SWAT 모형을 이용한 시.공간적 토지 이용변화에 따른 유량 및 유사량 특성분석

신용철,임경재,김기성,최중대,Shin, Yong-Chul,Lim, Kyoung-Jae,Kim, Ki-Sung,Choi, Joong-Dae 한국관개배수위원회 2007 한국관개배수논문집 Vol.14 No.1

In this study, the Soil and Water Assessment Tool (SWAT) model was used to assess spatiotemporal effects on watershed runoff and sediment characteristics due to land uses changes from 1999 to 2002 at the small watershed, located in Chuncheon-si, Gangwon province. The annual average flow rate of Scenario I (long-term simulation using land use of 1990), II (long-term simulation using land use of 1996), III(long-term simulation using land use of 200) and IV(simulation using land use of 1990, 1995, and 2000) in long-term simulation) using the SWAT model were 29,997,043 m3, 29,992,628 m3, 29,811,191 m3 and 29,931,238 m3, respectively. It was shown that there was no significant changes in estimated flow rate because no significant changes in land uses between 1990 and 2000 were observed. The annual average sediment loads of Scenarios I, II, III and IV for 15 year period were 36,643 kg/ha, 45,340 kg/ha , 27,195 kg/ha and 35,545 kg/ha, respectively. The estimated annual sediment loads from Scenarios I, II, and III, were different from that from the scenario IV, considering spatio-temporal changes in land use and meterological changes over the years, by 10%, 127%, and temporal changes in land use and meterological changes over the years, by 10%, 127%, and 77%. This can be explained in land use changes in high soil erosion potential areas, such as upland areas, within the study watershed. The comparison indicates that changes in land uses upland areas, within the study watershed. The comparison indicates that changes in land uses can affect on sediment yields by more than 10%, which could exceed the safety factor of 10% in Total Maximum Daily Loads (TMDLs). It is, therefore, recommended that not only the temporal analysis with the weather input data but also spatial one with different land uses need to be considered in long-term hydrology and sediment simulating using the SWAT model

가지야마공식과 SWAT 모델을 이용한 유출량산정

신용철 ( Yong Chul Shin ),신민환 ( Min Hawn Shin ),김웅기 ( Woong Ki Kim ),허성구 ( Sung Gu Heo ),임경재 ( Kyoung Jae Lim ),김기성 ( Ki Sung Kim ),최중대 ( Joong Dae Choi ) 한국물환경학회 2007 한국물환경학회·대한상하수도학회 공동 춘계학술발표회 Vol.2007 No.-

GIS 기반의 모형을 이용한 도암댐 유역의 산림 파편화에 따른 수(水)환경 영향 평가

허성구 ( Sung Gu Heo ),김기성 ( Ki Sung Kim ),안재훈 ( Jae Hun Ahn ),윤정숙 ( Jong Suk Yoon ),임경재 ( Kyoung Jae Lim ),최중대 ( Joong Dae Choi ),신용철 ( Yong Chul Shin ),유창원 ( Chang Won Lyou ) 한국지리정보학회 2006 한국지리정보학회지 Vol.9 No.4

도암댐 유역의 산림 파편화에 따른 수(水)환경 영향 평가

허성구 ( Sung-gu Heo ),김기성 ( Ki-sung Kim ),안재훈 ( Jae-hun Ahn ),윤정숙 ( Jong-suk Yoon ),임경재 ( Kyoung Jae Lim ),최중대 ( Joongdae Choi ),신용철 ( Yong-chul Shin ),유창원 ( Changwon Lyou ) 한국농공학회 2006 한국농공학회 학술대회초록집 Vol.2006 No.-

The water quality impacts of forest fragmentation at the Doam-dam watershed were evaluated in this study. For this ends, the watershed scale model, Soil and Water Assessment Tool (SWAT) model was utilized. To exclude the effects of different magnitude and patterns in weather, the same weather data of 1985 was used because of significant differences in precipitation in year 1985 and 2000. The water quality impacts of forest fragmentation were analyzed temporarily and spatially because of its nature. The flow rates for Winter and Spring has increased with forest fragmentations by 8,366㎥/month and 72,763㎥/month in the S1 subwatershed, experiencing the most forest fragmentation within the Doam-dam watershed. For Summer and Fall, the flow rate has increased by 149,901 ㎥/month and 107,109㎥/month, respectively. It is believed that increased flow rates contributed significant amounts of soil erosion and diffused nonpoint source pollutants into the receiving water bodies. With the forest fragmentation in the S1 watershed, the average sediment concentration values for Winter and Spring increased by 5.448mg/L and 13.354mg/L, respectively. It is believed that the agricultural area, which were forest before the forest fragmentation, are responsible for increased soil erosion and sediment yield during the spring thaw and snow melts. For Spring and Fall, the sediment concentration values increased by 20.680mg/L and 24.680mg/L, respectively. Compared with Winter and Spring, the increased precipitation during Summer and Fall contributed more soil erosion and increased sediment concentration value in the stream. Based on the results obtained from the analysis performed in this study, the stream flow and sediment concentration values has increased with forest fragmentation within the S1 subwatershed. These increased flow and soil erosion could contribute the eutrophication in the receiving water bodies. This results show that natural functionalities of the forest, such as flood control, soil erosion protection, and water quality improvement, can be easily lost with on-going forest fragmentation within the watershed. Thus, the minimize the negative impacts of forest fragmentation, comprehensive land use planning at watershed scale needs to be developed and implemented based on the results obtained in this research.

