Earticle

본 연구에서는 섬진강 상류 옥정호의 유역에서 유입하는 유량. 총인(TP), 총질소(TN) 및 총부유물질(TSS) 유입량을 산정하기 위해 널리 사용되는 SWAT (Soil and Water Assessment Tool) 모델 및 HSPF (Hydrological Simulation Program-Fortran)를 동시에 적용한 결과를 비교 분석하였다. 환경부에서 제공하는 2012년부터 2021년까지의 현장의 각종 자료를 위 두 가지 모델의 입력자료와 보정자료로 사용하였으며 결정계수(R²), Nash-Sutcliffe Efficiency (NSE) 및 Percent Bias (PBIAS)등을 정확도의 평가지표로 이용하였다. 유량 의 경우 SWAT 모델은 본 연구의 실측자료에 대해 평균 R² 0.82 및 NSE 0.72로, HSPF 모델의 R² 0.76 및 NSE 0.67의 경우보다 약간 더 정확하거나 유사한 성능을 보였다. 그러나 수질 모의의 경우 SWAT 모델은 TN 13.2%, TP 19.1%, TSS 31.5%의 평균 PBIAS를 보인 반면, HSPF 모델은 TN 17.2%, TP 23.2%, TSS 25.9%의 평균 PBIAS를 나타냈다. 이러한 결과는 두 모델 모두 실측 수질을 정확하게 모의하는 데에는 한계가 있음을 시사한다. 본 연구에서는 두 가지 모델의 예측 결과를 토대로 오차의 원인을 분석하고 비점오염 부하 저감 등 옥정호의 수질관리를 위해 적절한 유역모델을 선택하는 데에 유용한 사례를 제공할 수 있을 것으로 판단된다.

This study conducted a comparative analysis by simultaneously applying the widely used SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program-Fortran) models to estimate the inflow of discharge, total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) into Okjeong Lake, located in the upper reaches of the Seomjin River. Data provided by the Ministry of Environment from 2012 to 2021 were used as input and calibration data for both models, and performance evaluation metrics such as the coefficient of determination (R²), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) were utilized to assess model accuracy. For flow calibration, the SWAT model showed slightly better performance, with an average R² of 0.82 and NSE of 0.72 across all stations, compared to the HSPF model's R² of 0.76 and NSE of 0.67. However, for water quality calibration, the SWAT model had an average PBIAS of 13.2% for TN, 19.1% for TP, and 31.5% for TSS, while the HSPF model had an average PBIAS of 17.2% for TN, 23.2% for TP, and 25.9% for TSS. These results suggest that both models are limited in their ability to accurately simulate real world water quality. Based on the predicted results of the two models, this study analyzed the causes of the errors and provided useful examples for selecting an appropriate watershed model for water quality management of Okjeong Lake, including non-point source pollution load reduction.

The objective of this study was to evaluate the applicability of SWAT (Soil and Water Assessment Tool) model for runoff estimation in the Nam river dam watershed. Input data for the SWAT model were established using spatial data (land use, soil, digital elevation map) and weather data. The SWAT model was calibrated and validated using observed runoff data from 2003 to 2014 for three stations (Sancheong, Shinan, Changchon) within the study watershed. The $R^2$ (Determination Coefficient), RMSE (Root Mean Square Error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (Relative Mean Absolute Error) were used to evaluate the model performance. Parameters for runoff calibration were selected based on user's manual and references and trial and error method was applied for parameter calibration. Calibration results showed that annual mean runoff were within ${\pm}5%$ error compared to observed. $R^2$ were ranged 0.64 ~ 0.75, RMSE were 2.51 ~ 4.97 mm/day, NSE were 0.48 ~ 0.65, and RMAE were 0.34 ~ 0.63 mm/day for daily runoff, respectively. The runoff comparison for three stations showed that annual runoff was higher in Changchon especially summer and winter seasons. The flow exceedance graph showed that Sancheong and Shinan stations were similar while Changchon was higher in entire fraction.

