Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

Francesco Fusco,Domenico Calcaterra,Pantaleone De Vita,Rex Baum,Benjamin Mirus

doi:10.3390/w13050713

Abstract

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.

Highlights

Representative values of unsaturated/saturated hydraulic and geotechnical soil properties (Table 1), which were estimated as median of weighted harmonic means respectively of previous characterizations [76], allowed us to set the simplified regional-scale parameterization of the ash-fall pyroclastic soil cover used for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) modeling (Figure 5)
Other attempts to model hydrological and slope stability behavior of Sarno Mountain slopes have considered layered soils, but they have not taken into account the combined influence of the spatially variable soil thickness and antecedent soil hydrological conditions, as well as the influence of soil layering on the model parameterization for distributed applications [81,85,94]
The distributed modeling of the hydrological response at large spatial scales of slopes characterized by layered and thickness-varying soils represents a challenge for the assessment of hazard to rainfall-induced shallow landslides

Summary

Introduction

Assessing the timing and potential locations of landslides through mathematical models applied for transient, unsaturated infiltration and slope stability modeling requires understanding unsaturated soil hydrology and mechanics, as well as climate and topography [4]. The timing and spatial distribution of rainfall-triggered landslides depend on both quasi-static and dynamic variables [5]. The dynamic or transitory variables, such as relative soil saturation and strength, represent the landslide-inducing factors. Natural processes, such as climatic or hydrological and human activities, such as cut and fill works or forest clearing, respectively affect dynamic and quasi-static variables, conditioning the temporal and spatial patterns of landslides

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