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FS 3.147

Monitoring and Modeling of Landslides

Details

Full Title
FS 3.147: Monitoring, Assessment, and Multi-Scale Modeling of Landslides: Approaches for Hazard Analysis
Scheduled
TBA
Location
TBA
Convener
Barbara Schneider-Muntau
Co-Conveners
Christina Rechberger, Christine Fey,
Assigned to Synthesis Workshop
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Thematic Focus
Hazards, Monitoring, Multi-scale Modeling, Remote Sensing
Keywords
Natural Hazards, Landslides, Monitoring, Modeling

Description

Landslides such as rock- and soil slides, rockfalls, and debris flows, are significant geological processes that pose risks to infrastructure, and human lives. Effective management of these hazards relies on a comprehensive methodological approach with monitoring, assessment, and multi-scale modeling, which enables a the understanding of the dynamics of landslides. Advances in sensor technologies, computational modeling, and data integration enhance predictive accuracy and early warning capabilities. Such interdisciplinary efforts are critical for mitigating the impacts of landslides. The monitoring involves the continuous or periodic collection of data to track slope stability, movement dynamics, and environmental conditions. Advanced techniques include, but are not limited to: – Ground-Based Methods, such as GNSS and Total Stations and inclinometer measurements to detect movement in slopes. – Remote Sensing, such as LiDAR, InSAR or UAVs, to detect surface changes over time. – Geotechnical Sensors, such as piezometers, strain gauges, and accelerometers to collect real-time data. The assessment focuses on understanding the triggering mechanisms, susceptibility, and potential impacts of landslides, which includes:

Hazard analysis according to inventory mapping and susceptibility models.
Triggering Factors, like climatic influences, changes in pore water pressure, seismic events and anthropogenic activities, as excavation or deforestation.
The modeling of landslides across spatial and temporal scales bridges monitoring data with predictive capabilities.

Approaches include for example empirical models, physical models, numerical models or data driven models.

Submitted Abstracts

ID: 3.8195

Numerical multiscale modelling and monitoring of landslide hazard in Georgia

Aida Mehrpajouh
Engel, Jens

Abstract/Description

For landslides over Georgia Upper Svaneti, this study incorporates multi-scale numerical modeling, hazard assessment, and monitoring. After using FSLAM to identify high-risk regions, we used pore water pressure and surface deformation sensors in the field. RAMMS simulations are used to study semi-empirical models in various scenarios. The results provide a robust basis for hazard planning in areas susceptible to landslides by emphasizing the importance of slope geometry and pore pressure. The findings underline the importance scenario-based modeling and continuous monitoring to risk assessment and mitigation methods. In mountainous regions susceptible to landslides and debris flows, this method can help early warning systems for long-term hazard management.

ID: 3.8662

Complex spatial distribution and control of seismic landslides on the Tibetan Plateau

Lijun Su

Abstract/Description

Since 2000, there are 12 earthquakes that trigger more than 200000 landslides on the Tibetan Plateau (TP), causing disastrous impacts on human society and belonging to important driving forces in regional evolution. The rapid development of multiple advanced techniques and more relevant studies have contributed to much progress in understanding seismic landslides (SLs), but a synoptic survey that combines the insights of related studies to build a comprehensive understanding of SLs on the Tibetan Plateau is currently lacking. Here, we adopt recent landslides triggered by the 2005 Kashmir (Mw 7.6), 2008 Wenchuan (Mw 7.9), 2010 Yushu (Mw 6.9), 2013 Lushan (Mw 6.6), 2013 Minxian (Mw 5.5), 2015 Gorkha (Mw 7.8), 2017 Jiuzhaigou (Mw 6.5), 2017 Nyingchi (Mw 6.5), 2022 Maerkang (Mw 5.8) 2022 Lushan (Mw 5.8), 2022 Luding (Mw 6.6) and 2023 Jishishan (Mw 5.9) earthquakes on the Tibetan Plateau to illustrate the complex spatial distribution and control of SLs on the TP. The results show the seismic events are mainly concentrated in the eastern and southern margins of the TP, especially in the eastern margin of the Bayan Har fault block, as half of the total events are concentrated in the block; the SL distributions of thrust earthquakes follow the hanging wall effect, tectonic and geomorphic controls of SL distributions from different scales. As seismic magnitudes increase, the affected SL area, total SL number, total SL area and volume all follow an exponential growth. Compared to other earthquakes worldwide, the TP is more sensitive to SLs than other places, and slight seismic shaking (PGA < 0.1g) can trigger SLs; the landslide size distributions of the TP are similar to those of other earthquakes worldwide. SLs on the TP need longer recovery periods, approximately 3~10 times those of coastal earthquakes

ID: 3.9618

The LANDAM project: characterization, forecast and management of landslides damming rivers.

