Regional-scale susceptibility modelling of shallow landslides involving the weathered and fractured sub-surface bedrock

Landsliding is a complex phenomenon and its modelling aimed at predicting where the processes are most likely to occur is a tricky issue to be performed. Apart the chosen modelling approach, for both data-driven and physically-based models, paying adequate attention to the predisposing and triggering factors, as well as the input parameters is no less important. Generally, shallow landslides mobilize relatively small volumes of material sliding along a nearly planar rupture surface which is assumed to be roughly parallel to the ground surface. In the literature it is also widely accepted that shallow landslides involve only unconsolidated slope deposits (i.e., the colluvium), then the rupture surface corresponds to the discontinuity between the bedrock and the overlying loose soil. In this work, based on systematic field observations, we highlight that shallow landslides often involve also portions of the sub-surface bedrock showing different levels of weathering and fracturing. Then, we show that the engineering geological properties of slope deposits, as well as those related to the underlying bedrock, must be considered to obtain more reliable shallow landslides susceptibility assessment. As a first task, a multi-temporal shallow landslide inventory was built by photointerpretation of aerial orthoimages. Then, a new fieldwork-based method is proposed and implemented to acquire, process and spatialize the engineering geological properties of both slope deposits and bedrock. To support the regional scale approach, field observations were collected within, in the neighbour and far from the shallow landslide areas. Finally, both physically-based and data-driven methods were implemented to assess and compare shallow landslide susceptibility at regional scale, as well as to analyse the role of spatial distribution of rock mass quality for shallow slope failure development. The results highlight that, according to geology, structural setting and morphometric conditions, bedrock properties spatially change, defining clusters influencing both the distribution and characters of shallow landslides. As a consequence, the physically-based modelling provides better prediction accuracy when two possible rupture surfaces are analysed, the shallower one located at the slope deposit / bedrock discontinuity, and the deeper one located at the bottom of the fractured and weathered bedrock horizon. Even though the physically-based and data-driven models provide similar results in terms of ROC curves, the resulting susceptibility maps highlight quite substantial differences.

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Integrating Satellite Soil Moisture and Rainfall Data on a Data-Driven Model for the Assessment of Shallow Landslides Hazard

Proceedings ◽

10.3390/proceedings2019030042 ◽

2019 ◽

Vol 30 (1) ◽

pp. 42

Author(s):

Meisina ◽

Bordoni ◽

Lucchelli ◽

Brocca ◽

Ciabatta ◽

...

Keyword(s):

Soil Moisture ◽

Methodological Approach ◽

Regional Scale ◽

Shallow Landslides ◽

Predisposing Factors ◽

Data Driven ◽

Triggering Factors ◽

Physically Based ◽

Physically Based Models ◽

Triggering Events

Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the events, according to the return time of the triggering events, which generally correspond to intense and concentrated rainfalls. Susceptibility and hazard of a territory are usually assessed by means of physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall amounts. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Moreover, physically-based models require, sometimes, significant computation power, which limit their implementations at regional scale. Data-driven models could overcome both of these limitations, even if they are generally built up taking into only the predisposing factors of shallow instabilities. Thus, they allow usually to estimate the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the hazard. This work presents the preliminary results of the development and the implementation of data-driven model able to estimate the hazard of a territory towards shallow landslides. The model is based on a Genetic Algorithm Model (GAM), which links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to the soil moisture content and to the rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in different catchments of 30–40 km2 located in Oltrepò Pavese area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

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Application of a physically based model to forecast shallow landslides at a regional scale

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-1919-2018 ◽

2018 ◽

Vol 18 (7) ◽

pp. 1919-1935 ◽

Cited By ~ 21

Author(s):

Teresa Salvatici ◽

Veronica Tofani ◽

Guglielmo Rossi ◽

Michele D'Ambrosio ◽

Carlo Tacconi Stefanelli ◽

...