수학적 모델은 수량과 수질의 예측을 위해 현장 조사의 대안으로 사용되어지며 이러한 모델의 사용과 실측에 불확실성이 존재하게 된다. 불확실성에 대한 많은 연구들이 진행되어 왔으나 시나리오에 의한 모델링 과정에서 발생하는 불확실성에 대한 연구는 미흡한 실정이다. 본 연구에서는 산림이 농경지와 목초지로의 변화에 따른 시나리오를 설계한 후 시나리오 적용에 따른 SWAT (Soil and Water Assessment Tool) 매개변수의 불확실성을 분석하고자 하였다. 몬테카를로 기법 (Monte Carlo simulation)을 이용하여 각 매개변수별 1,000개의 난수를 발생하였으며 앙상블 유량모의 기법을 이용하여 미국 Alabama주 카하바강 상류 (50,967ha)를 대상으로 각 난수별 100개의 유량을 통해 불확실성을 분석하였다. 분석 결과 산림지역이 농경지와 목초지로 변화 되었을 때 유출량이 증가하는 것으로 분석되었으며, 임야가 목초지 보다 농경지로 변화되었을 때 유출량은 더욱 증가하는 것으로 나타났다. 각 시나리오별 SWAT 매개변수의 불확실성은 AWC (Available water capacity), CN (Curve number), GWREVAP (groundwater re-evaporation coeffeicient), REVAPMN (minimum depth of water in shallow aquifer for re-evaporation to occur)순으로 크게 나타났으며, Ksat (Saturated hydraulic conductivity)와 ESCO(Soil evaporation compensation factor)는 유출량의 변화에 큰 영향을 미치지 못하는 것으로 분석되었다. 토지피복별 산림 면적이 클 경우 불확실성이 크게 나타나 산림이 목초지와 농경지로 변함에 따라 불확실성은 감소하는 것으로 나타났다.

With the aid of SWAT/GRASS interface, SWAT model was applied to a representative rural watershed and evaluated for its applicability through calibration and verification using observed data. Results of the model application showed that the simulated runoff was in a good agreement with the observed data and indicated reasonable applicability of the model. In predicting sediment yield, however, there were differences between the simulated and the observed values. It seems due to the inaccurate determination of MUSLE factors, better prediction of sediment yield could be achieved by selecting more appropriate MUSLE factors for different land use types. In predicting nutrient loading, the simulation results showed a similar trend to observed data.

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.

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

The study aimed to project inflows and demmands for the agricultural reservoir watersheds in South Korea considering a variety of regional characteristics and the uncertainty of future climate information. The study bias-corrected and spatially downscaled retrospective daily Global Climate Model (GCM) outputs under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios using non-parametric quantile mapping method to force Soil and Water Assessment Tool (SWAT) model. Using the historical simulation, the skills of un-calibrated SWAT model (without calibration process) was evaluated for 5 reservoir watersheds (selected as well-monitored representatives). The study then, evaluated the performance of 9 GCMs in reproducing historical upstream inflow and irrigation demand at the five representative reservoirs. Finally future inflows and demands for 58 watersheds were projected using 9 GCMs projections under the two RCP scenarios. We demonstrated that (1) un-calibrated SWAT model is likely applicable to agricultural watershed, (2) the uncertainty of future climate information from different GCMs is significant, (3) multi-model ensemble (MME) shows comparatively resonable skills in reproducing water balances over the study area. The results of projection under the RCP 4.5 and RCP 8.5 scenario generally showed the increase of inflow by 9.4% and 10.8% and demand by 1.4% and 1.7%, respectively. More importantly, the results for different seasons and reservoirs varied considerably in the impacts of climate change.

