• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.3
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• pp.97-104
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• 2010

The purpose of this study is to evaluate sediment yield reduction under various field slope conditions with rice straw mat. The Vegetative Filter Strip Model-W (VFSMOD-W) and Soil and Water Assessment Tool (SWAT) were used for simulation of sediment yield reduction effect of rice straw mat. The Universe Soil Loss Equation Practice factor (USLE P factor), being able to reflect simulation of rice straw mat in the agricultural field, were estimated for each slope with VFSMOD-W and measured soil erosion values under 5, 10, and 20 % slopes. Then with the regression equation for slopes, USLE P factor was derived and used as input data for each Hydrological Response Unit (HRU) in the SWAT model. The SWAT Spatially Distributed-HRU (SD-HRU) pre-processor module was utilized, moreover, in order to consider spatial location and topographic features (measured topographic features by field survey) of all HRU within each subwatershed in the study watershed. Result of monthly sediment yield without rice straw mat (Jan. 2000 - Aug. 2007) was 814.72 ton/month, and with rice straw mat (Jan. 2000 - Aug. 2007) was 526.75 ton/month, which was reduced as 35.35 % compared without it. Also, during the rainy season (from Jun. to Sep. 2000 - 2007), when without vs. with rice straw mat, monthly sediment indicated 2,109.54 ton and 1,358.61 ton respectively. It showed about 35.60 % was reduced depending on rice straw mat. As shown in this study, if rice straw mat is used as a Best Management Practice (BMP) in the sloping fields, rainfall-driven sediment yield will be reduced effectively.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.457-467
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• 2014

This study is to evaluate the snowmelt impact on dam inflow for the Chungju Dam watershed $6,642.0km^2$ using Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) and Soil and Water Assessment Tool (SWAT). To determine the SWAT snowmelt parameter; snow cover depletion curve (SCDC) the snow depth distribution (SDD) using Terra MODIS was used, the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 10 sets (2000-2010) data during snowmelt period (November-April), the sno50cov parameter, that is, the 50% coverage at a fraction of SCDC which determines the shape of snow depletion process, showed the values of 0.4 to 0.7. The SWAT model was calibrated with average $R^2$ of 0.54 using the sno50cov of each year. The 10 years average streamflow during snowmelt period was 104.3 mm which covers 12.0% of the annual streamflow.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.39-50
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• 2022

In this study, we suggested the optimal training period for predicting the streamflow using the LSTM (Long Short-Term Memory) model based on the deep learning and CMIP5 (The fifth phase of the Couple Model Intercomparison Project) future climate scenarios. To validate the model performance of LSTM, the Jinan-gun (Seongsan-ri) site was selected in this study. We comfirmed that the LSTM-based streamflow was highly comparable to the measurements during the calibration (2000 to 2002/2014 to 2015) and validation (2003 to 2005/2016 to 2017) periods. Additionally, we compared the LSTM-based streamflow to the SWAT-based output during the calibration (2000~2015) and validation (2016~2019) periods. The results supported that the LSTM model also performed well in simulating streamflow during the long-term period, although small uncertainties exist. Then the SWAT-based daily streamflow was forecasted using the CMIP5 climate scenario forcing data in 2011~2100. We tested and determined the optimal training period for the LSTM model by comparing the LSTM-/SWAT-based streamflow with various scenarios. Note that the SWAT-based streamflow values were assumed as the observation because of no measurements in future (2011~2100). Our results showed that the LSTM-based streamflow was similar to the SWAT-based streamflow when the training data over the 30 years were used. These findings indicated that training periods more than 30 years were required to obtain LSTM-based reliable streamflow forecasts using climate change scenarios.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.7-14
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• 2009

The purpose of this study is to identify how much the MODIS NDVI (Normalized Difference Vegetation Index) can explain the forest soil moisture simulated from SWAT (Soil and Water Assessment Tool) model. For ChungjuDam watershed ($6,661.3\;km^2$) which covers 82.2% of forest, the SWAT model was calibrated for four years (2003-2006) at two locations of the watershed using daily streamflow data and was verified for three years (2000-2002) with average Nash and Sutcliffe model efficiencies of 0.69 and 0.75 respectively. For the period from March to June, the average spatial correlation between 16 days composite MODIS NDVI and the corresponding SWAT forest soil moisture was 0.90. The two variables averaged for each data set during that period showed an inverse relation with the average coefficient of determination of 0.55.