Keyword(s):

Slope Stability ◽

Regional Scale ◽

Shallow Landslide ◽

Shallow Landslides ◽

Richards Equation ◽

Mean Annual Precipitation ◽

Good Prediction ◽

Physically Based Model ◽

Good Prediction Accuracy ◽

Physically Based

Abstract. In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.

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Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model

Geosciences ◽

10.3390/geosciences11010035 ◽

2021 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

Luca Schilirò ◽

José Cepeda ◽

Graziella Devoli ◽

Luca Piciullo

Keyword(s):

Numerical Simulations ◽

Regional Scale ◽

Shallow Landslides ◽

South Eastern ◽

Physically Based ◽

Triggering Conditions ◽

Cover Thickness ◽

Regional Analyses ◽

Factor Maps ◽

Eastern Norway

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

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A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽

10.3390/w13091208 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1208

Author(s):

Massimiliano Bordoni ◽

Fabrizio Inzaghi ◽

Valerio Vivaldi ◽

Roberto Valentino ◽

Marco Bittelli ◽

...

Keyword(s):

Machine Learning ◽

Water Potential ◽

Soil Water ◽

Temporal Trends ◽

Soil Water Potential ◽

Shallow Landslides ◽

Water Dynamics ◽

Data Driven ◽

Machine Learning Techniques ◽

Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

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Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-1927-2011 ◽

2011 ◽

Vol 11 (7) ◽

pp. 1927-1947 ◽

Cited By ~ 61

Author(s):

L. Montrasio ◽

R. Valentino ◽

G. L. Losi

Keyword(s):

Real Time ◽

Regional Scale ◽

Back Analysis ◽

Early Warning Systems ◽

Shallow Landslides ◽

Landslide Risk ◽

Time Interval ◽

Slip Model ◽

Temporal Prediction ◽

Physically Based

Abstract. In the framework of landslide risk management, it appears relevant to assess, both in space and in time, the triggering of rainfall-induced shallow landslides, in order to prevent damages due to these kind of disasters. In this context, the use of real-time landslide early warning systems has been attracting more and more attention from the scientific community. This paper deals with the application, on a regional scale, of two physically-based stability models: SLIP (Shallow Landslides Instability Prediction) and TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis). A back analysis of some recent case-histories of soil slips which occurred in the territory of the central Emilian Apennine, Emilia Romagna Region (Northern Italy) is carried out and the main results are shown. The study area is described from geological and climatic viewpoints. The acquisition of geospatial information regarding the topography, the soil properties and the local landslide inventory is also explained. The paper outlines the main features of the SLIP model and the basic assumptions of TRIGRS. Particular attention is devoted to the discussion of the input data, which have been stored and managed through a Geographic Information System (GIS) platform. Results of the SLIP model on a regional scale, over a one year time interval, are finally presented. The results predicted by the SLIP model are analysed both in terms of safety factor (Fs) maps, corresponding to particular rainfall events, and in terms of time-varying percentage of unstable areas over the considered time interval. The paper compares observed landslide localizations with those predicted by the SLIP model. A further quantitative comparison between SLIP and TRIGRS, both applied to the most important event occurred during the analysed period, is presented. The limits of the SLIP model, mainly due to some restrictions of simplifying the physically based relationships, are analysed in detail. Although an improvement, in terms of spatial accuracy, is needed, thanks to the fast calculation and the satisfactory temporal prediction of landslides, the SLIP model applied on the study area shows certain potential as a landslides forecasting tool on a regional scale.