The objective of this study was to estimate the climate change impact on inflow to Namgang Dam using SWAT (Soil and Water Assessment Tool) model. The SWAT model was calibrated and validated using observed flow data from 2003 to 2014 for the study watershed. The $R^2$ (Determination Coefficient), RMSE (Root Mean Square Error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (Relative Mean Absolute Error) were used to evaluate the model performance. Calibration results showed that the annual mean inflow were within ${\pm}5%$ error compared to the observed. $R^2$ were ranged 0.61~0.87, RMSE were 1.37~7.00 mm/day, NSE were 0.47~0.83, and RMAE were 0.25~0.73 mm/day for daily runoff, respectively. Climate change scenarios were obtained from the HadGEM3-RA. The quantile mapping method was adopted to correct bias that is inherent in the climate change scenarios. Based on the climate change scenarios, calibrated SWAT model simulates the future inflow and evapotranspiration for the study watershed. The expected future inflow to Namgang dam using RCP 4.5 is increasing by 4.8 % and RCP 8.5 is increasing by 19.0 %, respectively. The expected future evapotranspiration for Namgang dam watershed using RCP 4.5 is decreasing by 6.7 % and RCP 8.5 is decreasing by 0.7 %, respectively.

In this study, the channel and floodplain widths are indirectly measured for three different watersheds using satellite images to reflect the shape of Korean channels in the Soil and Water Assessment Tool (SWAT) model. For measuring the channel and floodplain widths, multiple satellite images were referred to ensure the widest width of certain points. In the single channel, the widths at the multiple points were measured. Based on the measured data, the regression equations were derived to estimate the channel and floodplain widths according to watershed areas. Applying these developed equations, this study evaluated the effect of the change of channel and floodplain widths on the SWAT simulation by comparing to the measured streamflow data. The developed equations estimated larger channel width and smaller floodplain compared with those calculated in the current SWAT model. As shown in the results, there was no considerable changes in the predicted streamflow using the current and developed equations. However, the flow velocity and channel depth calculated from the developed equations were smaller than those of the current equations. The differences were caused by the effect of different channel geometries used for calculating the hydraulic characteristics. The channel geometries also affected the water quality simulation in channels because the hydraulic characteristics calculated by the channel geometries are directly related to the water quality simulation. Therefore, application of the river cross-sectional regression equation reflecting the domestic stream shape is necessary for accurate water quantity / quality and water ecosystem simulation using hydrological model.

하천을 수원으로 하는 상수도 시설은 강수량 및 하천 유량과 같은 모니터링 설비가 없는 곳이 대부분이며, 갈수기 등 하천 유량 감소기에도 취수량 조절과 같은 가뭄대응을 위한 판단 기준이 없어 사전 대응이 곤란한 실정이다. 또한, 실제 가뭄 업무를 일선에서 처리하는 지자체 공무원 등이 지금까지 제시된 가뭄지수(SPI, PDSI 등) 및 확산 모델 등을 사전 대응에 활용하기에는 한계가 있는 것으로 나타났다. 이에 따라 본 연구에서는 장기유출모형(SWAT)의 매개변수 검·보정을 통해 장기간의 일단위 유출량을 추정하였으며, 감수곡선식을 도출하였다. 또한, 일단위 취수량과 감수곡선식의 관계분석을 통해 취수가능일수를 정량적으로 산정하였으며, 이를 용수공급 네트워크에 대입하여 읍면동 단위 적용 가능 범위를 제시하였다.

Most of the water supply facilities that use rivers as sources do not have monitoring facilities such as precipitation and stream flow measurement, and there is no judgment standard for drought response such as water intake control in river flow during dry season. In addition, it was confirmed that local government officials, who deal with actual drought work, have limitations in applying the drought index (SPI, PDSI, etc.) and diffusion models that have been proposed so far in advance. Therefore, in this study, the drought prediction system was constructed to determine the number of water-intake available days using SWAT (Soil and Water Assessment Tool) and the water supply network from the intake source to the beneficiary area, suggesting the drought spreading time and space.

