Performance demonstration of a hydro-meteorological warning model for landslides at regional scale

The risk of rainfall-induced landslides is expected to rise as climate change intensifies and increases the frequency of extreme precipitation. In this context, territorial landslide early warning system (Te-LEWS) represent effective non-structural measures for landslide risk mitigation at regional scale. Currently, most operational territorial warning models worldwide are based on rainfall thresholds. Since a trigger-cause conceptual framework of hydro-meteorological thresholds was proposed, a growing number of studies report that such thresholds outperform conventional rainfall thresholds. Nevertheless, hydro-meteorological thresholds have rarely been implemented in operational Te-LEWSs, because real-time monitoring of hydrological variables require dense in-situ networks, whereas the use of satellite/reanalysis products is constrained by latency.Recently, the availability of weather and hydrological forecast products allows incorporating soil moisture information into an operational Te-LEWS. In this work, we present an operational hydro-meteorological warning model developed employing multiple hydro-meteorological thresholds derived from a probabilistic analysis, using soil saturation and precipitation data retrieved from the ERA5-Land product for one of the warning zones defined by Civil Protection for landslide risk management in Campania region, Italy. The performance of the developed model was demonstrated using the real-time forecasts from the Integrated Forecasting System High-Resolution (IFS-HRES) product and compared with the rainfall-only warning model currently operational in Campania in the period 2021–2024. The performance demonstration highlights that the hydro-meteorological model outperforms the regional model, reducing false alarms by 4.1% and shortening the duration of first warning levels not associated to landslides. In addition, the hydro-meteorological model decreases missed alarms by 1.2% and detects a large landslide event missed by the regional model.