• Journal of Korea Water Resources Association
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• v.40 no.3
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• pp.205-214
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• 2007

The objective of this study is to generate the regional scale runoff scenarios by using IPCC SRES A2 climate change scenario for analyzing the spatial variation of water resources in Korea. The PRMS model was adopted to simulate long-term stream discharge. To estimate the PRMS model parameters on each sub-basin, the streamflow data at 6 dam sites and Rosenbrock's scheme are used for model parameter calibration and those parameters are translated to ungauged catchments by regionalization method. The other 3 dam sites are selected for the verification of the adequateness of regionalized model parameters in ungagued catchments. The statistical results show that the simulated flows by using regionalized parameters well agree with observed ones. The generated runoff scenarios by climate change are compared with observed data on 4 dam sites for the reference period. The consequences show that the selection of climate station for generating climate scenario affects the reliability of climate scenario at sub-basin. The comparison results of the stream flows between the 30-year baseline period (1971-2000) and future 90-year (2001-2030, 2031-2060, 2061-2090) show that the long-term mean annual runoff in the Han River has increasing trend, while the Nakdong, the Gum, the Youngsan and the Sumjin Rivers have decreasing trend.

• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.311-321
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• 2009

Long-Term Hydrologic Impact Assessment (L-THIA) was modified to improve runoff and pollutant load prediction for Korean watersheds with changes in land use classification and event mean concentration produced from observed data in Korea. The L-THIA model was linked with SCE-UA, which is one of the global optimization techniques, to automatically calibrate direct runoff. Modified L-THIA model was applied to Gumho River Basins to analyze spatial distribution of nonpoint source pollution. The results of model calibration during 1991~2000 and validation during 1981~1990 for direct runoff represented high model efficiency of 0.76 for calibration and 0.86 for validation. As a results of spatial analysis of nonpoint source pollution, the BOD was mainly loaded from urban area but SS, TN, and TP from agricultural area which is mainly located along the stream. Modified L-THIA model improve its accuracy with minimum imput data and application efforts. From this study, we can find out the L-THIA model is very useful tool to predict direct runoff and pollutant loads from the watershed and spatial analysis of nonpoint source pollution.

• Journal of Korea Water Resources Association
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• v.37 no.7
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• pp.599-612
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• 2004

The continuous long-term rainfall-runoff simulation model SWAT has the advantage of being able to account for various land use, however, SWAT lacks the capability of simulating the drainage characteristics of urban area. On the other hand, SWMM, which is the most popular model for runoff analysis of urban watershed, has the advantage of being capable of considering surface and drainage characteristics in urban area, but SWMM cannot easily account for land use other than urban area within a watershed. In this study, SWAT-SWMM model, which builds on the strengths of SWAT and SWMM, has been applied to the Osan River Watershed which is a tributary watershed to the Gyung-Ahn River. From the application, the results from coupled SWAT-SWMM model has been compared to the ones from SWAT for each hydrologic component such as evapotranspiration, surface runoff, groundwater flow, and watershed and channel discharge, and the runoff characteristics of two models for each hydrologic component has been discussed.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.581-589
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• 2012

Non-point source pollutant is exerting a serious influence on the water quality, since the characteristics of stormwater runoff is varied by the land usage pattern of an area and a basin, and all sorts of pollutants on the earth in rainfall flow into the urban stream. This study estimated EMC of each pollutant to investigate the characteristics of stormwater runoff by separating the urban area as the housing area and industrial area. As a result of the analysis, the first flush effect occurred in the non-point source pollutant of housing area and industrial area, as the runoff concentration gradually reduces after it rapidly increases in the initial rainfall, and in case of the non-point source pollutant the control of first stage rain-water. It is considered to require the continuous follow-up study such as the scale of long-term rainfall event and water quality data, land usage pattern by GIS method, database of topography and geological features, and so forth.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.856-864
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• 2006

