Classification of large-scale catchments data-sets: use of seasonality statistics in the identification of flood typology

River flooding is one of the most destructive natural calamities, causing every year serious economic, societal and environmental consequences. It is therefore of vital importance the investigation of the causative mechanisms of past flooding events. The present research, in the framework of large-scale hydrological classification studies, is inspired by the methodology elaborated by Berghuijs et al. 2019. We here propose a multicriteria classification process, widely transferable across locations, based on seasonality statistics for the evaluation of the recurrence of different flood types at the catchment scale, recognising extreme (one-day) precipitation, soil-moisture excess and snowmelt as the main triggering mechanisms. We then discuss the methodology presented in Stein et al. 2019, which also investigates mixed-flood behaviour at the catchment scale, but using a single-event approach, comparing its outputs with ours for the CAMELS dataset, 671 catchments spread across the contiguous US.