Bayesian MCMC flood frequency analysis with historical information

This paper explores Bayesian Markov Chain Monte Carlo (MCMC) methods for evaluation of the posterior distributions of flood quantiles, flood risk, and parameters of both the log-normal and Log-Pearson Type 3 distributions. Bayesian methods allow a richer and more complete representation of large flo...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) 2005-11, Vol.313 (1), p.97-116
Main Authors: Reis, Dirceu S., Stedinger, Jery R.
Format: Article
Language:English
Subjects:
Bayesian MCMC
Bayesian theory
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Exact sciences and technology
flood frequency analysis
floods
Freshwater
historical flood information
hydrologic models
Hydrology
Hydrology. Hydrogeology
Log-normal distribution
Log-Pearson Type 3 distribution
mathematics and statistics
Monte Carlo method
Natural hazards: prediction, damages, etc
prediction
risk assessment
watershed hydrology
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Summary:This paper explores Bayesian Markov Chain Monte Carlo (MCMC) methods for evaluation of the posterior distributions of flood quantiles, flood risk, and parameters of both the log-normal and Log-Pearson Type 3 distributions. Bayesian methods allow a richer and more complete representation of large flood records and historical flood information and their uncertainty (particularly measurement and discharge errors) than is computationally convenient with maximum likelihood and moment estimators. Bayesian MCMC provides a computationally attractive and straightforward method to develop a full and complete description of the uncertainty in parameters, quantiles and performance metrics. Examples illustrate limitations of traditional first-order second-moment analyses based upon the Fisher Information matrix.
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2005.02.028