Soil characterization for shallow landslides modeling: a case study in the Northern Apennines (Central Italy)

Abstract

In this paper, we present preliminary results of the IPL project No. 198 BMulti-scale rainfall triggering models for Early Warning of Landslides (MUSE).^In particular, we perform an assessment of the geotechnical and hydrological parameters affecting the occurrence of landslides. The aim of this study is to improve the reliability of a physically based model high resolution slope stability simulator (HIRESSS) for the forecasting of shallow landslides. The model and the soil characterization have been tested in Northern Tuscany (Italy), along the Apennine chain, an area that is historically affected by shallow landslides. In this area, the main geotechnical and hydrological parameters controlling the shear strength and permeability of soils have been determined by in situ measurements integrated by laboratory analyses. Soil properties have been statistically characterized to provide more refined input data for the slope stability model. Finally, we have tested the ability of the model to predict the occurrence of shallow landslides in response to an intense meteoric precipitation.

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

“…Current practices for geotechnical parameterization in physically based landslide modeling include the application of averaged values from in situ measurements (e.g., Thiebes et al, 2014;Tofani et al, 2017;Zieher et al, 2017) or using values from existing databases, lookup tables, or other published/unpublished sources (e.g., Schmidt et al, 2008;Kuriakose et al, 2009;Mergili et al, 2014b). In the landslide research community, probabilistic treatment of input parameters for regional model application has seen a rise only in the last couple of years (e.g., Mergili et al, 2014a;Raia et al, 2013;Neves Seefelder et al, 2017).…”

“…Measuring geotechnical and hydrological parameters for large areas is difficult, time-consuming, and expensive. Therefore, applying spatially distributed physically based models with spatially variable geotechnical parameters is not straightforward and it is impossible to find an approach that is universally accepted (Tofani et al, 2017). Even if there is a sufficiently large number of measured values available for one, some, or even all parameter values in a model up to the point at which it is possible to specify distributions and covariances for the parameter values, there remain some methodological obstacles.…”

“…One reason usually mentioned is the poor comprehension of the spatial organization of the geotechnical and hydraulic input parameters (e.g., Tofani et al, 2017;Park et al, 2013). However, as outlined above, it does not make too much of a difference whether the underlying study area is 50 or 5000 km 2 investigated at a scale of 1 : 1000 or 1 : 25 000 -the model is still influenced by errors or uncertainties from the input parameters given how input parameters are derived, and one should not believe that uncertainties disappear (or even decrease) with more data and more measurements.…”

“…Tofani et al (2017) performed 59 site investigations to parameterize their distributed slope stability model; this number of in situ soil samplings with associated lab measurements is exceptional and a solid foundation for determining the pdf's. Although Tofani et al (2017) ultimately used the median value for each lithological class, the box plots suggested normal to lognormal parameter distributions.…”

“…Tofani et al (2017) performed 59 site investigations to parameterize their distributed slope stability model; this number of in situ soil samplings with associated lab measurements is exceptional and a solid foundation for determining the pdf's. Although Tofani et al (2017) ultimately used the median value for each lithological class, the box plots suggested normal to lognormal parameter distributions. This is a common observation and might be a result of the central limit theorem, which indicates that lumping data from many different sources (i.e., different in situ soil sampling sites in this case) tends to result in a normal or lognormal distribution .…”