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Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-559-2013 ◽

2013 ◽

Vol 13 (3) ◽

pp. 559-573 ◽

Cited By ~ 70

Author(s):

D. Zizioli ◽

C. Meisina ◽

R. Valentino ◽

L. Montrasio

Keyword(s):

Shallow Landslide ◽

Northern Italy ◽

Shallow Landslides ◽

Rainfall Event ◽

Aerial Photographs ◽

Intense Rainfall ◽

Field Surveys ◽

Ex Post ◽

Physically Based ◽

Physically Based Models

Abstract. On the 27 and 28 April 2009, the area of Oltrepo Pavese in northern Italy was affected by a very intense rainfall event that caused a great number of shallow landslides. These instabilities occurred on slopes covered by vineyards or recently formed woodlands and caused damage to many roads and one human loss. Based on aerial photographs taken immediately after the event and field surveys, more than 1600 landslides were detected. After acquiring topographical data, geotechnical properties of the soils and land use, susceptibility analysis on a territorial scale was carried out. In particular, different physically based models were applied to two contiguous sites with the same geological context but different typologies and sizes of shallow landslides. This paper presents the comparison between the ex-post results obtained from the different approaches. On the basis of the observed landslide localizations, the accuracy of the different models was evaluated, and the significant results are highlighted.

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1-D inversion analysis of a shallow landslide triggered by the 2018 Eastern Iburi earthquake in Hokkaido, Japan

Earth Planets and Space ◽

10.1186/s40623-021-01443-y ◽

2021 ◽

Vol 73 (1) ◽

Author(s):

Jun Kameda ◽

Atsushi Okamoto

Keyword(s):

Slope Failure ◽

Flow Model ◽

High Mobility ◽

Shallow Landslide ◽

Shallow Landslides ◽

Viscoplastic Fluid ◽

Rheological Parameters ◽

Slope Length ◽

Flow Duration ◽

Inversion Analysis

AbstractDestructive landslides were triggered by the 6.7 Mw Eastern Iburi earthquake that struck southern Hokkaido, Japan, on 6 September 2018. In this study, we carried out 1-D inversion analysis of one of the shallow landslides near the epicenter using a Bing debris-flow model. At this site, the slope failure comprised cover soil with an initial down-slope length of ~ 80 m and a thickness of ~ 7 m on a slope with < 20° dip. The landslide moved southeastward with a run-out distance of ~ 100 m. Inversion analysis of the post-failure deposit geometry was conducted with the Markov Chain Monte Carlo method (MCMC) to optimize the Bingham rheological parameters of the debris. The analysis reproduced several features of the deposit geometry with a yield stress of ~ 1500 Pa and dynamic viscosity of 800–3000 Pa s. The results suggest that the shallow landslide can be approximated by the flow of a viscoplastic fluid with high-mobility debris and a maximum frontal velocity of 6–9 m/s, with a flow duration of 2–4 min.

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Shallow landslides involving weathered and fractured bedrock: a comparative susceptibility analysis between deterministic and statistical models

10.5194/egusphere-egu2020-18614 ◽

2020 ◽

Author(s):

Enrico D'Addario ◽

Leonardo Disperati ◽

José Luís Zêzere ◽

Raquel De Melo ◽

Sérgio Oliveira

Keyword(s):

Statistical Models ◽

Regional Scale ◽

Central Italy ◽

Shallow Landslide ◽

Geological Strength Index ◽

Predictive Capacity ◽

Predisposing Factor ◽

Fractured Bedrock ◽

Susceptibility Analysis ◽

Physically Based

Shallow landslide susceptibility modelling at regional scale may be performed using both a physically based and statistical approach. For the same area, these two approaches can have inconsistent results, mainly because the two methods are conceptually different. Physically based models are based on the infinite slope model and consists on the computation cell by cell of a safety factor comparing between driving and resisting forces. The assumption that landslides occur in slopes that are characterized by predisposing factors similar to those in which landslides have occurred in the past, is the concept behind the statistical models. The aim of this work is to compare the two approach and investigate the differences between the two models. The study area is located in northern Tuscany, central Italy, in which an extensive field survey highlighted that about 60% of landslides involve bedrock. For this reason, we developed a physically based susceptibility analysis taking into account both the surficial layer (slope deposit, SD) and the underlying layer (BR), characterized by weathered and fractured bedrock. This model is compared to the statistically based one, which take into account topographic and geologic predisposing factor as well as bedrock geo-mechanical properties, such Geological Strength Index (GSI), Schmidt hammer rebound values (Rv) and Joint density (Jv). The accuracy of the models is evaluated using a multi-temporal landslide inventory, in which involving bedrock landslides are distinct from slope deposits landslides. Within this general framework results are discussed regarding the model&#8217;s predictive capacity and spatial agreement.