Sara Savi
Meisina, Claudia; Francioni, Mirko; Sciarra, Nicola; Revellino, Paola; Focareta, Mariano; Cencetti, Corrado

Abstract/Description

The LANDAM project focuses on the study of landslides that have the potential to dam the valley river. These landslides are difficult to forecast with statistical return intervals and can develop in secondary hazardous events, such as lake outburst floods, which may add risk to infrastructures and communities living in the downstream valley. The LANDAM project has three main objectives: 1) Characterization: it wants to create a database of all the known landslides-dams with information regarding the landslide, the dam, the river, and the eventual formed lake. The database will be open and will bring to a new landslide classification better suitable for the emergency management; 2) Forecast: by using selected criterium (e.g., active landslides, satellite data, InSAR data, etc.) it wants to locate all possible landslides that may evolve into natural dams to prevent the possible associated risks. Few selected case-studies will be further investigated to develop risk scenarios, based on the expected landslide volume and modelled runout, as well as on the modeled lake formation and eventual lake outburst flood. This will serve as base for the last objective, which is the 3) Emergency management plan in case of the event occurrence. This last phase aims at developing guidelines for intervention in case of signs of failure in the dam would become visible. The project is led by the University of Perugia with the help of 4 other Italian Universities and the MAPSAT society. Each partner will bring its own expertise and competences for the development of the above-mentioned objectives. The outcomes of the project are expected to be of primary importance to improve our knowledge of this particular type of landslide, and to provide solid basis for the management of the risks to it associated with. The research is part of the RETURN Partnership Project, Spoke 2 – Ground Instabilities (CUP B53C22004020002).

ID: 3.11119

Machine Learning-Based Analysis of Rheological Properties of Debris Flow and Their Impact on Pipeline Infrastructure: A Case Study in Cameron Highlands

Afnan Ahmad
Ali Khan, Mudassir; Sumair, Muhammad; Kumar, Manoj; Anggraini, Vivi

Abstract/Description

Debris flows pose significant geohazards to critical infrastructure, particularly water, oil, and gas pipelines in mountainous regions. Understanding the impact forces exerted by debris flows on pipelines is essential for enhancing their resilience. This study investigates the relationship between debris flow rheology—including shear stress, shear strain, viscosity, and normal stress—and the resulting impact forces on pipelines. The findings contribute to improved pipeline design, reducing failure risks and ensuring the safe operation of energy systems. In this study, experimental data were collected from debris flow samples with solid volume fractions ranging from 0.20 to 0.80 (S0–S7). The rheological properties were analyzed using a digital hybrid rotational rheometer with vane rotor and parallel plate geometry systems. The collected samples were prepared from reconstituted debris flow sediments located in Cameron Highland, Malaysia. The findings of the study indicate that increasing solid volume fraction (Cv) enhances yield stress and viscosity, exhibiting non-Newtonian behavior consistent with the Herschel-Bulkley model. The consistency coefficient (k) ranged from 0.00035 to 10.43 Pa·sⁿ, while the pseudoplastic index (n) varied from 0.16 to 1.91. While the yield stress was notably influenced by substituting 6% of coarser particles with finer material in samples S3 and S4, highlighting the critical role of particle size in debris flow mobilization. Moreover, machine learning techniques, including Random Forest and Gradient Boosting Machines (GBM), were employed to predict impact forces using experimental data. The Random Forest model demonstrated high predictive accuracy (RMSE = 0.12, R² = 0.95), while the GBM model achieved RMSE = 0.15 and R² = 0.93. These results emphasize the significant influence of rheological properties on impact forces and highlight the potential of data-driven modeling in geohazard mitigation. By integrating experimental rheological analyses with predictive modeling, this research offers valuable insights for improving pipeline resilience in debris flow-prone regions. The findings support the incorporation of rheological parameters into infrastructure design, aligning with sustainable engineering practices. Future work will focus on real-time monitoring and early warning systems to further enhance infrastructure safety.

