• 한국방재학회 논문집
• /
• 제11권3호
• /
• pp.165-174
• /
• 2011

본 연구의 목적은 하천의 홍수저감을 목적으로 설치하는 하천변 저류지의 홍수저감특성을 분석하기 위한 모형의 개발에 있다. 하천에 홍수가 발생하였을 경우에 하천변 저류지는 하천의 홍수첨두 일부를 분담하는 기능을 갖으며, 이는 제방의 일부를 낮춘 월류제를 통한 범람으로 가능하게 되며, 또한 범람된 물은 저류지 내에서 저류 하게 된다. 이러한 저류지가 갖는 홍수저감특성은 하천 홍수위, 월류제 제원(높이, 위치, 길이 등), 저류지의 수리거동 등에 의해 좌우되게 되므로, 본 연구에서는 이러한 일련의 물의 거동을 재현하기 위해서 1차원 하천부정류 모델, 월류제 상의 월류량 산정 모델 및 제내지 홍수범람 모델을 연계한 통합모형을 개발 하였다. 이상에서 개발된 연계 모형을 가상하도 및 실제하도에 적용하여 월류제가 갖는 기하적 특성이 홍수경감에 미치는 영향에 대해 분석하였으며, 이를 통해 향후 개선하여야 할 시사점에 대해 기술하였다.

• 한국물환경학회지
• /
• 제24권5호
• /
• pp.581-591
• /
• 2008

Most of our rivers are fragmented by the presence of at least one large dam. Dams are often the most substantial controller of the flow regimes and aquatic environments of natural river system. The quality of downstream water released from a stratified reservoir is highly dependent on upstream reservoir water quality. Thus, an integrated modeling approach is more efficient, compared to fragmented modeling approach, and necessary to better interpret the impact of dam operation on the down stream water quality. The objectives of this study were to develop an integrated reservoir-river modeling system for Daecheong Reservoir and its downstream using a two-dimensional laterally averaged hydrodynamic and water quality model, and evaluate the model's performance against field measurement data. The integrated model was calibrated and verified using filed data obtained in 2004 and 2006. The model showed satisfactory performance in predicting temporal variations of water stage, temperature, and suspended solid concentration. In addition, the reservoir-river model showed efficient computation time as it took only 3 hours for one year simulation using personal computer (1.88 Ghz, 1.00 GB RAM). The suggested modeling system can be effectively used for assisting integrated management of reservoir and river water quality.

• 한국물환경학회지
• /
• 제27권5호
• /
• pp.710-718
• /
• 2011

An integrated water quality management of reservoir and river would be required when the quality of downstream river water is affected by the discharge of upstream dam. In particular, for the control of downstream turbidity during flood events, the integrated modeling of reservoir and river is effective approach. This work was aimed to develop a laterally-averaged two-dimensional hydrodynamic and water quality model (CE-QUAL-W2), by which water quality can be predicted in the downstream of Yongdam dam in conjunction with the reservoir model, and to validate the model under two different hydrological conditions; wet year (2005) and drought year (2010). The model results clearly showed that the simulated data regarding water elevation and suspended solid (SS) concentration are well corresponded with the measured data. In addition, the variation of SS concentration as a function of time was effectively simulated along the river stations with the developed model. Consequently, the developed model can be effectively applied for the integrated water quality management of Yongdam dam and downstream river.

• 한국농공학회지
• /
• 제39권2호
• /
• pp.1-8
• /
• 1997

Abstract The need for integrated reservoir system operation become more intense as the demands from the system increase. A deterministic, three-dimensional discrete incremental dynamic programming approach is presented to derive reservoirs system operational planning strategies. The developed H3DP model optimizes the monthly operation of the Hwachon and Soyang Projects on the North Han river and Chungju Main Project on the South Han river. By using the H3DP model, Hwachon project was reevaluated as a component of the upstream multipurpose storage reservoirs in the basin based on 1993 hydrology. This case study demonstrates the practical use of the developed model for the basin multi-reservoir system operation in an integrated, multipurpose fashion.

