Younggu Her

Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case-dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor’s note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. (KEY TERMS: channel dimension; SWAT; equifinality; uncertainty; hydrology; sediment.) Her, Younggu, Jaehak Jeong, Katrin Bieger, Hendrik Rathjens, Jeffrey Arnold, and Raghavan Srinivasan, 2017. Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling. Journal of the American Water Resources Association (JAWRA) 1-23. DOI: 10.1111/1752-1688.12533

Alternative CN Averaging Methods for Determining the Representative CN of a Watershed

AbstractA representative curve number (CN) of a catchment is often calculated from the area-weighted average of CN values of unique land use and soil combinations. While convenient for implementation, this method is known to underestimate direct runoff depth. This paper investigates the mathematical mechanism of the expected underestimation and proposes alternative CN averaging methods called the quadratic and exact averaging methods, to handle the underestimation problem. Results suggest that the quadratic method could reduce the level of underestimation in direct runoff depth; further, the exact method could provide the same direct runoff depth as that of the area-weighted direct runoff depth averaging method. A representative CN is found to be a function of rainfall size and catchment landscape, and the underestimation is relatively large for small and mid-sized storm events. The improvement in runoff estimation with the newly proposed averaging methods is particularly beneficial to stormwater manageme...

Assessing Applicability of SWAT Calibrated at Multiple Spatial Scales from Field to Stream

유역 내부의 수문과정에 대한 관측치를 이용한 모형의 보정은 분산형 수문수질 모형의 적용성을 높이는 방법으로 권장되지만, 관측치가 충분하지 않은 경우가 많아 적용사 례가 드문 실정이다. 본 연구는 경지에서 유역의 출구까지 여러 단계의 수문과정을 고려하여 분산형 수문수질모형을 보정하는 방법을 제시하고, 이와 같은 방법으로 보정된 모형의 적용성을 검토하고자 하였다. 이를 위해 본 연구는 SWAT 모형을 이용하여 미국 조지아의 South Atlantic Coastal Plain에 있는 한 농업유역의 수문과정을 모의 하였다. 또한, 유사와 영양물질의 구체적인 하천운송과정을 모의하기 위해 SWAT모형의 QUAL2E 모듈을 이용하였다. 모형보정은 유역의 유출량 및 유사와 영양물질의 양 뿐만 아니라 경지에서 관측된 바이오매스, 토양 침식량 및 영양물질 발생량, 수변지역 (riparian buffer)에서 발생하는 유사와 영양물질 감소, 하천운송과정 등을 고려하 여 수행되었다. 모형의 보정 및 검증기간은 자료기간과 토양보전농법의 시행기간을 고려하여 선정되었으며, 보정된 모형의 적용성은 복수의 통계치 (NSE, RE, RSR 등) 를 이용하여 분석되었다. 보정된 모형은 유역의 출구에서 하천유량과 총질소량을 각각 NSE 0.93 및 0.59의 정확도로 모의하였다. 그러나 유사 (NSE: 0.40)와 총인량 (NSE: 0.45)모의에서는 상대적으로 낮은 정확도를 보였다. SWAT의 QUAL2E 모듈을 이용하여 하천에서의 유사 및 영양물질 운송과정을 고려하는 것은 총인과 총질소 모의결과의 정확도를 향상시킬 수 있는 것으로 나타났다. 본 연구에서 다단계 수문과정을 고려하여 보정된 SWAT모형은 유역 내부에서 발생하는 수문과정을 모의하는데 적용성이 있는 것으로 나타났다. 하지만, 계측방법 및 계측된 자료의 분석방법의 변화에 따른 오차와 토지이용의 시공간적 변화를 모의에서 고려하지 못해서 발생한 오차를 감안하더라도, 보정된 모형은 유역출구에서 관측되는 유사와 영양물질 양을 정확하게 모의하는데 실패 (NSE < 0.5)하였다고 평가할 수 있으며, 이는 SWAT 모형의 보정 에서 다단계 또는 유역내부의 수문과정을 고려하는 것이 유역출구에서 높은 모의 정확도를 얻는데 기여하지 못할 수도 있음을 보여주었다.

Evaluating Applicability of SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) in Hydrologic Analysis: A Case Study of Geum River and Daedong River Areas

Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) offers opportunities to make advances in many research areas including hydrology by providing near-global scale elevation measurements at a uniform resolution. Its wide coverage and complimentary online access especially benefits researchers requiring topographic information of hard-to-access areas. However, SRTM DEM also contains inherent errors, which are subject to propagation with its manipulation into analysis outputs. Sensitivity of hydrologic analysis to the errors has not been fully understood yet. This study investigated their impact on estimation of hydrologic derivatives such as slope, stream network, and watershed boundary using Monte Carlo simulation and spatial moving average techniques. Different amount of the errors and their spatial auto-correlation structure were considered in the study. Two sub-watersheds of Geum and Deadong River areas located in South and North Korea, respectively, were selected as the study areas. The results demonstrated that the spatial presentations of stream networks and watershed boundaries and their length and area estimations could be greatly affected by the SRTM DEM errors, in particular relatively flat areas. In the Deadong River area, artifacts of the SRTM DEM created sinks even after the filling process and then closed drainage basin and short stream lines, which are not the case in the reality. These findings provided an evidence that SRTM DEM alone may not enough to accurately figure out the hydrologic feature of a watershed, suggesting need of local knowledge and complementary data.