SlideforMap – a regional scale probabilistic model for shallow landslide onset analysis and protection forest management

2020 ◽  
Author(s):  
Feiko van Zadelhoff ◽  
Luuk Dorren ◽  
Massimiliano Schwarz
Keyword(s):  
Protective Effect ◽  
Probabilistic Model ◽  
Limit Equilibrium ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Soil Parameters ◽  
Root Reinforcement ◽  
Wetness Index ◽  
Wide Range

<p>In the Alps, shallow landslides repeatedly pose a risk to infrastructure and residential areas. For example, dozens of shallow landslides led to the destruction of several houses, killed one person and led to the evacuation of more than 50 houses, multiple road closure for several days in Austria in Nov. 2019. To analyse and predict the risk posed by shallow landslide, a wide range of scientific methods and tools for modelling disposition and runout exists, both for local and regional scale analyses. Most of these tools, however, do not take the protective effect, i.e. root reinforcement, of vegetation into account. Therefore, we developed SlideforMap (SfM), a probabilistic model that allows for a regional assessment of the disposition of shallow landslides while considering the effect of different scenarios of forest cover and management and of rainfall intensity.</p><p>SfM uses a probabilistic approach by attributing landslide surface areas, randomly selected from a gamma shaped distribution published by Malamud (2004), to random coordinates within a given study area. For each generated landslide, SfM calculates a factor of safety using the limit equilibrium infinite slope approach. Thereby, the relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are defined by normal distributions based on mean and standard deviation values representative for the study area. Hydrology is implemented using a stationary flow approach and the topographical wetness index. Root reinforcement is computed based on root distribution and root strength derived from single tree detection data and the root bundle model of Schwarz et al. (2013). Finally, the fraction of unstable landslides to the number of generated slides per raster cells is calculated and used as an index for landslide onset susceptibility. Inputs for the model are a Digital Terrain Model, a topographical wetness index and a file containing positions and sizes of trees.</p><p>Validation of SfM has been done by calculating the AUC (Metz, 1978) for three test areas with a reliable landslide inventory in Switzerland. These test areas are in mountainous areas ranging 0.5 – 7.5 km<sup>2</sup> with varying mean slope gradients (18 - 28°). The density of inventoried historical landslides varied from 0.4 – 59 slides/km<sup>2</sup>. This resulted in AUC values between 0.64 and 0.86. Our study showed that the approach used in SfM can reproduce shallow landslide onset susceptibility on a regional scale observed in reality.</p><p>SfM was developed to quantify the stabilizing effect of vegetation at regional scale and localize potential areas where the protective effect of forests can be improved. A first version of the model will be released in 2020 by the ecorisQ association (www.ecorisq.org).</p>

scholarly journals Introducing SlideforMap; a probabilistic finite slope approach for modelling shallow landslide probability in forested situations

2021 ◽  
Author(s):  
Feiko Bernard van Zadelhoff ◽  
Adel Albaba ◽  
Denis Cohen ◽  
Chris Phillips ◽  
Bettina Schaefli ◽  
...  
Keyword(s):  
Soil Depth ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Topographic Wetness Index ◽  
Root Reinforcement ◽  
Wetness Index ◽  
Tree Detection ◽  
Single Tree ◽  
Soil Cohesion

Abstract. Worldwide, shallow landslides repeatedly pose a risk to infrastructure and residential areas. To analyse and predict the risk posed by shallow landslides, a wide range of scientific methods and tools to model shallow landslide probability exist for both local and regional scale However, most of these tools do not take the protective effect of vegetation into account. Therefore, we developed SlideforMap (SfM), which is a probabilistic model that allows for a regional assessment of shallow landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SfM uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated from observed events. For each randomly generated landslide, SfM calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are assigned to the generated landslides from normal distributions based on mean and standard deviation values representative for the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed based on root proximity and root strength derived from single tree detection data. Ultimately, the fraction of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. Inputs for the model are a digital elevation model, a topographic wetness index and a file containing positions and dimensions of trees. We performed a calibration of SfM for three test areas in Switzerland with a reliable landslide inventory, by randomly generating 1000 combinations of model parameters and then maximising the Area Under the Curve (AUC) of the receiver operation curve (ROC). These test areas are located in mountainous areas ranging from 0.5–7.5 km2, with varying mean slope gradients (18–28°). The density of inventoried historical landslides varied from 5–59 slides/km2. AUC values between 0.67 and 0.92 indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modeling of shallow landslide probability are the soil depth, soil cohesion and the root reinforcement. Further, the use of single tree detection in the computation of root reinforcement significantly improved model accuracy compared to the assumption of a single constant value of root reinforcement within a forest stand. In conclusion, our study showed that the approach used in SfM can reproduce observed shallow landslide occurrence at a catchment scale.

scholarly journals Tree-roots control of shallow landslides

2017 ◽  
Author(s):  
Denis Cohen ◽  
Massimiliano Schwarz
Keyword(s):  
Slope Stability ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Tree Roots ◽  
Root Reinforcement ◽  
Apparent Cohesion ◽  
Root Strength ◽  
Tension And Compression ◽  
Weak Zones ◽  
Landslide Triggering

