• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.761-770
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• 2013

This study was carried out to forecast the flow rate and water quality at the inlet of the Saemangeum bay in Korea using the SWMM(Storm Water Management Model) and the WASP(Water Analysis Simulation Program), and to analyze the impacts of pollutant loading from non-point source on the water quality of the bay. The calibration and validation of flow rate and water quality were performed using those from two monitoring points in the Mankyeong river administrated by Korean Ministry of Environment as part of the national water quality monitoring network. When the river flow rate was calibrated and validated using the rainfall intensities during 2011-2012, $R^2$ (i.e., coefficient of determination) was ranged from 0.91 to 0.96. For water qualities, it was shown that $R^2$ of BOD(Biochemical Oxygen Demand) was ranged from 0.56 to 0.86, and $R^2$ of T-N(Total Nitrogen) was from 0.64 to 0.75, and $R^2$ of T-P(Total Phosphorus) was from 0.67 to 0.89. The integrated modeling system showed significant advances in the accuracy to estimate the water quality. Finally, further simulations showed that annual average flow of the river running into the bay was estimated to be $1.439{\times}10^9m^3/year$. The discharged load of BOD, T-N, and T-P into the bay were anticipated to be 618.7 ton/year, 331.5 ton/year, and 40.4 ton/year, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.281-285
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• 2007

본 연구에서는 보다 신뢰성 있고 정확한 정량적 강우예측자료를 생성하기 위하여 레이더강우 및 강우수치예보자료를 합성하는 기법을 제시하였고, 레이더 전처리 및 예측시스템, GIS와 연계한 물리적기반의 분포형모형인 Vflo모형 등 최신 수자원 IT기술을 활용하여 홍수기 돌발홍수에 대응한 초단기 정량적 강우-유출예측을 목적으로 향후 실시간으로 적용 가능한 분포형유출예측시스템의 기반을 구축하고자 하였다. 대상유역은 국지적인 고해상도 지형효과를 고려한 QPM이 개발되어 있는 금강권역의 용담댐유역이며, 예측 강우에 대한 호우사상은 2005년 이후 발생한 3개 강우사상을 대상으로 하였다. 한편, 기상 레이더 자료로부터 산정된 강수량의 수문학적 적용을 위하여 DEM, 토지피복도, 토양도 등의 기본 GIS자료들을 수집 및 구축하였고 물리적기반의 분포형모형(Vflo)의 입력인자로 사용하기 위한 12개의 공간분포형 수문매개변수들을 대표적인 GIS 소프트웨어인 ArcGIS 및 ArcView를 활용하여 추출하였으며, Vflo모형의 현업 적용가능성을 오프라인 상에서 검증해보았다. 모형 검증결과, GIS를 이용한 지형, 토양, 토지피복과 같은 물리적 특성을 사용한 모형의 초기 설정을 향상시킴에 의해 첨두유량, 유출량, 첨두도달시간차 등에서 만족할만한 결과를 보여주었다고 사료된다. 레이더 및 수치예보자료와 합성한 4가지의 형태(QPE, JQPE, QPM, BQPF)의 분포형 입력강우를 이용하여 적용해 본 결과 Nowcasting기법을 이용한 JQPF는 자료의 특성상 초기 1시간30분동안은 비교적 양호한 결과를 얻었으나 3시간 전후로 가면서 예측강우의 질이 저하되기 시작하였으나 QPM을 합성함으로써 생산한 BQPF는 보다 신뢰성있고 양호한 결과를 얻을 수 있었다. 이러한 결과들은 향후 정량적 분포형강우 예측을 이용한 실시간 홍수유출 예측시 댐운영자는 리드타임(홍수선행시간)을 충분히 확보함으로서 안정적이고 예측 가능한 홍수조절을 하는데 도움을 줄 수 있을 것으로 기대된다. 이와 같이 다양한 단기저수지 유입량의 예측정보 제공으로 다목적댐 저수지 운영모형의 효용성을 제고하여 향후 실제 저수지 유입량 예측에 이용함으로써 저수지 단기운영효율 개선에 기여할 수 있을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1498-1502
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• 2007

