• Journal of Korean Society of Disaster and Security
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• v.12 no.4
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• pp.63-72
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• 2019

There is an increasing demand for catchment runoff estimation to cope with the natural disasters such as typhoon, extreme rainfall. However, the ungauged catchments are common case in practices. This study suggested a rationalization of conceptual rainfall-runoff model for typhoon flood events in Geum river region. And the developed models were validated based on the observed hydrological data. Therefore, developed regionalization models could estimate catchment runoff for Typhoon flood events. It will be used as basic data for the river management for extreme flood conditions.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Water for future
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• v.19 no.4
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• pp.347-354
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• 1986

To predict flood runoff from rainfall and watershed Characteristics, Nash's parameters of N, K are needed to be determined. Also parameters of IUH N and K are derived by the moment method. Nash's model whose parameters are derived from rainfall characteristics is applied to the Wi-stream basin, which is a tributary located in the Nakdong river. For the derivation of IUH by applying linear conceptual model, the storage constant, K, with the rainfall characteristics was adopted as K=1.327 $$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. Gamma function argumetn, N, derived with such rainfall characteristics was found to be N=0.032$$.$$$.$$$.$$$.$$$.$$ having a highly significant correlation coefficient, 0.970. From the tested results it is proved that Nash's IUH and consequently flood runoff can be predicted from rainfall characteristics.

• Korean Journal of Agricultural Science
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• v.38 no.3
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• pp.513-524
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• 2011

To evaluate the applicability of inflow runoff model to reservoir operation in Korea, DAWAST model and TPHM model which are conceptual lumped daily runoff model and were developed in Korea, were selected and applied to simulate inflows to Daecheong multipurpose dam with watershed area of 4,134 $km^2$, and water storages in Geryong reservoir with watershed area of 15.1 $km^2$ and total water storage of 3.4 M $m^3$. Evaluating inflows on an yearly, monthly, ten-day, and daily basis, inflows by DAWAST model showed balanced scatters around equal value line. But inflow by TPHM model showed high in high flows. Annual mean water balance by DAWAST model was rainfall of 1,159.9 mm, evapotranspiration of 622.1 mm, and inflow of 644.6 mm, from which rainfall was 104.8 mm less than sum of evapotranspiration and inflow, and showed unbalanced result. Water balance by TPHM model showed satisfactory result. Reservoir water storages were shown to simulate on a considerable level from applying DAWAST and TPHM models to simulate inflows to Geryong reservoir. But it was concluded to be needed to improve DAWAST and TPHM model together from imbalance of water balance and low estimation in high flow.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.1-9
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• 2008

The impact of snow accumulation and snowmelt in rainfall-runoff modelling was analyzed for the Soyanggang dam basin by comparing the measured and simulated discharges simulated by the NWS-PC model. Sugawara's conceptual model was used to simulate the snow accumulation and snowmelt phenomena and NWS-PC model was employed to simulate rainfall-runoff. Parameters in model calibration were estimated by the Multi-step Automated Calibration Scheme and optimized using SCE-UA algorithm in each step. The results of the model calibration and verification show that the model considering snowmelt process is better than the one without consideration of snowmelt under the performance criteria such as RMSE, PBIAS, NSE, and PME. The measured discharge time series has over 60 days of persistence. Correlograms for each simulation showed that the simulated discharge with snowmelt model reproduce the persistence closely to the measured discharge's while the one without snow accumulation and snowmelt model reproduce only 20 days of persistence. The study result indicates that the inclusion of snow accumulation and snowmelt model is important for the accurate simulation of rainfall-runoff phenomena in the Soyanggang dam basin.

• Journal of Environmental Science International
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• v.22 no.5
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• pp.523-531
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• 2013

In this study, a method of simulating ephemeral stream runoff characteristics in Jeju watershed is newly suggested. The process based conceptual-physical scheme is established based on the SWAT-K and applied to Cheonmi-cheon watershed which shows the typical pattern of ephemeral stream runoff characteristics. For the proper simulation of this runoff, the intermediate flow and baseflow are controlled to make downward percolation should be dominant. The result showed that surface runoff simulated by using the modified scheme showed good agreement with observed runoff data. In addition, it was found that the estimated runoff directly affected the groundwater recharge rate. This conceptual model should be continuously progressed including rainfall interception, spatially estimated evapotranspiration and so forth for the reasonable simulation of the hydrologic characteristics in Jeju island.

• Korean Journal of Hydrosciences
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• v.8
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• pp.69-83
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• 1997

The Parameters of the storage function model (SFM) are taken as constants, while they have different values during every rainfall period and the duration of the runoff. Therefore, the results of the SFM generally show remarkably large errors. In this study, the modified storage function model (MSFM), in which the time variant parameters are introduced, is proposed to improve the SFM which is a conceptual rainfall-runoff model. The fuzzy reasoning method is applied as a real-time control one of the time variant parameters of the proposed model. The applicability of the MSFM was examined in the Bochung river, at a tributary of the Geum River, Korea. The pattern of the predicted runoff hydrograph and the peak discharge by the MSFM with fuzzy control are very similar to the measured values, compared with the results produced by the SFM.

• Water for future
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• v.28 no.6
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• pp.193-202
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• 1995

The subjective research attempts to apply a rainfall-runoff model capable of considering time-variation of soil water contents which are highly correlated to the river flows on the qpqyungchang river basin and to evaluate its performance for flow forecasting. The model used in this study is a physically-based conceptual time-continuous model, which is composed of the Sacramento soil moisture accounting model and the nonlinear multiple conceptual reservoirs model. The daily precipitation and evaporation data for 7 years and for 3 years were used for the parameter estimation and the model verification, respectively. As a result, the flows including a significant flood event were well simulated, and the cross-correlation coefficient between observed flows and computed flows for the verification periods was 0.87, but in general computed flows were underestimated for the low-flow periods. Also, the effects of precipitation and soil water content to the river flows were analysed for the flood and the drought.