ID: 3.11484

A Revision of the 1987 Parraguirre Ice-Rock Avalanche in the Semi-Arid Andes of Chile

Johannes Fürst
Farías-Barahona, David; Bruckner, Thomas; Scaff, Lucia; Mergili, Martin; Montserrat, Santiago; Peña, Humberto

Abstract/Description

Chile faces high vulnerability to mountain hazards along the Andean Cordillera. As climate change and urban development intensify, the frequency and impact of destructive debris flows are anticipated to rise. To inform mitigation and adaptation strategies, it is imperative to understand the characteristics of historical events in this region. A notable example is the Parraguirre rock avalanche that occurred on November 29, 1987, which transformed into a catastrophic debris flow, propagating 50 kilometers down-valley and causing severe damage and loss of human lives. The high destructive power is attributed to the considerable amount of water involved. Yet, the source of this water remains largely unidentified. Further unknowns are the initial trigger volume and the total mass transfer down the valley.

In this study, we revisit the past event by integrating new insights from remote sensing, climate and hydrological records as well as process-based modelling. Our results suggest important corrections for the trigger volume, the total fluid flood volume and a first estimate of the solid mass transfer out of the Parraguirre catchment. Moreover, we find that the elevated water content cannot be solely attributed to the entrainment of soil moisture and snow cover. It requires a considerable contribution from another source – likely in form of glacier ice. Furthermore, our simulations corroborate the damming hypothesis of Río Colorado, thereby reconciling the observations of multiple waves as well as on arrival times and run-out distance.

ID: 3.12014

Deciphering Present and Future Land use Land cover Change Effect on Landslide Susceptibility in Dharamshala Region, Himachal Pradesh

Ranjeet Verma
Kaur, Harsimran; Dwivedi, Amrita

Abstract/Description

Dharamshala is a major tourist destination and the fastest-growing urban center, situated in the picturesque Kangra Valley, Himachal Pradesh. In recent years, the region is experiencing rapid population growth demanding more infrastructural development, deforestation due to expansion of agricultural lands which significantly altering land use land cover (LULC). These changes disrupt slope stability and increase the likelihood of landslides occurrences. LULC changes are more dynamic factors among other causative factors and play a significant role in triggering landslides in geologically sensitive mountainous areas. Therefore, the study aims to examine and analyze the impact of LULC changes on landslide susceptibility in Dharamshala Region using remote sensing, Geographical Information System (GIS) and machine-learning techniques. The study will also assess the impact of landslide conditioning factors such as slope, aspect, lithology, vegetation cover, distance from settlements, distance from roads, and rainfall on landslide occurrences. Multi-temporal satellite imagery will be utilized to quantify changes in LULC classes. Cellular Automata-Artificial Neural Network (CA-ANN) model will be used to predict future LULC changes and landslide-prone areas. The findings of this study will highlight the need to incorporate LULC change assessment in landslide risk assessment and the potential use of machine learning models to enhance early warning systems in mountainous regions. The study will also provide insights to policymakers, planners, and stakeholders to focus on the need to monitor LULC changes to minimize its adverse impact on landslides in mountainous regions.

ID: 3.12101

What can we detect using SB-InSAR data? – The slow degradation of Tyrolean mountains.

Maria Honisch
Branke, Johannes; Keuschnig, Markus; Schneider-Muntau, Barbara

Abstract/Description

Mountain regions are especially prone to the increasing impact of mass-movement processes. Anthropogenic forced climate change increasingly triggers landslides due to changes in precipitation occurrence and magnitude and hence in pore-water pressure. This endangers valley-confined infrastructure and livelihoods. With respect to two case studies, the advantages and limitations of an additional application of satellite-based interferometry synthetic aperture radar (SB-InSAR) data to the existing methods, such as laser scanning (LiDAR), photogrammetry, differential global navigation systems (DGNSS), electric resistivity tomography (ERT), seismic and inclinometer measurements, are discussed. SB-InSAR data exists since 1992 and covers nearly the whole globe. Depending on the mean displacement rates of landslides, SB-InSAR data can be used to monitor surface deformations. As the case studies “Reissenschuh” and “Padauner Berg” in Tyrol will show, the complementary usage of SB-InSAR data enhances process delineation, provides additional data coverage in remote areas and a high temporal resolution due to repeated measurements every five days.

