• Journal of Wetlands Research
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• v.10 no.3
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• pp.69-78
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• 2008

In recent, we have a growing interest in the washland construction for the satisfaction of flood mitigation and ecological function in the river. This study performed the flood mitigation analysis for washland construction plan in Topyoung-cheon basin in Changyeong-gun, Gyeongnam. Several cases were considered for the washland construction on Topyoung-cheon basin, and we analyzed flood stage, inundation and flood damage mitigation for each case. From the result of flood mitigation analysis, we found some significant results according to the combination of each washland case and form. Therefore, to maximize flood mitigation effect by flood mitigation analysis is more desirable than guarantee the maximum storage area in washland construction.

• Journal of Wetlands Research
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• v.10 no.3
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• pp.125-132
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• 2008

Generally, stationary is considered as a basic assumption in frequency analysis. However, rainfall and flood discharge are changing due to the climate change and climate variability. Therefore, there is a new opinion that changing pattern of rainfall and flood discharge must be considered in frequency analysis. This study suggests the flood frequency analysis methodology using SIR algorithm which was developed from bootstrap could be used for considering climate change. Than is, SIR algorithm is selected for resampling method considering changing pattern of flood discharge and it has been used for resampling method with likelihood function. Resampled flood discharge data considering the increase of flood discharge pattern are used for parametric flood frequency analysis and this results are compared with frequency analysis results by Bootstrap and original observations. As the results, SIR algorithm shows the greatest flood discharge than other methods in all frequencies and this may reflect the increasing pattern of flood discharge due to the climate change and climate variability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.175-182
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• 2018

In this study, flood quantiles were estimated at ungauged watersheds by adjusting the flood quantiles from the design rainfall - runoff analysis (DRRA) method based on regional frequency analysis. Comparing the flood frequency analysis (FFA) and DRRA, it was found that the flood quantiles estimated by the DRRA method were overestimated by 52%. In addition, a practical method was suggested to make an flood index using natural flows to apply the regional frequency analysis (RFA) to ungauged watersheds. Considering the relationships among DRRA, FFA, and RFA, we derived an adjusting formula that can be applied to estimate flood quantiles at ungauged watersheds. We also employed Leave-One-Out Cross-Validation scheme and skill score to verify the method proposed in this study. As a result, the proposed model increased the accuracy by 23.2% compared to the existing DRRA method.

• Smart Structures and Systems
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• v.17 no.1
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• pp.149-165
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• 2016

Floods have been known to be one of the main causes of bridge collapse. Contrary to earthquakes, flood events tend to occur repeatedly and more frequently in rainfall areas; flood-induced damage and collapse account for a significant portion of disasters in many countries. Nevertheless, in contrast to extensive research on the seismic fragility analysis for civil infrastructure, relatively little attention has been devoted to the flood-related fragility. The present study proposes a novel methodology for deriving flood fragility curves for bridges. Fragility curves are generally derived by means of structural reliability analysis, and structural failure modes are defined as excessive demands of the displacement ductility of a bridge under increased water pressure resulting from debris accumulation and structural deterioration, which are known to be the primary causes of bridge failures during flood events. Since these bridge failure modes need to be analyzed through sophisticated structural analysis, flood fragility curve derivation that would require repeated finite element analyses may take a long time. To calculate the probability of flood-induced failure of bridges efficiently, in the proposed framework, the first order reliability method (FORM) is employed for reducing the required number of finite element analyses. In addition, two software packages specialized for reliability analysis and finite element analysis, FERUM (Finite Element Reliability Using MATLAB) and ABAQUS, are coupled so that they can exchange their inputs and outputs during structural reliability analysis, and a Python-based interface for FERUM and ABAQUS is newly developed to effectively coordinate the fragility analysis. The proposed framework of flood fragility analysis is applied to an actual reinforced concrete bridge in South Korea to demonstrate the detailed procedure of the approach.

• Water Engineering Research
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• v.1 no.1
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• pp.49-61
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• 2000

Magnitude-frequency relationships are used in the design of dams, highway bridges, culverts, water supply systems, and flood control structures. In this paper, possible techniques for analyzing flood frequency at a site are presented. A currently used approach to flood frequency analysis is based on the concept of parametric statistical inference. In this analysis, the assumption is make that the distribution function describing flood data in known. However, such an assumption is not always justified. Even though many people have shown that the nonparametric method provides a better fit to the data than the parometric method and gives more reliable flood estimates. the noparpmetric method implies a small probability in extrapolation beyond the highest observed data in the sample. Therefore, a remedy is presented in this paper by introducing an estimator which mixes parametric and nonparametric density estimate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1797-1807
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• 2013

