This article describes some of the recent and on-going research developments of the author at Colorado State University. Advances in the field of sedimentation and river mechanics include basic research and computer modeling on several topics. Only a few selected topics are considered here: (1) analytical determination of velocity profiles, shear stress and sediment concentration profiles in smooth open channels; (2) experiments on bedload particle velocity in smooth and rough channels; (3) field measurements of sediment transport by size fractions in curved flumes. In terms of computer modeling, significant advances have been achieved in: (1) flashflood simulation with raster-based GIOS and radar precipitation data; and (2) physically-based computer modeling of sediment transport at the watershed scale with CASC2D-SED. Field applications, measurements and analysis of hydraulic geometry and sediment transport has been applied to: (1) gravel-bed transport measurements in a cobble-bed stream at Little Granite Creek, Wyoming; (2) sand and gravel transport by size fraction in the sharp meander bends of Fall River, Colorado; (3) changes in sand dune geometry and resistance to flow during major floods of the Rhine River in the Netherlands; (4) changes in hydraulic geometry of the Rio Grande downstream of Cochiti Dam, New Mexico; and (5) analysis of the influence of water temperature and the Coriolis force on flow velocity and sediment transport of the Lower Mississippi River in Louisiana. Recent developments also include two textbooks on "Erosion and Sedimentation" and "River Mechanics" by the author and state-of-the-art papers in the ASCE Journal of Hydraulic Engineering.

Developed at Colorado State University, CASC2D-SED is a physically-based model simulating the hydrologic response of a watershed to a distributed rainfall field. The time-dependent processes include: precipitation, interception, infiltration, surface runoff and channel routing, upland erosion, transport and sedimentation. CASC2D-SED is applied to Goodwin Creek, Mississippi. The watershed covers 21 $\textrm{km}^2$ and has been extensively monitored both at the outlet and at several internal locations by the ARS-NSL at Oxford, MS. The model has been calibrated and validated using rainfall data from 16 meteorological stations,6 stream gaging stations and 6 sediment gaging stations. Sediment erosion/deposition rates by size fraction are predicted both in space and time. Geovisualization, a powerful data exploration technique based on GIS technology, is used to analyze and display the dynamic output time series generated by the CASC2D-SED model.

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States 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 as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, 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. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

In this paper, the status of forest and forestry together with the trend of forest hydrology in Japan are firstly overviewed for the mutual understanding between the Japan Society of Hydrology and Water Resources (JSHWR) and the Korean Water Resources Association (KWRA). Then, Long-Term Ecological Research recently introduced in Asia is briefly explained, and the establishment of Japan-Korea Long-Term Forest Hydrological Research Network is proposed.

An expert system, to assist dam managers for five dams along the Saikawa River, has been developed with a primary objective of achieving swift and accurate reservoir operation decision-makings during floods. The expert system is capable of supporting on decision-makings upon establishment of flood management procedure and release/storage planning. Furthermore, an attempt was made to improve reservoir inflow prediction models for better supporting capability. As a result, accuracy on prediction of inflow up to 7 hours ahead was improved, which is important for flood management of the five dams, using neural network. The neural network inflow prediction models were developed for each types of floods caused by frontal rainfalls, snowmelt and typhoons, after extracting relevant meteorological factors for each.

Weather Radar have played an important role in both precipitation observation and hydrological operations over several countries and evaluated its efficient and necessities for the developed flood management and control. This paper describe the factors influencing the design the hydrological radar network in Korea and develop Hydrological Radar Network Simulation Model (HRNSM) based on GIS and UI system. Moreover, the methodologies for geographical and hydrological feasibility analysis for radar network were provided in detail manner.

본 연구는 유역 수질의 효과적인 관리와 수질 모델의 효율적인 활용을 위하여 GIS를 기반으로 수질 모델과 지형 및 문자 데이터베이스를 통합한 모델링 시스템(Water Quality Management System)을 개발하고 전라북도 내 동진강에 대해 적용하였다. WQMS는 DOS 기반의 수질모델이 가지는 복잡성과 결과해석의 불편을 최소화하기 위하여 윈도우 기반의 하천모델을 운용하기 위한 시스템을 구축하고, 나아가 GIS 기반의 사용자에게 보다 편리한 모델링 환경을 제공하도록 설계되었다. 개발된 시스템의 구현 단계는 전처리와 모델링 실행, 후처리의 세단계로 구분될 수 있다. 전처리단계는 DB에 의해 모델실행을 지원하며 후처리 단계에서는 GIS를 이용하여 모델실행 결과를 그래프와 속성 자료로 확인할 수 있도록 하였다. 또한 실측자료를 활용하여 WQMS의 적용성을 평가한 결과 신뢰성이 높게 평가되었다. WQMS는 기존 DOS 기반의 모델링의 복잡성을 제거하고 정도 높은 수질분석을 수행하므로서 효율적인 유역 수질관리에 필요한 기본자료를 제공할 것으로 기대된다.

영산강 홍수사상에 의한 모형의 보정을 통하여 복합조도계수는 0.030-0.031의 범위로 적용하였고 난류교환계수는 나주수위표 관측수위에 가장 근접하는 난류교환계수 3000 N.s/$m^2$을 결정하였다. HEC-RAS에 의한 홍수위보다 RMA2에 의한 홍수위가 크게 나타난 일부 구간에서는 횡단 방향 홍수위의 변화를 고려하여 제방고를 계획하여야 한다. 횡단 방향 유속 분포에서 유심부에서는 RAM2에 의한 유속이 HEC-RAS에 의한 평균유속보다 상당히 크다. 따라서 단기적으로 볼 때 유심부에는 세굴이 진행되는 하상변동 그리고 양안부에서는 퇴적이 진행되리라 전망된다.