Toward More Robust Extreme Flood Prediction by Bayesian Hierarchical and Multimodeling

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Natural Hazards

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Flood information, especially extreme flood, is necessary for any large hydraulic structure design and flood risk management. Flood mitigation also requires a comprehensive assessment of flood risk and an explicit quantification of the flood uncertainty. In the present study, we use a multimodel ensemble approach based on Bayesian model averaging (BMA) method to account for model structure and distribution uncertainties. The usefulness of this approach is assessed by a case study over the Willamette River Basin (WRB) in Pacific Northwest, USA. Besides the standard log-Pearson Type III distribution, we also identified that the generalized extreme value and three-parameter lognormal distributions were both potential distributions in WRB. Three different statistical models, including the Bulletin-17B quantile model, index-flood model, and spatial Bayesian hierarchical model, were considered in the study. The BMA method is then used to assign weights to different models, where better performing model receives higher weights. It was found that the major uncertainty in extreme flood prediction is contributed by model structure, while the choice of distribution plays a lesser important role in quantification of flood uncertainty. The BMA approach provides a more robust extreme flood prediction than any single model.


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