• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.41-52
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• 2004

In this study, parameters of long and short term runoff model were optimized using genetic algorithm as a basic research for integrated water management in a watershed. In case of Korea where drought and flood occurr frequently, the integrated water management is necessary to minimize possible damage of drought and flood. Modified TANK model was optimized as a long term runoff model and storage-function model was optimized as a short term runoff model. Besides distinguished parameters were applied to modified TANK model for supplementing defect that the model estimates less runoff in the storm period. As a result of application, simulated long and short term runoff results showed 7% and 5% improvement compared with before optimized on the average. In case of modified TANK model using distinguished parameters, the simulated runoff after optimized showed more interrelationship than before optimized. Therefore, modified TANK model can be applied for the long term water balance as an integrated water management in a watershed. In case of storage-function model, simulated runoff in the storm period showed high interrelationship with observed one. These optimized models can be applied for the runoff analysis of watershed.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.4
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• pp.99-108
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• 1982

This thesis aims to estimate the rainfall runoff from paddy field in a small watershed during irrigation period. When the data observed at the proposed site are not available, the Monthly Runoff Equation of Korean Rivers which was derived from data observed under the following assumptions is used to study the water balance. a. Monthly base flow was assumed as 10. 2mm even if these is no mouthly rainmfall. b. Monthly comsumption of rainfall was ranged from 100 to 2OOmm without relation to the rainfall depth. However, the small watershed which consists mainly of paddy fields encounters severe droughts and accordingly the baseflow is negligible. Under the circumstances the author has developed the following equation called "Flood Irrigation Method for Rainfall Runoff "taking account of the evapotranspiration, precipitation, seepage, less of transportation, etc. R= __ A 7000(1 +F) -5n(n+1)+ (n+1)(Pr-S-Et)] where: R: runoff (ha-m) A: catchment area (ha) F: coefficient of loss (o.o-0. 20) Pr: rainfall (mm) S: seepage Er: evapotranspiration (mm) To verify the above equation, the annual runoff ratio for 28 years was estimated using the Monthly Runoff Equation of Korean Rivers the Flood Irrigation Method and the Complex Hydrograph Method based on meteorological data observed in the Dae Eyeog project area, and comparison was made with data observed in the Han River basin. Consequently, the auther has concluded that the Flood Irrigation Method is more consi- stent with the Complex Hydrograph Method and data observed than the Monthly Runoff Equation of Korean Rivers.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.527-534
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• 2007

To operate scientifical and integrated management of water resources, it needs to identify clearly the quantitative variation and moving pathway of water resources in a basin. Moreover, it needs to also estimate more precisely the amount of runoff generating from the precipitation. Thus, in this study, to carry out more reliable hydrologic analyses, the runoff characteristics according to detailed runoff components and water balance in a basin are analyzed. As a result of yearly water balance analyses, during the period of drought year, the loss is bigger than that of 6-year mean loss and the return flow of groundwater is the most dominant component of runoff. During the period of flood year, the loss is smaller about 4% than that of 6-year mean loss and the subsurface water is the most dominant component of runoff. The loss due to the interception and evapotranspiration for 6-year mean loss is about 53% of the total rainfall, the mean runoff ratio is about 27% and the baseflow is about 22%.

• Journal of Industrial Technology
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• v.22 no.B
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• pp.231-240
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• 2002

The rational method of estimating peak flow is used largely for the simplicity. But the accuracy of rational method is not easy to estimate, because the rational method is analyzed by the deterministic point or view and the runoff coefficients of the rational method are proposed from other countries. In this study the rational method is analyzed by the probabilistic way to be a more reliable method. The runoff coefficient is regarded to parameter that changes the probabilistic rainfall to the peak flow. The runoff coeffient for each return period is analyzed to be a reliable index which is used to estimate the peak flow of ungauged natural catchments.

• Journal of Environmental Health Sciences
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• v.31 no.1
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• pp.1-6
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• 2005

This study was conducted to identify the runoff characteristics of non-point source according to rainfall in Nam watershed. Land-uses of the Nam watershed were surveyed paddy field 4.5%, crop field 6.8%, mountainous 78.7%, urban 2.4%, and etc. 7.7%. Mean runoff coefficients in each area were observed Ⅰ area 0.08, Ⅱ area 0.08, and Ⅲ area 0.05. In the relationship between the rainfall and peak-flow, correlation coefficients(r) were investigated Ⅰ area -0.8609, Ⅱ area 0.6035, and Ⅲ area -0.4913. In the relationship between the antecedent dry period and first flow runoff, correlation coefficients(r) were investigated Ⅰ area -0.9093, Ⅱ area -0.1039, and Ⅲ area -0.7317. The discharge of pollutant concentrations relates to the flow rate of storm-water. In the relationship between the rainfall and watershed loading, exponent values of BOD, COD, SS, and T-N were estimated to 1.2751, 1.2003, 1.3744, and 1.1262, respectively.

