Multifactor analysis of IoT, satellite and reanalysis time-series for early warning of rainfall-induced shallow landslides, debris flows and flash floods at municipal scale

Empirical thresholds are crucial tools for predicting the occurrence of rainfall-induced shallow landslides, debris flows and flash floods at territorial scale. These thresholds are typically based only on rainfall data, but this approach overlooks the influence of predisposing conditions and complex hydrogeological processes in the area of interest. The soil response under intense meteorological events can be better investigated by using local monitoring data; indeed, a deeper knowledge of the possible effects in the ground of different rainfall events could provide fundamental support to decision makers towards warning for potential critical events over a relatively wide area (e.g., a catchment or a municipality).

To this aim, IoT monitoring networks have been installed within two small catchments in the municipalities of Amalfi and Sorrento (Campania region, southern Italy). The two monitoring networks―active since autumn 2023 and spring 2024 respectively―can be defined as multifactor networks; in fact, they include sensors installed to monitor the following variables: rainfall, soil water content, soil suction and water level in streams. The sensors have been installed at several locations, covering both the upstream and the downstream sections of the two catchments. This allows the combined use of widespread meteorological data and local real-time measurements coming from monitoring devices installed at specific spots of geomorphological interest. To fully characterize the weather conditions and their potential to cause shallow landslides, debris flows and flash floods, data from satellite observations and reanalysis products are also considered in the analysis. The multifactor time-series analysis is aimed at establishing correlations between the collected variables and at defining a relationship between the local meteorological conditions and the hydrogeological response in the shallower soil layers.

The final aim is the identification of proxies of “critical conditions” over time, that can be used to improve the performance of territorial warning models for rainfall-induced shallow landslides, debris flows and flash floods.