• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.4
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• pp.25-36
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• 2004

This study was conducted to derive the design rainfall by the consecutive duration using the at-site frequency analysis. Using the errors, K-S tests and LH-moment ratios, Log Pearson type 3 (LP3) and Generalized Extreme Value (GEV) distributions of Gamma and Non-Gamma Family, respectively were identified as the optimal probability distributions among applied distributions. Parameters of GEV and LP3 distributions were estimated by the method of L and LH-moments and the Indirect method of moments respectively. Design rainfalls following the consecutive duration were derived by at-site frequency analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE) and relative efficiency (RE) in RRMSE for the design rainfall derived by at-site analysis in the observed and simulated data were computed and compared. It has shown that at-site frequency analysis by GEV distribution using L-moments is confirmed as more reliable than that of GEV and LP3 distributions using LH-moments and Indirect method of moments in view of relative efficiency.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.479-485
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• 1999

This study was conducted to derived design floods by Generalized Extreme Value(GEV) distributiion for the annual maximum series at ten watersheds along Han, Nagdong, Geum , Yeongsan and Seomjin river systems. Adequency for the analysis of flood data used in this study was established by the test of Independence, Homogeneity , detection of Outliers. Coefficient of variation , skewness and kurtosis were calculated by the L-Moment, and LH-Moment ratio respectively. Parameters were estimated by the Method of L-Method of LH-Moment. Design floods obtained by Method of L-Moments and LH-Moments using different methods for plotting positions in GEV distributions and were compared with those obatined using the Method of L-Moments and LH-Moments by the Relative Mean Errors and Realtive Absoulte Errors. It was found that desgin floods derived by the method of L-Moments and LH-Moments using Cunnane plotting position foumula in the GEV distribution are much closer to those of the observed data in comparison with those obtained by methods of L-moments and LH-moments using the other formula for poltting postions from the viewpoint of Relative Mean Errors and Relative Absoulte Errors. In view of the fact that hydraulic structures indcluding dams and levees are generally usiong design floods with the return period of two hundred years or so, design floods derived by LH-Moments are seemed to be more reasonable than those of L-Moments in the GEV distribution.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.3
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• pp.41-50
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• 1999

Derivatio of reasonable design floods was attempted by comparative analysis of design floods derived by Generalized Extreme Value(GEV) distribution using methods of L-moments and LH-moments for the annual maximum series at ten watersheds along Han, Nagdong. Geum, Yeongsan and Seomjin river systems, LH-coefficient of variation, LH-skewness and Lh-kurtosis were calcualted by KH-moment ration respectively. Paramenters were estimated by the Method of LH-Moments, Design floods obtained by Method of LH-Moments using different methods for plotting positionsi n GEV distribution and design floods were compared with those obtained using the Method of L-Moments by the Relative Mean Errors(RME) and Relative Absolute Errors(RAE). The results was found that design floods derived by the method of L-Moments and LH-Moments using Cunnane plotting position formula in the GEV distribution are much closer to those of the observed data in comparison with those obtained by methods of L-moments and LH-moments using the other formula for plotting positions from the viewpoint of Relative Mean Errors and Relative Absolute Errors. In viewpoint of the fact that hydrqulic structures including dams and levees are genrally using design floods with the return period of two hundred years or so, design floods derived by LH-Moments are seemed to be more reasonable than those of L-Moments in the GEV distribution.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.596-596
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• 2015

수공구조물 설계의 기본이 되는 설계홍수량은 정상성 (Stationary) 가정 하에 산정되고 있다. 정상성은 분포형의 매개변수들이 시간에 따라 변화하지 않는 것을 의미한다. 그러나 최근 기후변화로 인한 극치사상의 크기와 빈도가 비정상적인 증가 추세를 나타내고 있어 강우자료의 변화 특성을 정확하게 파악하기 위해서는 비정상성 (Non-Stationary)에 대한 고려가 필요한 실정이다. 따라서 본 연구에서는 비정상성을 고려한 미래 IDF (Intensity-Duration-Frequency) 곡선을 산정하고, 기후변화에 의한 IDF 곡선의 특성 변화를 분석하고자 한다. 연구대상지로 충청남도 공주시에 위치한 계룡저수지를 선정하였고, 계룡저수지에 인접한 대전 관측소의 과거 강우자료 (1981-2010년)를 수집하였다. 또한 기상청에서 제공하는 RCP4.5 시나리오 기반의 미래 강우자료를 편의보정하여 3개 기간 (2011-2040년, 2041-2070년, 2071-2100년)에 대한 미래 강우자료를 구축하였고, 지속시간별 연최대치 강우자료를 추출하여 경향성 분석 및 비정상성 빈도해석을 실시하였다. 강우자료의 확률분포형과 매개변수 추정방법으로는 GEV (Generalized Extreme Value)분포와 L-모멘트법을 선정하였다. 이를 바탕으로 과거 및 미래 기간별 IDF 곡선을 산정하였으며, 그 특성을 비교분석하였다. 본 연구에서 도출한 IDF 곡선은 계룡저수지의 설계한도를 재검토하는데 활용될 수 있으며, 본 연구에서 제시한 방법은 기후변화에 따른 농업용저수지의 안정성 검토에 사용할 수 있을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.130-130
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• 2022

