For an accurate rainfall-runoff simulation in the river basin, it is important to consider not only evaluation of runoff model but also accurate runoff component. In this study long-term runoffs were simulated by means of watershed runoff model and the amounts of runoff components such as upstream inflow, surface runoff, return flow and dam release were evaluated based on the concept of water budget. SSARR model was applied to Daecheong basin, the upstream region of Geum river basin, and in turn the monthly runoff discharges of main control points in the basin were analyzed. In addition, for the purpose of providing the basic quantified water resources data the conceptual runoff amounts were estimated with water budget analysis and the reliability of the observations and the monthly runoff characteristics were investigated in depth. The yearly runoff ratios were also estimated and compared with the observations. From the results of the main control points, Yongdam, Hotan, Okcheon and Daecheong, the yearly runoff ratios of those points are consistent well with data reported previously.

기후변화와 지구온난화현상은 지구 전체에 걸쳐 분명하게 나타나고 있으며 그에 따라 발생할 수 있는 수문 변화에 대한 연구가 다양하게 이루어지고 있다. 본 연구에서는 기후변화에 따른 유역 유출의 민감도를 평가하기 위하여 SWAT 모형을 이용하였으며 대청댐유역에 적용하였다. 모형의 보정은 1982-1995년의 월평균 하천유량을 이용하였고 1996-2005년의 자료를 이용하여 검증하였다. 기후변화에 따른 수문 변동을 정량적으로 분석하기 위하여 1988-2002

미국농무성에서 토양과 토지이용 특성을 고려한 대규모 유역의 유출해석과 토양침식량 및 비점오염원 부하를 해석하기 위해 개발한 SWAT 모델을 대청댐 유역에 적용하여 토지이용 특성별 토양침식량을 산정하였다. 연구결과는 저수지관리자와 정책입안자들에게 저수지 탁수문제를 완화하기 위한 유역관리 대안의 효율성을 평가하는데 중요한 정보를 제공한다. 유출과 토양 유실량 산정에 영향을 미치는 주요한 매개변수들을 보정한 후, 모델은 실측 연간 유출성분과 월별 유황변화를

This study was performed to analyze the variation characteristics of water qulity, correlation analysis of water quality data at each site and among the items of water quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, T-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature + 1.23 (R^2 = 0.7616) . Results of the correlation analysis of water quality data showed that DO had higt correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, SS did site 5 and 6, T-N did 2 and 3, T-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. DO_1 = 4.46 + 0.59 DO_2 (R^2 = 0.8868), BOD_1 = 0.52 + 0.63 BOD_3 (R^2 = 0.6390) COD_2 = 0.44 + 0.71 COD_1 (R^2 = 0.9183), SS_6 = 0.89 + 0.70 SS_5 (R^2 = 0.9155) TN_3 = 0.151 + 0.886 TN_2 (R^2 = 0.9415), TP_4 = 0.004 + 0.758 TP_6 (R^2 = 0.9669)