ID: 3.12325

Investigation of landslide and rockfall impact against mitigation structures: a DEM- based study in an irregular mountainous terrain

Debayan Bhattacharya

Abstract/Description

Geohazards such as landslides, rockfalls, and debris flows have become quite prevalent in the geo-dynamically active Himalayan region, which accounts for nearly 76% of the landslide-prone area of the Indian subcontinent. These catastrophic events result in severe casualties and economic losses, often causing extensive structural failures. Characterized by the rapid downhill movement of granular materials, these high-energy flows generally occur due to the complex undulating terrain, necessitating effective mitigation strategies to be designed in the flow path. Conventional countermeasures based on classical earth pressure theories often overlook the dynamic nature of granular flow and impact forces on protective structures. Although existing literature sheds some light on this aspect, the effect of particle morphology – irregular realistic particle shapes with topographic undulations – rugged mountainous terrain on granular kinematics and flow-barrier interactions remains largely unexplored.
The present research employs a 3D Discrete Element Method (DEM)-based micromechanical framework to systematically examine the impact of dry granular flow on realistic mountainous terrain, incorporating two types of mitigation structures: rigid barriers and slit dams. Analysis of granular flow within complex, uneven terrain reveals intricate flow behaviours, such as turning and coalescence, which more accurately replicate real hazard scenarios, while idealistic inclined channelized flows are used for benchmarking the study. Using Wadell’s true sphericity as a measure of particle morphology, the study reveals that with a decrease in the sphericity of particles, flow resistance increases, leading to a reduction in flow velocity. Also, the peak of kinetic energy has been observed to be delayed and attenuated with increasing flow resistance. Similar trends have been observed with force-chain analysis (micro-response), highlighting how contact force distributions influence the total impact force (macro-response) that will eventually govern the design strategy of mitigation structures. The findings provide critical insights into how particulate behaviour at the microscale affects large-scale impact dynamics of granular mass flows. By bridging this gap, the study contributes to optimizing the design and placement of mitigation structures, enhancing their ability to dissipate impact energy and improve the overall resilience of the human commune against geophysical mass flows.

ID: 3.12855

Low-cost system for real-time measurement of ground movements in remote areas

Thomas Schmiedinger
Schafferer, Martin; Keßler, Bernarda; Brockschmidt, Sophia; Mandl, Bernhard

Abstract/Description

Assessment of ground movement is an important task in the field of geomonitoring. The data can be used to continuously monitor unstable areas, understand underlying physical principles, and to develop forecasting models. Therefore, it is crucial, to collect movement data at an adequate temporal and spatial resolution. Recent and ongoing advancement in the field of sensing devices and IoT in general provide a solid technological foundation which can be applied in geomonitoring. However, it is crucial to adopt these systems to the specific requirements of alpine regions (e.g. environmental conditions, accessibility etc.).
The aim of the work is to develop a low-cost system for assessing ground movement remotely and in real-time.
The system consists of several mobile units and a fixed gateway. A local network is established which enables the transmission of sensor data from the mobile units to the gateway. The gateway transmits the assessed data to an online database for further processing. Gateway and mobile units are equipped with a multiband GNNS receiver for position tracking in sub-centimeter level. The mobile units are equipped in addition with a 6-axis IMU allowing the continuous measurement of acceleration and orientation.
Movement data was successfully recorded by the mobile sensor units and transmitted to the gateway. Data was stored at the gateway as well as transmitted to the cloud storage enabling remote access of movement data. The combined approach based on GNNS and IMU data enables a robust system for accessing movement behavior.
Digital technologies as IoT enables a novel approach in geomonitoring. The combination of miniaturized sensors in combination with microcontrollers enables the realization of small measurement units which can be easily deployed also in harsh environments. The system will be scaled and tested for different applications in the geomonitoring field.

ID: 3.12880

Freeze-thaw cycles as a triggering factor for rockfalls in the upper Soča Valley (NW Slovenia) on limestone cliffs

