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Discussion papers
https://doi.org/10.5194/hess-2018-22
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-22
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 25 Jan 2018

Submitted as: research article | 25 Jan 2018

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This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.

Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin

Yenan Wu1, Upmanu Lall2,3, Carlos H.R. Lima4, and Ping-an Zhong1 Yenan Wu et al.
  • 1College of Hydrology and Water Resources, Hohai University, 5 Nanjing, 210098, China
  • 2Columbia Water Center, Columbia University, New York, NY, 10027, USA
  • 3Department of Earth and Environmental Engineering, Columbia University, New York, 10027, USA
  • 4Civil and Environmental Engineering, University of Brasilia, Brasilia, 70910-900, Brazil

Abstract. We develop a hierarchical, multilevel Bayesian model for reducing uncertainties in local (at-site) and regional (ungauged or short data sites) flood frequency analysis. This model is applied to the annual maximum streamflow of 17 gauged sites in the Huaihe River basin, China. A Generalized Extreme Value (GEV) distribution is considered for each site, and its location and scale parameters depend on the site’s drainage area. We assume the hyper-parameters come from Non-informative (independent, uniform) prior distribution and sample values from posterior distribution by the MCMC method using Gibbs sampling. For comparison, the ordinary GEV fitting by Maximum Likelihood Estimate (MLE) and index flood method fitted by L-moments are also applied. The local simulation results show that for most sites the 95 % credible interval simulated by the Hierarchical Bayesian model are narrower than the at site GEV outputs thus reducing uncertainty. By comparison, the homogeneity assumption of the index flood method often leads to large deviations from the empirical flood frequency curve. Cross validated flood quantiles and associated uncertainty intervals are also derived. These results show that the proposed model can better estimate the flood quantiles and their uncertainty than the index flood method.

Yenan Wu et al.
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Status: closed (peer review stopped)
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Yenan Wu et al.
Yenan Wu et al.
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Short summary
We develop a hierarchical Bayesian (HB) model to reduce uncertainties in flood frequency analysis and explore suitable mapping function linking GEV parameters with drainage area. The results of HB model are compared with ordinary GEV model and index flood method.The application shows HB model provides more adequate confidence intervals than other used methods. This model also provides a better way for using fragmented data with varying lengths for analyzing spatial distribution of flood frequency.
We develop a hierarchical Bayesian (HB) model to reduce uncertainties in flood frequency...
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