• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.709-712
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• 2004

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. h short-term water demand forecasting technology will be developed fatting into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff 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 the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Water for future
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• v.24 no.3
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• pp.95-104
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• 1991

The study was focused on developing a new model to estimate annual runoff. This model can be used to estimate the available water resources for ungaged catchments for long-term water resources development planning. Data used in the model development were daily rainfall and daily runoff of the sample basin with record length from 1945 to 1988 years in Korea. The sample basin selected by consideration whether the flow is virgin and quality of discharge data is good. As a result, 46 stage gaging station were selected. Annual runoff was determined by sum of daily runoff calculated by daily stage data of the sample basin. Also, the annual mean precipitation by using daily rainfall data was estimated and the annual runoff ratio for each sample basin was calculated, and the annual mean runoff ratio was estimated. The linear regression model was proposed and calibrated using auunal mean precipitation values and geomorphological characteristics of the basins. To verify reasonableness of this model, the regression model was applied to the gaging stations which have historical data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Water Engineering Research
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• v.5 no.3
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• pp.133-146
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• 2004

Soil and water assessment tool (SWAT) was simulated based on the default parameters and a priori soil parameter estimation method in Bocheong watershed of Korea. The performance of the model was tested against the measured daily runoff data for 5 years between 1993 and 1997. The sensitivity analysis of SWAT model parameters was conducted to identify the most sensitive model parameters affecting the model output. The results of SWAT simulation indicate that the overall performance of SWAT in calculating daily runoff is reasonably acceptable. However, there is a problem in estimating the low flow components of streamflow since the low flow components simulated by SWAT are significantly different from the measured low flow. The sensitivity analysis with SWAT points out that soil related parameters are the most sensitive parameters affecting surface and ground water balance components and groundwater flow related parameters exhibit negligible sensitivity.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.507-510
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• 2003

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. A short-term water demand forecasting technology will be developed taking into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff 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 the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.6
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• pp.11-16
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• 2009

This study is to suggest the SWAT model as inputs for the estimation of CN (Curve number) if we do not have hourly rainfall and runoff data in the ungaged watershed. The daily CNs were estimated by using SWAT model for Chungju dam watershed and the CNs by hourly rainfall and runoff data in the same period with daily CN estimation were also estimated. Then the daily and hourly CNs were compared each other. The CNs by SWAT model were larger than the actual CNs. 7.4% larger in AMC-I, 1.2% in AMC-II, and 6.3% in AMC-III respectively. If we consider various uncertainties in the estimation of CN, the error of 6.8% could be acceptable for the application in the field.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.1
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• pp.3-13
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• 2004

For optimal irrigation reservoir operation during flood and normal period, a general and systematic policy is suggested to make balance of the conflicting purposes between water conservation and flood control. We developed Open Water Management Program (OWMP) with an open architecture to deal with newly arising upgrade problems for optimal management of irrigation reservoir. And we evaluated the applicability of OWMP to estimate daily runoff from an agricultural watershed including irrigation reservoirs, and analyzed behaviour of irrigation reservoirs as irrigation water requirements considering frequency analysis of reservoir storage and frequency analysis water requirements for effective management of reservoir. When we executed OWMP with data produced from an experimental field, IHP basins, the mean relative errors of application of daily runoff and irrigation water requirement were less than 5％. We also applied OWMP to a Seongju irrigation reservoir to simulate daily runoff, storage and water requirement from 1998 to 2002, and the mean model efficiency between measured and simulated value was 0.76. Our results based on the magnitude of relative errors and model efficiency of the model simulation indicate that the OWMP can be a tool nicely adapted to the effective water management of irrigation reservoir for beneficial water use and flood disaster management.

• Magazine of the Korean Society of Agricultural Engineers
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• v.29 no.2
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• pp.53-63
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• 1987

Due to their wide range of application, deterministic comprehensive hydrologic models using digital computers have been developed in all countries of the world and researches are being undertaken for their appropriate applications. The aim of this study has been to demonstrate the practical implementation of a physically based distributed hydrologic model, the USDAHL-74 model and to investigate its ability to simulate the long term estimate of water balance quantities in a Korean mountainous watershed. Application of the model to Dochuk watershed indicates the following results. 1.Since the USDAHL-74 model includes all the major components of the hydrologic cycle in agricultural watersheds, thus is comprehnsive, the model seems to have a wide range of application from the fact that simulation results obtained are not only runoff volumes m various time units but their spatial variation as well as even soil moisture within the watershed. 2.An approximate calibration to determine the parameter values in the model using various data obtained from D0chuk shed shows that the simulation error of yearly runoff volume is only 0.6 % and a correlation coefficient between observed daily runoff volume and simulated one is 0.91 in all calibrated period.3.As a verification test of the model, runoff volumes are simulated using 1986 year data without changing the parameter values determined by 1985 year data. The tests show that the USDAHL-74 model is a flexible tool and that realistic production to simulate the long term estimate of runoff in Korean mountainous watershed could be obtained using only a short period of calibration.4. Despite of the encouraging results, there still remain minor problems concerning the practical application of the model to improve the result of simulations. Some of these are the small descrepancies between observed and simulated daily runoff volume appeared in the vicinity of peaks and the recession of1 the daily hydrographs and the model performance for the frozen ground and melting process in the model. 5. Alough the use of parameter with physical significance and the ability to improve calibrations on the basis of physical reasoning represents advantages in the simulation for ungaged watersheds, further researches are needed to use the USDAHL-74 mode to simulate runoff in ungaged watersheds.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.65-71
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• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.111-120
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• 2018

The purpose of this study was to develop a ridge regression (RR) model to estimate BOD and TP load using runoff weighted value. The concept of runoff weighted value, based on distributed curve-number (CN), was introduced to reflect the impact of land covers on runoff. The estimated runoff depths by distributed CN were closer to the observed values than those by area weighted mean CN. The RR is a technique used when the data suffers from multicollinearity. The RR model was developed for five flow duration intervals with the independent variables of daily runoff discharge of seven land covers and dependent variables of daily pollutant load. The RR model was applied to Heuk river watershed, a subwatershed of the Han river watershed. The variance inflation factors of the RR model decreased to the value less than 10. The RR model showed a good performance with Nash-Sutcliffe efficiency (NSE) of 0.73 and 0.87, and Pearson correlation coefficient of 0.88 and 0.93 for BOD and TP, respectively. The results suggest that the methods used in the study can be applied to estimate pollutant load of different land cover watersheds using limited data.