Milan Kobal
Jemec Auflič, Mateja

Abstract/Description

The morphology of the slopes, the geological and tectonic conditions and the climatic diversity contribute significantly to the high rockfall potential in the Slovenian Alps and other mountain regions around the world. In this study, the influence of freeze-thaw cycles on the occurrence of rockfall in the upper Soča Valley on limestone is analysed. The aim of the analysis was to identify periods in which fluctuations in air temperature cause pressure fluctuations in the rock that lead to volumetric expansion of fluids, which can contribute to the destabilisation of rocks and cliffs. A dataset from the Slovenian Infrastructure Agency, which contains records of rockfalls along the regional road R1-206/1029 (Trenta–Bovec) between 2015 and 2024. The temperature data comes from meteorological stations near the analysed test site. For each rockfall event, we interpolated temperature series based on nearby meteorological stations. We considered the altitude and distance to the rockfall location, with the closer stations having a greater influence. The definition of freeze-thaw cycles is based on different time periods in which the temperature fluctuates above and below the freezing point. The analysis summarises the relationship between the frequency of rockfall events and 1) the average temperatures, 2) the standard deviations of the temperatures and 3) the number of freeze-thaw cycles over periods of one week, two weeks, one month and three months prior to the rockfall events. The total length of the road is 22,124 metres, with a total of 1,045 recorded events over a period of 3,468 days. The rockfall frequency along the entire road section is therefore 0.301 events per day, or 0.014 events per day when converted to a length of 1 kilometre. The highest number of events was recorded in April (121), followed by May (116) and March (101). The lowest number of events was recorded in November (64). After August (77), a slight increase in rockfall frequency can be observed in September (82) and October (79). The results contribute to a better understanding of the relationship between temperature changes and the occurrence of rockfalls and have practical implications for risk assessment and the planning of protective measures in rockfall-prone areas.

ID: 3.12894

Investigating topographic and morphological differences between shallow landslides in forests and open land using a semi-automatic mapping method with bi-temporal airborne laser scanning data

Lotte De Vugt
Zieher, Thomas; Schneider-Muntau, Barbara; Adams, Marc; Perzl, Frank; Rutzinger, Martin

Abstract/Description

Different land cover types have different effects on the occurrence and processes of shallow landslides (translational earth and debris slides with a depth < 2 m). For a thorough shallow landside susceptibility and hazard assessment it is important to have a comprehensive understanding of the effects of land cover on slope stability. However, especially the effects of forest cover and its structure on the processes of shallow landslides are still largely unknown. Due to the bias of existing inventories against landslides in forests and the inability of commonly used analysis datasets (e.g., aerial imagery) to penetrate the forest canopy, most studies are unable to capture differences between landslides in forests and open land. Previous studies on landslide mapping instead used laser scanning data datasets, which are capable of capturing landslide signs beneath a forest canopy. In recent years it also became possible to use bi-temporal datasets for these analyses. Use of such datasets could provide not only a more complete picture of landslides that occurred under the forest canopy, but also provide more accurate representations of their topographic signatures. This study provides insights into how forest cover and structure affect the topography (e.g., slope values within the landslide) and morphology (e.g., their area and shape characteristics) of shallow landslide scarps. This is investigated using bi-temporal Airborne Laser Scanning (ALS) datasets. Based on the resulting Difference of Digital elevation models (DoD), a semi-automatic detection algorithm was developed to map landslides under forest canopy. The resulting mapped scarp segments were classified based on their canopy cover and used to analyse differences between landslides in forests and open land, using their topographic profiles and morphological features. The results show that there are significant differences between landslides in forests and open land. Landslides in forests within the study area are on average significantly smaller (23 m2) than those outside forests (33 m2) and have a narrower profile, with a mean cross-sectional curvature of -0.24 against -0.19 for landslides in open land. These are valuable insights which can be used to better inform shallow landslide modelling approaches in forested areas.

ID: 3.13315

Machine learning based characterization of landslides in north-western Himalayas

Ankit Singh
Dhiman, Nitesh; Praise Shukla, Dericks

Abstract/Description

Climate change coupled with global warming has led to an increase in natural hazards in mountainous regions of the world. Among these hazards, landslides are commonly occurring due to the presence of steep and fragile slopes. Minimizing the losses caused by landslides is important for effective planning and prevention strategies. Identifying landslides leads to studies related to landslide susceptibility mapping, which requires a landslide inventory. However, inventories largely do not take into account the types of landslides and their characteristics, resulting in generalized outcomes that only identify probable landslides.

This study aims to characterize landslides using three machine learning methods: logistic regression, bagging, and J48, while also assessing the capability of transfer learning to predict landslides in unknown regions with similar topographical characteristics. The results showed that J48 and bagging performed better in characterizing landslides. Additionally, transfer learning was effective in predicting landslide characteristics for Kullu district (target) based on knowledge acquired from Mandi district (source).

