Smooth copula-based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada

This paper proposes a smooth copula-based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two-step approach combines GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula-based model to take into account dependence between the recording stations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula-based dissimilarity measure recently introduced in the literature. Finally, we compare the classical GEV parameter interpolation approaches with the proposed smooth copula-based GEV modeling approach.