• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.1009-1019
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• 2009

In this research, various characteristics of South Korea's design flood have been examined by deriving appropriate design flood, using data obtained from careful observation of actual floods occurring in selected main watersheds of the nation. 19 watersheds were selected for research in Korea. The various characteristics of annual rainfall were analyzed by using a moving average method. The frequency analysis was decided to be performed on the annual maximum flood of succeeding one year as a reference year. For the 19 watersheds, tests of basic statistics, independent, homogeneity, and outlier were calculated per period of annual maximum flood series. By performing a test using the LH-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test, among applied distributions of Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distribution was found to be adequate compared with other probability distributions. Parameters of GEV distribution were estimated by L, L1, L2, L3 and L4-moment method based on the change in the order of probability weighted moments. Design floods per watershed and the periods of annual maximum flood series were derived by GEV distribution. According to the result of the analysis performed by using variation rate used in this research, it has been concluded that the time for changing the design conditions to ensure the proper hydraulic structure that considers recent climate changes of the nation brought about by global warming should be around the year 2002.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.47-57
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• 2019

Purpose: This paper aims to investigate the concentration of fine particulate matter (PM2.5) in the Seoul area by predicting unhealthy days due to PM2.5 and comparing the regional differences. Methods: The extreme value theory is adopted to model and compare the PM2.5 concentration in each region, and each best model is selected through the goodness of fitness test. The maximum likelihood estimation technique is applied to estimate the parameters of each distribution, and the fitness of each model is measured by the mean absolute deviation. The selected model is used to estimate the number of unhealthy days (above $75{\mu}g/m^3$ PM2.5 concentrations) in each region, with which the actual number of unhealthy days are compared. In addition, the level of PM2.5 concentration in each region is analyzed by calculating the return levels for periods of 6 months, 1 year, 3 years, and 5 years. Results: The Mapo (MP) area revealed the most unhealthy days, followed by Gwanak (GW) and Yangcheon (YC). On the contrary, the number of unhealthy days was low in Seodaemun (SDM), Songpa (SP) and Gangbuk (GB) areas. The return level of PM2.5 was high in Gangnam (GN), Dongjak (DJ) and YC. It will be necessary to prepare for PM2.5 than other regions. On the contrary, Gangbuk (GB), Nowon (NW) and Seodaemun (SDM) showed relatively low return levels for PM2.5. However, in most of the regions of Seoul, PM25 is generated at a very poor level ($75{\mu}g/m^3$) every 6months period, and more than $100{\mu}g/m^3$ PM2.5 occur every 3 years period. Most areas in Seoul require more systematic management of PM2.5. Conclusion: In this paper, accurate prediction and analysis of high concentration of PM2.5 were attempted. The results of this research could provide the basis for the Seoul Metropolitan Government to establish policies for reducing PM2.5 and measuring its effects.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.511-514
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• 2003

This study was conducted to estimate the design rainfall by the determination of best fitting order for Higher Probability Weighted Moments of the annual maximum series according to consecutive duration at sixty-five rainfall stations in Korea. Design rainfalls were obtained by generalized extreme value distribution which was selected to be suitable distribution in 4 applied distributions and by L, L1, L2, L3 and L4-moment. The best fitting order for Higher Probability Weighted Moments was determined with the confidence analysis of estimated design rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.278-278
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• 2023

강우는 수문 현상을 구성하는 가장 기본적인 요소로, 관측된 강우 자료의 정확한 분석 결과는 수자원 정책과 계획·관리에 합리적 판단 근거로 작용한다. 강원도는 지난 2002년 태풍 루사로 인하여 일 강수량 870.5mm의 폭우가 기록된 지역으로, 극한 강우로 인한 막대한 피해가 해마다 발생하고 있다. 특히, 강원도 지역은 태백산맥 중심의 산악지형과 동해의 영향을 직·간접적으로 받는 강우 사상의 특성이 집중호우, 폭설 등으로 나타난다. 본 연구에서는 강원도 지역 극한 강우의 통계적 특성을 파악하기 위하여 국가수자원관리종합정보시스템에서 제공하는 강우 자료를 수집하여 분석하였다. 또한, 최근 5년간 극한 강우의 변동 특성을 정량적으로 분석하고자 2022년까지의 자료를 구축하여 기존 『홍수량 산정 표준 지침』 작성 시 산정한 결과(2017년까지의 자료)와 비교·분석하였다. L-모멘트법 기반의 Generalized Extreme Value (GEV) 분포형을 이용하였고, 지역빈도해석을 수행하여 확률강우량을 산정하였다.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.3
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• pp.53-62
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• 2009

This study was carried out to select optimal probability distribution based on design accumulated monthly mean inflow from the viewpoint of drought by Gamma (GAM), Generalized extreme value (GEV), Generalized logistic (GLO), Generalized normal (GNO), Generalized pareto (GPA), Gumbel (GUM), Normal (NOR), Pearson type 3 (PT3), Wakeby (WAK) and Kappa (KAP) distributions for the observed accumulative monthly mean inflow of Chungjudam. L-moment ratio was calculated using observed accumulative monthly mean inflow. Parameters of 10 probability distributions were estimated by the method of L-moments with the observed accumulated monthly mean inflow. Design accumulated monthly mean inflows obtained by the method of L-moments using different methods for plotting positions formulas in the 10 probability distributions were compared by relative mean error (RME) and relative absolute error (RAE) respectively. It has shown that the design accumulative monthly mean inflow derived by the method of L-moments using Weibull plotting position formula in WAK and KAP distributions were much closer to those of the observed accumulative monthly mean inflow in comparison with those obtained by the method of L-moment with the different formulas for plotting positions in other distributions from the viewpoint of RME and RAE.

