WS 3.504
Numerical Modeling for Landslide Risk Assessment
Full Title
WS 3.504: Numerical Modeling for Landslide Risk AssessmentScheduled
TBALocation
TBAConvener
Assigned to Synthesis Workshop
---Thematic Focus
No focus definedKeywords
numerical modeling, landslides, risk assessment, geotechnical analysis, open source software
Description
Understanding and predicting landslide behavior is crucial for mitigating risks in mountainous regions. This session focuses on the application of numerical modeling techniques to assess soil behavior under stress conditions and to simulate landslide scenarios. Participants will be introduced to open source software tools such as R-slope or Avaflow, widely used in geotechnical engineering for analyzing soil-structure interactions.
The goal is to to set up a simple numerical model that represents the slope visited on the excursion session. The participants will learn how to use topografic data, INSAR-data, geological maps and laboratoric data to create a simple model close to reality to capture the behaviour of soil.
Furthermore participants will have a brief introdution to advanced numerical models, such as: coding, 3D-modelling and advanced soil constitutive models.
The session will include practical exercises where participants develop simple models to simulate landslides, evaluate soil stability, and predict potential failure mechanisms. Discussions will emphasize the interpretation of modeling results and their application in real-world hazard assessment and mitigation strategies.
Submitted Abstracts
InSAR-based Modelling and Monitoring of Permafrost-induced Deformation in Indian Himalayas
Luvkesh Attri
Ramsankaran, RAAJ
Abstract/Description
Permafrost is a crucial component of the Earth’s cryosphere, influencing global climate systems, ecosystems, and human infrastructure. While well-studied in the Arctic, the Alps, and other permafrost regions, similar studies monitoring permafrost degradation remain limited in the Himalayas. Understanding permafrost degradation in this region is important due to its implications for permafrost-related hazards such as landslides, ground instability, landscape changes and glacial lake outburst floods (GLOFs). To frequently monitor these landscapes, we employed a large-scale remote sensing approach, interferometric synthetic aperture radar (InSAR) in the Tso Kar valley, Ladakh to observe seasonal and annual ground deformation. This analysis offers insights to permafrost degradation and seasonal freeze-thaw cycle of active layer thickness (ALT). We utilized Sentinel-1A/B SAR data acquired from March 2019 to November 2023 to monitor seasonal and annual surface deformation using SBAS-InSAR approach. Two different inversion algorithms, namely least squares (LS) and weighted least squares (WLS), were applied to estimate the time-series deformation patterns. Our findings indicate that the seasonal deformation amplitude ranges from 10 to 25mm and the annual mean vertical deformation trend varies from -10 to -30mm/yr. To further characterize ground deformation, we combined data from both ascending and descending passes to derive the vertical and horizontal component of the deformation. Since line-of-sight (LOS) displacement alone cannot be directly linked to permafrost thawing or ALT changes, computing vertical deformation component is essential. Our preliminary findings indicate cumulative vertical deformation ranging from -10 to -40mm and the East-West movement between -15 to 18mm over study period. These preliminary results show noticeable variations in seasonal and annual ground deformation patterns suggesting ongoing changes in permafrost dynamics. This underscores the critical need for comprehensive studies in the Indian Himalayas to better understand permafrost dynamics, assess associated hazards, and establish long-term monitoring strategies. Given limitations of C-band SAR data, alternative SAR datasets such as NISAR and ALOS should be explored, as they may offer deeper insights for long-term monitoring. Simultaneously, there is an urgent need for extensive ground temperature monitoring to effectively study and model the current state of permafrost degradation and active layer dynamics on a spatial scale.
Influence of Water Content in Debris and Mud Flows on Superelevation: Two-Phase SPH-DEM Modelling
Philipp Friess
Vicari, Hervé; Aberg, Amanda; McArdell, Brian; Gaume, Johan
Abstract/Description
Debris and mud flows pose significant natural hazards, often resulting in severe damage and loss of life. Understanding their behavior, particularly in curved channels, is crucial for improving risk assessment and predictive models. As these flows navigate along bends, centrifugal forces cause a height difference between the inner and outer banks, a phenomenon known as superelevation. Analytical models describe this effect by relating superelevation to flow velocity, typically using a forced vortex approach. However, these models rely on simplifying assumptions, such as a linear flow surface and a rectangular cross-section, while neglecting complex rheological behaviors and solid-fluid interactions. Consequently, an empirical correction factor is introduced, though its mechanical basis remains unclear and is primarily determined through field and laboratory studies. This study enhances the forced vortex approach by integrating depth-resolved numerical simulations using a coupled SPH-DEM model, where SPH represents the fluid phase (fines and water) and DEM captures the coarse solid particles. The model is first validated against laboratory-scale superelevation experiments before being applied to analyze the effect of water content on flow behavior. Results reveal that higher water content leads to increased superelevation and influences the flow surface shape. Mud flows tend to exhibit convex upward surfaces, whereas granular debris flows display concave downward shapes. This distribution is governed by the equilibrium between boundary stresses and centrifugal forces. By balancing centrifugal forces and basal normal stresses along the channel boundary, we establish a correlation between the analytical model’s correction factor, material type, and water content. However, we notice significant variations in the correction factor throughout the bend, prompting questions about the presence of additional variables not accounted for in the model, like a run-up component. Large-scale SPH-DEM simulations of a real debris flow event at Illgraben (Switzerland) demonstrate good agreement with field data, highlighting the model’s potential for real-world applications. These findings contribute to a more accurate representation of debris flow dynamics in curved channels, improving hazard assessment and mitigation strategies.
