Bayesian regional flood frequency analysis with GEV hierarchical models under spatial dependency structures

Bayesian hierarchical models have been increasingly used in regional flood frequency analysis due to their flexibility and ability to accommodate the spatial variability of flooding processes in distribution parameters. Hierarchical models based on the generalized extreme value (GEV) distribution are useful since they may combine scaling properties and distinct degrees of pooling in the shape parameter for improving quantile estimation. In this paper, we evaluate the benefits of combining a partial pooling approach and a formal description of the spatial latent processes that govern the distribution parameters. The application of the model in the Alto do Sao Francisco River catchment (Brazil) suggests that, despite obtaining similar estimates at gauged sites, prediction at ungauged counterparts may be substantially improved in densely gauged regions, in terms of accuracy and precision, by accounting for spatial dependency. In poorly gauged areas, however, no benefits in utilizing latent spatial processes for inference were verified.