• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.39-39
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• 2015

극치 수문(Hydrologic extremes)분야에서는 수문자료의 분포에 따라 Gumbel, GEV, 그리고 GLO 분포와 같은 다양한 확률통계 분포형이 존재한다. GEV와 GLO 분포형의 경우 Gumbel 분포형과 달리 형상매개변수가 포함된 3변수 분포형으로써 이상 기후 현상으로 인한 잦은 극치 수문사상을 표현하는데 좀 더 유연한 것으로 알려져 있다. 특히 GLO 분포형의 경우 영국에서 홍수빈도해석 시 적정분포형으로 선정된바 있다(Institute of Hydrology, 1999). 다양한 분포형 중에서 표본 자료를 대표할 수 있는 분포형을 선정하는 통계적 기법이 적합도 검정이다. 적합도 검정에는 $x^2$-검정, Cramer von-Mises 검정, Kolmogorov-Smirnov 검정, PPCC(probability plot correlation coefficient, 확률도시 상관계수)검정 등이 있으며 그 중 PPCC 검정은 이용방법이 간편하면서도 뛰어난 기각능력을 보이는 것으로 알려져 있다. 본 연구에서는 극치 수문분야에서 널리 이용되고 있는 GLO 분포형을 대상으로 자료의 왜곡도 영향을 고려할 수 있는 확률도시 상관계수 검정의 검정통계량을 추정하여 보았다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.321-325
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• 2007

3개의 매개변수(location, scale, shape)로 이루어진 GEV와 GLO 분포는, 미국의 공식적인 홍수빈도 분포인 Log Pearson Type III와 함께 수문분야에서 중요한 위치를 차지하고 있다. 본 연구에서는 Monte Carlo 실험을 이용하여 GEV와 GLO 분포에서 서로 다른 두 지점의 유출량 자료를 생성하여 L-CV(L-moment Coefficient of Variation; $\tau_2$)와 L-CS(L-moment Coefficient of Skewness; $\tau_3$)를 추정하였으며, L-moment 추정값들 간의 교차상관$(\tau_2-\tau_2,\;\tau_3-\tau_3,\;\tau_2-\tau_3)$과 유출량 자료간의 교차상관의 관계를 Simple Power 함수를 이용하여 유도하였다. 실험 과정에서 GEV와 GLO 분포가 비현실적인 음수 유출량을 생성하여, 실험 결과에 큰 영향이 있음을 확인하여, 두 분포에서 생성된 유출량 자료에서 음수값을 제외한 GEV+와 GLO+ 분포를 이용하여 관계식을 유도하고 이를 GEV와 GLO 분포의 결과와도 비교하였다. 본 연구에서 도출된 관계식은 향후 Generalized Least Square 회귀식을 이용하여 홍수분포의 지역 매개변수를 추정하기 위해 활용성이 클 것으로 기대한다.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.19-36
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• 2024

The global seasonal forecasting system version 6 (GloSea6) operated by the Korea Meteorological Administration for 1- and 3-month prediction products does not include complex atmospheric chemistry-aerosol physical processes (UKCA). In this study, low-resolution GloSea6 and GloSea6 coupled with UKCA (GloSea6-UKCA) were installed in a CentOS-based Linux cluster system, and preliminary prediction results for the spring of 2000 were examined. Low-resolution versions of GloSea6 and GloSea6-UKCA are highly needed to examine the effects of atmospheric chemistry-aerosol owing to the huge computational demand of the current high resolution GloSea6. The spatial distributions of the surface temperature and daily precipitation for April 2000 (obtained from the two model runs for the next 75 days, starting from March 1, 2000, 00Z) were compared with the ERA5 reanalysis data. The GloSea6-UKCA results were more similar to the ERA5 reanalysis data than the GloSea6 results. The surface air temperature and daily precipitation prediction results of GloSea6-UKCA for spring, particularly over East Asia, were improved by the inclusion of UKCA. Furthermore, compared with GloSea6, GloSea6-UKCA simulated improved temporal variations in the temperature and precipitation intensity during the model integration period that were more similar to the reanalysis data. This indicates that the coupling of atmospheric chemistry-aerosol processes in GloSea6 is crucial for improving the spring predictions over East Asia.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.827-837
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• 2012

The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.645-656
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• 2016

