Large-scale instabilities in rock, weak rocks, debris, and ice represent enormous risks and are complex systems that are difficult to describe, investigate, monitor, and model. Their evolution can range from slow to fast complex mass movements and depends on forcing factors, geological and hydrological boundary conditions, and the evolution in space and time of thermo-hydro-mechanical controls, as well as the properties of the unstable mass. Many aspects remain understudied and debated due to difficult characterization and the limited number of thoroughly studied cases, and regional and temporal distribution and relationships with controlling and triggering factors are often poorly understood, resulting in poor predictions of behaviour and evolution under present and future climates. The session welcomes contributions on case studies, monitoring and modelling approaches and tools, numerical and physical modelling of dynamic loading and instability, deterministic event scenarios and probabilistic evaluations, threshold definition and offline data analyses, advanced numerical modelling and machine learning techniques, innovative dating and investigation methods, site effects such as amplification and the influence of pre-existing landslide masses, and impacts on structures and infrastructures including tunnels, dams, and roads, with the goal of improving hazard assessment and supporting early warning systems.
Earthquake-induced landslide (EQIL) models seek to map where landslide is likely to occur during earthquakes from ground-motion measures and environmental controls. Yet most models are trained almost exclusively on landslide-triggering earthquakes, encouraging overfitting to event-specific signatures, weakening transferability, and blurring how ground motion and predisposition jointly govern failure. Here we address both limitations by compiling a new global EQIL database that explicitly includes strong non-triggering earthquakes, and by developing a neural-network framework designed to learn transferable, mechanism-consistent controls on landslide occurrence. Our database extends existing public inventories by harmonizing 44 previously published landslide-triggering earthquakes and adding 24 newly mapped triggering events, alongside 44 strong earthquakes for which no widespread landslides is mapped. These non-triggering earthquakes provide event-level negative constraints that are rarely available in EQIL modelling. For each non-triggering event, we conducted a multi-temporal audit using 3-m PlanetScope imagery; any missed failures are expected to be sporadic and very small, and do not alter the event-level classification. Using the combined catalogue, we train pixel-level probabilistic models conditioned on ground motion and environmental covariates. Transferability is evaluated via leave-one-event-out cross-validation and an independent multi-continent test set spanning diverse climates and faulting styles. Incorporating non-triggering earthquakes markedly improves cross-event performance (mean ROC–AUC increases from 0.873 to 0.914) and reduces event-specific errors, yielding more robust probabilistic maps of landslide spatial patterns. To interpret learned controls, we apply SHAP-based explain ability supported by complementary statistical summaries. Terrain and material properties (for example slope/relief and lithology) exert strong inhibitory influences that keep predicted probabilities low even under high peak ground acceleration (PGA), whereas PGA acts primarily as a conditional amplifier where predisposition is high. Overall, explicitly modelling counterfactual non-triggering earthquakes offers a practical route to more accurate, transferable EQIL mapping and clearer insight into why strong earthquakes do—or do not—produce widespread landslides.
How to cite: Fang, C., Fan, X., Luigi, L., Hakan, T., and Cees van, W.: Global database and prediction model of earthquake-triggered landslides, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11390, https://doi.org/10.5194/egusphere-egu26-11390, 2026.
The seismic event that occurred on 6 February 2023 in southeastern Turkey (Mw 7.8 and Mw 7.6) caused widespread surface deformation, with lateral spreading being the dominant process in fluvial environments. Despite the extensive research conducted on earthquake-induced lateral spreading, the majority of studies have focused on liquefaction mechanisms, with comparatively less attention being paid to the geomorphic controls on its spatial occurrence. This study aims to address this research gap by investigating the distribution of lateral spreading along the Asi (Orontes) River and identifying the key geomorphic factors that shape its development. To achieve this objective, a multi-source high-resolution dataset was compiled, incorporating pre- and post-earthquake satellite imagery, UAV-based optical data, and aerial photographs. These data were used to map 328 cases of lateral spreading. The sedimentological context of deformation zones was further constrained through stratigraphic profiling of exposed sedimentary sections. The results obtained revealed a pronounced clustering of lateral spreading on pointbar surfaces (58.6%), largely associated with convex planform geometries (49.4%). A secondary accumulation along cutbank–concave margins (25.5%) underscore the strong role of channel-margin morphology in conditioning susceptibility. The former Amik Lake floodplain stands out as the region where 68% of all cases occurred. Beyond its characteristic sedimentary properties, zones corresponding to the paleo-meander belt—particularly at intersections of abandoned channels also constitute weak geomorphic domains that facilitated lateral spreading. Additional controls, including distance to the fault rupture and channel sinuosity, likewise influenced the spatial pattern. By integrating these geomorphic and spatial parameters and situating the findings within a broader comparative context (e.g., the Canterbury earthquake), this study delineates the principal drivers governing lateral spreading as a secondary seismic hazard and advances our understanding of the geomorphic conditions that amplify its occurrence in active fluvial systems.
