• Journal of Environmental Science International
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• v.18 no.9
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• pp.1045-1055
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• 2009

The discharge within the basin in Jeju Island was calculated by using SWAT model, which a Semi-distributed rainfall-runoff model to the important rivers. The basin of Chunmi river of the eastern region of Jeju Island, as the result of correcting as utilizing direct runoff data of 2 surveys, appeared the similar value to the existing basin average runoff rate as 22% of average direct runoff rate for the applied period. The basin of Oaedo river of the northern region showed $R^2$ of 0.93, RMSE of 14.92 and ME of 0.70 as the result of correcting as utilizing runoff data in the occurrence of 7 rainfalls. The basin of Ongpo river of the western region showed $R^2$ of 0.86, RMSE of 0.62 and ME of 0.56 as the result of correcting as utilizing runoff data except for the period of flood in $2002{\sim}2003$. Yeonoae river of the southern region showed $R^2$ of 0.85, RMSE of 0.99 and ME of 0.83 as the result of correcting as utilizing runoff data of 2003. As the result of calculating runoff for the long term about 4 basins of Jeju Island from the above results, SWAT model wholly appears the excellent results about the long-term daily runoff simulation.

• Magazine of the Korean Society of Agricultural Engineers
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• v.33 no.2
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• pp.51-60
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• 1991

The main objective of this study is to examine the adaptability for the large watershed of the storage tank model which can be applied for the analysis of both long and short terms runoff developed on the basis of hydrologic data for a smaH mountaineous watershed. The results obtained in this study are summarized as follows ; 1. Areal rainfalls of the Dae Chong watershed were calculated by Thiessen method composed of 9 Thiessen networks. 2. Optimal parameters for two types, Model A and Model B of tank models were derived through calibration procedure by standardized Powell method. 3. Monthly simulated flows of Model B are seemed to be closer to the monthly observed than those of Model A during calibration period in the long terms runoff. 4. Relative errors for the simulated flood flows of Model B were apperaed as lower percentage to the observed than those of Model A during calibration period in the short terms runoff. 5. Daily simulated hydrographs of Model B are seemed to be closer to the daily observed than those of Model A during verification period in the long terms runoff. Significance of Model B was highly acknowledged in comparison with Model A in the correlation analysis between annual observed and annual simulated runoff. 6. Reproducibility of simulated flows for Model B is generally seemed to be better than that of Model A during calibration period in the short terms runoff. 7. It can be concluded that reproducibility of Model B is superior to that of Model A in the long and short terms runoff even a large watershed like the result of the small one. 8. It was verified that adaptability for the large watershed of Model B is superior to that of Model A between the two models which were developed by a small watershed characteristics for both long and short terms runoff. 9. Further study for getting a suitable tank model is desirable to be established by the decision, calibration method of initial parameters of tank model and by additional application of another watershed with different watersheds and meterological characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.204-208
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• 2006

This study is to develop a distributed urban flood runoff model that simulates the road runoff and to test the applicability of the model by applying to Pyeongtaek city of $12.2km^2$. To generate the runoff along the runoff, agree burned DEM (Digital Elevation Model) with road networks was suggested and the proper spatial resolution of DEM was identified finer than 15 m. To test the model applicability, 32 points on the road networks were selected and the hydrographs of each point were generated. The test showed reasonable results that increase the road runoff from the high elevation roads to the low elevation roads and the road runoff considering rainwater drainage from the road also showed reasonable results.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.51-56
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• 1999

An object-orient watershed runoff model was formulated using the SCS curve number method and routing routines. The four objects included in the model were rainfall , hydrologic unit, reservoir, and channel. Each object considers the data and simulation method to depict the runoff processes. the details of which were presented and discusses in the paper. The resulting model was applied to the HS #3 watershed of the Balan Watershed Project, which is 412.5 ha in size and relatively steep in landscape. The simulated runoff hydrographs from the model were close to the observed data.

