• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.180-180
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• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• 한국농공학회지
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• 제45권2호
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• 대한토목학회논문집
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• 제31권1B호
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• pp.21-27
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• 2011

유출예측량을 모의할 때 과거와 현재의 수문자료를 이용한다는 측면에서 미래 예측결과의 불확실성을 완전히 제거할 수는 없겠지만, 다양한 기법별 분석에 의하여 불확실성을 감소시킬 수 있다. 본 연구에서는 유출예측의 정확성 향상을 위해 다양한 유출예측 기법을 적용 및 평가하였으며 확률론적 예측을 가능하게 하는 예측기법인 ESP와 관측 시계열 자료를 이용한 통계기법으로 공주지점의 유출예측을 수행하였다. 각 기법에 따른 유출예측 결과의 신뢰성 평가는 MAE(Mean Absolute Error), RMSE(Root Mean Squared Error), RRMSE(Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC(Theil Inequality Coefficient)를 이용하였다. ESP 확률을 이용하여 예측한 유출결과와 통계적 시계열 분석에 의해 예측된 유출결과를 MAE, RMSE, RRMSE, MAPE, TIC를 이용하여 비교 분석하였으며 유출예측의 개선효과를 확인해본 결과, ESP 확률을 이용한 예측이 MAE(10.6), RMSE(15.14), RRMSE(0.244), MAPE(22.74%), TIC(0.13)으로 평가되었으며 MAE(23.2), RMSE(37.13), RRMSE(0.596), MAPE(26.69%), TIC(0.30)으로 평가된 ARMA와 MAE(26.4), RMSE(34.44), RRMSE(0.563), MAPE(47.38%), TIC(0.25)으로 평가된 Winters 에 비해 신뢰성이 높게 나타났다.

• 한국농공학회논문집
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• 제58권4호
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• pp.9-19
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• 2016

The objective of this study was to evaluate the applicability of SWAT (Soil and Water Assessment Tool) model for runoff estimation in the Nam river dam watershed. Input data for the SWAT model were established using spatial data (land use, soil, digital elevation map) and weather data. The SWAT model was calibrated and validated using observed runoff data from 2003 to 2014 for three stations (Sancheong, Shinan, Changchon) within the study watershed. The $R^2$ (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. Parameters for runoff calibration were selected based on user's manual and references and trial and error method was applied for parameter calibration. Calibration results showed that annual mean runoff were within ${\pm}5%$ error compared to observed. $R^2$ were ranged 0.64 ~ 0.75, RMSE were 2.51 ~ 4.97 mm/day, NSE were 0.48 ~ 0.65, and RMAE were 0.34 ~ 0.63 mm/day for daily runoff, respectively. The runoff comparison for three stations showed that annual runoff was higher in Changchon especially summer and winter seasons. The flow exceedance graph showed that Sancheong and Shinan stations were similar while Changchon was higher in entire fraction.

• 한국물환경학회지
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• 제34권3호
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• pp.328-338
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• 2018

The objective of this study was to evaluate the applicability of a HSPF (Hydrological Simulation Program-Fortran) model for runoff estimation in the Namgang dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input for the HSPF model, which was calibrated and validated using observed runoff data from 2004 to 2015 for three stations (Sancheong, Shinan, Changchon) in the study watershed. Parameters for runoff calibration were selected based on the user's manual and references, and parameter calibration was done by trial and error. The $R^2$ (determination coefficient), RMSE (root-mean-square error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (relative mean absolute error) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within a ${\pm}5%$ error in Sancheong and Shinan, whereas there was a14% error in Changchon. The model performance criteria for calibration and validation showed that $R^2$ ranged from 0.80 to 0.92, RMSE was 2.33 to 2.39 mm/day, NSE was 0.71 to 0.85, and RMAE was 0.37 to 0.57 mm/day for daily runoff. Visual inspection showed that the simulated daily flow, monthly flow, and flow exceedance graph agreed well with observations for the Sancheong and Shinan stations, whereas the simulated flow was higher than observed at the Changchon station.

• 한국환경과학회지
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• 제20권1호
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• pp.93-106
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• 2011

Physically-based resampling scheme for roughness coefficient of surface runoff considering the spatial landuse distribution was suggested for the purpose of effective operational application of recent grid-based distributed rainfall runoff model. Generally grid scale(mother scale) of hydrologic modeling can be greater than the scale (child scale) of original GIS thematic digital map when the objective basin is wide or topographically simple, so the modeler uses large grid scale. The resampled roughness coefficient was estimated and compared using 3 different schemes of Predominant, Composite and Mosaic approaches and total runoff volume and peak streamflow were computed through distributed rainfall-runoff model. For quantitative assessment of biases between computational simulation and observation, runoff responses for the roughness estimated using the 3 different schemes were evaluated using MAPE(Mean Areal Percentage Error), RMSE(Root-Mean Squared Error), and COE(Coefficient of Efficiency). As a result, in the case of 500m scale Mosaic resampling for the natural and urban basin, the distribution of surface runoff roughness coefficient shows biggest difference from that of original scale but surface runoff simulation shows smallest, especially in peakflow rather than total runoff volume.