How to cite: Çetinkaya, A. and Görüm, T.: Determinants of the Spatial Distribution of Lateral Spreading: February 6, 2023 Kahramanmaraş EarthquakeAuthors and Affiliations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-794, https://doi.org/10.5194/egusphere-egu26-794, 2026.
Earthquake events that are often accompanied by prolonged rainfall before, during, or after the mainshock, usually result in thousands of landslides. To estimate landslide trigger factors in such scenarios, we propose a hybrid model combining a statistical model for cumulative rainfall with a physical model for coseismic landslide displacement. The statistical model is a Distributed Lag Nonlinear Model (DLNM) and the physical model is a rigorous Newmark's analysis. The chain of events that led to landsliding following the 2011 Sikkim earthquake is used as a case study. Trigger information of 164 landslide points from field investigations were used to train the model and predict the trigger for 1196 satellite-based landslide points. The hybrid model significantly improves predictions over generalized additive models. Cumulative rainfall shows a significant spatial correlation with trigger factors and heavy rainfall three weeks before the earthquake played a key role in preparing the ground for landslides.
https://www.sciencedirect.com/science/article/abs/pii/S1364815224003207
How to cite: Sridharan, A., Gutjahr, G., and Gopalan, S.: Estimating landslide trigger factors using distributed lag nonlinear models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-610, https://doi.org/10.5194/egusphere-egu26-610, 2026.
As human socioeconomic activities expand into complex and perilous mountainous areas, engineering projects in these areas face unprecedented landslide risks, particularly linear projects such as railways and highways. However, research on landslide risk assessment specifically tailored to linear projects in mountainous terrain remains limited. A novel technical framework for kinematic-based seismic landslide risk assessment to linear projects is proposed, whose key components include landslide development characteristics, seismic landslide hazard assessment, distributed kinematic-based landslide simulation, vulnerability assessment of linear projects, and integrated seismic landslide risk assessment. Depending on the type of linear projects, the linear elements at risk are discretized into a series of line segments or points to facilitate a more precise vulnerability assessment. Taking the Western Sichuan Railway as a case study, the seismic landslide hazard assessment is improved through kinematic-based landslide simulation. Six factors—derived from both attribute characteristics and post-disaster resilience—are used to construct a comprehensive vulnerability indicator system, enabling a full seismic landslide risk assessment for mountainous linear projects. The research findings provide a valuable reference and novel insight for landslide risk assessment of linear projects in mountainous areas.
How to cite: Yang, Z., Guo, C., and Yang, H.: Kinematic-based seismic landslide risk assessment to linear projects in mountainous areas: a case study of the Western Sichuan Railway, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9131, https://doi.org/10.5194/egusphere-egu26-9131, 2026.
Strong earthquakes can change hillslope stability beyond the shaking period by changing the effective strength of slope materials and generating discontinuities that influence subsequent slope response. These earthquake legacy effects are rarely represented in regional landslide modeling, largely because scalable ways to estimate coseismic and post-seismic property changes are limited. Here we present a regional, physically based framework that jointly accounts for coseismic shear modulus degradation, post-seismic strength reduction, and scenario ground shaking to assess future slope stability conditions. We apply the approach to the 2023 Kahramanmaraş earthquake sequence in Türkiye using a detailed landslide inventory to define failed and stable slope units. Coseismic displacements are simulated with finite element modeling, and a displacement threshold provides a regional constraint to back-analyze shear modulus under seismic loading. We then develop empirical relationships between shear modulus, peak ground acceleration, and slope geometry for regional extrapolation. Future ground shaking is represented by six maximum credible rupture scenarios on fault segments identified as seismic gaps, and these scenarios are combined with spatially variable post-seismic strength changes. The proposed workflow provides a transferable and scalable approach for improving long-term landslide hazard assessment in tectonically active regions.
