• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.709-720
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• 2010

This paper describes the modeling of climate change impact on drought using a conceptual soil moisture model and presents the results of the modeling approach. The future climate series is obtained by scaling the historical series, informed by CCCma CGCM3-T63 with A2 green house emission scenario, using a daily scaling method that considers changes in the future monthly precipitation and potential evapotranspiration as well as in the daily precipitation distribution. The majority of the modeling results indicate that there will be more frequent drought in Korea in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.36-36
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• 2016

하천정비나 유역종합 치수계획 등 수자원계획을 수립하는 과정에 있어 하천의 설계홍수량 추정은 필수적이며, 하천의 수공구조물의 안전성과 수문학적 위험도를 산정하는데도 활용되고 있다. 그러나 매년 관측되는 강우량 자료에 비해 유출량 자료의 길이가 비교적 짧아 신뢰성 있는 홍수량자료의 구축이 어려운 실정이며, 미계측 유역에 위치한 중소규모 하천의 설계홍수량과 같은 수문학적 자료는 매우 제한적이다. 이러한 이유로 본 연구에서는 기 수립된 하천정비기본계획의 자료들을 활용하여 유역의 특성(면적, 경사, 고도)이 고려되는 새로운 홍수량 산정식을 개발하였으며, Bayesian GLM(generalized linear method) 기법을 활용하여 미계측 유역의 지역화를 통한 홍수량의 추정이 가능하도록 하였다. 또한 Hierarchical Bayesian 기법을 활용하여 개발된 공식에 활용되는 매개변수의 불확실성을 구간을 산정하였다. Bayesian 기법의 도입으로 산정되는 홍수량의 불확실성 구간을 정량적으로 제시할 수 있었으며, 제안된 연구 결과는 미계측 유역의 홍수량을 추정하는 도구로서 활용성이 높을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.301-301
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• 2016

국내의 경우 수공구조물을 설계하기 위해서는 빈도해석을 통해 설계수문량을 산정한다. 일반적으로 실무에서는 지점빈도해석을 수행하게 되는데 설계빈도보다 대부분 짧은 기간의 자료를 이용하여 산정한다. 지역빈도해석은 이러한 자료기간이 가지는 문제점을 극복하기 위하여 확률수문량의 정확도와 신뢰도를 향상시키는 기법이다. 스케일 모델은 지속기간별로 관측된 강우자료를 이용하여 재현기간에 대한 지속기간의 함수로 표현이 가능하며, 이를 통해 강우의 IDF곡선을 제시할 수 있는 수학적 모델이다. 대상지역의 강우관측소에서 관측된 강우자료가 일단위이면, 기준지속기간이 24시간이 되며, 기준지속기간에 대한 확률강우량으로부터 임의의 지속기간에 대한 확률강우량을 스케일 모델을 이용하여 추정할 수 있다. 따라서 짧은 자료를 보유한 지역이거나 미계측 지역에 대한 확률강우량을 추정을 위해 지역빈도해석과 지역 스케일 모델을 이용하여 확률강우량을 추정하여 지점빈도해석과 비교하고자 한다. 본 연구를 위해 한강유역의 강우 관측소를 이용하였으며, 군집분석 중 k-means방법을 적용하여 수문학적 동질성을 확보한 후 지역을 구분하였다. 구분된 지역은 지점 및 지역빈도해석을 수행한 후 상대평균제곱근오차(relative root mean square error, RRMSE)를 비교하여 정확도를 판단하였고, 정확도가 높은 빈도해석에 지역 스케일 모델을 적용하여 미계측 지점에 대한 임의의 시간에 대한 확률강우량을 추정하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.249-249
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• 2020

수공구조물을 설계하기 위해서는 다양한 지속기간에 대한 설계수문량을 추정해야 한다. 국내의 경우도 기후변화로 인한 이상기후의 발생으로 1~2시간 동안 강우강도가 큰 집중호우가 발생하여 도시홍수를 발생시키며 직접 또는 간접적으로 피해를 주고 있다. 특히 북한 지역은 강우관측소가 존재하지 않은 지역이 많아 수공구조물 설계에 필요한 설계수문량을 추정하기에 많은 어려움이 있다. 본 연구에서는 하향스케일링(down-scaling)을 이용하여 북한 지역의 24시간 이내의 확률강우량을 추정하고자 한다. 이를 위하여 미계측 유역인 화천댐 상류유역의 지역빈도해석과 군집분석을 수행하여 수문학적 동질성을 확보하였고, 한강유역을 4개의 동질지역으로 구분하였다. 스케일 성질은 동일한 분포형을 가정하므로 수문학적 동질성이 확보된 기준 지속기간의 자료로부터 임의이 지속기간에 대한 확률강우량 추정이 가능하다. 따라서 북한지역의 짧은 지속기간에 대한 확률 강우량 추정을 위하여 동일한 지역 내의 지역 스케일 지수와 스케일 인자를 이용하여 하향스케일링을 적용할 수 있으며, 단기 혹은 장기에 해당하는 지속기간에 대한 확률강우량을 추정할 수 있다.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.1175-1180
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• 2001

