• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.481-488
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• 2006

This paper suggests a novel approach of integrating the quasi-distributed watershed model SWAT with the fully-distributed groundwater model MODFLOW. Since the SWAT model has semi distributed features, its groundwater components hardly considers distributed parameters such as hydraulic conductivity and storage coefficient. Generating a detailed representation of groundwater recharge, head distribution and pumping rate is equally difficult. To solve these problems, the method of exchanging the characteristics of the hydrologic response units (HRUs) in SWAT with cells in MODFLOW by fully combined manner is proposed. The linkage is completed by considering the interaction between the stream network and the aquifer to reflect boundary flow. This approach is provisionally applied to Gyungancheon basin in Korea. The application demonstrates a combined model which enables an interaction between saturated zones and channel reaches. This interaction plays an essential role in the runoff generation in the Gyungancheon basin. The comprehensive results show a wide applicability of the model which represents the temporal-spatial groundwater head distribution and recharge.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.413-413
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• 2022

본 연구에서는 통계적 방법으로 도출된 장기 기상예측정보를 이용하여 유역에서의 유출량 전망 가능성을 검토하였다. 먼저 한강권역의 월 강수량과 기온에 대해 글로벌 기후지수와의 원격상관성을 기반으로 다중회귀모형 기반의 통계적 예측모형을 구성하여 미래기간(1~12개월)에 대한 월 단위 기상예측정보를 도출하였다. 월 단위로 도출된 강수량과 기온은 통계적 상세화 기법을 통해 한강권역 주요 ASOS 관측소 지점별로 일 단위 강수량과 기온자료로 변환하였으며, 상세화된 일 자료를 유역모형인 SWAT의 입력자료로 활용하여 경안천 유역의 미래기간에 대한 유출량을 도출하였다. 유출량 예측성을 평가하기 위하여 과거기간(2003~2021년)을 대상으로 관측유출량과 예측기상정보로부터 산출된 예측유출량을 비교하였다. 각 월별로 예측된 유출량의 중앙값과 관측값의 적합도를 분석한 결과, PBIAS는 -5.2~-2.7%, RSR은 0.79~0.91, NSE는 0.34~0.38, r은 0.59~0.62로 강수량 및 기온의 예측성에 비해 낮게 나타났다. 전 기간에 대해 월별로 분석한 예측결과에 대한 3분위 확률은 5월, 6월, 7월, 9월, 11월은 평균 42.8%로 예측성이 충분한 것으로 나타났으나, 나머지 월에서의 평균 예측성은 17.3%로 매우 낮게 나타났다. 상세화된 기상정보를 이용하여 유역모델링을 통해 산정한 유출량에 대한 전망 결과는 기상예측결과에 비해 상대적으로 예측성이 낮은 것으로 분석되었다. 이는 관측값 자체에서 나타날 수 있는 불확실성에 기인할 수도 있으며, 유출량에 지배적인 영향을 주는 강수량의 예측성에 대한 문제가 유역 모델링 과정에서 증폭되어 나타나는 문제일 수도 있다. 또한 지점별 일 자료로 상세화되는 과정에서의 불확실성, 우리나라 여름철 유출량 변동성 등 여러 가지 요인이 복합적으로 영향을 주어 나타나는 것으로 생각된다. 향후 다양한 대상유역에 대한 검토와 기상예측모형의 보완, 상세화 과정에서의 불확실성 해소 등을 통해 예측성을 개선할 계획이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.37-37
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• 2022

