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Comparison of Bayesian and Spatial Bootstrap Methods for Estimating Rainfall Spatial Distribution

Author(s): Young-Min Seo; Ki-Bum Park; Sungwon Kim

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Keywords: Rainfall Spatial Distribution; Bayesian Method; Spatial Bootstrap Method; Model Parameters; Uncertainty Assessment

Abstract: This study compares Bayesian method with spatial bootstrap method for estimating spatial distribution of probability rainfall in Wi-Stream catchment, Republic of Korea. As a result, the estimates and uncertainty distribution of mean parameters are almost similar for both methods. For scale and correlation parameters, the mode and median of the uncertainty distribution estimated by Bayesian method are similar with those of the uncertainty distribution estimated by the conventional methods such as OLS, WLS, ML, and REML. The dispersion of uncertainty distribution estimated by Bayesian method is smaller than that of uncertainty distribution estimated by spatial bootstrap method. Therefore, it is found that Bayesian method is superior to spatial bootstrap method for the parameter estimates. It is expected that the uncertainty assessment of model parameters for estimating spatial distribution of probability rainfall can be used as basic data for the investigation and analysis of uncertainty factors involved in the estimation processes of probability flood discharge and flood risk.

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Year: 2012

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