• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.9-19
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• 2014

The objective of this study was to evaluate of the Tank model in simulating runoff discharge from rural watershed in comparison to the SWAT (Soil and Water Assessment Tool) model. The model parameters of SWAT was calibrated by the shuffled complex evolution-university Arizona (SCE-UA) method while Tank model was calibrated by genetic algorithm (GA) and validated. Four dam watersheds were selected as the study areas. Hydrological data of the Water Management Information System (WAMIS) and geological data were used as an input data for the model simulation. Runoff data were used for the model calibration and validation. The determination coefficient ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency index (NSE) were used to evaluate the model performances. The result indicated that both SWAT model and Tank model simulated runoff reasonably during calibration and validation period. For annual runoff, the Tank model tended to overestimate, especially for small runoff (< 0.2 mm) whereas SWAT model underestimate runoff as compared to observed data. The statistics indicated that the Tank model simulated runoff more accurately than the SWAT model. Therefore the Tank model could be a good tool for runoff simulation considering its ease of use.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.161-169
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• 2012

Recently various attempts have been made to apply HSPF model to calculate runoff and diffuse pollution loads of stream and reservoir watersheds. Because the role of standard flow is very important in the water quality modelling of Total Water Pollution Load Management, HSPF was used as a means of estimating standard flow. In this study, BASINS 4.0 and WinHSPF was applied to the Gomakwoncheon watershed, genetic algorithm(GA) and influence coefficient algorithm were used to calibrate the runoff parameters of the WinHSPF. The objective function is the sum of the squares of the normalized residuals of the observed and calculated flow and it is optimized using GA. Estimates of the optimum runoff parameters are made at each iteration of the influence coefficient algorithm. The calibration results showed a relatively good correspondence between the observed and the calculated values. The standard flow(Q275) of the Gomakwoncheon watershed was estimated using the ten years of weather data.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.2
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• pp.181-187
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• 2009

In this study, a runoff hydrograph and runoff volume were calculated by using the kinetic wave theory for small urban watersheds based on the concept of low impact development(LID), and the effective imperviousness was estimated based on these calculations. The degree of sensitivity of the effective imperviousness of small watersheds to the impervious to pervious area ratio, infiltration capability, watershed slope, roughness coefficient and surface storage depth was then analyzed. From this analysis, the following conclusions were obtained: The effective imperviousness and paved area reduction factor decreased as the infiltration capability of pervious area increased. As the slope of watersheds becomes sharper, the effective imperviousness and the paved area reduction factor display an increasing trend. As the roughness coefficient of impervious areas increases, the effective imperviousness and the paved area reduction factor tend to increase. As the storage depth increases, the effective imperviousness and the paved area reduction factor show an upward trend, but the increase is minimal. Under the conditions of this study, it was found that the effective imperviousness is most sensitive to watershed slope, followed by infiltration capability and roughness coefficient, which affect the sensitivity of the effective imperviousness at a similar level, and the storage depth was found to have little influence on the effective imperviousness.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.553-563
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• 2011

According to the improvement of computer's performance, the development of Geographic Information System (GIS), and the activation of offering information, a distributed model for analyzing runoff has been studied a lot in recently years. The distribution model is a theoretical and physical model computing runoff as making target basin subdivided parted. In the distributed model developed by this study, the volume of runoff at the surface flow is calculated on the basis of the parameter determined by landcover data and a two-dimensional diffusion wave equation. Most of existing runoff models compute velocity and discharge of flow by applying Manning-Strickler's mean velocity equation and Manning's roughness coefficient. Manning's roughness coefficient is not matched with dimension and ambiguous at computation; Nevertheless, it is widely used in because of its convenience for use. In order to improve those problems, this study developed the runoff model by applying not only Manning-Strickler's equation but also Chezy's mean velocity equation. Furthermore, this study introduced a power law of exponential friction factor expressed by the function of roughness height. The distributed model developed in this study is applied to 6 events of fan-shape basin, oblong shape test basin and Anseongcheon basin as real field conditions. As a result the model is found to be excellent in comparison with the exiting runoff models using for practical engineering application.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.193-199
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• 1993

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Dong Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas. Key Words : runoff, effective rainfall, SCS method, clark watershed iou상ng, hydrological parameters, parameter estimation, least square method, correlation function method, input-output model, typhoon gladys.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.204-208
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• 2004