• 대한토목학회논문집
• /
• 제33권3호
• /
• pp.975-985
• /
• 2013

본 연구에서는 도암댐 유역을 대상으로 유역모형과 호소 및 하천모형의 연계를 통해 통합모델링시스템을 구축하였다. 국내 기후 특성상 하절기에 집중되는 강우로 인해 댐의 건설은 홍수조절, 용수확보 및 전력생산 등의 목적에 있어서 불가피하다. 특히 이러한 목적의 댐 형태가 하천과 하천 사이에 위치할 경우에는 연계된 구간을 하나의 유역으로 보고 이를 통합적으로 모의 및 관리 할 수 있는 시스템이 필요하다. 본 연구에서는 도암댐 유역을 대상으로 유역모형인 SWAT 모형을 구축하고 저수지 및 하천의 수리 및 수질 모의를 위해 EFDC-WASP 모형을 구축하였다. 또한 현재 도암댐 상부에서 시범가동 중인 수질개선장치 효율이 반영된 시나리오를 모의하여 통합모델링시스템의 적용성을 검토하였다.

• Water Engineering Research
• /
• 제3권2호
• /
• pp.143-153
• /
• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2005년도 학술발표회 논문집
• /
• pp.1184-1188
• /
• 2005

The impacts of turbidity flow induced by summer rainfall events on water supply, aquatic ecosystems, and socioeconomics are significant and major concerns in most of reservoirs operations. As a decision support tool, the real-time turbidity flow monitoring and modeling system RTMMS is under development using a laterally integrated two-dimensional (2D) hydrodynamic and water quality model. The objectives of this paper is to present the preliminary field observation results on the characteristics of rainfall-induced turbidity flows and their density flow regimes, and the model performance in replicating the fate and transport of turbidity plume in a reservoir. The rainfall-induced turbidity flows caused significant drop of river water temperature by 5 to $10^{\circ}C$ and resulted in density differences of 1.2 to $2.6kg/m^3$ between inflow water and ambient reservoir water, which consequently led development of density flows such as plunge flow and interflow in the reservoir. The 2D model was set up for the reservoir. and applied to simulate the temperature stratification, density flow regimes, and temporal and spatial turbidity distributions during flood season of 2004 After intensive refinements on grid resolutions , the model showed efficient and satisfactory performance in simulating the observed reservoir thermal stratification and turbidity profiles that all are essentially required to enhance the performance of RTMMS.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.167-167
• /
• 2020

Annual sediment deposition in reservoirs behind weirs poses flood risk, while its accurate prediction remains a challenge. Sediment management by hydraulic flushing is an effective method to maintain reservoir storage. In this study, an integrated approach to predict sediment inflow and sediment flushing simulation in reservoirs is presented. The annual sediment inflow prediction was carried out with Artificial Neural Networks (ANN) modelling. RESCON model was applied for quantification of sediment flushing feasibility criteria. The integrated approach was applied on Sangju Weir and also on estuary of Nakdong River (NREB). The mean annual sediment inflow predicted at Sangju Weir and NREB was 400,000 ㎥ and 170,000 ㎥, respectively. The sediment characteristics gathered were used to setup RESCON model and sediment balance ratio (SBR) and long term capacity ratio (LTCR) were used as flushing efficiency indicators. For Sangju Weir, the flushing discharge, Q_f = 140 ㎥/s with a drawdown of 5 m, and flushing duration, T_f = 10 days was necessary for efficient flushing. At NREB site, the parameters for efficient flushing were Q_f = 80 ㎥/s, T_f = 5 days, N = 1, El_f = 2.24 m. The hydraulic flushing was concluded feasible for sediment management at both Sangju Weir and NREB.

• 한국농공학회지
• /
• 제37권3_4호
• /
• pp.48-60
• /
• 1995

A flood - flow forecasting system model of river basins has been developed in this study. The system model consists of the data management system(the observation and telemetering system, the rainfall forecasting and data-bank system), the flood runoff simulation system, the reservoir operation simulation system, the flood forecasting simulation system, the flood warning system and the user's menu system. The Multivariate Rainfall Forecasting model, Meteorological factor regression model and Zone expected rainfall model for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood - flow forecasting. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, 7 streamfiow and other hydrological data during the past flood periods.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2006년도 학술발표회 논문집
• /
• pp.354-359
• /
• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.