• Journal of Environmental Science International
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• v.18 no.6
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• pp.609-619
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• 2009

SWAT-K model is a modified version of the original SWAT, and is known to more accurately estimate the streamflows and pollutant loadings in Korean watersheds. In this study, its hydrological components were compared with those of HSPF in order to analyse the differences in total runoff including evapotranspiration(ET), surface flow, lateral flow and groundwater flow from the Chungju Dam watershed during $2000{\sim}2006$. Averaged annual runoff with SWAT-K overestimated by 1%, and HSPF underestimated it by 3% than observed runoff. Determination coefficients($R^2$) for observed and simulated daily streamflows by both the models were relatively good(0.80 by SWAT-K and 0.82 by HSPF). Potential ET and actual ET by HSPF were lower in winter, but similar or higher than those by SWAT-K. And though there were some differences in lateral and groundwater flows by two models because of the differences in hydrological algorithms, the results were to be reasonable. From the results, it was suggested that we should utilize a proper model considering the characteristic of study area and purposes of the model application because the simulated results from same input data could be different with models used. Also we should develop a novel model appropriate to Korean watersheds by enhancing limitations of the existing models in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.470-470
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• 2015

본 연구의 목적은 위성영상 기반의 SEBAL(Surface Energy Balance Algorithm for Land) 모형과 SWAT(Soil and Water Assessment Tool) 수문모형을 용담댐 유역($922.3km^2$)에 적용하여 증발산량을 산정하고 모형 간 공간 증발산량의 비교를 통해 각 모형의 적용성을 평가하는데 있다. 이를 위해 SEBAL모형의 입력자료로 Terra MODIS(Moderate Resolution Imaging Spectrometer) Product 중 Normalized Distribution Vegetation Index(NDVI), Albedo 영상을 2012년부터 2013년까지 월단위로 구축하고, 일단위의 Land Surface Temperature(LST) 영상을 구축하였다. 지형자료로는 Digital Elevation Model(DEM)과 Land use를 구축하였으며 SEBAL 모형의 구동을 위한 위성영상 및 지형자료는 500 m의 공간해상도로 재구축하였다. SWAT 모형의 모의를 위해 기상 및 유량 자료를 2000년부터 2013년까지 일단위로 구축하였고, DEM, Land use, 토양도의 지형자료를 30 m의 공간해상도로 구축하였다. SWAT 모형의 유출 검보정 후 수위관측소 지점에서 평균 $R^2$를 산정한 결과 도치(0.80), 동향(0.72), 석정(0.64), 주천(0.80), 천천(0.80), 용담댐(0.72)로 높은 상관성을 나타냈으며, 유출 검보정 후 SWAT 모형의 증발산량 모의 결과를 바탕으로 SEBAL 모형과의 공간 증발산량을 비교하였다. 두 모형의 증발산량은 SEBAL 모형의 경우 지형에 따라 SWAT 모형은 토양 특성에 따라 분포하는 경향이 다르게 나타났다. SEBAL 모형은 주로 저지대에서 증발산량이 높게 산정되며 고지대로 갈수록 감소하여 증발산량이 지형의 고저차에 따라 분포하는 모습을 보였다. SWAT 모형은 토양 특성에 따라 증발산량이 분포하며 유역 내에서 뚜렷한 차이를 나타내지는 않았다. 월별 총 증발산량은 SWAT 모형의 경우 7~8월에 약 90 mm/mon로 가장 높게 나타나고 1~2월은 0 mm/mon로 계절별 변화폭이 컸으나, SEBAL 모형의 경우 5~6월에 증발산량이 약 60 mm/mon로 가장 높게 나타났고 계절별 변화 폭이 SWAT 모형에 비해 적은 모습을 보였다. 이는 위성영상을 기반으로 하는 SEBAL 모형의 특성상 장마 기간에 해당하는 7~8월은 구름으로 인해 일사량이 적게 계산되고, 그 결과 5~6월에 비해 증발산량이 작게 산정되는 것으로 판단된다.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.47-57
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• 2018

