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.

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 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 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 A hydro-climatological approach to predicting regional landslide probability using Landlab

2017 ◽  
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Sai Siddhartha Nudurupati ◽  
Christina Bandaragoda ◽  
Nicole M. Gasparini ◽  
...  
Keyword(s):  
Soil Depth ◽  
Probability Distributions ◽  
Probability Model ◽  
Subsurface Flow ◽  
Grid Cell ◽  
Bimodal Distribution ◽  
Shallow Landslide ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Infinite Slope Stability Model

Abstract. We develop a hydro-climatological approach to modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation. The physically-based model couples the infinite slope stability model with a steady-state subsurface flow representation and operates on a digital elevation model. Spatially distributed raster data for soil properties and a soil evolution model and vegetation classification from National Land Cover Data are used to derive parameters for probability distributions to represent input uncertainty. Hydrologic forcing to the model is through annual maximum recharge to subsurface flow obtained from a macroscale hydrologic model, routed on raster grid to develop subsurface flow. A Monte Carlo approach is used to generate model parameters at each grid cell and calculate probability of shallow landsliding. We demonstrate the model in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting uncertainty of soil depth and its potential long-term variability. We found elevation dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests in low elevations, an increased landslide probability with forest decline at mid elevations (1,400 to 2,400 m), and soil limitation and steep topographic controls at high alpine elevations and post-glacial landscapes. These dominant controls manifest in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similar model confidence for the three hazard maps, suggesting suitable use as relative hazard products. Validation of the model with observed landslides is hindered by the completeness and accuracy of the inventory, estimation of source areas, and unmapped landslides. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

A GEOTECHNICAL-HYDROLOGICAL APPROACH FOR DEFINING CRITICAL RAINFALL-INDUCED SHALLOW LANDSLIDES AND WARNING SYSTEM AT LARGE SCALE

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Apip Apip ◽  
Takara K ◽  
Yamashiki Y ◽  
Ibrahim A.B ◽  
Sassa K.
Keyword(s):  
Slope Stability ◽  
Soil Depth ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Saturated Soil ◽  
Slope Instability ◽  
Steady State Condition ◽  
River Catchment ◽  
Stability Model ◽  
Critical Rainfall

This study proposes a novel method that combines deterministic slope stability model and hydrological approach for predicting critical rainfall-induced shallow landslides. The method first uses the slope stability model to identify “where” slope instability will occur potentially; the catchment is characterized into stability classes according to critical soil saturation index. The critical saturated soil index is calculated from local topographic components and soil attributes. Then, spatial distribution of critical rainfall is determined based on a hydrological approach under near-steady state condition as a function of local critical saturated soil depth, slope geometric, and upstream contributing drainage areas. The critical rainfall mapping is bounded by theoretically “always stable” and “always unstable” areas. To show how the method works, observed landslides (1985-2008) and a satellite-based rainfall estimates associated with a past new shallow landslide in the Upper Citarum River catchment (Indonesia) were used to validate the model. The proposed study is useful for rainfall triggered shallow landslide disaster warning at large catchment scale. Keywords: Critical rainfall, slope stability, hydrology, shallow landslide, Citarum River catchment

scholarly journals Evaluating performances of simplified physically based models for landslide susceptibility

2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace
Keyword(s):  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Test Case ◽  
Susceptibility Analysis ◽  
Physically Based ◽  
Private And Public ◽  
Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

scholarly journals Numerical Modelling of the Topographic Wetness Index: An Analysis at Different Scales

2011 ◽  
Vol 02 (04) ◽  
pp. 476-483 ◽  
Author(s):  
Anderson Luis Ruhoff ◽  
Nilza Maria Reis Castro ◽  
Alfonso Risso
Keyword(s):  
Numerical Modelling ◽  
Topographic Wetness Index ◽  
Wetness Index
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