• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.3-12
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• 1998

Rainfall intensity under storms affects peak discharge or its time of occurrence in watershed runoff. Thus, it is reasonable to reflect the effect on the parameters of rainfall-runoff models or the governing equations of the models. This paper relates the change of the runoff coefficient of the first tank in tank model to rainfall intensity under storms. The standard four tanks have made the basic structure of the flood event model. and its modifications are as follows: it has two equal runoff coefficients in the first tank: the runoffs from first and second tanks produce delayed response through a simple delaying parameter. Applying the event simulation model to flood data from Naerinchon. runoff coefficients were estimated and their relation to rainfall intensity was analyzed. The results showed the Weak relation of the two factors. The trend of the two was fitted with the equation a1=kI$. where a1is the runoff coefficient of the first tank: I is rainfall intensity; k and m are fitting coefficients. In the verification. the model used moving averages for the calculation of I(t). If the value I(t) gave more greater value of a1(t) than that of previous time(t-1). the flood simulation was performed again from the beginning with the updated greater value of a1. The reflection of rainfall intensity on the runoff coefficient showed far better results than that of a fixed parameter.

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.1
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• pp.40-49
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• 1993

The Objectives of this paper were to develop a modified tank model that is capable of simulating daily streamflow from a small watershed using daily watershed evapotranspiration and to test the applicability of the model to different watersheds. Tank model was restructured to consist of three series of tanks, each of which may mathematically reflect watershed runoff mechanisms from different components of surface runoff, interflow, and baseflow. And pan evaporation was correlated to potential evapotranspiration estimated from a combination method, and was multiplied by monthly crop and landuse coefficients, and watershed storage coefficient to estimate the watershed evapotranspiration losses. Ten watersheds were selected to calibrate model parameters that were defined using an optimization scheme, and the results were correlated with watershed parameters. Simulated daily runoff was compared to the observed ones from the tested watersheds. The simulating results were in good agreement with the observed values when optimal and calibrated parameters were used. Ungaged conditions were also applied to compare simulated values to the observed. And the results were in fair conditions for all the tested watersheds which differ considerably in their sizes, landuse types, and physiological features.

• Water for future
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• v.26 no.3
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• pp.47-61
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• 1993

A menu-driven shell program DSFS (Daily Streamflow Simulation Model), that can process the input data, optimize the parameters, execute the program, and graphically display the results of a modified tank model, was developed and applied to simulating daily streamflow from small watersheds. The model defines daily watershed evapotranspiration losses from potential values multiplied by monthly landuse coefficients and correction factors for soil water storage levels. The parameters were calibrated using observed hydrologic data for fifteen watersheds, and the results were correlated with watershed parameters to define empirical relationships. The proposed model was tested with streamflow data of ungaged conditions, and the simulation results overestimated the annual runoff.

• Journal of Korean Society on Water Environment
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• v.24 no.2
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• pp.221-229
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• 2008

This study revised a model for hydrologically analyzing rainwater harvesting facilities considering their rainfall-runoff properties and the data available. This model has only a few parameters, which can be estimated with rather poor measurements available. The model has a non-linear module for rainfall loss, and the remaining rainfall excess (effective rainfall) is assumed to be inflow to the storage tank. This model has been applied for the rainwater harvesting facilities in Seoul National University, Korea Institute of Construction Technology, and the Daejon World Cup Stadium. As a result, the runoff coefficients estimated were about 0.9 for the building roof as a rainwater collecting surface and about 0.18 for the playground. This result is coincident with that for designing the rainwater harvesting facilities to show the accuracy of model and the simulation results.

• 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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.1
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• pp.65-72
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• 1995

This paper describes the applications of the SCS TR-20 hydrologic model for simula- tion of hourly inflow rates from sixty-six ungaged tributaries and subareas between the Naju station and the estuarin dam at the Yongsan River Basin. The model was tested for the ungaged conditions with fifteen storm events at Naju station. Hourly simulated run- off data were compared with the observed, and the results showed less correlationships between the two data than those from TANK model. The coefficients of correlation ranged from 0.74 to 0.87. The curve numbers and time of concentration were defined from topographic dta for each of sixty-six tributaries for the estuarine dam and used for TR-20 applications. The results were within an acceptable range of errors in simulating the inflow fluctuations for the flood forecasting at the estuarine dam.