• 한국수자원학회논문집
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• 제31권2호
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• pp.177-187
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• 1998

하천과 대수층이 연결된 계에서 분산오염원이 유입될 경우에 지하수 수질을 평가할 수 있는 공간적분모형을 제안하였다. 제안된 공간적분모형은 불포화대의 영향이 고려되었으며, 다양한 수문 및 대수층 모의 조건에서 Richards 방정식과 이송-분산 방정식의 공간분포모형에 대한 수치해와 비교를 통하여 공간적분모형을 테스트하였다. 비교 결과에 의하면, 분산도비와 대수층의 두께가 큰 경우를 제외하고는 공간적분모형과 공간분포모형사이의 포화대수층의 평균농도 및 지하수유출의 평균농도는 일치된 반응을 보여주고 있다. 그리고 분산도비와 대수층의 두께가 큰 계에서는 비선형 공간적분모형이 선형 공간적분모형보다 더 좋은 결과를 보여주었다.

• 한국물환경학회지
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• 제36권4호
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• pp.300-313
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• 2020

The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized Likelihood Uncertainty Estimation (GLUE) method. These two methods were applied to the TANK model, a hydrological model comprising 13 parameters, to examine the uncertainty of the parameters of the model. The TANK model comprises a combination of multiple reservoir-type virtual vessels with orifice-type outlets and implements a common major hydrological process using the runoff calculations that convert the rainfall to the flow. As a result of the application to the Nam River A watershed, the two Bayesian methods yielded similar flow simulation results even though the parameter estimates obtained by the two methods were of somewhat different values. Both methods ensure the model's prediction accuracy even when the observed flow data available for parameter estimation is limited. However, the prediction accuracy of the model using the MH algorithm yielded slightly better results than that of the GLUE method. The flow duration curve calculated using the limited observed flow data showed that the marginal reliability is secured from the perspective of practical application.

• 한국농공학회논문집
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• 제62권1호
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• pp.39-50
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• 2020

The objective of this study was to assess the livestock nonpoint source pollutant impact on water quality in Namgang dam watershed using the HSPF (Hydrological Simulation Program-Fortran) model. The input data for the HSPF model was established using the landcover, digital elevation, and watershed and river maps. In order to apply the pollutant load to the HSPF model, the delivery load of the livestock nonpoint source in the Namgang dam watershed was calculated and used as a point pollutant input data for the HSPF model. The hydrologic and water quality parameters of HSPF model were calibrated and validated using the observed runoff data from 2007 to 2015 at Sancheong station. The R² (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. The simulation results for annual mean runoff showed that R² ranged 0.79~0.81, RMSE 1.91~2.73 mm/day, NSE 0.7~0.71 and RMAE 0.37~0.49 mm/day for daily runoff. The simulation results for annual mean BOD for RMSE ranged 0.99~1.13 mg/L and RMAE 0.49~0.55 mg/L, annual mean TN for RMSE ranged 1.65~1.72 mg/L and RMAE 0.55 mg/L, and annual mean TP for RMSE ranged 0.043~0.055 mg/L and RMAE 0.552~0.570 mg/L. As a result of livestock nonpoint pollutant loading simulation for each sub-watersehd using the HSPF model, the BOD ranged 16.6~163 kg/day, TN ranged 27.5~337 kg/day, TP ranged 1.22~14.1 kg/day.

• 한국환경과학회지
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• 제24권4호
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• pp.437-447
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• 2015

Estimation of runoff peak is needed to assess water availability, in order to support the multifaceted water uses and functions, hence to underscore the modalities for efficient water utilization. The magnitude of storm rainfall acts as a primary input for basin level runoff computation. The rainfall-runoff linkage plays a pivotal role in water resource system management and feasibility level planning for resource distribution. Considering this importance, a case study has been carried out in the Hancheon basin of Jeju Island where distinctive hydrological characteristics are investigated for continuous storm rainfall and high permeable geological features. The study aims to estimate unit hydrograph parameters, peak runoff and peak time of storm rainfalls based on Clark unit hydrograph method. For analyzing observed runoff, five storm rainfall events were selected randomly from recent years' rainfall and HEC-hydrologic modeling system (HMS) model was used for rainfall-runoff data processing. The simulation results showed that the peak runoff varies from 164 to 548 m3/sec and peak time (onset) varies from 8 to 27 hours. A comprehensive relationship between Clark unit hydrograph parameters (time of concentration and storage coefficient) has also been derived in this study. The optimized values of the two parameters were verified by the analysis of variance (ANOVA) and runoff comparison performance were analyzed by root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) estimation. After statistical analysis of the Clark parameters significance level was found in 5% and runoff performances were found as 3.97 RMSE and 0.99 NSE, respectively. The calibration and validation results indicated strong coherence of unit hydrograph model responses to the actual situation of historical storm runoff events.