Dams always have the possibility of failure due to unexpected natural phenomena. In particular, dam failure can cause huge damage including damage for humans and properties when dam downstream regions are densely populated or have important national facilities. Although many studies have been conducted on the analysis of flood waves about single dam failure thus far, studies on the analysis of flood waves about the sequential failure of dams are lacking. Therefore, the purpose of this study was to calculate the peak discharge of sequential failure of dams through flood wave analysis of sequential failure of dams and this analysis techniques to predict flood wave propagation situation in downstream regions. To this end, failure flood wave analysis were conducted for Lawn Lake Dam which is a case of sequential failure of dams among actual failure cases using DAMBRK to test the suitability of the dam failure flood wave analysis model. Based on the results, flood wave analysis of sequential failure of dams were conducted for A dam in Korea assuming a virtual extreme flood to predict flood wave propagation situations and 2-dimensional flood wave analysis were conducted for major flooding points. Then, the 1, 2-dimensional flood wave analysis were compared and analyzed. The results showed goodness-of-fit values exceeding 90% and thus the accuracy of the 1-dimensional sequential failure of dams simulation could be identified. The results of this study are considered to be able to contribute to the provision of basic data for the establishment of disaster prevention measures for rivers related to sequential failure of dams.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.257-267
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• 2010

A flood event can be characterized by three attributes such as peak discharge, total flood volume, and flood duration, which are correlated each other. However, the amount of peak discharge is only used to evaluate the flood events for the hydrological plan and design. The univariate analysis has a limitation in describing the complex probability behavior of flood events. Thus, the univariate analysis cannot derive satisfying results in flood frequency analysis. This study proposed bivariate flood frequency analysis methods for evaluating flood events considering correlations among attributes of flood events. Parametric distributions such as Gumbel mixed model and bivariate gamma distribution, and a non-parametric model using a bivariate kernel function were introduced in this study. A time series of annual flood events were extracted from observations of inflow to the Soyang River Dam and the Daechung Dam, respectively. The joint probability distributions and return periods were derived from the relationship between the amount of peak discharge and the total volume of flood runoff. Applicabilities of bivariate flood frequency analysis were examined by comparing the return period acquired from the proposed bivariate analyses and the conventional univariate analysis.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.637-640
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• 2008

Flood control and river improvement works are carried out every year for the defense of the flood disaster, it is impossible to avoid the damage when there is a flood exceeding the capacity of hydraulic structures. Therefore, nonstructural counter plans such as the establishment of flood hazard maps, the flood warning systems are essential with structural counter plans. In this study, analysis of the internal inundation effect using rainfall runoff model such as PC-SWMM was applied to Woo Ee experimental stream basin. Also, the design frequency analysis for effects of the external inundation was accomplished by main parameter estimation for conclusive hydraulic routing using HEC-RAS model. Finally, inundated areas for flood hazard map were estimated at Woo Ee downstream basin according to flood frequency using HEC-GeoRAS model linked by Arc View GIS.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.34-40
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• 1998

The existing flood runoff models, Complex Hydrograph and HEC-1, have some problems which do not properly represent runoff characteristics on the Korean paddy basin and their basin slopes. In this study, FAS(Flood Analysis System) was developed in order to supplement those problems, which was built calibrating runoff curve number for paddy basin and applying basin slopes to classify 5 levels. And also the FAS can synthesize the flood hydrographs of subbasin and analyze flood routing along a stream. To verify the applicability of the FAS, the computed flood hydrographs were compared with the observed hydrographs from the four watersheds. In the small basin smaller than 10$\textrm{km}^2$, the results of the FAS did not completely agree with the observed ones due to concentration time delay of paddy storage effect while in the medium and large size basin showed good agreements between the observed and computed ones. Therefore, it was concluded that the FAS could be applied for the flood analysis of Korean watershed which was characterized by paddy storage effect.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.194-194
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• 2017

Flooding is one of the most serious and frequently occurred natural disaster at many regions around the world. Especially, under the climate change impact, it is more and more increasingly trend. To reduce the flood damage, flood forecast and its accuracy analysis are required. This study is conducted to analyze the accuracy of the real-time flood forecasting of a coupled meteo-hydrological model for the Han River basin, South Korea. The LDAPS (Local Data Assimilation and Prediction System) products with the spatial resolution of 1.5km and lead time of 36 hours are extracted and used as inputs for the SURR (Sejong University Rainfall-Runoff) model. Three statistical criteria consisting of CC (Corelation Coefficient), RMSE (Root Mean Square Error) and ME (Model Efficiency) are used to evaluate the performance of this couple. The results are expected that the accuracy of the flood forecasting reduces following the increase of lead time corresponding to the accuracy reduction of LDAPS rainfall. Further study is planed to improve the accuracy of the real-time flood forecasting.