Shallow landslides susceptibility assessment in different environments

Persichillo, Maria Giuseppina; Bordoni, Massimiliano; Meisina, Claudia; Bartelletti, Carlotta; Barsanti, Michele; Giannecchini, Roberto; Avanzi, G. D’Amato; Galanti, Yuri; Cevasco, Andrea; Brandolini, Pierluigi; Arnedo, Jorge Pedro Galve

doi:10.1080/19475705.2016.1265011

Cited by 63 publications

(63 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different studies have been performed to evaluate the role of land use in shallow-landsliding, mainly through the use of statistical methods (e.g. Baeza and Corominas 2001;Beguerıa 2006;Piacentini et al 2012;Galve et al 2015;Trigila et al 2015;Persichillo et al 2016). These methods are based on conceptual models that describe the functional relationships between instability factors and the past and present distribution of slope failures (Carrara 1983).…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

Self Cite

View full text Add to dashboard Cite

In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

show abstract

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

Self Cite

View full text Add to dashboard Cite

show abstract

“…4. Such a procedure is similar to that one proposed by Persichillo et al (2017b) for the assessment of the shallow landslide susceptibility in different settings. In this paper, it was refined for the application to road susceptibility to landslides.…”

Section: Predictor Variablesmentioning

confidence: 88%

“…CS influences the destabilizing forces upstream that can provoke the development of a landslide (Brenning et al, 2015;Persichillo et al, 2017b). TWI highlights the water fluxes along the slopes and the position of the accumulation points in a catchment .…”

Section: Predictor Variablesmentioning

confidence: 99%

“…In the second step, a database formed of an equal number of road pixels affected or not affected by shallow landslides was implemented in order to avoid the over-estimation of non-landslide areas, which are much wider than landslide ones Ayalew and Yamagishi, 2005;Persichillo et al, 2017b). Then this database was subdivided into training and test sets.…”

Section: Implementation Of Gam Modelmentioning

confidence: 99%

“…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.…”

Section: Predictor Variablesmentioning

confidence: 99%

See 2 more Smart Citations

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

Bordoni

Persichillo

Meisina

et al. 2018

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Landslides cause severe damage to the road network of the hit zone, in terms of both direct (partial or complete destruction of a road or blockages) and indirect (traffic restriction or the cut-off of a certain area) costs. Thus, the identification of the parts of the road network that are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the financial expense caused by the damage. For these reasons, this paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to being hit by shallow landslides triggered in slopes upstream from the infrastructure. This model was based on the Generalized Additive Method, where the function relating predictors and response variable is an empirically fitted smooth function that allows fitting the data in the more likely functional form, considering also non-linear relations. This work also analyzed the importance, on the estimation of the susceptibility, of considering or not the sediment connectivity, which influences the path and the travel distance of the materials mobilized by a slope failure until hitting a potential barrier such as a road. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The most significant explanatory variables were selected by a random partition of the available dataset in two parts (training and test subsets), 100 times according to a bootstrap procedure. These variables (selected 80 times by the bootstrap procedure) were used to build the final susceptibility model, the accuracy of which was estimated through a 100-fold repetition of the holdout method for regression, based on the training and test sets created through the 100 bootstrap model selection. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model in which the index of connectivity was also calculated according to a linear relationship, was considered. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different land use scenarios were considered in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features. As a consequence, variations on the susceptibility of the road network according to different scenarios of distribution of land cover were limited. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give the managers of infrastructure information about the criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structur...

show abstract

Response of Terraced Slopes to a Very Intense Rainfall Event and Relationships with Land Abandonment: A Case Study from Cinque Terre (Italy)

et al. 2017

Self Cite

View full text Add to dashboard Cite

Agricultural terraces represent one of the best ways to prevent land degradation in hilly and mountainous landscapes. However, it is widely recognized that terraced slopes are threatened by agricultural land abandonment. In the literature, very few studies have quantitatively examined the influence of agricultural abandonment on the stability of terraced slopes. The goal of this research is to investigate the relationships between landslide magnitude and land use conditions of agricultural terraced slopes. In particular, Light Detection And Ranging (LiDAR) elevation data, coupled with aerial photo interpretation, were used for the computation of shallow landslide mobilized volumes on terraced slopes affected by an intense rainfall event. We performed the analysis within the Vernazza basin, a small Mediterranean coastal catchment located in the “Cinque Terre” area (Liguria, northwestern Italy), comparing pre‐event and post‐event LiDAR datasets. The results revealed that abandoned terraced slopes have been affected by a higher amount of mobilized debris volumes than still‐cultivated terraces. Furthermore, terraces abandoned for a short time (less than 25–30 years) resulted in the most hazardous land use class, showing erosion rates that were approximately 2 and 3 times higher than terraced slopes abandoned a long time ago (more than 25–30 years) and still‐cultivated terraces, respectively. These findings highlight that land abandonment and agricultural mismanagement can intensify the magnitude of rainfall‐induced shallow landslides. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Shallow landslides susceptibility assessment in different environments

Cited by 63 publications

References 62 publications

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

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

Response of Terraced Slopes to a Very Intense Rainfall Event and Relationships with Land Abandonment: A Case Study from Cinque Terre (Italy)

Contact Info

Product

Resources

About