How to cite: Wang, Y., van Westen, C., and Tanyaş, H.: Scenario-based regional landslide modeling with earthquake legacy effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1828, https://doi.org/10.5194/egusphere-egu26-1828, 2026.
Monitoring deep-seated landslides is an important task to prevent impacts on society. Their movement can be highly variable in space and time, ranging from millimetres to metres per year. In the Alps, their driving factors are typically prolonged rainfall events and snow melt, causing a rise of pore water pressure and reduced shear strength and subsequently higher movement rates. Nowcasting the movement of deep-seated landslides is an essential task in disaster risk management.
In the present study we employ a combination of a soil-vegetation-atmosphere transfer (SVAT) model and machine learning for nowcasting the movement of the Reissenschuh landslide in the Schmirn valley (Tyrol, Austria). The landslide’s movement has been monitored periodically since 2016 and continuously since 2020, with annual displacements up to more than 3 m and movement rates between 0.1 to 0.6 cm/day.
In a first step, we use the SVAT model LWF-Brook90 for reproducing subsurface runoff as a proxy of pore water pressure. The model includes vegetation dynamics and their interaction with incoming precipitation, snow accumulation and melt, as well as infiltration processes into porous media. We calibrated and validated the model using time series of snow water equivalent from two monitoring locations in Tyrol (Austria). For considering lag times between the infiltration and the onset of acceleration we computed running sums of subsurface runoff, considering time windows of up to 120 days.
Based on the continuous displacement time series and the outputs of the SVAT model, we trained machine learning models (support vector machines, SVM; random forest, RF) for reproducing the temporal displacement dynamics on a daily resolution. We validated the machine learning models then using the periodical displacement measurements. Based on the combined SVAT/machine learning models, we nowcast the movement of the Reissenschuh landslide using available meteorological products. With the growing displacement time series we will further refine the machine learning models and validate their predictive performance with periodical measurement campaigns. In a next step, we will employ the combined models for predicting the landslide’s movement under selected climate change scenarios.
How to cite: Zieher, T., Huber, T., Hagen, K., Branke, J., and Schneider-Muntau, B.: Nowcasting the movement of the deep-seated Reissenschuh landslide based on soil-vegetation-atmosphere transfer modelling and machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19194, https://doi.org/10.5194/egusphere-egu26-19194, 2026.
The Blatten rock-ice avalanche occurred on 28th May 2025 and induced a huge disaster downstream. The rock debris from the rock slope failure accumulated on the downslope glacier and destabilized it and finally induced the rock-ice avalanche on 28th. We analyzed the topography, geology and the monitoring webcam photographs of the source area of the Blatten rock slope failure that preceded the final rock-ice avalanche and obtained an idea about the gravitational slope deformation process until the rock slope failure event. We used 0.5-m DEMs and aerial photographs of swisstopo and the photographs of monitoring webcam of the canton of Valais (Switzerland).
The topographic features appearing in the aerial photographs before the 2025 event and the monitoring photographs of the source area of the Blatten rock slope failure strongly suggest that the slope was preceded by flexural toppling, which accelerated in May 2025 and finally collapsed to accumulate on the downslope glacier. The flexural toppling had made narrow terraces on the slope probably by preferential shearing along fault crush zones in a long term. There are traces of water gushing out along the terraces at least since September in 2005, which may be due to the damming up of groundwater by the probable fault crush zones and the following spill over. Such a sequence of flexural toppling, water pressure build up by fault crush zones, and landslide generation has been reported by Yokoyama (2020, Geomorphology) and Chigira (2024, Slope Tectonics) in Japan. Flexural toppling is probably continuing in the slopes on the west of the source area, preparing for the next rock slope failure.