유효우량 산정을 위하여 국내에서 주로 사용되는 모형은 NRCS-CN(Natural Resources Conservation Service - curve number) 모형으로, 유역의 유출 능력을 나타내는 유출곡선지수(runoff curve number, CN)와 같은 NRCS-CN 모형의 매개변수들은 관측 강우-유출자료 또는 토양도, 토지피복지도 등을 이용하여 유역마다 결정된 값이 사용되고 있다. 그러나 유역의 CN값은 유역의 토양 상태와 같은 환경적 조건에 따라 달라질 수 있으며, 이를 반영하기 위하여 선행토양함수조건(antecedent moisture condition, AMC)을 이용하여 CN값을 조정하는 방법이 사용되고 있으나, AMC 조건에 따른 CN 값의 갑작스런 변화는 유출량의 극단적인 변화를 가져올 수 있다. NRCS-CN 모형과 더불어 강우 손실량 산정에 많이 사용되는 모형으로 Green-Ampt 모형이 있다. Green-Ampt 모형은 유역에서 발생하는 침투현상의 물리적 과정을 고려하는 모형이라는 장점이 있으나, 모형에 활용되는 다양한 물리적인 매개변수들을 산정하기 위해서는 유역에 대한 많은 조사가 선행되어야 한다. 또한 이렇게 산정된 매개변수들은 유역 내 토양이나 식생 조건 등에 따른 여러 불확실성을 내포하고 있어 실무적용에 어려움이 있다. 따라서 본 연구에서는, 현재 사용되고 있는 강우손실 모형들의 매개변수를 추정하기 위한 방법을 제시하고자 하였다. 본 연구에서 제시하는 방법은 인공지능(AI) 기술 중 하나인 딥러닝(deep-learning) 기법을 기반으로 하고 있으며, 딥러닝 모형으로는 장단기 메모리(Long Short-Term Memory, LSTM) 모형이 활용되었다. 딥러닝 모형의 입력 데이터는 유역에서의 강우특성이나 토양수분, 증발산, 식생 특성들을 나타내는 인자이며, 모의 결과는 유역에서 발생한 총 유출량으로 강우손실 모형들의 매개변수 값들은 이들을 활용하여 도출될 수 있다. 산정된 매개변수 값들을 강우손실 모형에 적용하여 실제 유역들에서의 유효우량 산정에 활용해보았으며, 동역학파 기반의 강우-유출 모형을 사용하여 유출을 예측해보았다. 예측된 유출수문곡선을 관측 자료와 비교 시 NSE=0.5 이상으로 산정되어 유출이 적절히 예측되었음을 확인했다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.495-501
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• 2024

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.

• Journal of Korea Water Resources Association
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• v.39 no.5 s.166
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• pp.453-466
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• 2006

The objective of this study was to analyze the changes in the hydrological environment in Soyanggang-dam watershed due to climate change results (in yews 2050 and 2100) which were simulated using CCCma CGCM2 based on SRES A2 and B2. The SRES A2 and B2 were used to estimate NDVI values for selected land use using the relation of NDVI-Temperature using linear regression of observed data (in years 1998$\sim$2002). Land use change based on SRES A2 and B2 was estimated every 5- and 10-year period using the CA-Markov technique based on the 1985, 1990, 1995 and 2000 land cover map classified by Landsat TM satellite images. As a result, the trend in land use change in each land class was reflected. When land use changes in years 2050 and 2100 were simulated using the CA-Markov method, the forest class area declined while the urban, bareground and grassland classes increased. When simulation was done further for future scenarios, the transition change converged and no increasing trend was reflected. The impact assessment of evapotranspiration was conducted by comparing the observed data with the computed results based on three cases supposition scenarios of meteorological data (temperature, global radiation and wind speed) using the FAO Penman-Monteith method. The results showed that the runoff was reduced by about 50% compared with the present hydrologic condition when each SRES and periods were compared. If there was no land use change, the runoff would decline further to about 3$\sim$5%.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.737-748
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• 2022

Due to the lack of flood data, the water engineering practice calculates the design flood using rainfall frequency analysis and rainfall-runoff model. However, the rainfall frequency analysis for arbitrary duration does not reflect the regional characteristics of the duration and amount of storm event. This study proposed a practical method to calculate the design flood in a watershed considering the characteristics of storm event, based on the bivariate rainfall frequency analysis. After extracting independent storm events for the Pyeongchang River basin and the upper Namhangang River basin, we performed the bivariate rainfall frequency analysis to determine the design storm events of various return periods, and calculated the design floods using the HEC-1 model. We compared the design floods based on the bivariate rainfall frequency analysis (DF_BRFA) with those estimated by the flood frequency analysis (DF_FFA), and those estimated by the HEC-1 with the univariate rainfall frequency analysis (DF_URFA). In the case of the Pyeongchang River basin, except for the 100-year flood, the average error of the DF_BRFA was 11.6%, which was the closest to the DF_FFA. In the case of the Namhangang River basin, the average error of the DF_BRFA was about 10%, which was the most similar to the DF_FFA. As the return period increased, the DF_URFA was calculated to be much larger than the DF_FFA, whereas the BRFA produced smaller average error in the design flood than the URFA. When the proposed method is used to calculate design flood in an ungauged watershed, it is expected that the estimated design flood might be close to the actual DF_FFA. Thus, the design of the hydrological structures and water resource plans can be carried out economically and reasonably.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.875-889
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• 2021

