FIELD PARAMETRIZATION FOR REGIONAL PHYSICALLY BASED LANDSLIDE EARLY WARNING MODELS

This work deals with the characterization of geotechnical and hydrological properties of hillslopes affected by shallow landslides with the aim to improve the reliability of deterministic models for early warning purpose. In particular we apply the HIRESSS model that is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and in large areas. The software runs in real-time by assimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrological input parameters. In this context, an assessment of the factors controlling the geotechnical and hydrological features is crucial in order to understand the occurrence of slope instability mechanisms and to provide reliable forecasting of the hydrogeological hazard occurrence, especially in relation to weather events. In the selected study areas several campaigns, located in central and northern Italy, on site measurements and laboratory experiments were performed. The data obtained have been studied in order to assess the relationships existing among the different parameters and the bedrock lithology. The data collected contributes to generate input map of parameters for HIRESSS (static data). The contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. In particular, the HIRESSS model was applied in back analysis, in order to assess the reliability of the model through validation of the results with landslide events that occurred during the period. On selected past events in the study areas, the validation was performed. The simulations in general show substantial improvement of the reliability of the results compared to the use of literature parameters. A statistical analysis of the HIRESSS outputs in terms of failure probability has been carried out in order to define reliable alert levels for regional landslide early warning systems.