• Wind and Structures
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• v.19 no.5
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• pp.467-480
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• 2014

The estimation of wind speed values used in codes and standards is an integral part of the wind load evaluation process. In a number of codes and standards, wind speeds outside of tropical cyclone prone regions are estimated using a single probability distribution developed from observed wind speed data, with no distinction made between the types of causal wind hazard (e.g., thunderstorm). Non-tropical cyclone wind hazards (i.e., thunderstorm, non-thunderstorm) have been shown to possess different probability distributions and estimation of non-tropical cyclone wind speeds based on a single probability distribution has been shown to underestimate wind speeds. Current treatment of non-tropical cyclone wind hazards in worldwide codes and standards is touched upon in this work. Meteorological data is available at a considerable number of United States (U.S.) stations that have information on wind speed as well as the type of causal wind hazard. In this paper, probability distributions are fit to distinct storm types (i.e., thunderstorm and non-thunderstorm) and the results of these distributions are compared to fitting a single probability distribution to all data regardless of storm type (i.e., co-mingled). Distributions fitted to data separated by storm type and co-mingled data will also be compared to a derived (i.e., "mixed") probability distribution considering multiple storm types independently. This paper will analyze two extreme value distributions (e.g., Gumbel, generalized Pareto). It is shown that mixed probability distribution, on average, is a more conservative measure for extreme wind speed estimation. Using a mixed distribution is especially conservative in situations where a given wind speed value for either storm type has a similar probability of occurrence, and/or when a less frequent storm type produces the highest overall wind speeds. U.S. areas prone to multiple non-tropical cyclone wind hazards are identified.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.183-183
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• 2015

수공구조물 설계 시 중요한 요소 중 하나인 확률강우량은 일반적으로 고정지속기간별 강우량에 대하여 일변량 빈도해석을 수행하고 가장 적절한 분포형을 선택하는 지점빈도해석의 과정을 거친다. 그러나 일변량 빈도해석을 수행하기 위해서는 지속시간을 고정하고 강우량의 변화로만 해석해야 단점이 있으며 이를 보완하기 위해 본 연구에서는 다변량 확률모형인 copula 모형을 이용하여 이변량 빈도해석을 수행하였다. 확률변수로는 강우량과 지속기간(hr)을 사용하였고, 주변분포형으로 강수량 - Gumbel (GUM), generalized logistic (GLO) 분포형, 지속기간(hr) - generalized extreme value (GEV), GUM, GLO 분포형을 사용하였으며, copula 모형은 Gumbel-Hougaard 모형을 이용하였다. 주변분포형의 매개변수는 일반적으로 가장 많이 사용하는 확률가중모멘트법을 이용하여 추정하였으며, copula 모형의 매개변수는 maximum pseudolikelihood(MPL) 방법을 사용하였다. 이를 통해 얻어진 이변량 빈도해석의 확률강우량 결과와 기존 지점빈도해석의 결과를 비교하였다.

• Atmosphere
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• v.22 no.2
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• pp.221-231
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• 2012

Representative Concentration Pathways (RCP) are the latest emission scenarios recommended to use for the fifth assessment report of Intergovernmental Panel on Climate Change. This study investigates the projection of extreme precipitation in South Korea during the forthcoming 21st Century using the generalized extreme value (GEV) analysis based on two different RCP conditions i.e., RCP 4.5 and 8.5. Maximum daily precipitation required for GEV analysis for RCP 4.5 and 8.5 are obtained from a high-resolution regional climate model forced by the corresponding global climate projections, which are produced within the CMIP5 framework. We found overall increase in frequency of extreme precipitation over South Korea in association with climate change. Particularly, daily extreme precipitation that has been occurred every 20 years in current climate (1980~2005) is likely to happen about every 4.3 and 3.4 years by the end of 21st Century (2070~2099) under the RCP 4.5 and 8.5 conditions, respectively.

• 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.

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.889-902
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• 2011

In this study, frequency analysis using drought index had implemented for the derivation of drought severity-duration-frequency (SDF) curves to enable quantitative evaluations of past historical droughts having been occurred in Korean Peninsular. Seoul, Daejeon, Daegu, Gwangju, and Busan weather stations were selected and precipitation data during 1974~2010 (37 years) was used for the calculation of Standardized Precipitation Index (SPI) and frequency analysis. Based on the results of goodness of fit test on the probability distribution, Generalized Extreme Value (GEV) was selected as most suitable probability distribution for the drought frequency analysis using SPI. This study can suggest return periods for historical major drought events by using newrly derived SDF curves for each stations. In case of 1994~1995 droughts which had focused on southern part of Korea. SDF curves of Gwangju weather station showed 50~100 years of return period and Busan station showed 100~200 years of return period. Besides, in case of 1988~1989 droughts, SDF of Seoul weather station were appeared as having return periods of 300 years.