Bayesian regional flood frequency analysis for large catchments

TL;DR

This paper introduces a Bayesian hierarchical model for regional flood frequency analysis in large catchments, improving flood quantile estimates by incorporating geographic and meteorological covariates and assessing model uncertainty.

Contribution

It develops a novel Bayesian framework using GEV distribution and model averaging for regional flood analysis, enhancing predictive accuracy over existing methods.

Findings

Significantly better predictive performance than current Norwegian models.

Effective incorporation of geographic and meteorological covariates.

Quantitative assessment of model uncertainty.

Abstract

Regional flood frequency analysis is commonly applied in situations where there exists insufficient data at a location for a reliable estimation of flood quantiles. We develop a Bayesian hierarchical modeling framework for a regional analysis of data from 203 large catchments in Norway with the generalized extreme value (GEV) distribution as the underlying model. Generalized linear models on the parameters of the GEV distribution are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on the flood quantiles. A Bayesian model averaging component additionally assesses model uncertainty in the effect of the proposed covariates. The resulting regional model is seen to give substantially better predictive performance than the regional model currently used in Norway.