• 물과 미래
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• 제8권2호
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• pp.81-92
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• 1975

Calculation of the monthly water balance for Nakdong River basin for the period from 1958 to 1968 is made by determining three components independently: precipitation, runoff and evapotranspiration. The areal precipitation is computed by the Thiessen method using the records of nine meteorological stations in the basin, and the runoff is the flow gauged at Jindong which is located on the most downstream. For the computation of evapotranspiration, the Morton method is adopted because this method is relatively fit best in the calculation of water balance among the Morton, Penman and Thornthwaite methods. The values of Morton evapotransp iration are corrected by the factor of 0.82 in the basin in order to bring the error to zero. The areal evapotranspiration is the arithmetic mean of the Morton estimates at the stations. Mean water balance components in the Nakdong river basin are 1117.0mm, 600.6mm and 516.4m for precipitation, runoff and evapotranspiration respectively. Accordingly, the mean runoff ratio comes out to be 0.54. The smallest values of runoff coefficient are due for Daegu area, while the largest ones are for the southwest of the basin with the higher rainfall and high elevations there. The amount of runoff obtained by both Thornthwaite and Budyko methods for water balance computations indicate 59 and 60 per cent of actual values which are lower than the expected. An attempt is made to find the best reliable rainfall-runoff relation among the four methods proposed by Schreiber, 01'dekop, Budyko and Sellers. The modified equation of Schreiber type for annual runoff coefficient could be obtained with the smallest mean error of 11 per cent.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• 한국수자원학회논문집
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• 제35권3호
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• pp.307-319
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• 2002

본 연구에서는 WGR 강우모형으로부터 모의된 공간적으로 분포된 강우자료를 수정Clark방법으로 유출 해석하여 면적평균강우의 추정에 따른 오차와 유출오차사이의 관계론 고찰해 보았다. 이러한 관계는 강우관측소의 밀도를 다양하게 변화시켜가며 아울러 호우의 방향을 여러 가지 경우로 가정하여 살펴보았으며, 그 결과를 정리하면 다음과 같다. (1) 면적평균강우의 추정오차 및 이에 따른 유출오차는 강우관측소의 밀도가 높아짐에 따라 지수함수적으로 줄어들고 있으며, 어떤 밀도 이상이 되면 그 감소 폭이 크게 둔화되는 것으로 나타났다. (2) 면적평균강우의 추정오차는 강우관측소의 밀도가 작을수록 유출에 보다 큰 영향력을 미치고 있음을 알 수 있었다. 그러나 면적평균 강우-유출의 관계에서는 그 오차의 비가 1.0이하로 유역면적평균강우 추정시의 오차가 유출에 감소되어 전달되는데 비해 첨두유출량에는 그대로 또는 경우에 따라 증폭되어 전달됨을 파악할 수 있었다. (3) 호우의 방향성에 따른 강우오차는 크게 영향 받지 않는 것으로 판단된다. 그러나, 유출오차는 호우의 방향이 유역의 배수방향에 일치하는 경우에 더 크게 나타나고 있으며, 특히 수문곡선의 형상적인 측면에서보다는 첨두유출량에 더 많은 영향력을 미치고 있는 것으로 보여진다.

• 한국농공학회논문집
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• 제61권5호
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• pp.69-77
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• 2019

The objective of this study was to calibrate and validate the HSPF (Hydrological Simulation Program-Fortran) model for estimating the runoff of the Hapcheon dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input data for the HSPF model. Observed runoff data from 2000 to 2016 in study watershed were used for calibration and validation. Hydrologic parameters for runoff calibration were selected based on the user's manual and references, and trial and error method was used for parameter calibration. The $R^2$, RMSE (root-mean-square error), RMAE (relative mean absolute error), and NSE (Nash-Sutcliffe efficiency coefficient) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within ${\pm}4%$ error. The model performance criteria for calibration and validation showed that $R^2$ was in the rang of 0.78 to 0.83, RMSE was 2.55 to 2.76 mm/day, RMAE was 0.46 to 0.48 mm/day, and NSE was 0.81 to 0.82 for daily runoff. The amount of inflow to Hapcheon Dam was calculated from the calibrated HSPF model and the result was compared with observed inflow, which was -0.9% error. As a result of analyzing the relation between inflow and storage capacity, it was found that as the inflow increases, the storage increases, and when the inflow decreases, the storage also decreases. As a result of correlation between inflow and storage, $R^2$ of the measured inflow and storage was 0.67, and the simulated inflow and storage was 0.61.