본 연구에서는 충주댐 유역($6,642.0km^2$)을 대상으로 Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) 위성영상과 SWAT (Soil and Water Assessment Tool) 모형을 이용하여 융설이 댐유입량에 미치는 영향을 분석하였다. SWAT 모형의 융설 매개변수인 적설면적 감소곡선(Snow Cover Depletion Curve; SCDC)을 결정하기 위해 10 sets (2000-2010)의 Terra MODIS 위성영상으로부터 추출한 적설분포도와 지상 강설자료를 이용하여 구축한 적설심분포도(snow depth distribution; SDD)를 도출한 결과, 50% 적설면적일 때의 적설면적감소계수(sno50cov)는 0.4~0.7의 범위를 가지는 것으로 분석되었다. 매년의 융설 매개변수를 적용하여 융설기간 (11~4월)동안의 유출을 보정한 결과 평균 $R^2$는 0.54이었다. 10년 동안 융설기간의 유출량은 평균 104.3 mm로 연 유출량의 12.0%를 차지하는 것으로 분석되었다.

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.

본 연구에서는 2005년부터 2006년까지 충청남도 공주시 반포면에 위치한 봉곡천 유역의 경사지 밭을 포함하고 있는 산지하천에서 유출량, 총인, 총질소를 측정하였고 측정된 자료는 SWAT 모형을 통하여 장기간의 배출부하량 산정을 위해 모형의 보정 및 검정자료로 사용하였다. SWAT 모형의 보정 및 검정결과는 유출량은 일별자료를 이용하여 보정 및 검정을 실시하였다. 그 결과 결정계수 ($R^2$)가 0.80~0.83의 값을 보였으며 일별 T-N, T-P 부하량에 대한 보정 및 검정결과는 결정계수 ($R^2$)가 0.62~0.86의 값을 보였다. 모형의 보정 및 검정을 통해 결정된 최적매개변수를 적용하여 1997년부터 2006년까지 관측된 강우자료로 장기간의 유출량, T-N, T-P 배출부하량에 대한 SWAT 모형 시뮬레이션을 수행하였다. 또한 이를 바탕으로 하여 산지와 밭에 대한 원단위를 산정하였으며, 그 결과 산지에 대한 T-N의 원단위는 3.29 $kg/km^2/day$이었고 T-P에 대한 원단위는 0.15 $kg/km^2/day$로 나타났다. 또한 밭에서의 T-N에 대한 원단위는 11.15 $kg/km^2/day$이었고 T-P에 대한 원단위는 0.70 $kg/km^2/day$로 나타났으며 강우의 시간 및 공간적 변화에 따른 유출량을 고려한 산지와 밭에서의 영양염류 배출부하량을 산정하는데 SWAT모형을 적용하는 것이 타당성이 있는 것으로 판단되었다.

본 연구에서는 SWAT(Soil and Water Assessment Tool) 모형을 이용하여 경안천 유역($255.44km^2$)을 대상으로 미래 토지이용변화가 수문-수질에 미치는 영향을 분석하고자 하였다. Landsat TM(1987, 1991, 1996, 2004), $ETM^+$(2001) 위성영상으로부터 시계열 토지이용도를 작성하고, CA-Markov 기법을 이용하여 2030, 2060, 2090년도의 미래 토지이용변화를 예측하였다. 모형의 입력 자료인 수문 기상자료와 지형자료(DEM, 토양도, 하천도 등), 수질자료(SS, T-N, T-P)를 구축하고 1999, 2000년 자료를 이용하여 모형의 보정을 실시하였으며, 2001, 2002년에 대하여 검증하였다. 검보정 결과, 유출량에 대해 모형 효율성 계수는 0.59, 수질항목(Sediment, T-N, T-P)에 대한 결정계수는 0.88, 0.72, 0.68로 분석되었다. 미래 토지이용변화에 따른 유출량과 비점오염 부하량의 변화를 분석한 결과, 도시화가 진행되면서 2004년을 기준(76.3)으로 유역 평균 CN값이 2030년 76.9, 2060년 77.1, 2090년 77.4로 증가하면서 유출량이 1.4%, 2.0%, 2.7% 증가하는 것으로 분석되었다. 또한, 비점오염원의 증가로 유사량과 T-N, T-P 부하량은 2004년을 기준으로 2030년 51.4%, 5.0%, 11.7% 증가하였으며, 2060년 70.5%, 8.5%, 16.7% 2090년에 74.9%, 10.9%, 19.9% 증가하는 것으로 분석되었다.

