A stochastic rainfall model, NSRPM (Neyman-Scott Rectangular Pulse Model), is able to reflect the cluster characteristics of rainfall events which is unable in the RPM (Rectangular Pulse Model). Therefore NSRPM has advantage in the hydrological applications. The NSRPM consists of five model parameters and the parameters are estimated using optimization techniques such as DFP (Davidon-Fletcher-Powell) method and genetic algorithm. However the DFP method is very sensitive in initial values and is easily converge to local minimum. Also genetic algorithm has disadvantage of long computation time. Nelder-Mead method has several advantages of short computation time and no need of a proper initial value. In this study, the applicability of parameter estimation methods was evaluated using rainfall data of 59 national rainfall networks from 1973-2011. Overall results demonstrated that accuracy in parameter estimation is in the order of Nelder-Mead method, genetic algorithm, and DFP method.

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Typical urban flood simulation is conducted using physical models such as the 1D storm water analysis model and 2D inundation analysis model. Although 2D inundation analysis can predict flow velocity, inundation depth, and inundation area throughout an inundated urban area, it is difficult for it to produce a near real-time urban flood forecast for a metropolitan area such as Seoul. In this study, a physical urban flood forecast model was developed using lumped pipe networks to produce a near real-time urban inundation forecast. The dense pipe networks within a drainage basin were simplified as a single conceptual lumped pipe that has drainage and storage functions, and new pipe networks were constructed using lumped pipe networks. The model was applied to the August 2018 storm events in Seoul and showed a prediction accuracy of 0.71. The results demonstrated that the model can obviate the limitations of the near real-time operation of existing physical flood forecasting models to yield useful information for urban flood response, though showing room for improvement.

In this study, a nonstationary frequency analysis model was developed using a hierarchical Bayesian model. The model consists of 13 parameters which are 10 scale parameters according to different time windows, 2 hyper-parameters and 1 scale hyper-parameter. The model took use of extreme rainfall data based on POT (Peaks Over Threshold) and the GP (Generialized Pareto) distribution. The model parameters were estimated using a Gibbs sampler and Metropolis-Hastings algorithm. The model was applied for Seoul site to estimate target year probable rainfall amount and the probable rainfall estimate for the target year of 2045 is about 14-17% lager than that of current estimate according to different return periods. Results demonstrated that nonstationary frequency analysis is necessary for hydraulic structure design.

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