Characterizing compound floods in Italy by pooling precipitation and soil moisture seasonal ensemble re-forecasts

The increasing frequency and devastating impacts of compound hydro-meteorological extreme events underscore the urgent need for a deeper understanding of their dynamics and occurrence. Compound events involve the simultaneous or sequential occurrence of multiple natural hazards or drivers which may exacerbate impacts compared to individual events alone. Recent striking cases is the exceptional sequence of heavy rainfalls that struck northern Italy in 2023-2024, culminating in widespread flooding across the Emilia Romagna region. The severity and extent of the floods were amplified by antecedent precipitation, which saturated the soil, a pre-condition that resulted in substantial aggravation of the impacts. However, the rarity and unprecedented nature of such events pose major challenges for their robust characterization using observational records alone due to the limited temporal coverage, which can introduce substantial uncertainties. To address this, the UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach has emerged as a powerful method, leveraging large ensembles of seasonal re-forecasts from numerical weather prediction models. By pooling ensemble members, this approach effectively generates surrogate time series spanning thousands of years, enabling the exploration of low-probability, high-impact events within a statistically robust framework.
This study applies the UNSEEN methodology to compound flood events within a multivariate framework, focusing on the coupling between precipitation and soil moisture as a key preconditioning driver. The seasonal re-forecasts from SEAS5 (ECMWF) dataset for the period 1994-2023 are considered to characterize unprecedented compound extreme severe floodings in northern Italy, with particular focus on the Emilia-Romagna region. The ability of the UNSEEN ensemble to represent univariate extremes is firstly evaluated—an essential preliminary step to ensure the reliability of the pooled surrogate time series. Subsequently, an event coincidence analysis is conducted on the surrogate series of precipitation-soil moisture extremes to investigate the dominant spatio-temporal patterns arising from their interaction. Results show that the UNSEEN ensemble realistically captures the occurrence of historical extreme flood events, offering a more robust representation compared to observational climatologies alone. These findings underscore the potential of ensemble-based modeling approaches to improve our understanding of rare compound events and support the development of more effective adaptation and mitigation strategies in flood-prone regions.