• Applied Biological Chemistry
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• v.43 no.4
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• pp.285-290
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• 2000

This research was carried out to investigate the runoff pattern of nutrients by rainfall from the paddy field located at the southwest of Korea. Effective rainfalls were 17.0 and 21.2 mm for irrigation and non-irrigation period, respectively. The first runoff in paddy field was 6.3-33.7 mm (average=12.9 mm) and 5.9-12.5 mm (9.2 mm) for irrigation and non-irrigation period, respectively. And runoff ratio was 0.59-1.36 and 0.57-0.86 for irrigation and non-irrigation period, respectively. During irrigation and non-irrigation period, the outflow increased significantly with the precipitation. Pattern of precipitation-runoff differed between irrigation and non-irrigation period, as affected by precipitation intensity, hydrological condition, and cropping. There existed a linear relationship between the runoff loading of N and P and precipitation. During the irrigation and non-irrigation period, runoff loadings of N and P were not significantly different. However, there was significant difference in case of total suspended solids.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.633-641
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• 2018

The risk of disasters, such as floods and drought, has increased. Reliable hydrological data is important for analyzing the water resource and designing hydraulic structure to manage these risks. The Yongdam Guryang river catchment located in the central of Korea is the research catchment of K-water and UNESCO IHP, and the hydrological data, such as rainfall, runoff, evapotranspiration, etc. has been observed at the catchment. The aim of this study was to assess the runoff characteristics of the small mountainous catchment of Korea based on the observed hydrological data, and the Probability Distributed Model was applied as the Rainfall-Runoff Model at the Yongdam Guryang river catchment. The hydrological data was divided into the wet period from June to September and dry period from October to May according to data analysis. The runoff ratio was 0.27~0.41 in the wet period and 0.30~0.45 in the dry period. The calibration result by the Probability Distributed Model showed a difference in the calibrated model parameters according to the periods. In addition, the model simulated the runoff accurately except for the dry period of 2015, and the result revealed the applicability of the PDM. This study showed the runoff characteristics of the small mountainous catchment by dividing the hydrological data into dry and wet periods.

• Journal of Environmental Science International
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• v.7 no.4
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• pp.451-460
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• 1998

The objectives of this study Is to evaluate the total runoff yield, peak flow and peak flow travel time depending on the urbanization, return period and rainfall patterns at the downstream of Manchon urban watershed in TaeGu City. SWM(Storm Water Management Model) is used for runog analysis based on 5 different steps of urbanization and 4 different types of Hufrs quartile according to 8 return periods. It is analyzed that the order of total runoff yield according to raiun patterns is Huffs 4, Huffs 2. Huffs 3 and Huffs 1 quartile, that of peak flow magnitude is Huffs 2, Huffs 1, Huffs 4 and Huffs 3 quartile at present development ratio. under the 60, 70, 80 and 90ft of urbanization to the 50% of urbanization by means of the rainfall patterns, the mean Increasing ratio of total runoff yield for each case is 4.55, 11.43, 16.07 and 20.02%, that of peak flow is 5.82, 13.61, 17.15 and 18.83%, the mean decreasing ratio of peak flow travel time Is 0.00, 2.44, 5.07 and 6.26%, the mean increasing ratio of runoff depth Is 4.51, 11.42, 16.02 and 20.05% respectively. the mean increasing ratio of total runoff yield by means of each and 19.71%. Therefore, as the result of this study. it can be used for principal data as to storm sewage treatment and flood damage protection planning in urban small watershed.

• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.155-162
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• 2007

Water balance is the major factor in forest ecosystem, and is closely related to the vegetation and topographic characteristics within a watershed. The hydrologic response of a forest watershed was investigated with the hydrological model. The deterministic, lumped parameter model (BROOK90) was selected and used to evaluate the applicability of the model for simulating daily runoff on the steep, forested watershed. The model was calibrated and validated against the streamflow data measured at the Bukmoongol watershed. The deviation in runoff volume $(D_v)$ was -1.7% for the calibration period, and the $D_v$ value for the validation period was 4.6%. The correlation coefficient (r) and model efficiency (E) on monthly basis were 0.922,0.847, respectively, for the calibration period, while the r- and E-value for the validation period were 0.941, 0.871, respectively. Overall, the simulated streamflows were close to the observations with respect to total runoff volume, seasonal runoff volume, and baseflow index for the simulation period. BROOK90 model was able to reproduce the trend of runoff with higher correlation during the simulation period.

• Korean Journal of Ecology and Environment
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• v.41 no.1
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• pp.43-53
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• 2008

This study was conducted to simulate storm hydrographs on a small forested watershed using TOPMODEL, which is a distributed hydrological model. The Myeongseong watershed, which is 58.3 ha in size, was selected to monitor rainfall and runoff data. The Monte Carlo simulation was also used to calibrate parameters of TOPMODEL. Six rainfall-runoff pairs collected at the watershed in the year 1997 were used for parameter calibration, and eight rainfall-runoff pairs collected during the period of $1998\sim1999$ were used for validation effort. The errors of runoff volume ranged from -2.74% to 1.81%, and an average value of model efficiency in terms of runoff volume was 0.92 for the calibration period. The average value of observed peak discharge was $0.324m^3\;s^{-1}$ for six rainfall-runoff pairs, while the prediction value was $0.295m^3\;s^{-1}$. The simulation errors of peak discharge varied according to rainfall characteristics and antecedent condition, within ranges of -27.65% to -1.13%. The model efficiency for the validation period was 0.92. For the validation period, observed peak discharges have an average value of $0.087m^3\;s^{-1}$ and average value of simulated peak discharge was $0.090m^3\;s^{-1}$. Observed and simulated values of time to peak for the calibration period were 18.3 hrs and 11.0 hrs, respectively, and 16.6 hrs and 13.5 hrs, respectively, for the validation period.