This study is to assess the impact of future land use change on hydrology and water quality in Gyungan-cheon watershed ($255.44km^2$) using SWAT (Soil and Water Assessment Tool) model. Using the 5 past Landsat TM (1987, 1991, 1996, 2004) and $ETM^+$ (2001) satellite images, time series of land use map were prepared, and the future land uses (2030, 2060, 2090) were predicted using CA-Markov technique. The 4 years streamflow and water quality data (SS, T-N, T-P) and DEM (Digital Elevation Model), stream network, and soil information (1:25,000) were prepared. The model was calibrated for 2 years (1999 and 2000), and verified for 2 years (2001 and 2002) with averaged Nash and Sutcliffe model efficiency of 0.59 for streamflow and determination coefficient of 0.88, 0.72, 0.68 for Sediment, T-N (Total Nitrogen), T-P (Total Phosphorous) respectively. The 2030, 2060 and 2090 future prediction based on 2004 values showed that the total runoff increased 1.4%, 2.0% and 2.7% for 0.6, 0.8 and 1.1 increase of watershed averaged CN value. For the future Sediment, T-N and T-P based on 2004 values, 51.4%, 5.0% and 11.7% increase in 2030, 70.5%, 8.5% and 16.7% increase in 2060, and 74.9%, 10.9% and 19.9% increase in 2090.

SWAT-K(Soil and Water Assessment Tool-Korea) 모형은 토양 중심의 물수지 방정식을 기본으로 하는 장기유출모형이다. 모형의 필수 입력자료로 공간적인 정보를 나타내는 수치주제도인 토양도를 적용할 경우, 토양통(Soil series)으로 분류된 각 토양의 속성정보에 대한 데이터베이스(usersoil.dbf)의 구축을 필요로 한다. 국외에서 개발된 토양전이함수에 의해 추정된 토양 속성정보는 국내 토양특성을 반영하지 못하기 때문에 한국건설기술연구원에서는 국립농업과학원의 국내 토양 조사결과 및 연구결과를 반영한 토양DB를 구축한 바 있다. 본 연구에서는 토양 속성정보의 수문성분 모의과정을 보다 상세히 기술하고, 기구축된 토양DB를 최신 SWAT 모형에서 구동되도록 수정·보완하였다. 또한, 통합물관리 플랫폼을 통하여 구축된 토양DB를 제공함으로써, SWAT-K 모형 뿐만 아니라 토양을 고려하여 개발된 다양한 유역수문모형에 활용될 수 있을 것으로 기대된다.

SWAT-K (Soil and Water Assessment Tool-Korea) model is a long-term runoff model using a soil-centered water balance equation. Soil is crucial for simulating hydrological components, requiring a database (usersoil.dbf) with soil series attribute information. Since the soil property information estimated by soil transfer functions developed overseas does not reflect the characteristics of domestic soil, the Korea Institute of Civil Engineering and Building Technology has established the soil database, which incorporates the results of domestic soil surveys and research from the National Institute of Agricultural Sciences. This study provides a more detailed description of the hydrological component simulation process using soil property information and revises and supplements the previously established soil database to operate in the latest SWAT model. Additionally, by providing this database through the integrated water management platform, it is expected to be utilized not only in the SWAT-K model but also in various watershed hydrological models developed considering soil characteristics.

강우-유출 모의를 수행할 때 기상 및 강우관측소의 자료를 이용하는 것이 일반적이다. 그러나 유역면적이 클 경우 기상 및 강우관측소의 자료만으로 신뢰성 있는 유출량을 산정하기란 어렵다. 따라서 본 연구에서는 이용되는 강우자료에 따라 준분포형 모형에 의해 산정되는 유출량에 미치는 영향을 검토하기 위해 대상유역에 위치하고 있는 기상관측소의 강우자료, 기상 및 강우관측소의 강우자료, 크리깅 기법에 의해 기상 및 강우관측소의 강우자료를 공간적으로 분포시켜 얻은 가상 지점의 관측 강우자료를 이용해 각 소유역의 면적 강우량을 산정하였다. 또한 각각의 강우자료들을 비교하였으며, 분포형 모형인 SWAT모형을 이용하여 각각의 강우자료에 따른 유출량을 비교 분석하였다. 본 연구는 공간 분포된 면적강우량을 이용해 산정된 유출량의 정확성을 검토하기 위한 것으로써 분석 결과, 공간 분포된 면적 강우량을 이용한 유출량이 기상 및 강우관측소의 강우량을 이용한 유출량보다 실제 유출량을 보다 더 잘 모의하는 것으로 나타났다. 이는 공간 분포된 강우가 실제 강우패턴을 가장 잘 반영한다고 할 수 있다.

