Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS

Abstract

Abstract. Rainfall-induced shallow landslides are common phenomena in many parts of the world, affecting cultivation and infrastructure and sometimes causing human losses. Assessing the triggering zones of shallow landslides is fundamental for land planning at different scales. This work defines a reliable methodology to extend a slope stability analysis from the site-specific to local scale by using a wellestablished physically based model (TRIGRS-unsaturated). The model is initially applied to a sample slope and then to the surrounding 13.4 km 2 area in Oltrepò Pavese (northern Italy). To obtain more reliable input data for the model, longterm hydro-meteorological monitoring has been carried out at the sample slope, which has been assumed to be representative of the study area. Field measurements identified the triggering mechanism of shallow failures and were used to verify the reliability of the model to obtain pore water pressure trends consistent with those measured during the monitoring activity. In this way, more reliable trends have been modelled for past landslide events, such as the April 2009 event that was assumed as a benchmark. The assessment of shallow landslide triggering zones obtained using TRIGRSunsaturated for the benchmark event appears good for both the monitored slope and the whole study area, with better results when a pedological instead of geological zoning is considered at the regional scale. The sensitivity analyses of the influence of the soil input data show that the mean values of the soil properties give the best results in terms of the ratio between the true positive and false positive rates. The scheme followed in this work allows us to obtain better results in the assessment of shallow landslide triggering areas in terms of the reduction in the overestimation of unstable zones with respect to other distributed models applied in the past.

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

“…According to Model 1, the most susceptible road segments are those located downstream to slopes characterized by high slope gradient (> 20 • ), limited height (< 50 m), high catchment slope (28-30 • ) and shallow landslides triggering zones located very close to the road network (40-100 m). These settings are widespread in the entire study area (Bordoni et al, 2015;Persichillo et al, 2017bPersichillo et al, , 2018, but these particular features are not enough to discriminate more accurately those routes where damages provoked by sediments mobilized by shallow landslides are probable.…”

“…The morphological structure is typical of the Pede-Apennine margin of Oltrepò Pavese and is closely related to both the lithology and the tectonic or neotectonic setting of the Apennine margin. It is characterized by a medium-high slope gradient, with slope angles higher than 10 • , with prominent altimetric irregularities along ridge lines and channel network in narrow valleys (Bordoni et al, 2015). Bedrock is characterized by a Mio-Pliocenic succession formed by medium low-permeable arenaceous conglomeratic materials (Monte Arzolo Sandstones, Rocca Ticozzi Conglomerates) overlying impermeable silty-sandy marly bedrock (Montù Beccaria Formation, Sant'Agata Fossili Marls) and evaporitic chalky marls and gypsum (Gessoso-Solfifera Formation) (Vercesi and Scagni, 1984).…”

“…This area was recently affected by several shallow landslides triggered by intense rainfall events (Bordoni et al, 2015). These landslides had an average length of about 35 m and area varied from a minimum of 13 m 2 to a maximum of almost 9000 m 2 , with an average of about 477 m 2 .…”

“…The index of sediment connectivity (IC), defined by Borselli et al (2008), evaluates the potential connection between hillslopes and features that act as targets or storage areas (sinks) for mobilized sediments (e.g., channels, basin outlet, lakes or road networks). In the proposed model, IC, calculated according to the approach of Cavalli et al (2013), was implemented for a better characterization of surface processes and properties and to exploit a high-resolution DEM.…”

“…This is consistent with the features of the slopes where shallow landslides occurred in the past in the study area. In fact, from the source area to the accumulation zone of each landslide, the failed slopes kept similar morphological and hydrological features, in terms of slope angle, exposition, curvature and hydrological features (Bordoni et al, 2015;Persichillo et al, 2017b). Maps of the predictor variables were produced for the study area at a resolution of 1 m, as the input DEM.…”