기후변화로 인한 기상이변 현상으로 폭우와 홍수 등 수문학적 극치 사상의 출현 빈도가 잦아지고 있다. 따라서 이러한 기상이변 현상에 적응하기 위하여 보다 정확한 확률강우량 측정의 필요성이 증가하고 있다. 대장 지점의 미래 확률강우량 계산을 위해선 기후변화 시나리오의 비정상성을 고려해야 한다. 본 연구는 비정상적인 미래 기후에서 확률강우량이 어떻게 변화하는지 측정하는 것을 목표로 한다. Representative Concentration Pathway (RCP4.5)에 따른 우리나라의 확률강우량 계산에 인공신경망을 포함한 정상성, 비정상성 확률강우량 산정 모델들이 사용되었다. 지점빈도해석(AFA), 홍수지수법(IFM), 모분포홍수지수법(PIF), 인공신경망을 이용한 Quantile & Parameter regression technique(QRT & PRT)이 정상성 자료에 대해 확률강우량을 계산하는 모델로 사용되었으며, 비정상성 자료에 대해서는 비정상성 지점빈도해석(NS-AFA), 비정상성 홍수지수법(NS-IFM), 비정상성 모분포홍수지수법(NS-PIF), 인공신경망을 사용한 비정상성 Quantile & Parameter regression technique(NS-QRT & NS-PRT)이 사용되었다. Rescaled Akaike information criterion(rAIC)를 사용한 불확실성 분석과 적합도 검정을 통해서 generalized extreme value(GEV) 분포형 모델이 정상성 및 비정상성 확률강우량 산정에 가장 적합한 모델로 선정되었다. 이후, 관측자료가 GEV(0,0,0)을 따르고 시나리오 자료가 GEV(1,0,0)을 따르는 지점들을 선택하여 미래의 확률강우량 변화를 추정하였다. 각 빈도해석 모델들은 몬테카를로 시뮬레이션을 통해 bias, relative bias(Rbias), root mean square error(RMSE), relative root mean square error(RRMSE)를 바탕으로 측정하여 정확도를 계산하였으며 그 결과 QRT와 NS-QRT가 각각 정상성과 비정상성 자료로부터 가장 정확하게 확률강우량을 계산하였다. 본 연구를 통해 향후 기후변화의 영향으로 확률강우량이 증가할 것으로 예상되며, 비정상성을 고려한 빈도분석 또한 필요함을 제안하였다.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.661-671
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• 2017

The objective of this study was to analyze the flood stage considering the uncertainty caused by the river roughness coefficients and discharge. The methodology of this study involved the GLUE (Generalized Likelihood Uncertainty Estimation) to quantify the uncertainty bounds applying three different storm events. The uncertainty range of the roughness was 0.025~0.040. In case of discharge, the uncertainty stemmed from parameters in stage-discharge rating curve, if h represents stage for discharge Q, which can be written as $Q=A(h-B)^C$. Parameters in rating curve (A, B and C) were estimated by non-linear regression model and assumed by t distribution. The range of parameters in rating curve was 5.138~18.442 for A, -0.524~0.104 for B and 2.427~2.924 for C. By sampling 10,000 parameter sets, Monte Carlo simulations were performed. The simulated stage value was represented by 95% confidence interval. In storm event 1~3, the average bound was 0.39 m, 0.83 m and 0.96 m, respectively. The peak bound was 0.52 m, 1.36 m and 1.75 m, respectively. The recurrence year of each storm event applying the frequency analysis was 1-year, 10-year and 25-year, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.2
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• pp.120-127
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• 2012

Exploratory data analysis was carried out by using the long-term wave climate data in Sokcho coastal zone. The main features found in this study are as follows. The coefficient of variations on the wave height and period are about 0.11 and 0.02, respectively. It also shows that the annual components of the wave height and period are dominant and their amplitudes are 0.24 m and 0.56 seconds, respectively. The amount of intra-annual variation range is about two times greater than that of the inter-annual variation range. The distribution shapes of the wave data are very similar to the log-normal and GEV(generalized extreme value) functions. However, the goodness-of-fit tests based on the KS test show as "rejected" for all suggested density functions. Then, the structure of the timeseries wave height data is roughly estimated as AR(3) model. Based on the wave duration results, it is clearly shown that the continuous and maximum duration is decreased as a power function shape and the total duration is exponentially decreased. Meanwhile, the environment of the Sokcho coastal zone is classified as a wave-dominated environment.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.4
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• pp.45-57
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• 1998