• Journal of Industrial Technology
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• v.3
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• pp.95-101
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• 1983

This study aims at the determination of the coefficienties of runoff and infiltration affecting runoff. The rating curve is more available than the peak flood runoff to determine flood control plan of flood control reservoir and the volume of hydroelectric power plant, or to make multipurpose dam. In hydrologic analysis and design, it is necessary to develop relations between precipitation and runoff, possible using some of the factors affecting runoff as parameters. In order to calculate the runoff discharge, the runoff process constituting elements are divided to the surface runoff, the subsurface runoff and the groundwater runoff. By comparing the computed hydrograph with the measured hydrograph, determinned the watershed TANK Model constant Varying the tank model constant for approximating the computed hydrograph to the measured hydrograph.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.33-46
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• 1990

Abstract Over 700/0 of the rural land area in Korea is mountainous and small watersheds provide most of the water resources for agricutural use. To provide an appropriate tool for the agricultural water resource development project, SNUA2, a mathematical model for simulating the physical processes governing the precipitation-runoff relationships and predicting the storm and long-term runoff quantities from the small mountainous watersheds was developed. The hydrological characteristics of small mountainous watersheds were reviewed to select appropriate theories for the simulation of the runoff processes, and a deterministic and distributed model was developed. In this, subsurface flows are routed by solving Richard's two dimensional equation, the dynamics of soil moisture contents are simulated by the consideration of phenological factors of canopy plants and surface flows are routed by solving the kinematic wave theory by numerical analysis. As a result of an application test of the model to the Sanglim watershed, peak flow rates of storm runoff were over-estimated by up to 184.2%. The occurence time of peak flow and total runoff volume of storm runoffs simulated were consistent with observed values and the annual runoff volumes were simulated in the error range of less than 5.8%.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.439-439
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• 2015

The temporal and spatial relationship of the weather elements such as rainfall and temperature is closely linked to the streamflow simulation, especially, to the flood forecasting problems. For the study area, Imjin river basin, which has the specific characteristics in geography with river cross operation between North and South Korea, the meteorological information in the northern area is totally deficiency, lead to the inaccuracy of streamflow estimation. In the paper, this problem is solved by using the combination of global (such as soil moisture content, land use) and local hydrologic components data such as weather data (precipitation, evapotranspiration, humidity, etc.) for the model-driven runoff (surface flow, lateral flow and groundwater flow) data in each subbasin. To compute the streamflow in Imjin river basin, this study is applied the hydrologic model SURR (Sejong Univ. Rainfall-Runoff) which is the continuous rainfall-runoff model used physical foundations, originally based on Storage Function Model (SFM) to simulate the intercourse of the soil properties, weather factors and flow value. The result indicates the spatial variation in the runoff response of the different subbasins influenced by the input data. The dependancy of runoff simulation accuracy depending on the qualities of input data and model parameters is suggested in this study. The southern region with the dense of gauges and the adequate data shows the good results of the simulated discharge. Eventually, the application of SURR model in Imjin riverbasin gives the accurate consequence in simulation, and become the subsequent runoff for prediction in the future process.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.603-613
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• 2019

This study proposes a method to generate runoff ensemble members using a rainfall-runoff model based on the shot noise process (hereafter the rainfall-runoff model). The proposed method was applied to generate runoff ensemble members for three drainage basins of Daerim 2, Guro 1 and the Jungdong, whose results were then compared with the observed. The parameters of the rainfall-runoff model were estimated using the empirical formulas like the Kerby, Kraven II and Russel, also the concept of modified rational formula. Gamma and exponential distributions were used to generate random numbers of the parameters of the rainfall-runoff model. Especially, the gamma distribution is found to be useful to generate various random numbers depending on the pre-assigned relationship between mean and standard deviation. Comparison between the generated runoff ensemble members and the observed shows that those runoff ensemble members generated using the gamma distribution with its standard deviation twice of the mean properly cover the observed runoff.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.771-780
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• 2004

The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. As the forecasting models for flood runoff the neural network model was tested with the observed flood data at Gongju and Buyeo stations. The neural network model consists of input layer, hidden layer, and output layer. For the flood events tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer. To make a choice the forecasting model which would make up of runoff forecasting system properly, real-time runoff of river when flood periods were forecasted by using neural network model and state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff. The neural network model developed to be used in the Web was loaded into the server and was applied to the main stream of Geum river. For the main stage gauging stations mentioned above the applicability of the selected forecasting model, the Neural Network Model, was verified in the Web.