The source area of the Blatten rock slope failure is located within the zone of permafrost (Islam et al. 2025) but recent climatic change is supposed to have possibly warmed permafrost and increased the water (Farinotti et al., 2025). This is consistent with the presence of the water gushing traces.
The faults stated above are important but are inferred from the topographic features, so need to be investigated at the site.
Recent climate change has warmed the permafrost in high mountains, potentially increasing rockslope failures like the Bratten incident in many places..
How to cite: Chigira, M. and Jaboyedoff, M.: Gravitational slope deformation that preceded the Blatten rock slope failure in 2025, Switzerland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4559, https://doi.org/10.5194/egusphere-egu26-4559, 2026.
The Himalayan region is characterized by complex tectonic activity, steep terrain, and highly variable soil and rock mass conditions. Among the main geomorphic processes shaping Himalayan slopes, Deep-Seated Gravitational Slope Deformations (DGSDs) is a fundamental component of engineering geomorphology that directly and/or indirectly possessing the engineering geological and geotechnical challenges. Many researchers investigated many mountain slopes globally, which have distinct geomorphic signatures due to DGSDs movement. These deformation processes are generally classified into three main types: sackung (or sagging), lateral spreading, and rock block sliding. In many cases, these DGSDs are commonly associated with active fault systems. In the Himalayan context, DGSDs are often expressed as Large-Scale Landslide Topography (LLT), with inferred ages ranging from approximately 100 years BP to 10,000 years BP. Evidence of DGSDs can also be evaluated through the presence of ancient landslides and landslide dams. These large-scale slope deformations are most likely initiated following major Himalayan earthquakes and are driven by a combination of gravitational forces and progressive weakening of slip zones. This encourages to use DGSDs for study of paleo seismicity in Nepal. The weakening of slip zones is usually enhanced by clay mineralization along rock joints and shear zones resulting from hydrothermal alteration associated with Main Central Thrust (MCT).
Based on present activity in the Himalaya, DGSDs can be broadly classified into four types: active, dormant, reactivated, and extinct. Each category presents different levels of risk and civil engineering relevance. The engineering geological and geotechnical challenges associated with DGSDs can be effectively assessed through combining topographical maps, satellite and remote sensing data, engineering geological mapping and detailed field investigations.
After few extensive observations along roadside slopes, tunnel portals, and tunnel alignments in the Nepal Himalaya, a strong relationship between DGSDs and adverse engineering geological conditions are well identified. DGSD-prone terrain presents specific challenges, including difficulty in identifying, quantifying, and extrapolating deformation zones, limited working areas for investigations, elevated construction risks, increased engineering geological uncertainties in tunnels portals and roadside slopes.
Through selected case studies, this paper highlights key engineering geological and geotechnical challenges encountered in DGSD-affected areas in the Himalaya and demonstrates how proper geomorphological site evaluation can support optimal alignment selection for roads, hydropower projects, and tunnels. Ultimately, improved understanding of DGSDs is essential for reducing landslide risks and minimizing the tendency to attribute project difficulties to undefined “geological problems” in the Himalayan region.
How to cite: Dahal, R. K.: Engineering Geological Characterization of DGSDs in the Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5692, https://doi.org/10.5194/egusphere-egu26-5692, 2026.
Early warning of catastrophic landslides remains challenging due to the multiscale and intermittent nature of precursory deformation. Existing warning systems often rely on empirical thresholds or short-term acceleration criteria, limiting their transferability and physical interpretability across sites and landslide types. Here, we propose a general, physics-based hierarchical framework for landslide forecasting that integrates complementary statistical diagnostics operating across distinct temporal horizons. The framework is grounded in principles of statistical physics and explicitly links observable statistical signatures to underlying transitions between system deformation regimes. For long-term forecasting (months to years), we track the temporal decline of the velocity b value—defined as the power law tail exponent of the probability density function of slope velocities—which reflects an increasing frequency of medium-to-large velocities and a progressive shift towards critical behaviour. For medium-term forecasting (weeks to months), we apply the log-periodic power law singularity (LPPLS) model combined with a Lagrange regularisation approach to objectively identify the onset of a critical phase, marking the transition from stable or quasi-stable behaviour to accelerating deformation. For short-term forecasting (days to weeks), we detect dragon-kings, defined as statistically exceptional outliers that deviate from the background power law scaling of slope velocities and emerge only during the final stage preceding failure. The framework is designed to operate on time-series monitoring data commonly available at landslide sites, without reliance on site-specific empirical thresholds. We test the approach on well-instrumented historical landslide events, demonstrating that the combination of long-, medium-, and short-term indicators provides a coherent and hierarchical early warning strategy. By explicitly linking statistical signatures to distinct stages of instability development, the proposed framework offers a pathway towards more robust, transferable, and physically interpretable landslide early warning systems.
