Exploring Uncertainties in Flood Frequency Analysis Using Bayesian MCMC Method

Abstract:

The evaluation and reducing of uncertainty is central to the task of hydrological frequency analysis. In this paper a Bayesian Markov Chain Monte Carlo (MCMC) method is employed to infer the parameter values of the probabilistic distribution model and evalue the uncertainties of design flood. Comparison to the estimated results of three-parameter log-normal distribution (LN3) and the three-parameter generalized extreme value distribution (GEV), the Pearson Type 3 distribution (PIII) provides a good approximation to flood-flow data. The choice of the appropriate probabilistic model can reduce uncertainty of design flood estimation. Historical flood events might be greatly reduced uncertainty when incorporating past extreme historical data into the flood frequency analysis.

• Periodical: Advanced Materials Research (Volume 663)
• Pages: 768-772
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.663.768
• Online since: February 2013
• Authors: Li Jie Zhang
• Keywords: Bayesian MCMC Method, Flood Frequecy Analysis, Historicaly Flood, Pearson Type 3 Distribution, Uncertainty

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