• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.1
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• pp.27-38
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• 2006

In this study, we developed a program to estimate discharge efficiently considering major hydraulic characteristic including water level, river bed, water slope and roughness coefficient in a natural river. Stream discharge was measured at Gongju gauge station located in the down stream of the Daechung Dam during normal and dry seasons from 2003 to 2004. The developed model was compared with the results from the existing rating curve at T/M gage stations, and was used for runoff analyses. Evaluating the developed river discharge estimation program, it was applied during 1983-2004 that base flow separation method and RRFS (Rainfall Runoff Forecasting System) which is based on SSARR (Streamflow Synthesis And Resevoir Regulation). The result presents the stage-discharge curve creator range at the Gong-ju is overestimated by approximately $10-20\%$, especially at the low stage. It is attributed to the hydraulic characteristics at the study. The discharge simulated by the RRFS and base flow separation, which is calibrated using the measurement at the early spring and late fall season during relatively d]v season, shows the least errors. The coefficient of roughness at Gongju station varied with the high and low water level.

• Water Engineering Research
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• v.5 no.3
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• pp.111-121
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• 2004

For the long-term strategic water resources planning, forecasting the future streamflow change is important to meet the demand of a growing society. The streamflow variation to the decade-long precipitation was investigated for the two major stage gauging stations in Korea. Precipitation and runoff characteristics have been analyzed at Yongwol stream stage in the Han River as well as Sutong stream stage in the Kum River for the future water resources management strategies. Monte Carlo method has been applied to estimate the future precipitation and runoff. Based on the trend line of 10-year moving average of runoff depth for the historical runoff records, the relation between runoff and the time variation was examined in more detail using regression analysis. This study showed that the surface flows have been significantly decreased while precipitation has been stable in these basins. Decreasing in runoff reflects the regional watershed characteristics such as forest cover changes. The findings of this study could contribute to the planning and development for the efficient water resources utilization.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.398-402
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• 2012

최근 집중호우 및 홍수범람으로 인한 연평균 피해는 침수면적 26,757.17 ha, 수리시설 파괴 1,122개소, 그로 인한 피해액 약 580억원 등으로 집계되었으며 이상기후로 인한 집중호우 빈도 증가에 따른 잦은 홍수범람으로 그 피해액도 늘어나고 있는 것으로 조사되었다(국가재난정보센터). 이와 같은 피해를 최소화하기 위해서 홍수를 미리 예보하고 경보하는 시스템이 필요하며 시스템의 정확도 역시 중요하다.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.507-510
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• 2003

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. A short-term water demand forecasting technology will be developed taking into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.301-304
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• 2012

홍수예경보는 강우로 인하여 발생되는 홍수의 규모와 시간을 가능한 한 정확하고 빨리 예측하여 홍수에 대비할 수 있도록 유관기관 및 지역주민에게 사전에 홍수에 관한 정보 즉 예측되는 수위와 시간을 제공함으로써 홍수로부터의 피해를 최소화하는 것이다. 이와 같은 목적을 성공적으로 완수하기 위해서는 홍수시 급변하는 하천유량에 영향을 미치는 모든 수문학적 기상학적 자료를 신속 정확하게 수집할 수 있는 관측 시스템의 구축 뿐 아니라 이들 수집된 자료를 이용하여 실시간 홍수추적을 할 수 있는 효율적인 유출량 계산모형이 조화를 이룰 때 가능하다. 이에 본 연구에서는 중 소하천에서 홍수예경보를 위한 지능형 U-River 시스템의 실시간 모니터링 기술을 조사하고 하천수위를 이용한 예측시스템에 대해 연구하였다. 기존의 홍수예경보의 문제점을 해결하기 위해 간단한 입력자료만으로 홍수예측이 가능한 인공지능 기반의 신경망 모형을 이용 하였으며, 예측 모형의 효율성과 적용성을 높이기 위해 유사한 수문 사상을 가지는 상 하류간 입력 자료를 동시에 사용하였다. 또한 하천수위를 이용한 모델의 수행은 각 지점별 훈련성과를 토대로 최적의 은닉층 노드수를 선발하여 실시간 수위예측에 활용하였으며 수치적 기준을 적용하여 실측 수위와 모형에 의해 예측된 수위를 이용하여 평가하였다.

• Journal of Korea Water Resources Association
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• v.47 no.6
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• pp.561-572
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• 2014

The backwater effect at a tributary junction increases the risk of flood damage such as inundation and levee overflow. In particular, the rapid increase in water level may cause injury to persons. The purpose of this research is the development of the real-time flood forecasting technique as a part of the non-structural flood damage reduction measures. To this end, the factors causing a water level rising at a junction were examined, and the empirical formula for predicting flood level at a junction was developed using the calculated discharge and water level data from the well-constructed hydraulic model. The water level predictions show that average absolute error is about 0.2~0.3m with the maximum error of 1.0m and peak time can be captured prior to 0~5 hr. From the results of this study, the real-time flood forecasting system of a tributary junction can be easily constructed, and this system is expected to be utilized for reduction of flood inundation damage.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.408-412
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• 2006

Growing needs for efficient management of water resources urge integrated management of whole basin. As one of the tools for supporting above tasks, this study aims to indicate a hydrologic model that can simulate the streamflow discharges at some control points located both upper and down stream of dams. For the development and utilization of non analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-Time Water Information System. The well-known SSARR model was selected for basis of continuous daily runoff model for forecasting short and long-term national river flows in this paper.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.51-65
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• 2010

We suggest a simple and practical flood forecasting and warning system, which can predict change in the water level of a river in a small to medium-size watershed where flash flooding occurs in a short time. We first choose the flood defense target points, through evaluation of the flood risk of dike overflow and lowland inundation. Using data on rainfall, and on the water levels at the observed and prediction points, we investigate the interrelations and derive a regression formula from which we can predict the flood level at the target points. We calculate flood water levels through a calibrated flood simulation model for various rainfall scenarios, to overcome the shortage of real water stage data, and these results as basic population data are used to derive a regression formula. The values calculated from the regression formula are modified by the weather condition factor, and the system can finally predict the flood stages at the target points for every leading time. We also investigate the applicability of the prediction procedure for real flood events of the Jungnang Stream basin, and find the forecasting values to have close agreement with the surveyed data. We therefore expect that this suggested warning scheme could contribute usefully to the setting up of a flood forecasting and warning system for a small to medium-size river basin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2687-2692
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• 2010

There have been performed many researched for flood magnitude analysis, for example, the Flood-Duration-Frequency relations in the west. Because flood water stage data are more available rather than flood amount data at flood gauge stations of Korea, this study developed Flood water level-Duration-Frequency (Fwl-D-F) curves using rainfall Intensity-Duration-Frequency(I-D-F) curves for the quantitative flood risk assessment in urban watersheds. Fwl-D-F curve is made from water level data for 18 years at Joongrayng bridge station of Joongrayng River basin in Han River drainage area. Fwl-D-F curve can estimate the occurrence frequency for a certain flood elevation, which can be used for urban flood forecasting. It is expected that the flood elevation can be estimated from the forecasted rainfall data using both Fwl-D-F and I-D-F curves.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.