In general, the rainfall-runoff simulation is performed using rainfall data from meteorological and observational rain gauge stations. However, if we only use rainfall data from meteorological and observational rain gauge stations for runoff simulation of a large watershed, the problem in the reliability of the simulated runoff can be occurred. Therefore, this study examined the influence of the rainfall data on the simulated runoff volume by a Semi-distributed model. For this, we used rainfall data from meteorological stations, meteorological and observational stations, and a spatially distributed rainfall data from hypothetical stations obtained by kriging method. And, we estimated the areal rainfall of each sub-basin. Also the estimated areal rainfall and the observed rainfall were compared and we compared the simulated runoff volumes using SWAT model by the rainfall data from meteorological and observational rain gauge stations and runoff volume from the estimated areal rainfall by Kriging method were analyzed. This study was performed to examine the accuracy of calculated runoff volume by spatially distributed areal rainfall. The analysis result of this study showed that runoff volume using areal rainfall is similar to observed runoff volume than runoff volume using the rainfall data of weather and rain gauging station. this means that spatially distributed rainfall reflect the real rainfall pattern.

수리계수는 하천의 수리학적 특성을 대표하는 인자라고 할 수 있다. 따라서 우리는 하천정비기본계획에 의하여 하천을 측량하고 그 측량성과를 바탕으로 수리계수를 산정하여 왔다. 이러한 수리계수는 하천의 홍수위, 유사량, 수질 산정을 위하여 중요한 매개변수로 활용할 수 있다. 하지만, 하천 측량성과가 없는 미계측 유역의 경우 수리계수를 산정할 수 없는 한계가 있는 것이 사실이다. 이러한 문제점을 극복하고자 SWAT 모형의 방법론을 검토하고 미계측 유역에서 수리계수를 산정하는 방안을 제시하였다.

A hydraulic coefficient is a factor representing the hydraulic characteristics of the stream or river. For that reason, we survey stream characteristics such as cross section for performing the stream improvement plan and then we calculate hydraulic coefficient based on its surveyed results. This hydraulic coefficient can be used as an important parameter to calculate flood water level in stream, sediment discharge and water quality. However, we cannot calculate the hydraulic characteristics in an ungaged basin. To overcome this problem, we used the SWAT model for calculating the hydraulic coefficient in the ungaged basin.

본 연구에서는 상류의 휴·폐광산을 비롯한 다양한 점오염원과 비점오염원으로부터 유실되는 오염물질의 영향을 받는 안동호를 대상으로 수질 및 수생태 모델인 SWAT-WET을 구축하고, 적용성을 평가하고자 한다. 안동호르 유입되는 오염물질에 대한 정량적인 평가를 위해 유역모델인 SWAT을 안동댐 유역(1,584 km²)을 대상으로 구축하여, 검보정을 수행하였다. 유출량과 수질 부하량(SS, T-N, T-P)에 대한 검보정 결과 R²는 각각 0.76, 0.69, 0.84, 0.60 이상으로 분석되었다. 검보정된 SWAT의 유출량과 영양물질의 농도를 WET의 입력자료로 적용하여 안동호에 대한 수질, 수생태 모델 WET을 구축하였다. WET의 매개변수 민감도 분석 결과를 이용하여, 안동호의 수온, 용존산소, 수질농도(T-N, T-P)를 대한 검보정을 수행하였다. 검보정 결과 PBIAS는 각각 +19%, -13%, +4%, +26.5% 로 분석되었으며, 관측 자료를 유의한 수준으로 모의하는 것을 확인하였다. 저서동물의 관측 건중량(gDW/m²)은 0.5 이하였으나 모의 건중량의 평균은 0.8로 분석되었으며, 이는 WET 모델이 더 넓은 개념의 저서동물을 고려하기 때문으로 나타났다. 관측자료 부족으로 정확한 검보정은 어렵지만, 유역모델과의 연계를 통한 장기간 모의와 상류 유역환경변화가 호소에 미치는 영향을 분석할 수 있으므로 안동호의 수환경 관리를 위한 기초 자료로 활용될 수 있을 것으로 판단된다.