Gerald Innocent Otim
Barbato, Giulio; Rocchi, Irene; Sorrentino, Gianmario; Trapp, Stefan; Zhelezova, Alena
Abstract/Description
The diverse and variable nature of ground conditions and soil properties presents a challenge when it comes to determining design parameters for a soil model. In these numerical analyses, one of the key properties to focus on is shear strength. It’s important to balance the number of data sets to achieve an accurate linear Mohr-Coulomb failure surface within budget and time constraints. Careful consideration of important parameters such as normal stress is necessary, and it’s recommended to use a set of three data sets in practice. However, this approach may still result in a notable amount of data variability. To evaluate this, the direct shear test was performed in accordance with ASTM D3080/D3080M-23 on loose sand of size 0.3-0.5 mm on a set of 6 normal loads (0 – 100 kPa) each with 10 repetitions. This sand was a simplified material that could be compared to alluvial fans in mountainous subsoils but equally cohesionless. By leveraging the Mohr-coulomb shear strength equation and applying an area correction to the data sets, the uncertainty in this measurement was determined using the guide to the expression of uncertainty in measurement approach. The relative expanded uncertainty obtained from normal stress ranged between 3.2 – 3.5 % while that from the shear stress was 3.9 %. When applying the shear strength equation, a combined relative expanded uncertainty of 5.3 % was observed. Furthermore, the uncertainty measurements for the angle of internal friction and cohesion were determined as 33 ± 10 and 7.5 ± 1.1 kPa respectively, with residuals exhibiting randomness. The three primary factors influencing the uncertainty in both the normal and shear stresses included reproducibility and bias on the measuring forces, and bias on the diameter of the specimen. Combinations within the data set were derived to investigate the variability in shear strength parameters. The variability in the angle of internal friction and cohesion were 34 ± 1.80 and 3.6 ± 2.0 kPa respectively. The exploration of the variability in shear strength parameters with sand demonstrated the impact on the long-term stability of a natural homogeneous slope when simulated in plaxis software.
Rockfall Hazard Assessment and Propagation Modeling: A Comparative Study of Static Block Remobilization in Manikaran, NW Himalaya, India
Raj Kiran Dhiman
Bourrier, Franck; Thakur, Mahesh
Abstract/Description
Manikaran, located in Kullu district, Himachal Pradesh, NW Himalaya, India, is a renowned tourist destination, famous for its hot springs, ancient Ram Mandir, and Gurudwara Sahib. The region has seen an exponential increase in tourists, drawn to its natural and cultural attractions. However, the area is also prone to frequent rockfall events, which pose a significant risk to public safety. A major rockfall disaster occurred in August 2015, when a rockfall hit the Gurudwara, destroying its four-story building, killing around 10 people, and injuring 15 others who were asleep in the Gurudwara’s Sarai. The town and surrounding areas are regularly affected by rockfall activity, especially during the monsoon season, making it critical to study and predict future rockfall hazards. This study combines geological field investigations, geomorphic mapping, field-based rockfall datasets (rock shape and volume), high-resolution digital elevation models (DEM) using drone survey, and numerical simulations using the open-source SICONOS software. Field investigations revealed that the August 2015 rockfall event was not due to a single rock block, but rather a chain reaction, where a primary rockfall event triggered the remobilization of static blocks along the slope’s runout path. A novel rockfall propagation model was developed to incorporate rock-rock interactions and simulate realistic rockfall events, which may better predict future hazards. This model, the first to account for the impact of pre-fragmented rock mass as a source area on static blocks in rockfall propagation, enables a comparative hazard assessment by simulating rockfall trajectories both with and without the presence of static blocks on the slope. The findings of this study offer new insights into rockfall dynamics and provide valuable predictive capabilities, which can be applied to rockfall-prone regions worldwide, particularly those with large static rock blocks in the run-out paths of moving rock blocks.
Geospatial Modelling of Landslide Susceptibility in the Himalayas: A Comparative Analysis of EBF, FR and Shannon Entropy Models
Hemant Singh Bisht
Chen, Ruishan; Rongpi, Rumi
Abstract/Description
Landslides are one of the most prevalent and destructive natural hazards in mountainous regions, causing significant damage to infrastructure, loss of life, and economic disruptions. Their frequency and severity are particularly high in ecologically fragile regions like the Himalayas, where fatal landslides commonly occur during the monsoon season. Landslide susceptibility maps are essential tools for effective land-use planning, disaster management, and risk mitigation in such regions. This study aimed to investigate and identify landslide-prone zones in the Himalayas using spatially explicit models—the Evidence Belief Function (EBF), Frequency Ratio (FR), and Shannon Entropy (SE) focusing on the Pithoragarh district of Kumaon Himalayas. A comprehensive landslide inventory was developed, which identified 366 landslide events; 70% (256) of these events were utilized for model training/testing, while the remaining 30% (110) were used for validation. The study identified and analyzed 14 key landslide conditioning factors, including topography, geology, land cover, and precipitation data. These factors were extracted from spatial databases and used to assess the relationship between environmental conditions and landslide occurrence. The results revealed that the moderate, high, and very high landslide susceptibility classes of the EBF and FR models covered approximately 39.04%, 33.10%, and 13.47% of the study area, respectively, with higher susceptibility areas concentrated along roads, especially in the Berinag, Didihat, Gangolihat, and Pithoragarh tehsils in the southern part of the district. Model performance was validated using the Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) metrics, which correlated well with landslide data. The EBF, FR, and SE models provided success rates of 73.36%, 81.1%, and 79.3%, respectively, and predictive rates of 56.6%, 61.5%, and 60.0%. The findings demonstrate that these methodologies are proficient in predicting and mapping landslide susceptibility, providing essential insights for disaster risk management and planning in the Himalayas.
