• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1388-1392
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• 2008

Water resources management depends on dealing inherent uncertainties stemming from climatic and hydrological inputs and models. Dealing with these uncertainties remains a challenge. Streamflow forecasts basically contain uncertainties arising from model structure and initial conditions. Recent enhancements in climate forecasting skill and hydrological modeling provide an breakthrough for delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The approach here proposes integration and coupling of global climate models (GCM), multiple regional climate models, and numerous hydrological models to improve streamflow forecasting and characterize system uncertainty through generation of ensemble forecasts.

• 한국수자원학회논문집
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• 제41권9호
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• pp.907-917
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• 2008

본 연구에서는 PRMS, SLURP, SWAT 모형을 이용하여 유출모형에 따라 수자원의 기후변화 영향평가 결과에서 발생할 수 있는 차이를 평가하였다. 이를 위해 먼저 각 모형을 안동댐유역에 적용하여 관측자료에 대한 모의능력을 비교하였다. 그 다음 기온과 강수 변화를 가정한 합성시나리오 상황에서 각 모형별 모의결과를 비교하였다. 분석결과 세 모형은 관측기간에 대해서는 관측유량에 근접한 모의를 하였다. 그러나 강수와 기온의 변화를 고려하였을 경우에는 유출량의 변화량 모의에서 각 모형별로 상이한 결과를 보였다. 특히 기온이 크게 증가할 경우 모형별 결과차이가 증가하는 것으로 분석되었는데, 이것은 각 모델에서 이용하는 증발산량 추정방법의 차이가 가장 크게 영향을 미치는 것으로 분석되었다. 따라서 이러한 불확실성을 고려한 수자원 영향평가 방법이 필요할 것으로 판단되었다.

• 환경영향평가
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• 제9권1호
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• pp.25-37
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• 2000

This paper presents a study of hydrological and hydraulic model applications in environmental impact statements which were submitted during recent years in Korea. In many cases (almost 70 %), the hydrological and hydraulic changes were neglected from the impact identification processes, even if the proposed actions would cause significant impacts on those environmental items. In most cases where the hydrological and hydraulic impacts were predicted, simple equations were used as an impact prediction tool. Computer models were used in very few cases(5%). Even in these few cases, models were improperly applied and thus the predicted impacts would not be reliable. The improper applications and the impact neglections are attributed to the fact that there are no available model application guidelines as well as no requirements by the review agency. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is presented in this paper. This study suggested that the model application should be required and guided in detail by the review agency. It is also suggested that the hydrological and hydraulic items shoud be integrated with the water quality predictions in future, since the non-point source pollution runoff is based on the hydrologic phenomena and the water quality reactions on the hydraulic nature.

• 한국지역지리학회지
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• 제17권2호
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• pp.245-258
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• 2011

최적관리방안 (Best Management Practices)은 토양 침식과 비점오염원으로 인한 수질악화를 개선하는 방안으로 널리 이용된다. 모델을 이용하여 토양침식이나 최적관리관행의 잠재적 효과를 추정하는 것은 해당 지역의 전반적인 조건과 문제점을 식별하고 이에 대한 보전계획을 수립하는데 도움이 된다. 그러나 데이터, 특히 GIS (Geographic Information System) 데이터, 데이터 스케일의 문제, 혹은 모델의 선택 등에서 오는 불확실성은 최적관리방안의 효과를 예측하는데 있어서 정확성과 신뢰성을 떨어뜨리고 있다. 따라서 이 논문에서는 수리모델의 발전과 배경, 데이터의 불확실성, 모델의 선택, 그리고 데이터의 스케일 등을 참고문헌을 통하여 전반적으로 정리하고 살펴봄으로서 불확실성의 전반적인 이해를 돕고자 하였다. 또한 모델을 이용한 최적관리방안의 효과를 예측함에 있어서 소규모(small scale) 모델과 분포형 (spatially distributed) 모텔의 장점에 대해서도 논의하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.327-327
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• 2019

The study will quantify the total uncertainties in streamflow and precipitation projections for Upper Awash River Basin located in central Ethiopia. Three hydrological models (GR4J, CAT, and HBV) will be used to simulate the streamflow considering two emission scenarios, six high-resolution GCMs, and two downscaling methods. The readily available hydrometeorological data will be applied as an input to the three hydrological models and the potential evapotranspiration will be estimated using the Penman-Monteith Method. The SCE-UA algorithm implemented in PEST will be used to calibrate the three hydrological models. The total uncertainty including the incremental uncertainty at each stage (emission scenarios and model) will be presented after assessing a total of 24 (=$2{\times}6{\times}2$) high-resolution precipitation projections and 72 (=$2{\times}6{\times}2{\times}3$) streamflow projections for the study basin. Finally, the primary causes that generate uncertainties in future climate change impact assessments will be identified and a conclusion will be made based on the finding of the study.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.178-183
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• 2004

수문학적 모형들은 지구 물 순환에 있어서의 지표 성분을 모의하고 기후의 변화나 변동이 수자원에 미치는 영향을 평가하는데 메카니즘을 제공한다. 이러한 모형들에 있어서 증발산량(Evapotranspiration, ET)은 매우 중요한 요소이다. 본 연구에서는 SLURP 모형에서 증발산량 산정을 위하여 제시하고 있는 FAO Penman-Monteith, Motorn CRAE(Complementary Relationship Area Evapotranspiration), Spittlehouse-Black, Granger, the Linarce 등, 5 종류의 모형에 대하여 각각의 모형이 일 하천유출량에 미치는 영향을 분석해 보았다. 또한, 각 증발산 방법과 SLURP 모형의 매개변수와의 민감도 분석을 실시하였다.

• 한국기후변화학회지
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• 제7권4호
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• pp.407-419
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• 2016

The low flow is the necessary and important index to establish national water planning, however there are lots of uncertainty in the low flow estimation. Therefore, the objectives of this study are to assess the climate change uncertainty and the effects of hydrological models on low flow estimation. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods and 2 hydrological models were applied for evaluation. The study area were selected as Chungju dam and Soyang river dam basin, and the 30 days minimum flow is used for the low flow evaluation. The results of the uncertainty analysis showed that the hydrological model was the largest source of uncertainty about 41.5% in the low flow projection. The uncertainty of hydrological model is higher than the other steps (RCM, statistical post-processing). Also, VIC model is more sensitive for climate change compared to SWAT model. Therefore, the hydrological model should be thoroughly reviewed for the climate change impact assessment on low flow.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• 한국환경과학회지
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• 제18권6호
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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.