For estimating the minimum discharge to maintain a river, low flow analysis is required and long term runoff records are needed for the analysis. However, runoff data should be estimated to run a hydrologic model for ungaged river basin. For the reason, parameter estimation is crucial to simulate rainfall-runoff events for those basins using Tank model. In this study, only runoff data recorded for dry season are used for parameter estimation, which is different to other methods based on runoff data recorded for wet and dry seasons. The Harmony Search algorithm is used to determine the optimum parameters for Tank model. The coefficient of determination ($R^2$) is served as the objective function in the Harmony Search. In cases that recorded data are insufficient, the recording interval is changed and Empirical CDF is adopted to analyze the estimated parameters. The suggested method is applied to Yongdam dam, Soyanggang dam, Chungju dam and Seomjingang dam basins. As results, the higher $R^2s$ are obtained when the shorter recording interval, the better recorded data quality, and the more rainfall events recorded along with certain rainfall amount is. Moreover, when the total rainfall is higher than the certain amount, $R^2$ is high. Considering the facts found from this study for the low flow analysis, it is possible to estimate the parameters for Tank model properly with the desired confidence level.

• Journal of Korea Water Resources Association
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• v.30 no.4
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• pp.379-387
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• 1997

This study involves field monitoring of hydrlolgic characteristics of paddy fields under common irrigation practice, statistical analysis of maximum retention storage, determination of CNs for antecedent moisture conditions. Curve numbers were estimated from observed rainfall-runoff relationship of two years data. The estimated CN for AMC-II was 78, and the CNs for AMC-I and II were 63 and 88, respectively. A water balance model was used to find the effect of ridge height changes and initial ponding depth in paddy fields on runoff. The probability distribution of initial ponding depth was also investigated. The initial ponding depth follows normal probability distribution. Initial ponding depth corresponding 10%, 50%, and 90% probability were considered to be equivalent to AMC-I, AMC-II, and AMC-III, respectively. Long-term runoff data from paddy fields were simulated by a water balance model using recorded climate data, ridge height and estimated initial ponding depth derived from probability distribution. The estimated CNs using simulated runoff were 70, 79, and 89 for CN-I, CN-II, and CN-III, respectively.

• Water for future
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• v.24 no.3
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• pp.71-82
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• 1991

Methods of predicting water resources availiability of a river basin can be classified as empirical formula, water budget analysis and regression analysis. The purpose of this study is to develop a method to estimate the monthly runoff required for long-term water resources development project. Using the monthly runoff data series at gaging stations alternative multiple regression models were constructed and evaluated. Monthly runoff volume along with the meteorological and physiographic parameters of 48 gaging stations are used, those of 43 stations to construct the model and the remaining 5 stations to verify the model. Regression models are named to be Model-1, Model-2, Model-3 and Model-4 developing on the way of data processing for the multiple regressions. From the verification, Model-2 is found to be the best-fit model. A comparison of the selected regression model with the Kajiyama's formula is made based on the predicted monthly and annual runoff of the 5 watersheds. The result showed that the present model is fairly resonable and convinient to apply in practice.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.493-505
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• 2020

The rainfall-runoff model has been generally adopted to obtain a consistent runoff sequence with the use of the long-term ground-gauged based precipitation data. The Thiessen polygon is a commonly applied approach for estimating the mean areal rainfall from the ground-gauged precipitation by assigning weight based on the relative areas delineated by a polygon. However, spatial bias is likely to increase due to a sparse network of the rain gauge. This study aims to generate continuous runoff sequences with the mean areal rainfall obtained from radar rainfall estimates through a PRMS rainfall-runoff model. Here, the systematic error of radar rainfall is corrected by applying the G/R Ratio. The results showed that the estimated runoff using the corrected radar rainfall estimates are largely similar and comparable to that of the Thiessen. More importantly, one can expect that the mean areal rainfall obtained from the radar rainfall estimates are more desirable than that of the ground in terms of representing rainfall patterns in space, which in turn leads to significant improvement in the estimation of runoff.