An Euler-Lagrange two-phase flow model is presented fur simulation sheet-flow transport under wave and current. The flow is computed by solving the Reynolds Averaged Navier-Stokes equation in conjunction with the k-$\varepsilon$ turbulence model for turbulence closure. The sediment transport is introduced as a motion of granular media under the action of unsteady flow from the Lagragian point of view. In other word, motion of every single particle is numerically traced with Movable Bed Simulator (MBS) code based on the Distinct Element Method (DEM), in which the frequent interparticle collision of the moving particles during the sheet-flow transport is sophisticatedly taken into account. The particle diameter effect on time-dependent developing process of sheet-flow transport is investigated, by using three different diameter sizes of sediment. The influence of an imposed current on oscillatory sheet-flow transport is also investigated. It is concluded that the sediment transport rate increases due to the relaxation process related to the time-lag between flow velocity and sediment motion.

There has been continuous efforts to manage water resources for the required water quality criterion at river channel in Korea. However, we could obtain the partial improvement only for the point sources such as, waste waters from urban and factory site through the water quality management. Therefore, it is strongly needed that the best management practice throughout the river basin fur water quality management including non-point sources pollutant loads. This problem should be resolved by recognizing the non-point sources pollutant loads from the upstream river basin to the outlet of the basin depends on the landuse and soil type characteristics of the river basin using the computer simulation by a distributed model based on the detailed investigation and application of Geographic Information System (GIS). The purpose of this study is consisted of the three major distributions, which are the investigation of spread non-point sources pollutants throughout the river basin, development of the base maps to represent and interpret the input and outputs of the distributed simulation model, and prediction of non-point sources pollutant loads at the outlet of a up-stream river basin using Agricultural Non-Point Sources Model (AGNPS). For the validation purpose, the Seom-Jin River basin was selected with two flood events in 1998. The results of this application showed that the use of combined a distributed model and an application of GIS was very effective fur the best water resources and quality management practice throughout the river basin

In this paper, mixing characteristics and dilution of the merging buoyant discharges from array of multiple jets has been extensively studied in the hydraulic model experiments. New equations for dilution, which include the merging effects correctly, were derived. Experiments were constructed in a 20-m long, 4.9-m wide and 0.6-m deep flume, and the model diffuser was manufactured to indicate the typical characteristics of the existing ocean wastewater outfall in South Korea. Buoyant discharge from the diffuser was reproduced using heated water. Water temperature was measured using CC-Type thermocouple sensors, which were connected to a 40-channel data logger. Experimental results show that merging between ports in a particular riser is dependent upon the discharge densimetric Froude number, whereas merging between two ports which are facing each other at 90$\circ$ at the adjacent risers is dependent upon the discharge densimetric Froude number and distance from the port and port spacing. Centerline dilution increase with distance from the port outlet until two plumes has merged. However, after merging occurs, increase of the centerline dilution almost stops. Further distance from the position where merging occurs, centerline dilution increases again.

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

This study investigates the effects of sediment on the flow characteristics such as velocity distribution, friction velocity, turbulent intensities, Reynolds stress, etc. Particle tracking velocimetry (PTY) is used to measure the vertical flow field. Results show that flow over the high bed-load concentration region has larger values of mean velocity and friction velocity and smaller values of turbulence intensities, compared to those for flow over the low bed-load concentration region.

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.