ID: 3.13369

Towards Realtime Monitoring of Natural Hazards at Unprecedented Temporal and Spatial Resolution

Christian Bermes
Aaron, Jordan; Walter, Fabian; Bermes, Christian; Hirschberg, Jacob; Spielmann, Raffaele; Roebrock, Philipp

Abstract/Description

Landslides pose a global threat, causing numerous fatalities and extensive damage annually. Climate change, population growth and infrastructure development are expected to exacerbate these risks, particularly in alpine regions. To cope with this changing risk, we urgently need improved monitoring and early warning technologies. Here we describe a new approach that leverages recent advancements in environmental seismology, mobile robotics, and artificial intelligence (AI) to monitor landslides at unprecedented spatial and temporal resolution. We aim to overcome the main limits to current technologies, which often require direct line of sight or event arrival at monitoring locations, providing either high temporal or spatial resolution, but rarely both. To do so, our proposed system utilizes environmental seismology for improved event detection and mobile robotics sensors with AI algorithms for high temporal and spatial resolution measurements of in-situ flow parameters. We plan to collaborate with cantonal authorities and relevant industry partners to access field sites and incorporate feedback for system optimization. Once validated and tested, this system will fill a critical gap in landslide characterization, and help to cope with changing risk in the future.

ID: 3.13373

Identifying slow-moving landslides and slope instabilities in glacier surroundings using InSAR time series analysis

Zahra Dabiri
Hölbling, Daniel; Streifeneder, Vanessa; Albrecht, Florian; Nafieva, Elena; Abad, Lorena; Laher, Matthias

Abstract/Description

Glacial retreat and associated geomorphological and preglacial processes lead to significant changes in the surrounding environment and increase the risk for natural hazards, such as slope instabilities and landslides. Time series Interferometric Synthetic Aperture Radar (InSAR) is a powerful technique for measuring and monitoring surface deformation with sub-centimeter accuracy. However, linking measured surface deformation to slope instability and landslides remains challenging and requires further investigation. This study examines the application of the InSAR methodology in combination with geomorphological slope characteristics to identify slow-moving landslides and unstable slopes in selected high-mountain areas in Tyrol and Salzburg, Austria. We use freely available Sentinel-1 SAR time series data and the Small Baseline Subset (SBAS) InSAR technique to detect surface deformation and measure displacement velocities from 2015 to 2024. The InSAR results are used to locate areas exhibiting high deformation rates. The areas are then integrated with Slope Units (SUs) derived from very high-resolution digital elevation model (DEM) data. SUs are morphological terrain units defined by drainage and dividing lines. A critical threshold, based on statistical measures, such as standard deviation, is applied to distinguish active versus stable areas. The identified slow-moving landslides and unstable SUs are interpreted in relation to changing glacier outlines to better understand the potential linkages between landslides and glacier retreat. Furthermore, we overlay and assess the results with alpine hiking infrastructure data, i.e. trails and huts, to identify potentially affected trail sections or huts. This research provides valuable insights into the consequences of glacier retreat and related environmental spatio-temporal changes. The findings can contribute to natural hazard and risk assessments and support monitoring and mitigation strategies.

ID: 3.13423

Reopening of the Marienschlucht by Lake Constance

Achilles Häring

Abstract/Description

The Marienschlucht is probably the “most picturesque gorge by Lake Constance” and is situated within a unique nature conservation and recreation area. Since a landslide in May 2015 which resulted in a fatality, the gorge has been closed to the public. This situation is about to change with the introduction of a new walkway at a height of roughly 10 m above the stream bed and additional rock stabilisation measures, so the public will soon be able to experience the Marienschlucht at first hand once again. In addition to the work in the Marienschlucht itself, a fundamentally new concept for a system of paths has been developed. To make the approach from Bodman along the foot of the Mondfelsen rock face safe, a strategy involving rangers with regular inspections of the steep slopes combined with instrumentation techniques is being deployed. The instrumentation comprises humidity sensors, inclinometers and crack-opening sensors. The values are transmitted and evaluated in real time. Access can temporarily be blocked via a warning and alarm system if a particularly hazardous situation arises. By combining the above measures, it is anticipated that the public will once again be able to experience the beauty of the Marienschlucht towards the end of 2025.