FRAGSTATS 모형을 이용한 도암댐 유역의 경관 분석

허성구 ( Sung-gu Heo ),김기성 ( Ki-sung Kim ),안재훈 ( Jae-hun Ahn ),윤정숙 ( Jong-suk Yoon ),임경재 ( Kyoung Jae Lim ),최중대 ( Yong-chul Shin ),신용철 ( Changwon Lyou ),유창원 ( Joongdae Choi ) 한국농공학회 2006 한국농공학회 학술대회초록집 Vol.2006 No.-

The Doam-dam watershed, located at Kangwon Province, Korea, has been experiencing significant changes in land uses, conversion from forest to agricultural/urban areas, with human involvements. However, no thorough investigation of the landscape impacts of land use changes was performed at this watershed using scientific analytical tool. Thus, the FRAGSTATS model was utilized to quantitatively analyze the landscape impacts of forest fragmentation in this study. To provide the detailed explanations for 11 landscape indices considered in this study, two artificial and simplified landscapes, before and after fragmentations, were constructed. Using these 11 indices, the landscape impacts of forest fragmentation in 19 subwatersheds of the Doam-dam watershed were analyzed. The S1 subwatershed, one of 19 subwatersheds of the Doam-dam watershed, was found to have experienced the significant forest fragmentation from 1985 to 2000 based on landscape analysis. The results obtained in this study can be used to evaluate the water quality impacts of forest fragmentations/landuse changes at watershed scale level, and establish environment-friendly land use planning based on the results obtained using landscape analytical tool, FRAGSTATS.

FRAGSTATS 모형을 이용한 도암댐 유역의 산림 파편화 분석

허성구 ( Sung Gu Heo ),김기성 ( Ki Sung Kim ),안재훈 ( Jae Hun Ahn ),윤정숙 ( Jong Suk Yoon ),임경재 ( Kyoung Jae Lim ),최중대 ( Joong Dae Choi ),신용철 ( Yong Chul Shin ),유창원 ( Chang Won Lyou ) 한국지리정보학회 2007 한국지리정보학회지 Vol.10 No.1

강원도 평창군에 위치한 도암댐 유역은 인간의 개발행위에 따른 산림지역 파편화로 인해 산림지역의 상당부분이 농업/도시 지역으로 변화되어 왔다. 이러한 토지이용변화로 인해 하류 수역에서는 많은 부정적인 영향이 발생하고 있다. 그러나 토지이용변화가 도암댐 유역내 경관에 미치는 영향을 과학적인 분석 툴을 이용하여 수계단위로 분석한 예는 그리 많지 않다. 따라서 본 연구에서는 산림의 파편화가 경관에 미치는 영향을 정량적으로 분석하기 위하여 경관분석 프로그램인 FRAGSTATS를 이용하였다. 복잡한 계산식으로 구성된 경관지수를 자동으로 산출해주는 FRAGSTATS 프로그램은 경관분석에 많이 이용해 왔으나, 각 경관지수별 설명이 충분하지 않아 FRAGSTATS를 처음 사용하는 사용자가 이를 이용하여 정량적 경관분석을 수행하기에는 다소 어려움이 있어 왔다. 따라서 본 연구에서는 경관 파편화가 발생하기 전과 후의 가상적이면서 단순화 된 경관을 구성하여 경관지수를 설명하였다. 본 연구에서 기술된 경관지수를 이용하여 도암댐 유역내에서 산림 파편화가 경관에 미치는 영향을 정량적으로 평가하였다. 총 19개 소유역중 S1 유역이 1985년부터 2000년까지 가장 많은 산림 파편화가 진행된 것으로 분석되었다. 본 연구의 결과는 산림지역의 파편화, 이에 따른 토지이용변화가 수질에 미치는 영향을 수계단위로 평가하는데 매우 유용하게 사용될 수 있으리라 판단되며, FRAGSTATS과 같은 경관분석 프로그램의 결과를 바탕으로 한 환경친화적 토지이용계획을 수립하는데 매우 유용하리라 판단된다. The Doam-dam watershed, located at Kangwon Province, Korea, has been experiencing significant changes in land uses, conversion from forest to agricultural/urban areas, with human involvements. However, no thorough investigation of the landscape impacts of land use changes was performed at this watershed using the scientific analytical tool. Thus, the FRAGSTATS model was utilized to quantitatively analyze the landscape impacts of forest fragmentation in this study. To provide the detailed explanations for 11 landscape indices considered in this study, two artificial and simplified landscapes, before and after fragmentations, were constructed. Using these 11 indices, the landscape impacts of forest fragmentation in 19 subwatersheds of the Doam-dam watershed were analyzed. The S1 subwatershed, one of 19 subwatersheds of the Doam-dam watershed, was found to have experienced the significant forest fragmentation from 1985 to 2000 based on landscape analysis using the FRAGSTATS model. The results obtained in this study can be used to evaluate the water quality impacts of forest fragmentations/land use changes at watershed scale level, and establish environment-friendly land use planning based on the results obtained using landscape analytical tool, FRAGSTATS.

