Assessment of Shallow Landslides Induced by Mitch Using a Physically Based Model

2016 ◽  
pp. 319-330
Author(s):  
Zonghu Liao ◽  
Kun Yang ◽  
Yang Hong ◽  
Dalia Kirschbaum
Keyword(s):  
Shallow Landslides ◽  
Physically Based Model ◽  
Physically Based

scholarly journals A model for triggering mechanisms of shallow landslides

2008 ◽  
Vol 8 (5) ◽  
pp. 1149-1159 ◽  
Author(s):  
L. Montrasio ◽  
R. Valentino
Keyword(s):  
Safety Factor ◽  
Limit Equilibrium ◽  
Back Analysis ◽  
Shallow Landslides ◽  
Physically Based Model ◽  
Analysis Process ◽  
Physically Based ◽  
Triggering Mechanisms ◽  
Set Up ◽  
The University

Abstract. Rainfall-induced shallow landslides, also called "soil slips", are becoming ever more frequent all over the world and are receiving a rising interest in consequence of the heavy damage they produce. At the University of Parma, a simplified physically based model has been recently set up for the evaluation of the safety factor of slopes which are potentially at risk of a soil slip. This model, based on the limit equilibrium method applied to an infinite slope, takes into account some simplified hypotheses on the water down-flow and defines a direct correlation between the safety factor of the slope and the rainfall depth. In this paper, this model is explained in detail and is used in a back analysis process to verify its capability to foresee the triggering instant of rainfall-induced shallow landslides for some recent case studies in the Emilia Romagna Apennines (Northern Italy). The results of the analyses and of the model implementation are finally shown.

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

2014 ◽  
Vol 2 (12) ◽  
pp. 7409-7464 ◽  
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.

Predictive Power Evaluation of a Physically Based Model for Shallow Landslides in the Area of Oltrepò Pavese, Northern Italy

2014 ◽  
Vol 32 (4) ◽  
pp. 783-805 ◽  
Author(s):  
Roberto Valentino ◽  
Claudia Meisina ◽  
Lorella Montrasio ◽  
Gian Luca Losi ◽  
Davide Zizioli
Keyword(s):  
Predictive Power ◽  
Northern Italy ◽  
Shallow Landslides ◽  
Physically Based Model ◽  
Physically Based ◽  
Power Evaluation

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.

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

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