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Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Hierarchical Bayesian Model (HBM) based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. Furthermore, this study investigates the choice of the object function in the calibration process within the Hierarchical Bayesian framework. The proposed models are tested to ensure model performance throughout the rainfall networks of more than 50 rain gauges in South Korea. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series


Session R64, Parameter Estimation: Calibration



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