• 대한토목학회논문집
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• 제37권1호
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• pp.231-238
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• 2017

용수공급시설의 설계 및 운영에 기준이 되는 이수안전도는 용수공급안전도의 평가를 위한 중요한 지표이지만, 이수안전도에 대한 일관된 지침이 없어 댐 설계 과정에서 저마다 사용된 자료기간과 평가 방법이 서로 상이한 문제점이 있다. 따라서 본 연구에서는 다목적댐의 용수공급능력을 일관성 있게 평가하기 위해 가뭄의 심도와 지속기간을 동시에 고려할 수 있는 이변량 가뭄빈도해석을 수행하였다. 확률론적 개념을 바탕으로 가뭄사상을 평가하여 댐유역의 가뭄특성을 분석하였으며, 우리나라의 5개 다목적댐(소양강, 충주, 안동, 대청, 섬진강)을 대상으로 특정 가뭄상황에서의 용수공급능력을 평가하였다. 그 결과 여름과 가을에는 자체 유입량만으로 안정적인 용수공급이 가능하지만, 강수량이 적은 봄과 겨울에는 1년 빈도의 작은 가뭄에도 용수부족이 발생하는 것으로 나타났다.

• 한국수자원학회논문집
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• 제38권1호
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• pp.11-23
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• 2005

본 연구에서는 단기 예측강우를 활용하여 실시간 유량을 예측할 수 있는 기상-수자원 연계기법을 개발하였다. 이를 위해 기상청의 RDAPS 강수자료와 저류함수(SFM) 모델을 통해 소양강댐 상류유역의 댐유입량을 계산하고 그 정확도를 분석하였다. 대상 사례기간인 2003년 7월 18일부터 2003년 7월 24일까지 RDAPS 강우예측자료의 정확도를 평가한 결과 RDAPS 및 관측 강수량 사이의 정성적 평가에서 매우 우수한 정확도를 보이고, 수자원 측면에서 필요한 정량적 성격을 충족시키는 것으로 나타났다. RDAPS-SFM 연계기법을 통한 하천유량 계산에서도 그 정확도가 비교적 높은 것으로 검토되어 현재의 하천 유량 예측에서 기상 수치예보자료의 활용성은 매우 높은 것으로 사료된다.

• 한국수자원학회논문집
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• 제41권5호
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• pp.527-544
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• 2008

GPD 모형은 수문학 극치확률량 해석에 주로 적용되어 왔다. 극치 통계의 주목적은 드문 사상의 예측이며, 주요 문제점으로는 임계값 또는 임계값 초과치들에 대한 정확한 산정방법이 없어 그 추정이 매우 어렵다는 것이다. 본 연구에서는 임계값 또는 임계값 초과치들을 산정하기 위하여 4가지 방법을 적용하였다. 그 비교를 위하여 GPD 모형에 적용하여 7개의 지속시간(1, 2, 3, 6, 12, 18 및 24시간)과 10개의 재현기간(2, 3, 5, 10, 20, 30, 50, 70, 80 및 100년)에 대한 매개변수 및 Quantile을 추정하였다. 3변수 GPD의 매개변수 및 Quantile을 추정하기 위하여 MOM, ML과 PWM을 적용하였다. 적합도를 추정하기 위하여 K-S, CVM 및 A-D 검정을 수행하였고 Monte Carlo 실험으로 상대 제곱근오차를 산정하였다. 이러한 방법들을 이용하여 임계값 산정방법들을 비교하여 최적화된 방법을 추정하였다.

• 자원환경지질
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• 제44권6호
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• pp.525-532
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• 2011

지속가능한 지하수 자원의 개발과 관리능력의 수단으로서의 지하댐의 이용과 그 효율성에 관한 분석을 수행하기 위해 완전연동형 지표수-지하수 통합해석 모형인 SWAT-MODFLOW를 쌍천유역에 적용하여 지하댐 건설에 따른 영향평가를 수행하였다. 지하댐 건설 후에는 댐 상류의 지하수위 상승이 일어나며 하류부는 지하수가 감소하는 현상이 나타난다. 이에 따라 지표수-지하수의 상호교환량은 상류에서 더 커지는 것으로 나타났다. 지하댐 건설에 따라 상류부 대수층의 저류량이 증가함으로써 가용한 지하수의 개발량은 그만큼 증가하는 것으로 나타났다. 본 연구를 통해 지하댐의 기능이 대수층의 가용 저류량을 증대시키는 매우 유용한 시설임을 확인할 수 있었고, 통합 수문해석 방법론은 지하댐 건설에 따른 영향 뿐 아니라 설계와 운영을 평가하기 위한 적절한 기법으로 이용될 수 있을 것으로 판단된다.

• 한국농공학회지
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• 제26권2호
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• 한국농공학회논문집
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• 제55권4호
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.