Multiseasonal probabilistic slope stability analysis of a large area of unsaturated pyroclastic soils

Abstract

The analysis of slope stability over large areas is a demanding task for several reasons, such as the need for extensive datasets, the uncertainty of collected data, the difficulty of accounting for site-specific factors, and the considerable computation time required due to the size of investigated areas, which can pose major barriers, particularly in civil protection contexts where rapid analysis and forecasts are essential. However, as the identification of zones of higher failure probability is very useful for stakeholders and decision-makers, the scientific community has attempted to improve capabilities to provide physically based assessments. This study combined a transient seepage analysis of an unsaturated-saturated condition with an infinite slope stability model and probabilistic analysis through the use of a high-computing capacity parallelized platform. Both short- and long-term analyses were performed for a study area, and roles of evapotranspiration, vegetation interception, and the root increment of soil strength were considered. A model was first calibrated based on hourly rainfall data recorded over a 4-day event (December 14–17, 1999) causing destructive landslides to compare the results of model simulations to actual landslide events. Then, the calibrated model was applied for a long-term simulation where daily rainfall data recorded over a 4-year period (January 1, 2005–December 31, 2008) were considered to study the behavior of the area in response to a long period of rainfall. The calibration shows that the model can correctly identify higher failure probability within the time range of the observed landslides as well as the extents and locations of zones computed as the most prone ones. The long-term analysis allowed for the identification of a number of days (9) when the slope factor of safety was lower than 1.2 over a significant number of cells. In all of these cases, zones approaching slope instability were concentrated in specific sectors and catchments of the study area. In addition, some subbasins were found to be the most recurrently prone to possible slope instability. Interestingly, the application of the adopted methodology provided clear indications of both weekly and seasonal fluctuations of overall slope stability conditions. Limitations of the present study and future developments are finally discussed.

Root Reinforcement in Slope Stability Models: A Review

“…The approach to estimate the root reinforcement distribution in such a wide area adopted by the authors was the following: distribution of plant species was first obtained by in situ observation and preexisting vegetational maps, then a value of root cohesion and a range of variation was defined for each plant species based on the most recent literature, finally, the root reinforcement was treated as variable in Monte Carlo simulations (as well as the other geotechnical parameters) to stochastically reproduce its natural variability. Same approach was used also by Cuomo et al 2020 [91]. In this case, the authors also consider another process works by vegetation in addition to the root reinforcement, they indeed account for the effect of the evapotranspiration on the soil moisture budget in their distributed analyses for shallow rainfall-triggered landslides.…”

“…The following step was the introduction of this vegetational effect into stability models and it has been achieved gradually, starting from slope scale applications and achieving successful tests at the regional scale only recently. In the majority of the cases, this aim was persecuted starting from well-developed and largely tested slope stability models such as model TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis) [92,93] or HIRESSS [89,91].…”

“…Measures on Roots Roots Modeling Slope or Smaller Scale SSM Basin or Larger Scale SSM Other Abdi et al 2018 [49] x Arnone et al 2016 [87] x x Bordoni et al 2020 [102] x x x Bordoni et al 2016 [100] x x Chiaradia et al 2016 [104] x x x Chok et al 2015 [36] x x Cislaghi et al 2017 [101] x x Cislaghi et al 2017 [85] x x x Cislaghi et al 2018 [86] x Cislaghi et al 2019 [114] x x x Cuomo et al 2020 [91] x Dazio et al 2018 [57] x x Gehring et al 2019 [121] x x Giadrossich et al 2017 [48] x Gonzalez-Ollauri 2017 [99] x x x Hales et al 2018 [82] x x Hales and Miniat 2017 [43] x x x Hwang et al 2015 [81] x x x Kokutse et al 2016 [35] x x Likitlersuang et al 2017 [106] x Masi et al 2020 [70] x Moos et al 2016 [111] x x x Rickli et al 2019 [112] x Rossi et al 2017 [107] x Saadatkhah et al 2016 [93] x x Salvatici et al 2018 [89] x Schmaltz and Mergili 2018 [113] x x Schmaltz et al 2019 [116] x x Switala and Wu 2018 [95] x Switala and Wu 2019 [97] x Vergani et al 2017 [11] x Vergani et al 2017 [118] x x Vergani et al 2015 [21] x x Wang et al 2017 [105] x x x Wang et al 2018 [117] x x Wang et al 2019 [103] x In the last few years, a challenging research trend emerged in the field of slope stability modelling: the progressive widening of application areas from slope to catchment and regional scale-i.e., over dozens or hundreds of squared kilometer-wide areas. Although advances made in the modelling of the mechanica...…”

“…Although advances made in the modelling of the mechanical behavior of roots in soils [13,51], in the evaluation of root tensile strength [44,47,50], and in the spatial distribution modelling of root reinforcement at the stand level [3,54], we could find that estimating the root reinforcement at the regional scale remains challenging, and it represents one of the main limits of the distributed slope stability analyses in wide areas. Slopes scale methodologies of analysis are quite well established, applications over small catchments have become recurrent, but large-dozens or hundreds of km 2 -areas are investigated only in a few recent works: Cuomo (2020) [91], Hales (2018) [82], Hwang (2015) [81], Salvatici (2018) [89], Rossi (2017) [107], and Saadtkhah (2016) [93]. Studies reporting on slope scale analyses or numerical simulations of synthetic slopes are also essential for the scientists working on wider areas, as they investigate new parameterization strategies, new modeling approaches and sensitivity issues.…”

“…The consequent challenge posed to the research of finding indirect methods to evaluate such parameter in wide areas resulted in the development of various approaches based on remote sensing and inference technics starting from aboveground parts of plants [76][77][78][79]. Such approaches allow the application of slope stability modelling in increasingly large areas using the distributed slope stability models recently modified to account for the root cohesion [87,91].…”