• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.1
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• pp.55-64
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• 1987

The schemes synthesizing the instantaneous unit hydrograph(IUH) are presented by using the geomorphologic parameters of a basin. To this end, the channels in the network are numbered according to the Strahler scheme, and the mathematical formulation corresponding to a dynamic probability theory for deriving the geomorphologic IUH(GUH) is refered to the existing techniques adopted by Rodriguez-Iturbe and Valdes. Also, the mean runoff velocity is applied for expressing a dynamic state of flow. The applicability of the GUH to the real drainage basins is tested by using the data observed in a few basins with areas of the order of 9.2, 20, 33.63, and $109.73km^2$ in Korea. The test is carried out by checking the discrepancies between the observed and simulated values for the peak discharge and its time of occurrence which are the most important parameters of an IUH by varing the mean runoff velocity and the inputs. As a result, good agreement is found between them, and it is shown that the variability in peak discharge of hydrograph depends on the mean runoff velocity more than the constant loss rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.52-67
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• 2008

Recently, frequent occurrence of flash floods caused by climactic change has necessitated prompt and quantitative prediction of precipitation. In particular, the usability of rainfall radar that can carry out real-time observation and prediction of precipitation behavior has increased. Moreover, the use of distributed hydrological model that enables grid level analysis has increased for an efficient use of rainfall radar that provides grid data at 1km resolution. The use of distributed hydrologic model necessitates grid-type spatial data about target basins; to enhance reliability of flood runoff simulation, the use of visible and precise data is necessary. In this paper, physically based $Vflo^{TM}$ model and ModClark, a quasi-distributed hydrological model, were used to carry out flood runoff simulation and comparison of simulation results with data from Imjin River Basin, two-third of which is ungauged. The spatial scope of this study was divided into the whole Imjin River basin area, which includes ungauged area, and Imjin River basin area in South Korea for which relatively accurate and visible data are available. Peak flow and lag time outputs from the two simulations of each region were compared to analyze the impact of uncertainty in topographical parameters and soil parameters on flood runoff simulation and to propose effective methods for flood runoff simulation in ungauged regions.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.895-905
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• 2008

To analyze hydrologic processes in a watershed requires both various geographical data and hydrological time series data. Recently, not only geographical data such as DEM(Digital Elevation Model) and hydrologic thematic map but also hydrological time series from numerical weather prediction and rainfall radar have been provided as grid data, and there are studies on hydrologic analysis using these grid data. In this study, GRM(Grid based Rainfall-runoff Model) which is physically-based distributed rainfall-runoff model has been developed to simulate short term rainfall-runoff process effectively using these grid data. Kinematic wave equation is used to simulate overland flow and channel flow, and Green-Ampt model is used to simulate infiltration process. Governing equation is discretized by finite volume method. TDMA(TriDiagonal Matrix Algorithm) is applied to solve systems of linear equations, and Newton-Raphson iteration method is applied to solve non-linear term. Developed model was applied to simplified hypothetical watersheds to examine model reasonability with the results from $Vflo^{TM}$. It was applied to Wicheon watershed for verification, and the applicability to real site was examined, and simulation results showed good agreement with measured hydrographs.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.111-123
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• 2004

Recently, though damage caused by intensive rainfall and typhoon happens frequently, we could not forecast or predict a disaster, due to the difficulty of obtaining exact information about it. For efficient disaster management, the most urgent need is the preparation of a flood forecast-warning system. Therefore, we need to provide a program that has the ability of inundation analysis and flood forecast-warning using a geographic information system, and using domestic technology rather than that from foreign countries. In this research, we constructed a FDMS(Flood Disaster Management System) that is able to analyze real-time inundation data, and usins the GIS(Ceographic Information System) with prompt analyzing of hydrologic-topographical parameters and runoff-computation. Moreover, by expressing inundation analysis in three-dimensions, we were able to get to the inundation area with ease. Finally, we expect that the application of this method in the (food forecast-warning system will have great role in reducing casualties and damage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.

• Water for future
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• v.29 no.1
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• pp.151-161
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• 1996

The proposed flood forecasting system combines a flood routing model with a parameter estimation model. In the parameter estimation model system states and parameters are treated with the extended state-space formulation. The extended Kalman filter is adopted to estimate the states and parameters. A sensitivity analysis is used to investigate the relative significance of the parameters. Insensitive parameters are treated as constants and parameters that are mutually correlated are combined in a simplified form. The developed estimation methodology is applied todam sites of the multi-purpose reservoirs in Korea. The forecasted hydrographs from the extended Kalman filter satisfactorily coincide with the observed. From the time sequence plots of estimated parameters, it is found that the storage coefficient is almost constant, but exponent varies appreciably in time.

• 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.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.