ID: 3.13428

Sensor-based structural monitoring of rockfall protection fences

Matthias Toppe

Abstract/Description

The city of Essen has been monitoring a part of its rockfall protection fences with the help of sensors as part of a pilot project since 2022. Therefore the system GUARD is used, which registers impacts and rope forces as well as recording other environmental data. In the past, this has made it possible to recognise falling rocks and trees and initiate a targeted inspection of the structures. The rope forces are used to draw conclusions about the fill level of the structure. Continuous monitoring by the installed sensor technology can make the inspection of the structure more efficient and reduce the inspection effort. The presentation presents the experience gained from the planning, installation and operation of the system and discusses the potential of sensor-based monitoring for the structural monitoring of geotechnical protective structures.

ID: 3.13431

Long-term monitoring of a landslide

Matthias Toppe
Spang, Christian

Abstract/Description

In the 1980s, a large-scale landslide occurred during the construction of a bypass around the town Herdecke (NRW), which was stopped by backfilling and secured with prestressed anchors, soil anchoring and a complex drainage concept. A comprehensive measuring system was installed to monitor the slope movements. Since 2021, we have been recording movements using inclinometers, extensometers, anchor force measurements and groundwater measuring points. The lecture presents our experience in measuring and analysing these highly sensitive instruments and discusses challenges in data analysis. Particular attention is paid to the interpretation of long-term measurement series and the recognition of critical movement patterns. The results provide valuable insights for the geotechnical monitoring of slopes and their permanent stabilisation.

ID: 3.13565

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.

ID: 3.13731

Landslide Forecasting Using EO Data: A Review of Current Global Approaches

Kamini Sharma
Tiwari, Kailash; Van Wyk De Vries, Maximillian

Abstract/Description

Landslide forecasting, the prediction of spatio-temporal probability of slope instability, is essential for effective Landslide Early warning systems. Landslides are a significant threat globally, yet data scarcity limits traditional forecasting methods. This review paper examines global approaches for landslide forecasting using Earth Observation (EO) data, which provides comprehensive spatial coverage and enhanced data accessibility. We review the use of satellite-derived precipitation products (using radar, passive microwave, and infrared) for rainfall threshold determination, the use of radar and optical imagery for Soil moisture estimation, DEM generation, and other variables integrating into landslide susceptibility maps for ML/ANN based forecasting models, We also review GNSS and InSAR data for ground displacement prediction, aiding in landslide forecasting, while infrared thermal data to detect landslide precursors. We conclude by highlighting current limitations and proposing future directions of research.

ID: 3.14850

Geospatial Solutions for Landslide Risk Assessment and Mitigation Strategy

Muhammad Qasim

Abstract/Description

Landslides1 are a major hazard in mountainous regions, causing infrastructure damage, economic losses, and fatalities. Existing studies emphasize the need for accurate landslide susceptibility assessment, but many lack comprehensive multi-scale modeling approaches that integrate both local and regional influencing factors. This research bridges this gap by employing geospatial techniques for systematic monitoring, assessment, and hazard analysis in Neelum, Muzaffarabad, Bagh, and Poonch districts of AJK, Pakistan. This study utilizes GIS-based spatial analysis and remote sensing datasets to assess landslide-prone areas while incorporating multi-scale modeling techniques for risk assessment. Analytical Hierarchy Process (AHP) and weighted overlay analysis are used to quantify the impact of geological, hydrological, and topographic parameters. The research also focused on rainwater harvesting as a mitigation strategy to reduce slope instability caused by excessive surface runoff. The methodology involves processing DEM, geological maps, fault lines, rainfall data, Sentinel-2 imagery, and landslide inventory based on historical data and on-ground surveys of landslide areas to develop a robust assessment framework. By integrating high-resolution terrain data with historical landslide occurrences, the study enhances predictive accuracy and ensures a detailed spatial understanding of risk factors. The final landslide susceptibility map is generated through weighted overlay analysis, offering a reliable tool for hazard identification and risk management. Findings indicate that steep slopes, weak geological formations, and high drainage density significantly contribute to landslide occurrence, particularly in highly fragmented landscapes. Rainwater harvesting is identified as a sustainable intervention to minimize surface runoff and enhance slope stability in vulnerable regions. The study aligns with SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by promoting disaster risk reduction and sustainable land-use planning while supporting resilient infrastructure development.

FS 3.146: Assessing the impacts of environmental drivers on natural capital, ecosystem condition and services, and human well-being in mountain areas

FS 3.148: Glacier and permafrost risks in a changing climate

FS 3.147

Monitoring and Modeling of Landslides

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