본 연구에서는 격자기반의 레이더강우 등과 같은 향후 제공될 분포형 강수를 활용하기 위해 국내 유역에서 GIS와 연계한 물리적 기반의 수문학적 분포형모형의 적용성을 검토하고, 향후 저수지 유입량 예측을 위해 수자원공사 현업에서 실시간 물관리에 사용하고 있는 개념적기반의 집중형모형인 Kwater홍수분석모형과 실시간 홍수조절을 목적으로 미국 오클라호마대학의 백스터교 수측에서 개발된 물리적기반의 분포형모형인 Vflo모형을 이용하여 낙동강권역의 남강댐유역을 대상으로 유출해석을 수행하여 양 모형의 구조적인 장단점 등을 비교분석하였다. 입력이 되는 분포형 강우는 지상관측강우, 레이더추정강우를 적용하였고, GIS수문매개변수를 ArcGIS 및 ArcView를 활용하여 DEM, 토지피복도, 토양도 등의 기본 GIS자료들로 부터 추출, 물리적기반의 분포형모형(Vflo)의 입력인자로 사용하여 모형의 초기설정을 향상시켰다. 모형에서 계산된 방법이 물리성을 구비하여 타당한 매개변수의 값으로 현실의 유출량을 재현할 수 있는지를 실제 유역 규모의 스케일로 검증하고자 하였으며 홍수기 댐유역의 유출모의를 위한 모형의 장단점을 파악하고 분포형모형의 향후 실용화 가능성을 검토하였다. 모형 수행 결과, 모형보정은 물리적기반의 분포형모형인 Vflo모형이 집중형모형인 Kwater모형에 비하여 GIS를 이용하여 지형공간 자료와 토양, 토지피복과 같은 물리적 특성을 사용한 모형의 초기 설정을 향상시킴에 따라 평균적으로 첨두유량에서 $\pm254\;cms$, 유출량에서 $\pm14\;mm$, 첨두도달 시간차에서 $\pm15$분 이내의 정확도 향상을 가져왔다. 물리적 기반의 분포형모형인 Vflo모형은 남강댐유역 대다수 관측소에서 별다른 매개변수의 보정없이도 합리적이고 유용한 결과를 보여주었다. 이러한 결과는 GIS와 연계한 물리적기반의 분포형모형이 향후 돌발홍수나 게릴라성 집중호우 등의 악기상에 대응하여 레이더 등의 정확하고 신뢰할만한 강우예측이 입력자료로 생성되었을 때 다목적댐 저수지 운영에 있어서 리드타임을 충분히 확보하여 안정적이고 예측가능한 홍수조절을 수행할 수 있는 가능성을 보여주었다고 사료된다.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.963-972
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• 2009

The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.813-826
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• 2018

Recently, weather radar system has been widely used for effectively monitoring near real-time weather conditions. The radar rainfall estimates are generally relies on the Z-R equation that is an indirect approximation of the empirical relationship. In this regards, the bias in the radar rainfall estimates can be affected by spatial-temporal variations in the radar profile. This study evaluates the uncertainty of the Z-R relationship while considering the rainfall types in the process of estimating the parameters of the Z-R equation in the context of stochastic approach. The radar rainfall estimates based on the Bayesian inference technique appears to be effective in terms of reduction in bias for a given season. The derived Z-R equation using Bayesian model enables us to better represent the hydrological process in the rainfall-runoff model and provide a more reliable forecast.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.755-758
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• 2008

Currently, the increased run-off and the shortened arrival time are one of the causes of the city environmental disasters in urbanization. Therefore, it is necessary to properly design the hydrologic structures, but it is very difficult to forecast the values necessary to design from the planning stage. Moreover, as the parameter is changed due to the urban development, it is difficult not only to analyze the run-off influences but also to find the related studies and literatures. The purpose of this study is to utilize the results as the important basic data of the hydrologic structures, its proper design and run-off influences through the sensibility analysis of the model parameter variables. In this study, the absolute and relative sensibility analysis method were used to find out the correlation through the sensibility analysis of the topology and hydrology parameters. Especially, in this study, the changes in the run-off amount and volume were calculated according to increase/decrease in CN, the coefficient of discharge, and the empirical formula is prepared and proposed through the regressive analysis among the parameters. In the meantime, the parameter sensibility analysis was performed through the simulation HEC-HMS that is used and available in Korea. From the results of this study, it was found that the run-off amount is increased about by 10% when the CN value is increased by 5% before and after the development through the HEC-HMS simulation and data analysis. As long as there will be additional data collection analysis and result verification, and continuous further studies to find out the parameters proper to the domestic circumstances, it is expected to considerably contribute to the proper design of the hydrologic structures with respect to the ungauged basin.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.2
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• pp.67-74
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• 2011