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Using satellite soil moisture and rainfall data for the monitoring and the prediction of natural hazards

10.5194/egusphere-egu21-14812 ◽

2021 ◽

Author(s):

Valerio Vivaldi ◽

Massimiliano Bordoni ◽

Luca Brocca ◽

Luca Ciabatta ◽

Claudia Meisina

Keyword(s):

Methodological Approach ◽

Shallow Landslides ◽

Predisposing Factors ◽

Data Driven ◽

Probability Of Occurrence ◽

Mechanical Responses ◽

Triggering Factors ◽

Physically Based ◽

Adaptive Regression ◽

Spatio Temporal

Rainfall-induced shallow landslides affect buildings, roads, facilities, cultivations, causing several damages and, sometimes, loss of human lives. It is necessary assessing the most prone zones in a territory where these phenomena could occur and the triggering conditions of these events, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the spatial and temporal probability of occurrence are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, they are limited to be applied in a reliable way in little catchments, where geotechnical and hydrological characteristics of the materials are homogeneous. Data-driven models could constraints these, when the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, are combined with triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the implementation of a data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven techniques. The models are based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available thanks to satellite measures (ASCAT and GPM). The methodological approach is testing in different catchments of Oltrep&#242; Pavese hilly area (northern Italy), that is representative of Italian Apeninnes environment. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.

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Envisaging post-earthquake snowmelt-induced shallow landslides under climate change

10.5194/egusphere-egu2020-12199 ◽

2020 ◽

Author(s):

Srikrishnan Siva Subramanian ◽

Xuanmei Fan ◽

Ali. P. Yunus ◽

Theo van Asch ◽

Qiang Xu ◽

...

Keyword(s):

Climate Change ◽

Prediction Models ◽

Risk Mitigation ◽

Regional Scale ◽

Numerical Approach ◽

Shallow Landslides ◽

Slope Instability ◽

Time Step ◽

Macro Scale ◽

Physically Based

Seasonal snow cover occupies around 33 % of the earth&#8217;s surface and draws the underlying landscape to serious natural hazards under climate change. The frequency of shallow landslides in seasonal cold regions is increasing, i.e., in the French Alps, Umbria in Italy, and Hokkaido in Japan. Further, tectonically active seasonally cold areas are more susceptible to spring landslides if an earthquake occurs during pre-winter. Hazard assessment and risk mitigation of snowmelt-induced landslides in such a scenario requires physically-based prediction models. However, studies focusing on the impacts of future snowmelt on shallow landslides are scarce. To comprehend these, the complex interactions between the atmosphere, hydrological, and geomechanical systems within a catchment under future climate need detailed studies. Present methods for snowmelt induced soil slope instability analysis are single-slope based and applied for individual cases. The challenge remain is to simulate the interactions between the atmosphere, hydrological, and geomechanical systems by coupling micro and macro-scale processes within a catchment for regional-scale future forecasts. In this perspective, we developed a novel spatially distributed, a physically-based numerical approach to compute slope stability within a basin, explicitly considering the atmosphere-ground, hydrology, and mechanical interactions on a day to day time step. Using this model, we envisaged future snowmelt-induced landslides under increased and decreased melt rates and post-earthquake settings. We obtained the probability density curves of these future landslides and found that under slower snowmelt rates, the occurrence probability of individual landslides remains the same, whereas, under rapid and increased snowmelt rates, the size-distribution of the landslides increase one magnitude and doubles if rapid snowmelt follows an earthquake.

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