• Journal of Korea Water Resources Association
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• v.38 no.9 s.158
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• pp.713-725
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• 2005

The objective of this study is to evaluate the applicability and simulation capability of PRMS, developed by U.S. Geological Survey, over the seven multi-purpose dam watersheds in Korea. The basic concepts of model components and their parameters are investigated for the evaluation of model applicability and the possibility of model parameter estimation is suggested based on the data availibility. For model parameter estimation, some parameters are directly estimated from measurable basin characteristics, but the others are estimated by Rosenbrock's automatic optimization scheme. The results show that the simulated flows from the model were very close to the observed ones. Although the default values for snowmelt model parameter are used, the results from snowmelt simulation is also acceptable. The model shows that the simulation capability is not sensitive to the basin size, however, according to increasing basin area, simulation characteristics are close to those for lumped model rather than semi-distributed model.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.182-182
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• 2015

우리나라에서는 강우관측시스템의 지역적 불균형으로 상대적으로 소규모 저수지의 경우 미계측유역의 특성을 가지며, 신뢰성 있는 강우량, 유출량, 증발량 자료가 매우 부족한 실정이다. 다목적댐 유역과 같은 계측유역의 경우 상류유역의 유입량 자료의 확보가 용이하지만 대부분의 유역의 경우 계측장비가 부족하여 신뢰성이 확보된 유입량 자료를 얻는데 많은 어려움이 있다. 본 연구에서는 미계측유역의 유입량 산정을 위하여 계측유역을 대상으로 강우-유출 모형의 매개변수를 산정하였으며, 산정된 매개변수를 유역특성인자와의 상관성을 토대로 다중선형회귀분석기법(multiple linear regression, MLR)을 적용하여 지역화(regionalization)를 위한 회귀식을 도출하였다. 이를 위해 양질의 유량자료가 확보된 K-water 17개 댐 유역을 대상으로 매개변수를 산정하였으며 이 중 2개의 댐 유역을 미계측유역으로 간주하여 개발된 모형을 검증하였다. 대부분의 통계 지표에서 우수한 모의능력을 확인하였으며, 본 연구를 통하여 개발된 지역화 기법을 미계측유역에 활용한다면 보다 정량적이고 효율적인 수자원 계획이 가능할 것으로 판단된다. 향후 연구로는 불확실성을 고려한 Bayesian GLM 모형을 이용한 지역화기법을 개발하여 매개변수의 불확실성까지 고려할 수 있는 방안을 모색하고자 한다.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.90-97
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• 2019

In recent years, flood due to the consecutive storm events have been occurred and property damage and casualties are in increasing trend. This study calls the consecutively occurred storm events as a mega rainfall scenario and the discharge by the scenario is defined as a mega flood discharge. A mega rainfall scenario was created on the assumption that 100-year frequency rainfall events were consecutively occurred in the Gyeongancheon stream basin. The SSARR (Streamflow Synthesis and Reservoir Regulation) model was used to estimate the mega flood discharge using the scenario in the basin. In addition, in order to perform more reasonable runoff analysis, the parameters were estimated using the SCE_UA algorithm. Also, the calibration and verification were performed using the objective functions of the weighted sum of squared of residual(WSSR), which is advantageous for the peak discharge simulation and sum of squared of residual(SSR). As a result, the mega flood discharge due to the continuous occurrence of 100-year frequency rainfall events in the Gyeongan Stream Basin was estimated to be 4,802㎥/s, and the flood discharge due to the 100-year frequency single rainfall event estimated by "the Master Plan for the Gyeongancheon Stream Improvement" (2011) was 3,810㎥/s. Therefore, the mega flood discharge was found to increase about 992㎥/s more than the single flood event. The results of this study can be used as a basic data for Comprehensive Flood Control Plan of the Gyeongan Stream basin.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.930-930
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• 2012