This research focused on the investigation of runoff and nonpoint sources (NPS) pollution characteristics from small soil box plots treated by livestock waste composts. An indoor rainfall simulation was performed over the plots for 60 minutes. Simulated rainfall intensities were 32.4, 43.2, 50.3 and 57.1 mm/hr respectively. Slope of soil box plots was $10^{\circ}$ and $20^{\circ}$, respectively. Rainfall simulation replicated 5 times and the experiment was conducted every four days five times. As the slope of soil box increased, NPS pollution loads increased. And as rainfall intensity was increased from 32.4 to 57.1 mm/hr, NPS pollution loads gradually increased, too. Discharge of NPS pollution loads was the largest in the first simulation and thereafter decreased gradually. Discharged BOD load to the total applied load from $10^{\circ}$ plots, ranged 0.2 to 0.7 %, was 8.4 to 50.0 % lower than slope $20^{\circ}$ plots. When the application rate increased twice, the increase of pollution load was between 1.7~5.7 times. Analysis of Pearson's correlation coefficient showed that organic matter content in pig compost and NPS pollution loads were correlated well. While under liquid compost application, the correlation coefficients between them were not good. It was concluded that application of livestock resources need to consider long-term weather forecast and if necessary, NPS reduction measures must be preceded in order to reduce NPS pollution discharge.

• Journal of Korean Society on Water Environment
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• v.32 no.2
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• pp.173-182
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• 2016

The objective of this study is to quantitatively analyze climate change effects by using statistical trends and a watershed model in the Yongdam dam watershed. The annual average air temperature was found to increase with statistical significance. In particular, greater increases were observed in autumn. Also, this study was performed to evaluate the potential climate change in the streamflow and water temperature using a watershed model (HSPF) with RCP climate change scenarios. The streamflow of Geum river showed a decrease of 5.1% and 0.2%, respectively, in the baseline data for the 2040s and 2080s. The seasonal impact of future climate change on the streamflow showed a decrease in the summer and an increase in the winter. The water temperature of Geum river showed an average increase of 0.7~1.0℃. Especially, the water temperature of Geum river showed an increase of 0.3~0.5℃ in the 2040s and 0.5~1.2℃ in the 2080s. The seasonal impact of future climate change on the water temperature showed an increase in winter and spring, with a decrease in summer. Therefore, it was determined that a statistical analysis-based meteorological and quantitative forecast of streamflow and water temperature using a watershed model is necessary to assess climate change impact and to establish plans for future water resource management.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.303-314
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• 2020

The hydrological characteristics of watersheds and hydraulic systems of urban and river floods are highly nonlinear and contain uncertain variables. Therefore, the predicted time series of rainfall-runoff data in flood analysis is not suitable for existing neural networks. To overcome the challenge of prediction, a NARX (Nonlinear Autoregressive Exogenous Model), which is a kind of recurrent dynamic neural network that maximizes the learning ability of a neural network, was applied to forecast a flood in real-time. At the same time, NARX has the characteristics of a time-delay neural network. In this study, a hydrological model was constructed for the Taehwa river basin, and the NARX time-delay parameter was adjusted 10 to 120 minutes. As a result, we found that precise prediction is possible as the time-delay parameter was increased by confirming that the NSE increased from 0.530 to 0.988 and the RMSE decreased from 379.9 ㎥/s to 16.1 ㎥/s. The machine learning technique with NARX will contribute to the accurate prediction of flow rate with an unexpected extreme flood condition.