• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.1-11
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• 2017

The analysis of baseflow contribution is very significant in Korea because most rivers have high variability of streamflow due to the monsoon climate. Recently, the importance of such analysis is being more evident especially in terms of river management because of the changing pattern of rainfall and runoff resulted from climate change. Various baseflow separation methods have been developed to separate baseflow from streamflow. However, it is very difficult to identify which method is the most accurate way due to the lack of measured baseflow data. Moreover, it is inappropriate to analyze the annual baseflow contribution for Korean rivers because rainfall patterns varies significantly with the seasons. Thus, this study compared the baseflow contributions at various time-scales (annual, seasonal and monthly) for the 4 major river basins through BFI (baseflow index) and suggested baseflow contribution of each basin by the BFI ranges searched from different baseflow separation methods (e.g., BFLOW, HYSEP, PART, WHAT). Based on the comparison of baseflow contributions at the three time scales, this study showed that the baseflow contributions from the monthly and seasonal analysis are more reasonable than that from the annual analysis. Furthermore, this study proposes that defining BFI with its range is more proper than a specific value for a watershed, considering the difference of BFIs between various baseflow separation methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.146-146
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• 2015

하천관리는 직접유출과 기저유출로 구성된 하천유량의 특성을 이해하는 것으로부터 시작된다. 따라서 직접유출과 기저유출의 특성은 직접적으로 하천유량의 변동성으로 전달된다. 이러한 점을 고려했을 때 전략적인 하천관리를 위해서는 직접유출/기저유출과 하천유량의 관계를 분석하는 것이 필요하다. 이를 대해 본 연구의 목적은 하천유량의 변동성과 기저유출비(Baseflow Index, BFI)의 관계를 분석하고, 이러한 관계를 일관성 있게 나타낼 수 있는 하천유량 변동성 지표(Streamflow Variability Index, SVI)를 제시하는 것이다. 본 목적을 달성하기 위해 국내 총 91개 유량관측소를 대상으로 유황계수, R-B Index(Richards-Baker Flashiness Index), 변동계수(Coefficient of Variation), Q5:Q95 ratio 등의 SVI를 적용하였다. 이와 더불어, BFI를 산정하기 위하여 기저유출 분리 모형인 WHAT, PART, HYSEP, BFLOW를 사용하였다. 연구결과는 BFI값이 증가 할수록 SVI의 값은 감소하는 경향을 나타냈다. 다양한 SVI 가운데 BFI에 대한 R-B Index의 평균 결정계수가 0.66로 가장 큰 값을 나타냈으며, 특히 WHAT 모델의 Local Minimum Method(LMM)과의 결정계수가 0.82로 가장 밀접한 관계를 나타냈다. 변동계수 또한 평균 결정계수 값이 0.60으로 높은 값을 보였다. 그 외 유황계수와 Q5:Q95의 평균 결정계수는 각각 0.04와 0.07로 매우 낮은 값을 보였다. 결론적으로 다양한 SVI 가운데 R-B Index가 여러 기저유출 방법으로 산정된 BFI 값들에 대하여 가장 일관성 있는 결과를 보였다. 본 연구의 결과는 하천의 생태적 기능 유지와 치수안정성을 확보를 고려한 하천관리에 기여할 것이다.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.474-481
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• 2007

With population growth, industrialization, and urbanization within the watershed, the hydrologic response changed dramatically, resulting in increases in peak flow with lesser time to peak and total runoff with shortened time of concentration. Infiltration is directly affected by initial soil moisture condition, which is a key element to determine runoff. Influence of the initial soil moisture condition on hydrograph analysis should be evaluated to assess land use change impacts on runoff and non-point source pollution characteristics. The Long-Term Hydrologic Impact Assessment (L-THIA) model has been widely used for the estimation of the direct runoff worldwide. The L-THIA model was applied to the Little Eagle Creek (LEC) watershed and Its estimated direct runoff values were compared with the BFLOW filtered direct runoff values by other researchers. The $R^2$ value Was 0.68 and the Nash-Sutcliffe coefficient value was 0.64. Also, the L-THIA estimates were compared with those separated using optimized $BFI_{max}$ value for the Eckhardt filter. The $R^2$ value and the Nash-Sutcliffe coefficient value were 0.66 and 0.63, respectively. Although these higher statistics could indicate that the L-THIA model is good in estimating the direct runoff reasonably well, the Antecedent Moisture Condition (AMC) was not adjusted in that study, which might be responsible for mismatches in peak flow between the L-THIA estimated and the measured peak values. In this study, the L-THIA model was run with AMC adjustment for direct runoff estimation. The $R^2$ value was 0.80 and the Nash-Sutcliffe coefficient value was 0.78 for the comparison of L-THIA simulated direct runoff with the filtered direct runoff. However there was 42.44% differences in the L-THIA estimated direct runoff and filtered direct runoff. This can be explained in that about 80% of the simulation period is classified as 'AMC I' condition, which caused lower CN values and lower direct runoff estimation. Thus, the coefficients of the equation to adjust CN II to CN I and CN III depending on AMC condition were modified to minimize adjustments impacts on runoff estimation. The $R^2$ and the Nash-Sutcliffe coefficient values increase, 0.80 and 0.80 respectively. The difference in the estimated and filtered direct runoff decreased from 42.44% to 7.99%. The results obtained in this study indicate the AMC needs to be considered for accurate direct runoff estimation using the L-THIA model. Also, more researches are needed for realistic adjustment of the AMC in the L-THIA model.