• 한국수자원학회논문집
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• 제37권9호
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• pp.745-758
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• 2004

수문 모형들은 물 순환에 있어서의 지표 성분을 모의하고 기후 변동이 수자원에 미치는 영향을 평가하는데 메커니즘을 제공한다. 이러한 모형들에 있어서 증발산량(Evapoanspiration, ET)은 매우 중요한 요소이다. 본 연구에서는 SLURP 모형에서 증발산량 산정을 위하여 제시하고 있는 FAO Penman-Monteith, Morton CRAE(Complementary Relationship Area Evapotranspiration), Spittlehouse-Black, Granger, the Linacre 등, 5 가지의 방법론이 일 하천유출량에 미치는 영향을 분석하고, 각 증발산 방법과 SLURP 모형의 매개변수와의 민감도 분석을 실시하였다. 분석 결과, 본 논문에서는 SLURP 모형을 이용하여 용담댐 유역의 일 유출량을 모의할 경우 여러 증발산 모형 중 Morton CRAE 모형 이 가장 적합함을 확인하였다.

• 한국농공학회지
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• 제42권
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• pp.17-23
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• 2000

A decision support system for determining reservoir capacity, named as KORESIDSS (KOwaco's REservoir SIzing Decision Support System), was developed. The system is composed of three subsystems; a database/information subsystem, a model subsystem, and an output subsystem. This system is operated using MS-Windows with a GUI (Graphic User Interface) system developed using Visual Basic 5.0. As a continuous runoff model, the DAWAST model (DAily WAtershed STreamflow model) developed by Noh(1991) was and its analysis module was developed. This system was applied to a newly-planned dam, the Cheongyan Dam, Which will be located in Cheongyang-Gun, Chungcheongnam-Do and it was proved to be applicable in determining reservoir storage.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.428-428
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• 2017

In this study, three optimization techniques efficiency is assessed for calibration of the GR4J model for streamflow simulation in Selmacheon, Boryeong Dam and Kyeongancheon watersheds located in South Korea. The Penman-Monteith equation is applied to estimate the potential evapotranspiration, model calibration, and validation is carried out using the readily available daily hydro-meteorological data. The Shuffled Complex Evolution-University of Arizona(SCE-UA), Uniform Adaptive Monte Carlo (UAMC), and Coupled Latin Hypercube and Rosenbrock (CLHR) optimization techniques has been used to evaluate the robustness, performance and optimized parameters of the three catchments. The result of the three algorithms performances and optimized parameters are within the recommended ranges in the tested watersheds. The SCE-UA and CLHR outputs are found to be similar both in efficiency and model parameters. However, the UAMC algorithms performances differently in the three tested watersheds.

• 자원환경지질
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• 제56권1호
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• 한국물환경학회지
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• 제28권3호
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• pp.347-358
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• 2012

The SWAT (Soil and Water Assessment Tool) should be calibrated and validated with observed data to secure accuracy of model prediction. Recently, the SWAT-CUP (Calibration and Uncertainty Program for SWAT) software, which can calibrate SWAT using various algorithms, were developed to help SWAT users calibrate model efficiently. In this study, three algorithms (GLUE: Generalized Likelihood Uncertainty Estimation, PARASOL: Parameter solution, SUFI-2: Sequential Uncertainty Fitting ver. 2) in the SWAT-CUP were applied for the Soyang-gang dam watershed to evaluate these algorithms. Simulated total streamflow and 0~75% percentile streamflow were compared with observed data, respectively. The NSE (Nash-Sutcliffe Efficiency) and $R^2$ (Coefficient of Determination) values were the same from three algorithms but the P-factor for confidence of calibration ranged from 0.27 to 0.81 . the PARASOL shows the lowest p-factor (0.27), SUFI-2 gives the greatest P-factor (0.81) among these three algorithms. Based on calibration results, the SUFI-2 was found to be suitable for calibration in Soyang-gang dam watershed. Although the NSE and $R^2$ values were satisfactory for total streamflow estimation, the SWAT simulated values for low flow regime were not satisfactory (negative NSE values) in this study. This is because of limitations in semi-distributed SWAT modeling structure, which cannot simulated effects of spatial locations of HRUs (Hydrologic Response Unit) within subwatersheds in SWAT. To solve this problem, a module capable of simulating groundwater/baseflow should be developed and added to the SWAT system. With this enhancement in SWAT/SWAT-CUP, the SWAT estimated streamflow values could be used in determining standard flow rate in TMDLs (Total Maximum Daily Load) application at a watershed.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.384-389
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• 2000