This study implemented the rainfall-runoff analysis of the Mekong River basin using the SWAT (Soil and Water Assessment Tool). The runoff analysis was simulated for 2000~2007, and 11 parameters were calibrated using the SUFI-2 (Sequential Uncertainty Fitting-version 2) algorithm of SWAT-CUP (Calibration and Uncertainty Program). As a result of analyzing optimal parameters and sensitivity analysis for 6 cases, the parameter ALPHA_BF was found to be the most sensitive. The reproducibility of the rainfall-runoff results decreased with increasing number of stations used for parameter calibration. The rainfall-runoff simulation results of Case 6 showed that the RMSE of Nong Khai and Kratie stations were 0.97 and 0.9, respectively, and the runoff patterns were relatively accurately simulated. The runoff patterns of Mukdahan and Khong Chaim stations were underestimated during the flood season from 2004 to 2005 but it was acceptable in terms of the overall runoff pattern. These results suggest that the combination of SWAT and SWAT-CUP models is applicable to very large watersheds such as the Mekong for rainfall-runoff simulation, but further studies are needed to reduce the range of modeling uncertainty.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.868-872
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• 2012

유역의 유입 및 유출은 강수에 의해 발생하며 여러 가지 기후 조건과 토지 상태의 영향을 받는다. SWAT(Soil and Water Assessment Tool) 모형은 이러한 여러 가지 복잡한 기후 조건과 토지 상태를 반영하며, 장기간 입력 자료에 따른 유출량을 산출할 수 있다. 본 연구에서는 시험유역을 안동댐 유역으로 선정하였으며, SWAT 모형을 이용하여 10년(2000년 ~ 2010년) 동안의 유출량을 산정하였고 이를 안동댐의 실측 유입량과 비교 분석하였다. ArcSWAT을 이용하여 분석하였고 입력 자료는 SWAT의 분석단위인 HRU(Hydrologic Response Unit)를 산정하기 위한 정밀 토양도 및 토지피복도와 기상입력 자료인 강우 및 최고기온, 최저기온, 습도, 풍속, 일사량 등을 사용하였다. 강우관측소는 안동댐 유역의 고선, 남회룡, 도천, 미질, 석동, 석포, 석현, 의촌, 재산, 황지를 선정하였고, 일사량관측소는 안동, 대관령, 포항을 선정하였으며, 기온, 습도, 풍속관측소는 안동, 봉화, 태백, 영주를 선정하였다. 또한 기상입력자료 중 결측값은 역거리 자승법을 이용하여 보완하였다. SWAT 모형은 유출량 계산 시 여러 가지 다양한 매개변수가 사용되며, 이러한 매개변수들의 검 보정을 통하여 실제 유역의 특성과 하천 흐름특성을 반영할 수 있다. 본 연구의 시험유역인 안동댐유역은 산림과 초지가 많은 지역이기 때문에 식물에 의해 차단되는 강우에 관한 매개변수와 지하로 침투되는 강우량에 관한 매개변수 등을 보정하여 실제 유역특성을 반영하였다. 본 연구에서는 이러한 과정을 통해 안동댐 유역의 10년 동안의 일 유출량을 산정한 결과, 홍수기의 첨두유량 및 첨두시간에는 실측자료와 약간의 차이가 있었지만 전체적으로 실측자료와 매우 유사한 유출량을 산정하였다.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.703-716
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• 2006

The flow duration curves in the present and the ideal hydrologic cycle were derived using SWAT model. The present situation is the landuse and the groundwater withdrawal in the year of 2000 and the ideal situation is the landuse of 1975 and no groundwater withdrawal. These results were compared with the previous instream flow requirements which are the larger flow between the average drought flow and environmental control flow. As a result, the present and ideal drought flows of Ojeoncheon, Hakuicheon, Samseongcheon, and Sammakcheon, were the same and the drought flows of Samseongcheon and Sammakcheon were even zero since the baseflow is very little due to the small and mountainous watersheds. The previous instream flow requirement for the riverine function is also larger than the low flow of the ideal hydrologic cycle. The present method to set the instream flow requirement is not proper for the small mountainous watershed since it can be usually overestimated and drive the artificial measures to secure the streamflow Therefore, another method should be developed such as the low flow and the average flow between the drought flow and the low flow of the ideal hydrologic cycle using the proper hydrologic simulation model such as SWAT which can consider the landuse.