eurasia and negative SLP changes over the subtropical western Pacific off the east coast of south China. The frequent generation of typhoons over the warm eastern Philippine Sea and their tracks appear to influence the extreme rainfall events in Korea during the month of August. The typhoons in August mainly passed the western coast of Korea, resulting in the frequent occurrence of extreme rainfall events in this region. Furthermore, anomalous cyclonic circulations over the eastern Philippine Sea also promoted the generation of tropical cyclones. The position of pressure systems - positive SLP over Eurasia and negative SLP over the subtropical Pacific - in turn provided a pathway for typhoons. The moisture is then effectively transported further north toward Korea and east toward the southern parts of China during the extreme rainfall period.

• 한국방재안전학회논문집
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• 제8권1호
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• pp.21-27
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• 2015

전세계적으로 기상이변이 빈번하게 발생하면서 기후변화가 수문환경에 미치는 영향에 대한 연구가 활발히 진행되고 있다. 기후변화 연구에는 대체로 이산화탄소 배출 시나리오에 근거한 GCM 모의 결과가 사용되며, GCM 자료를 바탕으로 미래의 수문량 변화를 예측하는 방법으로 진행된다. 기후변화가 강우에 미치는 영향과 관련해서는 기후변화가 총강우량에 미치는 영향에 대한 연구가 주를 이뤄왔으나 극한강우량에 미치는 영향에 대한 연구는 미흡한 실정이다. 또한 상세화 된 강우 자료가 월단위 또는 일 단위이기 때문에 극한홍수량 산정에 필요한 시단위 극한강우량 추정에는 한계가 있다. 본 연구에서는 기후변화가 극한강우량에 미치는 영향을 분석하기 위해 A2 시나리오에 근거한 ECHO-G GCM 모델의 모의 결과를 상세화 시켜 얻은 한강 유역내의 9개 강우 관측 지점의 일강우 자료를 바탕으로 강우의 scale invariance 특성에 근거한 시단위 확률강우량을 추정하였고, NSRPM(Neymann-Scott Rectangular Pulse Model)을 적용하여 시단위 확률강우량을 추정하였다. 이러한 방법으로 추정된 9개 지점의 확률강우량과 한강유역종합치수계획(국토해양부, 2008)에서 산정한 확률강우량을 비교하여 미래의 확률강우량 변화를 분석하였다. 분석된 한강 유역 내 강우 관측 지점의 확률강우량 변화 추이는 지점에 따라, 미래기간에 따라 상이하게 나타났으나 대체로 scaling에 의한 결과가 관측값에 근거한 확률강우량보다 대체로 큰 값을 보였고, NSRPM에 의한 결과는 미래 기간에 따라 관측값보다 크거나 작은 값을 보였다.

• 한국수자원학회논문집
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• 제43권4호
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• pp.337-351
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• 2010

극치사상을 예측하기 위한 기존의 빈도분석 결과의 이용에 대한 많은 문제점들이 부각되고 있다. 특히, 통계적 모형을 이용하기 위해서 흔히 사용되는 점근적 모형 (asymptotic model)의 합리적인 검토 없는 외삽 (extrapolation)은 산정된 확률 값을 과대 또는 과소평가하는 문제를 일으켜, 예측결과에 대한 불확실성을 과다하게 산정함으로써 불확실성에 대한 신뢰도를 감소시키는 문제가 있다. 그러므로 본 연구에서는 국내에서 극치강우사상을 포함한 강우자료의 빈도분석에 대한 연구사례를 제공하고 점근적 모형을 사용하는 경우 발생되는 불확실성을 감소시키기 위한 방법론을 제시하였다. 이를 위하여 본 연구에서는 극치강우사상의 빈도분석을 수행하는 데 있어서 최근 들어 여러 분야에서 다양하게 적용되고 있는 Bayesian MCMC (Markov Chain Monte Carlo) 방법을 사용하였으며, 그 결과를 최우추정방법 (Maximum likelihood estimation method)과 비교하였다. 특히 강우사상의 점 빈도분석에 흔히 이용되는 확률밀도함수로 GEV (Generalized Extreme Value) 분포와 Gumbel 분포를 모두 고려하여 두 분포의 결과를 비교하였으며, 이 과정에서 각각의 산정결과 및 불확실성은 근사식을 이용한 최우추정방법과 Bayesian 방법을 이용하여 각각 비교 및 분석되었다.

• 대기
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• 제32권1호
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.