In the forthcoming 21C, the barometer of cultural lives depends on that the water demand will increase or not. On the opposite site of that, the small watersheds will influence directly on how to cover the surface of watersheds with land use, no planning developing watersheds, and the rearrangement of small rivers. Espacially as the exordinary climatic phenomena, water resources and water content of the small watersheds will be confused oil exactly not to make a plan of water resources. This study area has four small watersheds groups in Gangwon-Do Province, that is, group I five small river watersheds including Changchoncheon etc., group II fiver rivers watersheds including to Hwalsanmogicheon etc., group III five small river watersheds including Singicheon etc., group IV including to Sabulanggolcheon etc. According to the land use such as dry field(or farm), ice field, forest land, building lot arid others, in small watersheds, the amount of runoff will be impacted by precipitation. The comparison between the runoff was getting from Kajiyama Formular and calculated runoff from multi-linear regressed equations by land use percentage was performed. Its correlation which was estimated by coefficient of correlation will be accepted or not, as approched 1.00000 values. As the monthly water resources amount is estimated by multi-linear regressed equations, we make a plan to demand and supply the water quantity from small river watersheds during any return periods.

• Water for future
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• v.19 no.4
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• pp.347-354
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• 1986

To predict flood runoff from rainfall and watershed Characteristics, Nash's parameters of N, K are needed to be determined. Also parameters of IUH N and K are derived by the moment method. Nash's model whose parameters are derived from rainfall characteristics is applied to the Wi-stream basin, which is a tributary located in the Nakdong river. For the derivation of IUH by applying linear conceptual model, the storage constant, K, with the rainfall characteristics was adopted as K=1.327 $$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. Gamma function argumetn, N, derived with such rainfall characteristics was found to be N=0.032$$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. From the tested results it is proved that Nash's IUH and consequently flood runoff can be predicted from rainfall characteristics.

• Journal of Environmental Science International
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• v.27 no.10
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• pp.845-851
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• 2018

In this study, the runoff characteristics of the non-rainfall period were examined using daily rainfall data from 1977 to 2017 and the data of runoff into the dam. Results showed that, the mean runoff decreases with longer non-rainfall periods in the Andong dam basin. The correlation coefficient between non-rainfall days and average runoff reaches 0.85. The results of the analysis of the runoff characteristics during the non-rainfall period, based on the preceding rainfall of Andong dam are as follows. The runoff characteristics of the entire non-rainfall period, shows that, for a rainfall of 1.0 mm or less, the runoff height was larger than the rainfall size and the base runoff larger. The correlation between the antecedent rainfall and runoff height was reached as high as 0.9864 in the 30 ~ 50 mm interval of the antecedent rainfall period, and this is the interval where the linearity of rainfall and runoff was at its maximum in the Andong dam basin. The correlation between the antecedent rainfall and the runoff height reached 0.92 for rainfalls of 100.0 mm. However, for rainfalls of 100.0 mm greater, the correlation between the antecedent rainfall and runoff height during the rainfall period was 0.64, which is relatively small. In this study, we investigated the runoff characteristics of the rainfall period in the Andong dam watershed. As a result, it was confirmed that the mean runoff decreased with rainfall duration. The linearity was found to be weak for rainfall events greater than 100.0 mm. The results of this study can be used as data for water balance analysis and for formulating a water supply plan to establish water resource management of Andong dam.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.615-626
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• 2019

Soil moisture and runoff have very close relationship. Especially the water retention capacity and drainage characteristics of the soil are determined by various factors of the soil. In this study, a total of 40 rainfall events were identified from the entire rainfall events of Sulma basin in 2016 and 2017. For each selected events, the constant-K method was used to separate direct runoff and baseflow from total flow and calculate the runoff coefficient which shows positive exponential curve with Antecedent Soil Moisture (ASM). In addition to that, the threshold of soil moisture was determined at the point where the runoff coefficient starts increasing dramatically. The threshold of soil moisture shows great correlation with runoff and depth to water table. It was founded that not only ASM but also various factors, such as Initial Soil Moisture (ISM), storage capacity of soil and precipitation, affect the results of runoff response. Furthermore, wet condition and dry condition are separated by ASM threshold and the start and peak response are analyzed. And the results show that the response under wet condition occurred more quickly than that of dry condition. In most events occurred in dry condition, factors reached peak in order of soil moisture, depth to water table and runoff. However, in wet condition, they reached peak in order of depth to water table, runoff and soil moisture. These results will help identify the interaction among factors which affect the runoff, and it will help establish the relationship between various soil conditions and runoff.