The objective of this study is to develop the water quality and aquatic ecosystem model for Andong lake using SWAT-WET (Soil and Water Assessment Tool-Water Ecosystem Tool) and to evaluate the applicability of WET. To quantify the pollutants load flowing into Andong lake, a watershed model of SWAT was constructed for Andong Dam basin (1,584 km²). The calibration results for Dam inflow and water quality loads (SS, T-N, T-P) were analyzed that average R² was more than 0.76, 0.69, 0.84, and 0.60 respectively. The calibrated SWAT results of streamflow and nutrients concentration was used into WET input data. WET was calibrated and validated for water temperature, dissolved oxygen, and water quality concentration (T-N, T-P) of Andong lake. The WET calibrated results was analyzed that PBIAS was +19%, -13%, +4%, and +26.5% respectively and showed that it was simulated to a significant level compared with the observation data. The observed dry weight (gDW/m²) of zoobenthos was less than 0.5, but the average value of simulation was analyzed to be 0.8, which is because the WET model considers zoobenthos with a broader concept. Although accurate calibration is difficult due to the lack of observed data, SWAT-WET can analyze the effects of environmental change in the upstream watershed on the lake based on long-term simulation based on watershed model. Therefore, the results of this study can be used as basic data for managing the aquatic environment of Andong lake.

Aim of this research was to investigate the effects of Digital Elevation Model (DEM) for sensitivity analysis with two types of DEMs: 1 to 24,000 and 1 to 250,000 DEM. Another emphasis was given to the development of methodology for processing DEMs to create ArcGIS Pro and GRASS layers. This was done while developing water quality system modeling using DEMs which were used to model hydrological processes and SWAT model. Sensitivity analysis with DEMs resulted in different runoff volumes in the model simulation. Runoff volume was higher for the 1:24,000 DEM than 1:250,000 DEM, probably due to the finer resolution and slope which increased the estimated runoff from the watershed. Certainly the DEMs were factors in precision of the simulations and it was obvious during sensitivity analysis that DEMs had significant effect on runoff volumes. We suggest, however, that additional comparative research could be conducted involving more parameters such as soil and hydrologic parameters to provide insight into the overall physical system which the SWAT model represents.

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

Understanding how water moves through a landscape is essential for managing water resources, especially in small areas where direct measurements are scarce. In this study, we used the soil and water assessment tool (SWAT) model to estimate how much water flows out of a watershed using limited data. Our analysis included information on land use, soil type, and weather patterns from 2021 to 2024, helping us account for changes in rainfall, including extreme weather events. Over these four years, the area received an average of 1,481 mm of rainfall per year, with 397 mm running off the surface. The total amount of water flowing out, including underground water movement, was estimated at 711 mm per year, which made up about 48% of the total rainfall. To improve accuracy, we adjusted CN2, SOL_K, and ALPHA_BF key model settings related to how water moves through the soil and how streams are fed by underground sources. The model showed that underground water movement varied throughout the year, with the lowest values in April (9 mm) and the highest in August (39 mm), reflecting wetter conditions. Water lost through plant use and evaporation also changed with the seasons, reaching its peak in July (115 mm) and dropping to its lowest levels in winter (17 mm in January and December). When we compared the model's predictions with actual data, the results showed a strong match. The model slightly underestimated water flow in October 2024, but overall, it provided reliable estimates. Despite the limited data, our findings show that this approach is useful for understanding water flow in small watersheds and can support better planning for water use and flood prevention.