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

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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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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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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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

<p>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.</p>

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

10.5194/egusphere-egu21-13474 ◽

2021 ◽

Author(s):

Enrico D'Addario ◽

Leonardo Disperati ◽

Josè Luis Zezerè ◽

Raquel Melo ◽

Sergio Cruz Oliveira

Keyword(s):

Slope Failure ◽

Regional Scale ◽

Shallow Landslide ◽

Shallow Landslides ◽

Data Driven ◽

Field Observations ◽

Rupture Surface ◽

Slope Deposits ◽

Physically Based ◽

Engineering Geological Properties

<p>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.</p>

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Regional physically based landslide early warning modelling: soil parameterisation and validation of the results

10.5194/nhess-2017-425 ◽

2018 ◽

Author(s):

Teresa Salvatici ◽

Veronica Tofani ◽

Guglielmo Rossi ◽

Michele D'Ambrosio ◽

Carlo Tacconi Stefanelli ◽

...

Keyword(s):

Slope Stability ◽

Real Time ◽

Early Warning ◽

Regional Scale ◽

Early Warning Systems ◽

Shallow Landslides ◽

Mean Annual Precipitation ◽

Good Prediction Accuracy ◽

Physically Based ◽

Landslide Early Warning

Abstract. In this work, we apply a physically-based model, namely the HIRESSS (High REsolution Stability Simulator) model, to forecast the occurrence of shallow landslides at regional scale. The final aim is the set-up of an early warning system at regional scale for shallow landslides. HIRESSS is a physically based distributed slope stability simulator for analysing shallow landslide triggering conditions in real time and in large areas using parallel computational techniques. The software can run in real-time by assimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrological input parameters. The test area is a portion of the Valle d'Aosta region, located in North-West Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. of Dora Baltea's river floodplain to 4810 m a.s.l. of Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features lead to a high hydrogeological hazard in the whole territory, as mass movements interest the 70 % of the municipality areas (mainly shallow rapid landslides and rock falls). 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 landslides formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed with 12 survey points. The data collected contributes to generate input map of parameters for HIRESSS model. In order to take into account the effect of vegetation on slope stability, the contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. The model was applied in back analysis on two past events that have affected Valle d'Aosta region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, has provided good results and a good prediction accuracy of the HIRESSS model both from temporal and spatial point of view. A statistical analysis of the HIRESSS outputs in terms of failure probability has been carried out in order to define reliable alert levels for regional landslide early warning systems.

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From slope- to regional-scale shallow landslides susceptibility assessment using TRIGRS

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-2-7409-2014 ◽

2014 ◽

Vol 2 (12) ◽

pp. 7409-7464 ◽

Cited By ~ 1

Author(s):

M. Bordoni ◽

C. Meisina ◽

R. Valentino ◽

M. Bittelli ◽

S. Chersich

Keyword(s):

Regional Scale ◽

Field Measurements ◽

Shallow Landslides ◽

Susceptibility Assessment ◽

Physically Based Model ◽

Distributed Models ◽

Landslides Susceptibility ◽

Physically Based ◽

Monitoring Activity

Abstract. Rainfall-induced shallow landslides are common phenomena in many parts of the world, affecting cultivations and infrastructures and causing sometimes human losses. Assessing the shallow landslides susceptibility is fundamental for land planning at different scales. This work defines a reliable methodology to extend the slope stability analysis from the local to the regional scale by using a well established physically-based model (TRIGRS-Unsaturated). The model is applied at first for a sample slope and then to the surrounding area of 13.4 km2 in Oltrepo Pavese (Northern Italy). In order to obtain more reliable input data for the model, a long-term hydro-meteorological monitoring has been carried out at the sample slope, that has been assumed as representative of the study area. Field measurements allowed for identifying the triggering mechanism of shallow failures and were used to calibrate the model. After obtaining modelled pore water pressures at the slope scale consistent with those measured during the monitoring activity, more reliable trends have been modelled also for past landslide events, as the April 2009 event that has been assumed as benchmark. The shallow landslides susceptibility assessment obtained using TRIGRS-Unsaturated for the benchmark event appears good for both the monitored slope and the whole study area, with better results if a pedological instead of geological zoning is considered at regional scale. The scheme followed in this work allows for obtaining better results of shallow landslides susceptibility assessment in terms of reduction of overestimation of unstable areas with respect to other distributed models applied in the past.

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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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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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