This study tests the Revitalised Flood Hydrograph (ReFH) rainfall runoff model at two Korean catchments (Bukil and Jeungpyeong) and its application results are compared with HEC-HMS model. These two rainfall-runoff models are conceptual models, which are widely used as a standard tool in UK and Korea. The flood events (Bukil-8 events and Jeungpyeong-6 events) are applied to ReFH model and HEC-HMS with calibration and validation approaches. The main findings of this study are. a) ReFH model shows a potential as a rainfall runoff model for Korean catchments. Although it is not an overwhelmed comparing to HEC-HMS, the performance in objective function (NSE*) and simulated hydrographs in calibration/validation are promising. b) The results of general and cross validation indicate that ReFH could provide acceptable simulations in the flood events, which HEC-HMS fail to simulate and HMS has wide spectrum of the model performance. c) ReFH model shows the limitations in the simulation of peak flow in large catchment, Bukil (906.6 km²), while HEC-HMS shows good simulations in both catchments. It is due to a lumped concept in the ReFH modeling and the semi distributed modeling concept of HEC-HMS is important in the peak flow in large catchment. Further study will focus on testing the ReFH rainfall runoff model with the wide ranges of catchments in Geum river basin. It is necessary to generalize this study and build up the data base for the further regionalization of rainfall runoff model in Korea.

This study investigates the impact of event characteristics on runoff dynamics during extreme flood events observed in an 8.5-km2 experimental watershed located in South Korea. A high-quality data set containing the 31 most extreme flood events with event rainfall in excess of 50 mm were analyzed using an event-based rainfall-runoff model; the revitalized flood hydrograph (ReFH) routinely used for design flood estimation in the United Kingdom. The ReFH model was fitted to each event in turn, and links were investigated between each of the two model parameters controlling runoff volume and response time, respectively, and event characteristics such as rainfall depth, duration, and intensity, and also antecedent soil moisture. The results show no link between the parameter controlling runoff volume and any of the event characteristics, but identified a dependence between response time and rainfall depth. These results show that the linear unit hydrograph fails to adequately represent a reduction in ...

This study investigated the effects of rainfall input uncertainty on parameter estimation and predictions of stream flow in Weida catchment, Germany. Based on rainfall systematic and non-systematic errors, the uncertainty in the rainfall input data was implemented using the rainfall data of five gauging stations. 100 rainfall time series were generated based on precipitations for each gauging station. These randomly generated rainfall time series were employed with a type of Probability Distribution Model (PDM). Using the Monte Carlo method, the posterior distributions of the model parameters were computed, and the effects of the input uncertainty were assessed. This was done by following the concept of the extended GLUE. The hydrographs were simulated using all combinations of feasible rainfall data (i.e., 100 series) and model parameters (i.e., behavioural model parameter sets), the aim of which was to include the uncertainty sources of input data and model parameter. The 90% confidence interval of these hydrographs covers 31% of observed flows. Although the results in this study provide no clear evidence of the effects of rainfall uncertainty on parameter estimation, it does indicate that the suggested method in this study has the potential to cover major uncertainty in input data and model parameter.

This study aims to regionalize a rainfall-runoff model within the mountainous Geum River catchment, Korea. A version of the Probability Distributed Moisture model is applied to 19 gauged sub-catchments. A Monte Carlo based method is used for the calibration and validation of the model using three objective functions targeting overall performance, as well as low and high flow regimes specifically. A set of multivariate regression models linking model parameters and catchment characteristics is developed. The regionalised and locally calibrated models are compared using the leave-one-out cross-validation method. The validation results show that the regionalised model has equal or better performance than the locally calibrated model at 12 (for high flow model), 10 (for low flow model) and 10 catchments (for overall flow regime model) respectively. This study shows the potential of the regionalisation of the Probability Distributed Moisture model within the Geum River region. The results show that the suggested regionalized models for high and low flow regimes are better than a single model for the overall flow regime model. It is expected that this approach can usefully support water resource management in comparable ungauged mountainous, monsoon-affected catchments.

Typhoons are the main type of natural disaster in Korea, and accurately predicting typhoon-induced flood flows at gauged and ungauged locations remains an important challenge. Flood flows caused by six typhoons since 2002 (typhoons Rusa, Maemi, Nari, Dienmu, Kompasu and Bolaven) are modeled at the outlets of 24 Geum River catchments using the Probability Distributed Moisture model. The Monte Carlo Analysis Toolbox is applied with the Nash Sutcliffe Efficiency as the criterion for model parameter estimation. Linear regression relationships between the parameters of the Probability Distributed Moisture model and catchment characteristics are developed for the purpose of generalizing the parameter estimates to ungauged locations. These generalized parameter estimates are tested in terms of ability to predict the flood hydrographs over the 24 catchments using a leave-one-out validation approach. We then test the hypothesis that a more complex generalization approach, the Generalized Estimating Equation, which includes properties of the typhoons as well as catchment characteristics as predictors of PDM model parameters, will provide more accurate predictions. The results show that the predictions of Generalized Estimating Equation are comparable to those of the simpler, conventional regression. The simpler approach is therefore recommended for practical applications; however, further refinements of the Generalized Estimating Equation approach may be explored.