A artificial neural network model was developed to analyze and forecast the flow fluctuation at small streams in the Balan watershed. Backpropagation neural networks were found to perform very well in forecasting daily streamflows. In order to deal with slow convergence and an appropriate structure, two algorithms were proposed for speeding up the convergence of the backpropagation method, and the Bayesian Information Criterion(BIC) was proposed for obtaining the optimal number of hidden nodes. From simulations using daily flows at the HS#3 watershed of the Balan Watershed Project, which is 412,5 ㏊ in size and relatively steep in landscape, it was found that those algorithms perform satisfactorily.

• 한국습지학회지
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• 제26권2호
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• pp.147-159
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• 2024

부족한 하천유출 관측 데이터는 모델 보정 작업을 어렵게 만들어 모델의 성능 향상을 제한한다. 위성 기반 원격탐사 자료는 수문 관련 데이터의 확보에 적극적으로 활용될 수 있으므로 새로운 대안이 될 수 있다. 최근에는 여러 연구를 통하여 기존의 개념적/물리적 모델보다는 인공지능을 이용한 해법이 더 적절하다는 평가를 받고 있다. 본 연구에서는 다양한 순환 신경망들과 의사결정나무 기반 알고리즘들을 결합한 자료 기반 접근 방식을 제안하였다. 또한 인공지능 학습을 위하여 인공위성 원격탐사 정보의 활용성을 조사하였다. 본 연구에서 위성영상은 MODIS와 SMAP의 자료가 사용된다. 공적으로 공개된 25개 유역의 자료를 사용하여 제안된 접근 방식을 검증하였다. 전통적인 지역화 접근법에서 착안하여 모든 유역의 자료를 통합하여 하나의 자료 기반 모델을 학습하는 전략을 채택하였으며, Leave-one-out cross-validation 지역화 설정을 이용하여 하나의 모델이 다양한 유역의 하천유출을 예측함으로써 제안된 접근 방식의 잠재력을 평가하였다. GRU + Light GBM 모델이 대상 유역에 적합한 모델 조합으로 판명되었으며(25개 미계측 유역 일 하천유량 예측 모형효율계수 평균 0.7187) 하천유출이 매우 작은 시기를 제외하면 우수한 미계측 유역의 하천유출 예측 성능을 보여주었다. 인공위성 원격탐사 정보의 영향력은 최대 10% 정도로 파악되었으며, 위성 정보의 추가 적용이 풍수기 또는 평수기보다는 저수기 또는 갈수기의 하천유출 예측에 더 큰 영향을 미쳤다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.204-207
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• 2008

The purpose of this study is to identify how much the MODIS NDVI (Normalized Difference Vegetation Index) can explain the soil moisture simulated from SWAT (Soil and Water Assessment Tool) continuous hydrological model. For the application, ChungjuDam watershed (6,661.3 $km^2$) was adopted which covers land uses of 82.2 % forest, 10.3 % paddy field, and 1.8 % upland crop respectively. For the preparation of spatial soil moisture distribution, the SWAT model was calibrated and verified at two locations (watershed outlet and Yeongwol water level gauging station) of the watershed using daily streamflow data of 7 years (2000-2006). The average Nash and Sutcliffe model efficiencies for the verification at two locations were 0.83 and 0.91 respectively. The 16 days spatial correlation between MODIS NDVI and SWAT soil moisture were evaluated especially during the NDVI increasing periods for forest areas.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.143-148
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• 2005

The main purpose of this study is to suggest and evaluate an operational method for assessing the potential impact of climate change on hydrologic components and water resources of regional scale river basins. The method, which uses large scale climate change information provided by a state of the art general circulation model(GCM) comprises a statistical downscaling approach and a spatially distributed hydrological model applied to a river basin located in Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The values are used to modify the parameters of the stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of $2CO_2$. This approach is applied to the Yongdam dam basin in southern part of Korea. The results show that under the condition of $2CO_2$, about $7.6\% of annual mean streamflow is reduced when it is compared with the observed one. And while Seasonal streamflows in the winter and autumn are increased, a streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern and the analysis of the duration cure shows the mean of averaged low flow is increased while the averaged wet and normal flow are decreased for the climate change.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.243-243
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• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.