Abstract. Tree roots have long been recognized to increase slope stability by reinforcing the strength of soils. Slope stability models include the effects of roots by adding an apparent cohesion to the soil to simulate root strength. No model includes the combined effects of root distribution heterogeneity, stress-strain behavior of root reinforcement, or root strength in compression. Recent field observations, however, indicate that shallow landslide triggering mechanisms are characterized by differential deformation that indicates localized activation of zones in tension, compression, and shear in the soil. These observations contradict the common assumptions used in present models. Here we describe a new model for slope stability that specifically considers these effects. The model is a strain-step discrete element model that reproduces the self-organized redistribution of forces on a slope during rainfall-triggered shallow landslides. We use a conceptual sigmoidal-shaped hillslope with a clearing in its center to explore the effects of tree size, spacing, weak zones, maximum root-size diameter, and different root strength configurations. The model is driven by root data of Norway spruce obtained from laboratory and field measurements. Simulation results indicate that tree roots can stabilize slopes that would otherwise fail without them and, in general, higher root density with higher root reinforcement results in a more stable slope. Root tension provides more resistance to failure than root compression but roots with both tension and compression offer the best resistance to failure. Lateral (slope-parallel) tension can be important in cases when the magnitude of these forces is comparable to the slope-perpendicular tensile forces. In these cases, lateral forces can bring to failure tree-covered areas with high root reinforcement. Slope failure occurs when downslope soil compression reaches the soil maximum strength. When this occurs depends on the amount of root tension upslope in both the slope-perpendicular and slope-parallel directions. Roots in tension can prevent failure by reducing soil compressive forces downslope. When root reinforcement is limited, hillslopes form a crack parallel to the slope near its top. Simulations with roots that fail across this crack always resulted in a landslide. Slopes that did not form a crack could either fail or remain stable, depending on root reinforcement. Tree spacing is important for the location of weak zones but tree location on the slope (with respect to where a crack opens) is as important. Finally, for the specific cases tested here, large roots, greater than 20 mm, are too few too contribute significantly to root reinforcement. Omitting roots larger than 8 mm predicted a landslide when none should have occurred. Intermediate roots (5 to 20 mm) appear to contribute most to root reinforcement and should be included in calculations. To fully understand the mechanisms of shallow landslide triggering requires a complete re-evaluation of the traditional apparent-cohesion approach that does not reproduce the incremental loading of roots in tension or in compression. Our model shows that it is important to consider the forces held by roots in a way that is entirely different than done thus far. Our work quantifies the contribution of roots in tension and compression which now finally permits to analyze more realistically the role of root reinforcement during the triggering of shallow landslides.

scholarly journals Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS

2015 ◽  
Vol 15 (5) ◽  
pp. 1025-1050 ◽  
Author(s):  
M. Bordoni ◽  
C. Meisina ◽  
R. Valentino ◽  
M. Bittelli ◽  
S. Chersich
Keyword(s):  
Input Data ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Local Scale ◽  
Shallow Landslides ◽  
Sensitivity Analyses ◽  
Site Specific ◽  
Landslide Triggering

Abstract. Rainfall-induced shallow landslides are common phenomena in many parts of the world, affecting cultivation and infrastructure and sometimes causing human losses. Assessing the triggering zones of shallow landslides is fundamental for land planning at different scales. This work defines a reliable methodology to extend a slope stability analysis from the site-specific to local scale by using a well-established physically based model (TRIGRS-unsaturated). The model is initially applied to a sample slope and then to the surrounding 13.4 km2 area in Oltrepò Pavese (northern Italy). To obtain more reliable input data for the model, long-term hydro-meteorological monitoring has been carried out at the sample slope, which has been assumed to be representative of the study area. Field measurements identified the triggering mechanism of shallow failures and were used to verify the reliability of the model to obtain pore water pressure trends consistent with those measured during the monitoring activity. In this way, more reliable trends have been modelled for past landslide events, such as the April 2009 event that was assumed as a benchmark. The assessment of shallow landslide triggering zones obtained using TRIGRS-unsaturated for the benchmark event appears good for both the monitored slope and the whole study area, with better results when a pedological instead of geological zoning is considered at the regional scale. The sensitivity analyses of the influence of the soil input data show that the mean values of the soil properties give the best results in terms of the ratio between the true positive and false positive rates. The scheme followed in this work allows us to obtain better results in the assessment of shallow landslide triggering areas in terms of the reduction in the overestimation of unstable zones with respect to other distributed models applied in the past.

scholarly journals Use of the deterministic approach for the landslide susceptibility mapping, Vsetínské vrchy Highland, Czechia

Geografie ◽  
2008 ◽  
Vol 113 (1) ◽  
pp. 48-60
Author(s):  
Jan Klimeš
Keyword(s):  
Regional Scale ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Landslide Susceptibility Mapping ◽  
Deterministic Approach ◽  
Landslide Occurrence ◽  
Outer Western Carpathians ◽  
Susceptibility Model ◽  
The City

The work is seeking accurate spatial prediction of shallow landslide occurrence on regional scale, through field mapping of present day landslide activity and identifying the most susceptible parts of the study area by the means of the SINMAP susceptibility model. The SINMAP ArcView extension is also used to characterize shallow landslide and to study conditions for their spatial occurrence. The study area lays in the Outer Western Carpathians east of the city of Vsetín and it was subject of an avalanche like occurrence of mostly shallow landslides during the floods caused by heavy rains between the 4th and 8th and between the 17th and 21st of July, 1997.