최근 기후변화로 인하여 전 세계적으로 물 부족현상이 대두되고 있는 가운데 우리나라 또한 물 부족국가에 편재되어 있다. 더군다나 제주도는 최대 다우지임에도 불구하고 다른 타 지역과 마찬가지로 제주도 곳곳에서 물 부족 현상이 야기되고 있다. 이러한 현상은 제주도의 강우가 대부분 년 중 6월~9월에 집중되고, 그 기간 홍수유출 역시 경사가 급하고 15km 미만의 짧은 유로연장을 갖는 대부분의 하천의 특성상 홍수유출 지속시간이 단 시간에 불과하여 수자원 관리에 큰 어려움이 있기 때문으로 사료된다. 다만 일부하천의 경우 해안과 가까운 지점의 하상과 측벽에서 기저유출 성분인 용천수가 유출됨으로 1970년대부터 상수원으로 개발하여 일부분 이용되고 있다. 하지만 이 또한 정확한 용출량 산정이 어려워 수자원으로써의 활용이 미흡한 실정이다. 따라서 본 연구에서는 대상유역의 기저유출로 발생되는 용천수의 용출량을 산정하여 용천수의 이용 및 보전관리계획을 수립하는 중요한 척도를 제시함은 물론 제주 수자원의 대부분을 차지하는 지하수의 함양, 배출, 순환연구에 중요한 기초자료로 활용하기 위하여 진행되었다. 본 연구는 제주도 중서귀수역 주요 기저유출유역(강정천, 악근천)에 대하여 연구대상기간(2011.7~2011.12) 동안 하천의 유출량을 ADCP(Accoustic Doppler Current Profiler, SonTek사)와 전자기식 유속계(FLOW-MATE)를 이용하여 매월 2회 현장 계측을 실시하여 유출량 자료를 확보하였고, 일 단위의 모의가 가능한 유역단위의 준 분포형 장기 강우-유출 모델(continuous rainfall-runoff model)인 SWAT(Soil Water & Assessment Tool) 모델을 적용한 물수지 결과값과 비교 검정하여 제주도 기저유출 하천의 유출율 및 수문곡선의 특성을 분석하고 SWAT 모델을 통한 물수지 산정을 실시하였다. 중서귀수역의 기저유출 대상유역에 대한 지속적인 관측자료와 세계적인 SWAT모델을 이용한 정밀 수자원 해석결과는 서귀포시의 주 식수원으로 이용되고 있는 대상유역의 물순환 및 물수지분석과 통합 수자원의 연구진행, 향후 기후변화에 대비한 효율적인 수자원의 이용 및 확보를 위한 귀중한 자료로 이용될 수 있을 것으로 사료된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.755-765
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• 2013

This study developed a real-time dam's hydrologic variables prediction model (DHVPM) and evaluated its performance for simulating historical dam inflow and outflow in the Chungju dam basin. The DHVPM consists of the Sejong University River Forecast (SURF) model for hydrologic modeling and an autoreservoir operation method (Auto ROM) for dam operation. SURF model is continuous rainfall-runoff model with data assimilation using an ensemble Kalman filter technique. The four extreme events including the maximum inflow of each year for 2006~2009 were selected to examine the performance of DHVPM. The statistical criteria, the relative error in peak flow, root mean square error, and model efficiency, demonstrated that DHVPM with data assimilation can simulate more close to observed inflow than those with no data assimilation at both 1-hour lead time, except the relative error in peak flow in 2007. Especially, DHVPM with data assimilation until 10-hour lead time reduced the biases of inflow forecast attributed to observed precipitation error. In conclusion, DHVPM with data assimilation can be useful to improve the accuracy of inflow forecast in the basin where real-time observed inflow are available.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.980-987
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• 2012

Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

• Journal of Korea Water Resources Association
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• v.39 no.11 s.172
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• pp.935-946
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• 2006

This study proposed a new procedure of sustainable water resources planning to prevent the urban streamflow depletion, based on the Heathcote's study in 1998: (1) to understand the watershed component and processes, (2) to identify and quantify problems within the watershed, (3) to set clear and specific goals, (4) to develop a list of management options, (5) to eliminate infeasible options, (6) to test the effectiveness of remaining feasible options, and (7) to develop the final options. PSR(Presure-State-Response) concept was used for the determination of indicators of PSD(Potential Streamflow Depletion; step 2) and effect equation (step 7) and composite programming for the calculation of PSD. The instreamflow requirement was proposed as clear and specific goal (step 3) and was determined by the larger of the PHABSIM's environmental flow and the drought flow. A continuous rainfall-runoff model is necessary to test the effectiveness of alternatives. It should estimate not only the exact runoff but also the effect of landuse change, reservoir, infiltration facility and so on like SWAT(Soil and Water Assessment Tool). The proposed procedure will be applied on the corresponding paper.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.98-104
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• 2014

This study aims to predict the amount of soil loss from Mt. Palgong's small basin, by using influence factors derived from related models, including RUSLE and MUSLE models, and verify the validity of the model through a comparative analysis of the predicted values and measured values, and the results are as follows: The amount of soil loss were greatly affected by LS factor. In comparison with the measured value of the amount of total soil loss, the predicted values by the two models (RUSLE and MUSLE), appeared to be higher than those of the measured soil loss. Predicted values by RUSLE were closer to values of measured soil loss than those of MUSLE. However, coefficient of variation of MUSLE were lower, but two model's coefficient of variation in similar partial patterns in the prediction of soil loss. RUSLE and MUSLE, prediction soil loss models, proved to be appropriate for use in small mountainous basin. To improve accuracy of prediction of soil loss models, more effort should be directed to collect more data on rainfall-runoff interaction and continuous studies to find more detailed influence factors to be used in soil loss model such as RUSLE and MUSLE.