World Environmental and Water Resources Congress 2018
Bayesian Augmented L-Moment Approach for Regional Frequency Analysis
Publication: World Environmental and Water Resources Congress 2018: Groundwater, Sustainability, and Hydro-Climate/Climate Change
ABSTRACT
The standard L-moment based regional frequency analysis (RFA) involves four steps: data screening, identification of homogenous region, selection of distribution, and estimation of associated parameters. This study augments the last two steps of RFA using Bayesian statistics in order to limit the subjectivity of distribution selection and account for parameter and model uncertainty. Bayesian model averaging (BMA) was employed along with multiple performance measures to combine distributions and provide a consensus prediction that avoids subjectivity and quantify modeling uncertainty. The differential evolution adaptive metropolis (DREAM) algorithm, the latest addition in MCMC sampling, was adopted to estimate parameters of selected distributions and quantify parameter uncertainty. The results based on extreme precipitation data from 85-station and 10-duration in Eastern U.S. suggest that the coupled BMA and DEARM delivers a less subjective prediction with a better performance than the single best distribution as measured by Taylor diagram, Anderson Darling, and Bootstrap efficiency measures.
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Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2018: Groundwater, Sustainability, and Hydro-Climate/Climate Change
Pages: 165 - 180
Editor: Sri Kamojjala, Las Vegas Valley Water District
ISBN (Online): 978-0-7844-8141-7
Copyright
© 2018 American Society of Civil Engineers.
History
Published online: May 31, 2018
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