• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.178-182
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• 2005

In the forthcoming 21C, the development of cultural lives depends on that the water demand will increase or not. On the opposite site of that circumstance, many factors of 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. Especially as the extraordinary climatic Phenomena, exhaust of $CO_2$ and destruction of 03 layer, water resource and water foresting content of the small watersheds will be decreased by confusing on the malting a plan of water resources. For example, those are Typhoon Rusa in 2002, Typhoon Maemi in 2003 and heavy storms in 2004. This study area has three group and one of them having three small watersheds, total five small watersheds. That is, Sabukmyeon small watersheds in Chuncheon, Three small watersheds in Wonju(Jeoncheon, Jupocheon and Hasunamcheon), and Suipcheon in Yanggu-Gun which are located far away each other three group and different precipitation data. According to the land use such as dry field(or farm), rice field, forest land. building site and others in small watersheds, the amount of runoff will be impacted by monthly precipitation. The comparison between the runoff was getting from Kajiyama Formula and calculated runoff from multi-linear regressed equations by land use Percentage was performed with different precipitation data and different small watersheds. Its correlations which are estimated by coefficient of correlation will be accepted or not, as approached 1.0000 values. As the monthly water resources amount is estimated by multi-linear regressed equations with different precipitation data and different small watersheds having no gauging station, we make a plan in order to demand and supply the water quantity from small river watersheds during return periods.

• 한국농공학회지
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• 제22권3호
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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.