The study performed a regional flood frequency analysis and proposed a regression equation to estimate design floods corresponding to return periods for ungauged basins in Geum-river basin. Five preliminary tests were employed to investigate hydrological independence and homogeneity of streamflow data, i.e. the lag-one autocorrelation test, time homogeneity test, Grubbs-Beck outlier test, discordancy measure test ($D_i$), and regional homogeneity measure (H). The test results showed that streamflow data were time-independent, discordant and homogeneous within the basin. Using five probability distributions (generalized extreme value (GEV), three-parameter log-normal (LN-III), Pearson type 3 (P-III), generalized logistic (GLO), generalized Pareto (GPA)), comparative regional flood frequency analyses were carried out for the region. Based on the L-moment ratio diagram, average weighted distance (AWD) and goodness-of-fit statistics ($Z^{DIST}$), the GLO distribution was selected as the best fit model for Geum-river basin. Using the GLO, a regression equation was developed for estimating regional design floods, and validated by comparing the estimated and observed streamflows at the Ganggyeong station.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.761-771
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• 2016

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km ($0.83^{\circ}{\times}0.56^{\circ}$) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation ($R^2=0.60$, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a -3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.335-335
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• 2018

최근 이상기후현상으로 지구상의 여러 지역에서 극치 수문 사상의 발생 빈도와 강도가 날로 증가하고 있는 추세이다. 이에 대해 수공구조물의 설계를 위한 극치강우사상의 빈도해석에 있어서 적절한 확률분포모형의 적용은 매우 중요하다. 이에 수문통계분야에서는 generalized extreme value(GEV), generalized logistic(GLO), Gumbel(GUM) 모형과 같은 극치 분포를 이용한 수문통계적 특성에 대한 접근이 주로 이루어지고 있다. 하지만 우리나라 강우 사상의 경우 GEV 분포와 GUM 분포가 비교적 적합한 것으로 알려져 있지만 하나의 형상매개변수를 가지고 있어 분포 모형이 표현할 수 있는 통계적 특성에 한계를 가지고 있다. 기존의 GEV나 GUM분포로는 적절히 재현되지 않는 자료들을 분석하기 위해서 두 개의 형상매개변수를 가지는 분포형에 대한 연구가 진행되고 있다. 이에 본 연구에서는 두 개의 형상매개변수를 가지는 Burr XII 분포형의 우리나라 극한 강우자료에 대한 적용성을 평가하였다. Burr XII 분포형은 gamma나 exponential 분포 모형처럼 양의 확률변수만을 가지고, Cauchy나 Pareto 분포 모형처럼 두꺼운 꼬리(heavy-tailed distribution) 형상을 나타내기 때문에 비교적 큰 확률변수가 빈번히 나타나는 극치사상에도 적합한 것으로 알려져 있다. 이를 위해 Burr XII 분포 모형을 이용하여 우리나라 강우자료에 대해 지점빈도해석 및 지역빈도해석을 수행하고 우리나라 강우자료에 비교적 적합하다고 알려진 분포인 GEV, GLO, GUM 분포형을 통해 산정된 결과와 비교하였다.

• Korean Management Science Review
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• v.24 no.2
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• pp.115-126
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• 2007

Weekly minima of daily log returns of Korean composite stock price index 200 and its five industry-based business divisions over the period from January 1990 to December 2005 are fitted using two block-based extreme distributions: Generalized Extreme Value(GEV) and Generalized Logistic(GLO). Parameters are estimated using the probability weighted moments. Applicability of two distributions is investigated using the Monte Carlo simulation based empirical p-values of Anderson Darling test. Our empirical results indicate that both the GLO and GEV models seem to be comparably applicable to the weekly minima. These findings are against the evidences in Gettinby et al.[7], who claimed that the GEV model was not valid in many cases, and supported the significant superiority of the GLO model.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.551-562
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• 2017

In order to utilize 6-month precipitation forecasts (6 months at maximum) of Global Seasonal Forecast System version 5 (GloSea5), which is being provided by KMA (Korea Meteorological Administration) since 2014, for water resources management as well as other applications, it is needed to correct the forecast model's quantitative bias against observations. This study evaluated applicability of bias-correction skill in GloSea5 and selected an optimal method among 11 techniques that include probabilistic distribution type based, parametric, and non-parametric bias-correction to fix GloSea5's bias in precipitation forecasts. Non-parametric bias-correction provided the most similar results with observed data compared to other techniques in hindcast for the past events, yet relatively generated some discrepancies in forecast. On the contrary, parametric bias-correction produced the most reliable results in both hindcast and forecast periods. The results of this study are expected to be applicable to various applications using seasonal forecast model such as water resources operation and management, hydropower, agriculture, etc.