SWAT ArcView GIS Extension Patch를 활용한 SWAT 예측유사량 분석

김종건 ( Kim Jonggun ),박윤식 ( Park Younshik ),강성근 ( Kang Sung-keun ),임경재 ( Lim Kyoung Jae ),신용철 ( Shin Yongchul ),김기성 ( Kim Ki-sung ),최중대 ( Choi Joongdae ) 한국농공학회 2007 한국농공학회 학술대회초록집 Vol.2007 No.-

The Soil and Water Assessment Tool (SWAT) model has been widely used in simulating hydrology, soil erosion/sediment, pesticide, and nutrient within the watershed. The SWAT model estimates the slope length of each Hydrologic Response Unit (HRU) within subwatershed with the average slope of subwatershed. However topographic information extraction module in the SWAT ArcView system cannot be applied for steep watershed. Thus, the slope length values were modified with the ArcView Avenue programming. In this study, the 1:5000 digital maps were used to generate 7 DEMs (10m, 15m, 20m, 30m, 50m, 70m, 100m) for the Imha-dam watershed. The slope, slope length, estimated streamflow and sediment using various DEMs were compared in this study. As shown in this study, the use of DEM cell size of 100m or above could results in approximately 760% difference without SWAT ArcView GIS Extension Patch in slope length and 10% difference with SWAT ArcView GIS Extension Patch in slope length in estimated sediment yield although the same input data were used in model runs. Therefore, it is suggested that researchers or modelers use the detailed topographic data for accurate modeling of watershed hydrology and water quality using SWAT ArcView GIS Extension Patch utilized in this study.

머신러닝 기법을 활용한 유황별 LOADEST 모형의 적정 회귀식 선정 연구: 낙동강 수계를 중심으로

김종건 ( Jonggun Kim ),박윤식 ( Youn Shik Park ),이서로 ( Seoro Lee ),신용철 ( Yongchul Shin ),임경재 ( Kyoung Jae Lim ),김기성 ( Ki-sung Kim ) 한국농공학회 2017 한국농공학회 학술대회초록집 Vol.2017 No.-

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model(adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load(SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

SWAT과 SATEEC 모형을 이용한 토양유실량 비교

박윤식 ( Park Younshik ),김종건 ( Kim Jonggun ),신용철 ( Shin Yongchul ),안재훈 ( Ahn Jaehun ),박준호 ( Park Joonho ),김기성 ( Kim Ki-sung ),임경재 ( Lim Kyung Jae ) 한국농공학회 2007 한국농공학회 학술대회초록집 Vol.2007 No.-

Soil erosion is a natural process and has been occurring in most areas in the watershed. However, accelerated soil erosion rates have been causing numerous environmental impacts in recent years. To reduce soil erosion and sediment inflow into the water bodies, site-specific soil erosion best management practices (BMPs) need to be established and implemented. The most commonly used soil erosion model is the Universal Soil Loss Equation (USLE), which have been used in many countries over 30 years. The Sediment Assessment Tool for Effective Erosion Control (SATEEC) ArcView GIS system has been developed and enhanced to estimate the soil erosion and sediment yield from the watershed using the USLE input data. In the last decade, the Soil and Water Assessment Tool (SWAT) model also has been widely used to estimate soil erosion and sediment yield at a watershed scale. The SATEEC system estimates the LS factor using the equation suggested by Moore and Burch, while the SWAT model estimates the LS factor based on the relationship between sub watershed average slope and slope length. Thus the SATEEC and SWAT estimated soil erosion values were compared in this study. The differences in LS factor estimation methods in the SATEEC and SWAT caused significant difference in estimated soil erosion. In this study, the difference was -51.9%(default threshold)~-54.5%(min. threshold) between SATEEC and non-patched SWAT, and -7.8%(default threshold)~+3.8%(min. threshold) between SATEEC and patched SWAT estimated soil erosion.

머신러닝 기법을 활용한 유황별 LOADEST 모형의 적정 회귀식 선정 연구: 낙동강 수계를 중심으로

김종건,박윤식,이서로,신용철,임경재,김기성,Kim, Jonggun,Park, Youn Shik,Lee, Seoro,Shin, Yongchul,Lim, Kyoung Jae,Kim, Ki-sung 한국농공학회 2017 한국농공학회논문집 Vol.59 No.4

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.