• Atmosphere
• /
• v.25 no.2
• /
• pp.271-281
• /
• 2015

The objectives of this study are to develop a hydrological drought outlook system using GloSea5 (Global Seasonal forecasting system 5) which has recently been used by KMA (Korea Meteorological Association) and to evaluate the forecasting capability. For drought analysis, the bilinear interpolation method was applied to spatially downscale the low-resolution outputs of GloSea5 and PR (Predicted Runoff) was produced for different lead times (i.e., 1-, 2-, 3-month) running LSM (Land Surface Model). The behavior of PR anomaly was similar to that of HR (Historical Runoff) and the estimated values were negative up to lead times of 1- and 2-month. For the evaluation of drought outlook, SRI (Standardized Runoff Index) was selected and PR_SRI estimated using PR. ROC score was 0.83, 0.71, 0.60 for 1-, 2- and 3-month lead times, respectively. It also showed the hit rate is high and false alarm rate is low as shorter lead time. The temporal Correlation Coefficient (CC) was 0.82, 0.60, 0.31 and Root Mean Square Error (RMSE) was 0.52, 0.86, 1.20 for 1-, 2-, 3-month lead time, respectively. The accuracy of PR_SRI was high up to 1- and 2-month lead time on local regions except the Gyeonggi and Gangwon province. It can be concluded that GloSea5 has high applicability for hydrological drought outlook.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.6
• /
• pp.81-87
• /
• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• Journal of Wetlands Research
• /
• v.7 no.3
• /
• pp.57-66
• /
• 2005

This paper is to present the determination of the optimal loss rate parameters and unit hydrographs from the observed single rainfall-runoff event using optimization model. The linear program models has been formulated to derive the optimal unit hydrographs and loss rate parameters for the site of the Su-Jik Bridge in the HwangGuJichen River; one minimizes the summation of the absolute residual between predicted and observed runoff ordinates. In the perturbation stage of parameters the trial and error method has been adopted to determine the loss rate parameters for Kostiakov's, Philip's, Horton's, and Green-Ampt's equation. The unique unit hydrograph ordinates for a given rainfall-runoff event is exclusively obtained with ${\Phi}$ index, but unit hydrograph ordinates depend upon the parameters for each loss rate equations. In this paper the single rainfall-runoff event observed from the sample watershed is considered to test the proposed method. The optimal unit hydrograph obtained by the optimization model has smaller deviations than the ones by the conventional method.

• Journal of Wetlands Research
• /
• v.21 no.1
• /
• pp.1-8
• /
• 2019

In this study, the multi-objective optimization method is attemped to optimize the hydrological model to estimate the runoff through two hydrological processes. HL-RDHM, a distributed hydrological model that can simultaneously estimate the amount of snowfall and runoff, was used as the distributed hydrological model. The Durango River basin in Colorado, USA, was selected as the watershed. MOSCEM was used as a multi-objective optimization method and parameter calibration and hydrologic model optimization were tried by selecting 5 parameters related to snow melting and 13 parameters related to runoff. Data from 2004 to 2005 were used to optimize the model and verified using data from 2001 to 2004. By optimizing both the amount of snow and the amount of runoff, the RMSE error can be reduced from 7% to 40% of the simulation value based on the initial solution at three SNOTEL points based on the RMSE. The USGS observation point of the outflow is improved about 40%.

• Journal of Korean Society for Geospatial Information Science
• /
• v.18 no.1
• /
• pp.11-20
• /
• 2010

The distributed rainfall-runoff model which is developed in the country requires a lot of time and effort to generate input data. Also, it takes a lot of time to calculate discharge by numerical analysis based on kinematic wave theory in runoff process. Therefore, most river basins using the distributed model are of limited scale, such as small river basins. However, recently, the necessity of integrated watershed management has been increasing due to change of watershed management concept and discharge calculation of whole river basin, including upstream and downstream of dam. Thus, in this study, the feasibility of the GIS based physical distributed rainfall-runoff model, K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model) which has been developed by own technology was reviewed in the flood discharge process for the Geum River basin, including Yongdam and Daecheong Dam Watersheds. GIS hydrological parameters were extracted from basic GIS data such as DEM, land cover and soil map, and used as input data of the model. Problems in running time and inaccuracy setting using the existing trial and error method were solved by applying an auto calibration method in setting initial soil moisture conditions. The accuracy of discharge analysis for application of the method was evaluated using VER, QER and Total Error in case of the typhoon 'Ewiniar' event. and the calculation results shows a good agreement with observed data.

• Journal of Korea Water Resources Association
• /
• v.45 no.1
• /
• pp.15-27
• /
• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• Journal of Korea Water Resources Association
• /
• v.30 no.1
• /
• pp.83-93
• /
• 1997

The purpose of the study is to analyze the sensitivity of the parameters that affect the runoff and water quality in the studied drainage basins. SWMM model is applied to the four drainage basins located at Namgazwa and Sanbon in Seoul and Gray Haven and Kings Creek in the USA. first of all, the optimum values of the parameters which have least simulation error to the observed data, are detected by iteration procedure. These are used as the standard values which are compared against the procedure. These are used as the standard values which are compared against the varied parameter values. In order to catch the effectiveness of the parameters to the computing result, the parameters are changed step by setp, and the results are compared to the standard results in flowerate and quality of the sewer. The study indicates that the discharge is greatly affected by the types of runoff surface, i.e., impervious area remarkably affects the peak flow and runoff volume while the surface storage affects the runoff volume at mild sloped basins. In addition, the major parameters affecting the pollution concentrations and loadings are the contaminant accumulation coefficient per unit area per time and the continuous dry weather days. Furthermore, the factors that affect the water quality during the initial rainfall period are the rainfall intensity, transport capacity coefficient and its power coefficient. Consequently, in order to simulate the runoff-water quality, it is needed to evaluate previous data in the research performed for the studied basins. To accurately estimated from the tributary areas and the rational computation methods of the pollutants calculation should be introduced.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1998.10a
• /
• pp.359-365
• /
• 1998

A global optimization method known as the Shuffled Complex Evolution method from the University of Arizona(SCE-UA) was used for calibrating a Tank model. The model was calibrated with error-free synthetic data, and the SCE-UA method was found to effectively search optimal parameters. Historical data from an agricultural watershed was used to calibrate and validate the model parameters. The simulated results were in good agreement with the observed.

• Journal of the Korean earth science society
• /
• v.35 no.2
• /
• pp.97-103
• /
• 2014

An isotopic hydrograph separation technique has been able to determine the contribution of new water (event water such as rain or snowmelt) and old water (pre-event water like groundwater) to a stream hydrograph for last several decades using stable water isotopes. It is based on the assumption that the isotopic compositions of both new water and old water at a given instant in time are known and the stream water is a mixture of the two waters. In this study, we show that there is a systematic error (standard error in the new water fraction) in the isotopic hydrograph separation if the average isotopic compositions of new water were used ignoring the temporal variations of those of new water. The standard error in the new water fraction is caused by: (1) the isotopic difference between the average value and temporal variations of new water; (2) the new water fraction as runoff contributing to the stream during rainfall or spring melt; and (3) the isotopic differences between new and old water (inversely). The standard error is large, in particular, when new water dominates the stream flow, such as runoff during intense rainfall and in areas of low infiltration during spring melt. To reduce the error in the isotopic hydrograph separation, incorporation of fractionation in the isotopic composition of new water observed at a point should be considered with simultaneous sampling of new water, old water and stream water.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.59 no.2
• /
• pp.69-79
• /
• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.