This study was conducted to derive optimal design floods by Generalized Extreme Value (GEV) distribution for the annual maximum series at ten watersheds along Han, Nagdong, Geum, Yeongsan and Seomjin river systems. Adequacy for the analysis of flood data used in this study was established by the tests of Independence, Homogeneity, detection of Outliers. L-coefficient of variation, L-skewness and L-kurtosis were calculated by L-moment ratio respectively. Parameters were estimated by the Methods of Moments and L-Moments. Design floods obtained by Methods of Moments and L-Moments using different methods for plotting positions in GEV distribution were compared by the Relative Mean Errors(RME) and Relative Absolute Errors(RAE). The results were analyzed and summarized as follows. 1. Adequacy for the analysis of flood data was acknowledged by the tests of Independence, Homogeneity and detection of Outliers. 2. GEV distribution used in this study was found to be more suitable one than Pearson type 3 distribution by the goodness of fit test using Kolmogorov-Smirnov test and L-Moment ratios diagram in the applied watersheds. 3. Parameters for GEV distribution were estimated using Methods of Moments and L-Moments. 4. Design floods were calculated by Methods of Moments and L-Moments in GEV distribution. 5. It was found that design floods derived by the method of L-Moments using Weibull plotting position formula in GEV distribution are much closer to those of the observed data in comparison with those obtained by method of moments using different formulas for plotting positions from the viewpoint of Relative Mean Errors and Relative Absolute Errors.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.102-102
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• 2015

최근 중소규모 하천은 하천수 및 하천변 지하수 이용의 증가와 토지이용변화 등 유역 내 수문인자의 특성 변화로 인해 하천의 건천화가 점증하고 있어 하천 환경이 악화되고 있는 실정이다. 본 연구에서는 격자기반의 분포형 장기수문 모델(PGA-CC)을 이용하여 과거 수십년동안 토지이용변화에 따른 하천 건천화 영향을 평가하였다. 실제 건천화가 진행되고 있는 삽교천 상류유역($358.8km^2$)을 선정하였고 토지이용변화분석을 위해 과거 1975년 토지이용도(Past), 현재 2008년 토지이용도(Present)를 구축하였다. 각각의 토지이용 항목 중 변화율이 가장 높은 도시비율은 과거 토지이용도에서는 2.6 %였으며 현재 토지이용도에서는 11.3 %로 8.7 %가 증가하였다. 모델 검보정은 최근 7년(2005-2011)동안 최종유역출구지점에서 유출 검보정을 실시하였다. 그 결과 NSE (Nash-Sutcliffe model efficiency)은 평균 0.71로 유출량의 모의값과 실측값이 유효한 것으로 나타났다. 건천화를 평가하기 위해 시험유역에서의 5 WPs (Watching Points)를 선정하여 과거 및 현재 토지이용조건을 모의하고 유황분석을 통한 갈수 변화량 분석을 실시하였다. 건천화 빈도분석을 위해 GEV (Generalized Extreme Value) 갈수빈도분석을 실시하여 과거 토지이용 모의결과 산정된 평균 갈수량($m^3/s$) 이하로 낮아지는 유출량 일수를 계산하였다. 최종유역출구에서 과거 및 현재 토지이용도에서 모의된 평균갈수량은 각각 $3.27m^3/s$ 및 $3.11m^3/s$로 나타났다. GEV 갈수빈도분석결과 과거 토지이용조건에서의 평균갈수량은 $3.20m^3/s$(재현기간 2.33년)으로 나타났다. 도시증가에 따른 인구증가는 지하수 사용량에 증가를 가져온다. 이는, 건천화에 영향을 미치며 본 연구에서는 지하수이용량 자료(1998-2011)를 이용하여 도시면적과 지하수이용량의 선형회귀분석을 실시하여 과거 22년 지하수 사용량을 예측하였다. 그 결과 지하수사용량 증가는 토지이용변화와 복합적으로 상류유역에 하천의 변화를 가속시키는 것으로 나타냈다.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.475-488
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• 2017

To simulate accurate drought, a drought index is needed to reflect the hydrometeorological phenomenon. Several studies have been conducted in Korea using the Modified Surface Water Supply Index (MSWSI) to simulate hydrological drought. This study analyzed the limitations of MSWSI and quantified the uncertainties of MSWSI. The influence of hydrometeorological components selected as the MSWSI components was analyzed. Although the previous MSWSI dealt with only one observation for each input component such as streamflow, ground water level, precipitation, and dam inflow, this study included dam storage level and dam release as suitable characteristics of the sub-basins, and used the areal-average precipitation obtained from several observations. From the MSWSI simulations of 2001 and 2006 drought events, MSWSI of this study successfully simulated drought because MSWSI of this study followed the trend of observing the hydrometeorological data and then the accuracy of the drought simulation results was affected by the selection of the input component on the MSWSI. The influence of the selection of the probability distributions to input components on the MSWSI was analyzed, including various criteria: the Gumbel and Generalized Extreme Value (GEV) distributions for precipitation data; normal and Gumbel distributions for streamflow data; 2-parameter log-normal and Gumbel distributions for dam inflow, storage level, and release discharge data; and 3-parameter log-normal distribution for groundwater. Then, the maximum 36 MSWSIs were calculated for each sub-basin, and the ranges of MSWSI differed significantly according to the selection of probability distributions. Therefore, it was confirmed that the MSWSI results may differ depending on the probability distribution. The uncertainty occurred due to the selection of MSWSI input components and the probability distributions were quantified using the maximum entropy. The uncertainty thus increased as the number of input components increased and the uncertainty of MSWSI also increased with the application of probability distributions of input components during the flood season.