Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach

Abstract. Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables -the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

Section: Preparatory and Triggering Factors Driving Slope Instabilitymentioning

confidence: 99%

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Almeida

Holcombe

Pianosi

et al. 2017

“…In fact, adequate historical data on landslides and simultaneous rainfall are in most cases available only for a relatively short period, which may not be sufficiently significant from a statistical point of view. Furthermore, rainfall intensity and duration alone may not be able to capture most of the uncertainty related to landslide triggers (Peres and Cancelliere, 2014). Another drawback of the empirical rainfall thresholds is a general lack of spatial resolution.…”

Section: Schilirò Et Al: Evaluation Of Shallow Landslide-triggerimentioning

confidence: 99%

Evaluation of shallow landslide-triggering scenarios through a physically based approach: an example of application in the southern Messina area (northeastern Sicily, Italy)

Schilirò

Esposito

Mugnozza

2015

Abstract. Rainfall-induced shallow landslides are a widespread phenomenon that frequently causes substantial damage to property, as well as numerous casualties. In recent years a wide range of physically based models have been developed to analyze the triggering process of these events. Specifically, in this paper we propose an approach for the evaluation of different shallow landslide-triggering scenarios by means of the TRIGRS (transient rainfall infiltration and grid-based slope stability) numerical model. For the validation of the model, a back analysis of the landslide event that occurred in the study area (located SW of Messina, northeastern Sicily, Italy) on 1 October 2009 was performed, by using different methods and techniques for the definition of the input parameters. After evaluating the reliability of the model through comparison with the 2009 landslide inventory, different triggering scenarios were defined using rainfall values derived from the rainfall probability curves, reconstructed on the basis of daily and hourly historical rainfall data. The results emphasize how these phenomena are likely to occur in the area, given that even short-duration (1-3 h) rainfall events with a relatively low return period (e.g., 10-20 years) can trigger numerous slope failures. Furthermore, for the same rainfall amount, the daily simulations underestimate the instability conditions. The high susceptibility of this area to shallow landslides is testified by the high number of landslide/flood events that have occurred in the past and are summarized in this paper by means of archival research. Considering the main features of the proposed approach, the authors suggest that this methodology could be applied to different areas, even for the development of landslide early warning systems.

“…Aronica et al (2012a) published a detailed description of the 2009 event, with an insight into the saturation conditions of the soils and an evaluation of the difference of DEMs in the total volume of mobilized material for the Giampilieri catchment. Rainfall thresholds for the landslide activations have been investigated by Gariano et al (2015), in the framework of a regional study, and by Peres and Cancelliere (2014), who conducted a specific study on the Ionian-Peloritan area, hit by the 2009 event. Lombardo et al (2014) tested spatial exportation techniques for logistic regression-based susceptibility models, in the Briga and Giampilieri catchments.…”

Section: Introductionmentioning

confidence: 99%

“…In order to predict these phenomena, together with physically based approaches, which are mainly focused on the detection of the rainfall thresholds responsible for their triggering (e.g. Peres and Cancelliere, 2014;Bordoni et al, 2015) and on the physical modelling of the propagation phase (e.g. Schraml et al, 2015), susceptibility models (Brabb, 1984), suitable to depict prediction images of the sites where these phenomena are more likely to activate on a catchment/regional scale, are required as well.…”

Section: Introductionmentioning

confidence: 99%

Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

Cama

Lombardo

Conoscenti

et al. 2015

Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios.The study area is the small catchment of the Itala torrent (10 km 2 ), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chronovalidation, based on the time partition of the landslide in- Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chronovalidated models. These are interpreted as effects of the nonlinearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.