Reinforced concrete deck slabs are directly affected by traffic loads and they are also susceptible to weather-related problems, such as cracking, reinforcement corrosion, spatting, scaling, delamination, leakage, efflorescence and so on. Some of these defects are caused by water which seeps through pavements and trapped between pavements and deck slabs. For durability of reinforced concrete deck slabs and pavements, it is very important to protect deck slabs and drain the trapped water out. To develop the trapped water drainage system, the following studies have been performed in Korea Highway Cooperation: related researches are reviewed; for six bridges, deck slabs are thoroughly investigated; new system to effectively drain the trapped water out is proposed; the proposed system is installed and evaluated. The proposed system is proved to be effective to drain the trapped water out and is expected to increase the durability of reinforced concrete deck slabs.

• Ocean and Polar Research
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• v.32 no.1
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• pp.1-13
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• 2010

To assess short-term variation of summer phytoplankton community structure in different water masses, phytoplankton and environmental factors were monitored from 31 stations on and off the southern coasts of Korea, from June 18 to June 20 2009. According to multidimensional scaling (MDS) and cluster analysis based on phytoplankton community data from each station, the southern sea was divided into two groups. The first group included stations in the south-eastern region of Jeju Island, which is strongly influenced by the Kuroshio warm current. The second group located along the coastal region of the southern sea, which was mainly comprised of Bacillariophyceae and Crytophyceae. Of these stations, St. 13 and 28 formed a temperature front caused by different hydrological conditions. In particular, nutrients and Chl.a concentrations in these two stations were significantly higher compared to those in the other stations. This indicates that phytoplankton population and subsequent microalgal growth under high nutrient concentrations vary in different water masses. Our results support the theory that phytoplankton community structure in the southern sea of Korea can be influenced on a short-term scale by different water masses and currents.

• Fisheries and Aquatic Sciences
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• v.10 no.2
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• pp.93-101
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• 2007

Concentrations of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) were determined in marine sediments from Mokpo coastal water of Korea. Total and toxic equivalent (TEQ) concentrations of PCDD/Fs ranged from 19.4 to 175pg/g dry weight and ranged from 0.195 to 2.87 pg WHO-TEQ/g dry weight, respectively. These PCDD/F concentrations were below the safety sediment value (20 pg TEQ/g dry weight) of chronic toxicity. A highly significant correlation between the concentrations of PCDD/Fs and total organic carbon (TOC) in marine sediments was observed. Overall PCDD/F concentrations in the sediments were elevated in rivers and at inner locations close to harbors, indicating that these contaminants derived from local discharges of human activities. PCDD/F concentrations measured in our study were lower than those in several industrialized areas in Korea. Nonparametric multidimensional scaling (MDS) ordination showed that combustion processes from industrial complexes are the major source of PCDD/Fs in Mokpo coastal water.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.29-41
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• 2017

Design rainfalls are one of the most important hydrologic data for river management, hydraulic structure design and risk analysis. The design rainfalls are first estimated by a point frequency analysis and the IDF (intensity-duration-frequency) curve is then constructed by a nonlinear regression to either interpolate or extrapolate the design rainfalls for other durations which are not used in the frequency analysis. It has been widely recognised that the more reliable approaches are required to better account for uncertainties associated with the model parameters under circumstances where limited hydrologic data are available for the watershed of interest. For these reasons, this study developed a hierarchical Bayesian based GLM (generalized linear model) for a regional frequency analysis in conjunction with a scaling function of the parameters in probability distribution. The proposed model provided a reliable estimation of a set of parameters for each individual station, as well as offered a regional estimate of the parameters, which allow us to have a regional IDF curve. Overall, we expected the proposed model can be used for different aspects of water resources planning at various stages and in addition for the ungaged basin.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1431-1438
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• 2018

In this paper, we proposed a river water level detection and tracking of the river or dams based on image processing system. In past, most of the water level detection system used various water sensors. Those water sensors works perfectly but have many drawbacks such as high cost and harsh weather. Water level monitoring system helps in forecasting early river disasters and maintenance of the water body area. However, the early river disaster warning system introduces many conflicting requirements. Surveillance camera based water level detection system depends on either the area of interest from the water body or on optical flow algorithm. This proposed system is focused on water scaling area of a river or dam to detect water level. After the detection of scale area from water body, the proposed algorithm will immediately focus on the digits available on that area. Using the numbers on the scale, water level of the river is predicted. This proposed system is successfully tested on different water bodies to detect the water level area and predicted the water level.