This study compares and analyzes the Soil and Water Assessment Tool (SWAT) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) as coniferous, deciduous and mixed forest with Yongdam Dam upstream (904.4 km²). The hydrologic evaluation period was set to 10 years from 2010 to 2019, and the applicability of the 8-day MOD15A2 Leaf Area Index (LAI) data, 3 TDR (Time Domain Reflectometry) (GB, JC, CC), and 1 Flux Tower (DU) evaporation volume (YDD) data was simulated. As a result, the R² of coniferous forest, deciduous forest and mixed forest are 0.95, 0.89, 0.90, soil moisture and evaportranspiration stations R² were analyzed at 0.50 to 0.55 and 0.51, respectively, with R² at 0.74, RMSE 2.75 mm/day, NSE 0.70 and PBIAS 14.3% for Yongdam inflow. Based on the calibrated and validated watersheds, the annual average evaportranspiration was calculated as coniferous 469.7 mm, deciduous 501. mm and 511.5 mm mixed forest, total runoff were estimated at coniferous 909.8 mm, deciduous 860.6 mm and 864.2 mm mixed forest. In the case of annual average evaportranspiration, it was evaluated that deciduous were high, but in the case of streamflow, it was evaluated that coniferous were high. Unlike other hydrologic with similar patterns throughout the year, the average annual evapotranspiration was about 7% higher than coniferous due to the higher evapotranspiration of deciduous with high leaf area index in summer and fall. In addition, deciduous were 9% and 6% higher for surface runoff and lateral flow, but the groundwater of coniferous was 77% higher. Therefore, it was confirmed that the total runoff was in order of coniferous, mixed forest, and deciduous.

• Journal of Soil and Groundwater Environment
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• v.11 no.5
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• pp.20-34
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• 2006

This study was undertaken to assess the anthropogenic impact on trace metal concentrations (Zn, Cu, Pb, Cr, Ni, and Cd) of roadside sediments (N = 70) from No.7 national road within the watershed of Hoidong Reservoir in Pusan City and to estimate the potential mobility of selected metals using sequential extraction. We generally found high concentrations of metals, especially Zn, Cu and Pb, affected by anthropogenic inputs. Compared to the trace metal concentrations of uncontaminated stream sediments, arithmetic mean concentrations of roadside sediments were about 7 times higher for Cu, 4 times higher for Zn, 3 times higher for Pb and Cr and, 2 times higher for Ni and As. Speciation data on the basis of sequential extraction indicate that most of the trace metals considered do not occur in significant quantities in the exchangeable fraction, except for Cd and Ni whose exchangeable fractions are appreciable (average 29.3 and 25.8%, respectively). Other metals such as Zn (51.4%) and Pb (45.2%) are preferentially bound to the reducible fraction, and therefore they can be potentially released by a pH decrease and/or redox change. Copper is mainly found in the organic fraction, while Cd is highest in the exchangeable fraction, and Cr and Ni in the residual fraction. Considering the proportion of metals bound to the exchangeable and carbonate fractions, the comparative mobility of metals probably decreases in the order of Cd>Ni>Pb>Zn>Cr>Cu. Although the total concentration data showed that Zn was typically present in potentially harmful concentration levels, the data on metal partitioning indicated that Cd, Ni and Pb pose the highest potential hazard for runoff water. As potential changes of redox state and pH may remobilize the metals bound to carbonates, amorphous oxides, and/or organic matter, and may release and flush them through drain networks into the watershed of Hoidong Reservoir, careful monitoring of environmental conditions appears to be very important.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.677-690
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• 2006

The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages of global and local sensitivity analysis effectively identified the sensitivity ranking for the parameters of SWAT model over feasible parameter space. Use of this information allows us to select the calibrated parameters for the automatic calibration process. The performance of the automatic calibration of SWAT model using SCE-UA method depends on the length of calibration period, the number of calibrated parameters, and the selection of statistical error criteria. The performance of SWAT model in terms of RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), and NMSE (Normalized Mean Square Error) becomes better as the calibration period and the number of parameters defined in the automatic calibration process increase. However, NAE (Normalized Average Error) and SDR (Standard Deviation Ratio) were not improved although the calibration period and the number of calibrated parameters are increased. The result suggests that there are complex interactions among the calibration data, the calibrated parameters, and the model error criteria and a need for further study to understand these complex interactions at various representative watersheds.