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

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>

scholarly journals Tree-root control of shallow landslides

2017 ◽  
Vol 5 (3) ◽  
pp. 451-477 ◽  
Author(s):  
Denis Cohen ◽  
Massimiliano Schwarz
Keyword(s):  
Slope Stability ◽  
Traditional Approach ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Tree Roots ◽  
Root Reinforcement ◽  
Apparent Cohesion ◽  
Root Strength ◽  
Weak Zones ◽  
Landslide Triggering

Abstract. Tree roots have long been recognized to increase slope stability by reinforcing the strength of soils. Slope stability models usually include the effects of roots by adding an apparent cohesion to the soil to simulate root strength. No model includes the combined effects of root distribution heterogeneity, stress-strain behavior of root reinforcement, or root strength in compression. Recent field observations, however, indicate that shallow landslide triggering mechanisms are characterized by differential deformation that indicates localized activation of zones in tension, compression, and shear in the soil. Here we describe a new model for slope stability that specifically considers these effects. The model is a strain-step discrete element model that reproduces the self-organized redistribution of forces on a slope during rainfall-triggered shallow landslides. We use a conceptual sigmoidal-shaped hillslope with a clearing in its center to explore the effects of tree size, spacing, weak zones, maximum root-size diameter, and different root strength configurations. Simulation results indicate that tree roots can stabilize slopes that would otherwise fail without them and, in general, higher root density with higher root reinforcement results in a more stable slope. The variation in root stiffness with diameter can, in some cases, invert this relationship. Root tension provides more resistance to failure than root compression but roots with both tension and compression offer the best resistance to failure. Lateral (slope-parallel) tension can be important in cases when the magnitude of this force is comparable to the slope-perpendicular tensile force. In this case, lateral forces can bring to failure tree-covered areas with high root reinforcement. Slope failure occurs when downslope soil compression reaches the soil maximum strength. When this occurs depends on the amount of root tension upslope in both the slope-perpendicular and slope-parallel directions. Roots in tension can prevent failure by reducing soil compressive forces downslope. When root reinforcement is limited, a crack parallel to the slope forms near the top of the hillslope. Simulations with roots that fail across this crack always resulted in a landslide. Slopes that did not form a crack could either fail or remain stable, depending on root reinforcement. Tree spacing is important for the location of weak zones but tree location on the slope (with respect to where a crack opens) is as important. Finally, for the specific cases tested here, intermediate-sized roots (5 to 20 mm in diameter) appear to contribute most to root reinforcement. Our results show more complex behaviors than can be obtained with the traditional slope-uniform, apparent-cohesion approach. A full understanding of the mechanisms of shallow landslide triggering requires a complete re-evaluation of this traditional approach that cannot predict where and how forces are mobilized and distributed in roots and soils, and how these control shallow landslides shape, size, location, and timing.

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

2015 ◽  
Vol 15 (9) ◽  
pp. 2091-2109 ◽  
Author(s):  
L. Schilirò ◽  
C. Esposito ◽  
G. Scarascia Mugnozza
Keyword(s):  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Landslide Event ◽  
Wide Range ◽  
Physically Based ◽  
Triggering Process ◽  
Landslide Triggering ◽  
Northeastern Sicily

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.

A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale

Landslides ◽  
2017 ◽  
Vol 14 (5) ◽  
pp. 1731-1746 ◽  
Author(s):  
Diana Salciarini ◽  
Giulia Fanelli ◽  
Claudio Tamagnini
Keyword(s):  
Probabilistic Model ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Landslide Prediction

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

2015 ◽  
Vol 3 (5) ◽  
pp. 2975-3022 ◽  
Author(s):  
L. Schilirò ◽  
C. Esposito ◽  
G. Scarascia Mugnozza
Keyword(s):  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Landslide Event ◽  
Wide Range ◽  
North Eastern ◽  
Physically Based ◽  
Triggering Process ◽  
Landslide Triggering

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 has 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 TRIGRS numerical model. For the calibration of the model, a back-analysis of the landslide event occurred in the study area (located SW of Messina, north-eastern 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 the 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 (3–6 h) rainfall events having a relatively low return period (e.g. 10 years) can trigger numerous slope failures. On the contrary, for the same rainfall amount, the daily simulations overestimate the instability conditions. The tendency of shallow landslides to trigger in this area agrees with the high number of landslide/flood events occurred in the past and summarized in this paper by means of archival researches. 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.

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

2018 ◽  
Vol 18 (7) ◽  
pp. 1919-1935 ◽  
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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