• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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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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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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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.

• Journal of Korean Society on Water Environment
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• v.33 no.3
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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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.

• Journal of the Korean association of regional geographers
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• v.17 no.5
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• pp.505-520
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• 2011

This study is aimed to examine the change on extreme precipitation events in South Korea. The country is divided into six basins, and seven extreme precipitation indices-related to heavy rainfall are analyzed at sixty weather stations. The increasing trend in amount of heavy rainfall is more stable than that in days of heavy rainfall. The increasing trend is the most stable when days of rainfall are more than 50 mm, or rainfall is over the 95th percentile. The precipitation indices-related to heavy rainfall was mostly increasing during analysis period. Especially, basins of the Han river, the upper Nakdong river, and the Eastern coast show significantly increasing trends compared to the other basins. However, the increasing trends of the Geum river and the Seomjin river are not statistically significant. Heavy rainfall events had stably increased in the Han and the Nakdong rivers since the mid-1970s. However, the number of stably increasing regions has decreased since the mid-2000s. It means that the frequency and intensity of the recent heavy rainfall become more irregular.

• Journal of the Korean earth science society
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• v.28 no.5
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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.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.21-27
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• 2015

Recent researches show that climate change has impact on the rainfall process at different temporal and spatial scales. The present paper is focused on climate change impact on sub-daily rainfall quantile of Han River basin in South Korea. Climate change simulation outputs from ECHO-G GCM under the A2 scenario were used to estimate daily extreme rainfall. Sub-daily extreme rainfall was estimated using the scale invariance concept. In order to assess sub-daily extreme rainfall from climate change simulation outputs, precipitation time series were generated based on NSRPM (Neyman-Scott Rectangular Pulse Model) and modified using the ratio of rainfall over projection periods to historical one. Sub-daily extreme rainfall was then estimated from those series. It was found that sub-daily extreme rainfall in the future displayed increasing or decreasing trends for estimation methods and different periods.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Atmosphere
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• v.32 no.1
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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.