Casagli, Nicola

Catani, Filippo

D&apos

Masi, Elena Benedetta

Pazzi, Veronica

Rosi, Ascanio

Rossi, Guglielmo

Salvatici, Teresa

Tacconi Stefanelli, Carlo

Tofani, Veronica

Vannocci, Pietro

English

Florence Research

Geophysical Research AbstractsVol. 19, EGU2017-13712, 2017EGU General Assembly 2017© Author(s) 2017. CC Attribution 3.0 License.Application of regional physically-based landslide early warning model:tuning of the input parameters and validation of the resultsMichele D’Ambrosio, Veronica Tofani, Guglielmo Rossi, Teresa Salvatici, Carlo Tacconi Stefanelli, AscanioRosi, Elena Benedetta Masi, Veronica Pazzi, Pietro Vannocci, Filippo Catani, and Nicola CasagliDepartment of Earth Sciences, University of Florence, Florence, Italy (michele.dambrosio@unifi.it)The Aosta Valley region is located in North-West Alpine mountain chain. The geomorphology of the region ischaracterized by steep slopes, high climatic and altitude (ranging from 400 m a.s.l of Dora Baltea’s river floodplainto 4810 m a.s.l. of Mont Blanc) variability. In the study area (zone B), located in Eastern part of Aosta Valley, heavyrainfall of about 800-900 mm per year is the main landslides trigger. These features lead to a high hydrogeologicalrisk in all territory, as mass movements interest the 70% of the municipality areas (mainly shallow rapid landslidesand rock falls).An in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslidesformation was conducted, with the aim to improve the reliability of deterministic model, named HIRESS (HIghREsolution Stability Simulator). In particular, two campaigns of on site measurements and laboratory experimentswere performed. The data obtained have been studied in order to assess the relationships existing among the dif-ferent parameters and the bedrock lithology.The analyzed soils in 12 survey points are mainly composed of sand and gravel, with highly variable contents ofsilt. The range of effective internal friction angle (from 25.6◦ to 34.3◦) and effective cohesion (from 0 kPa to 9.3kPa) measured and the median ks (10E-6 m/s) value are consistent with the average grain sizes (gravelly sand).The data collected contributes to generate input map of parameters for HIRESS (static data). More static data are:volume weight, residual water content, porosity and grain size index. In order to improve the original formulationof the model, the contribution of the root cohesion has been also taken into account based on the vegetation mapand literature values.HIRESS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering con-ditions in real time and in large areas using parallel computational techniques. The software runs in real-time byassimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrolog-ical input parameters. In this context, an assessment of the factors controlling the geotechnical and hydrologicalfeatures is crucial in order to understand the occurrence of slope instability mechanisms and to provide reliableforecasting of the hydrogeological hazard occurrence, especially in relation to weather events.In particular, the model and the soil characterization were applied in back analysis, in order to assess the reliabilityof the model through validation of the results with landslide events that occurred during the period.The validation was performed on four past events of intense rainfall that have affected Valle d’Aosta region be-tween 2008 and 2010 years triggering fast shallows landslides. The simulations show substantial improvement ofthe reliability of the results compared to the use of literature parameters. A statistical analysis of the HIRESSS out-puts in terms of failure probability has been carried out in order to define reliable alert levels for regional landslideearly warning systems.

Application of regional physically-based landslide early warning model: tuning of the input parameters and validation of the results

https://flore.unifi.it/bitstream/2158/1094363/1/D%27Ambrosio%20et%20al%20EGU%202017.pdf

Application of regional physically-based landslide early warning model: tuning of the input parameters and validation of the results

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