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Regional low flow frequency analysis using Bayesian regression and prediction at ungauged catchment in Korea

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KSCE Journal of Civil Engineering Aims and scope

Abstract

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence intervals, at two ungauged catchments using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchments.

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Authors and Affiliations

  1. Land, Transport and Maritime Affairs Team, National Assembly Research Service, Seoul, 150-703, Korea

    Sang Ug Kim

  2. Dept. of Civil and Environmental Engineering, Seoul National University, Seoul, 151-742, Korea

    Kil Seong Lee

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  1. Sang Ug Kim
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  2. Kil Seong Lee
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Correspondence to Sang Ug Kim.

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Kim, S.U., Lee, K.S. Regional low flow frequency analysis using Bayesian regression and prediction at ungauged catchment in Korea. KSCE J Civ Eng 14, 87–98 (2010). https://doi.org/10.1007/s12205-010-0087-7

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  • DOI: https://doi.org/10.1007/s12205-010-0087-7

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