How to cite: Lei, Q. and Sornette, D.: A physics-based hierarchical framework for landslide early warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8115, https://doi.org/10.5194/egusphere-egu26-8115, 2026.
Landslides pose a persistent threat to infrastructure and communities across the rapidly changing Himalayan landscape. Despite the advances in remote sensing, rapid and accurate landslide mapping remains limited due to complex topography, frequent cloud cover and the need for updated inventories to support forecasting models. In this study, a potential landslide detection method was implemented on Google Earth Engine (GEE) using multi-temporal Sentinel-2 imagery and terrain-based masking. A buffer region in Himachal Pradesh was analysed using satellite observations acquired between July and September 2022. Cloud-filtered image pairs were processed to compute NDVI for each date, and significant vegetation loss was used as a proxy for fresh slope disturbances. Terrain parameters derived from the SRTM DEM, specifically slope and surface roughness, were applied to exclude flat or stable areas and enhance the specificity of detection. Pixels showing a substantial decline in NDVI on steep, rugged terrain were automatically flagged as potential landslides and exported as geolocated point features. The results demonstrate that multispectral NDVI change analysis can rapidly highlight areas of probable slope failure within the monsoon season. The validation against published research and news reports demonstrated strong spatial agreement between detected zones of ground displacement and the NDVI-based outputs. Therefore, this confirms the effectiveness of the proposed method in capturing event-scale landslide patterns across Himalayan landscapes. The study presents a fast, scalable and operationally practical method for preliminary landslide screening. Also, it provides valuable support for the growing need for machine-learning-based susceptibility modelling and early warning systems.
How to cite: Sharma, A., Tiwari, Dr. R. K., and James, Dr. N.: Rapid Detection of Landslides Using Sentinel-2 NDVI Change and DEM Metrics on Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1629, https://doi.org/10.5194/egusphere-egu26-1629, 2026.
Most landslides in tropical regions initiate in steep terrain, but we investigate an earth slide on a gently inclined pyroclastic slope in West Java. On 18.02.2024, cracks were observed on a slope with a gradient of only 12°, composed of completely weathered pyroclastic rocks, indicating precursory signs of a large-scale earth slide. Eleven days later, on 29.02.2024, slope failure occurred, leading to the displacement of more than 90,000 m3 of material and causing damage to 39 houses, one elementary school, and an inter‑village road. We acquired UAV-based photogrammetry to build a digital elevation model and we reconstructed the pre‑failure topography. Fieldwork based on geomorphological-geological mapping and geotechnical drilling was performed to investigate the spatial distribution, thickness, geometry and kinematics, geological structure, and hydrogeological setting. Samples from selected outcrops and core drillings were collected for comprehensive laboratory testing to determine grain-size distribution, clay mineralogy composition, strength and hydrogeological properties. In addition, meteorological data were analyzed and geomechanical modeling were done to elucidate the failure mechanism. Our geological-geometrical model indicates a translational earth slide with a gently dipping basal shear zone of about 10° at a depth of 13–14 m, and a rotational secondary slide at the toe. Geologically, a three‑layer model from bottom to top is derived: Layer 1 consists of a sequence of sandstones and claystones with a gently dipping bedding that locally aligns with the sliding direction; Layer 2 is composed of highly weathered pyroclastic rocks, i.e., tuffaceous claystone and sandstone; and Layer 3 is a completely weathered tuff unit. Our investigations indicate that the clay-rich basal shear zone is located at the contact between Layer 1 and 2. Extensive clay mineralogy analyses of 37 samples show that smectite and vermiculite are the primary clay minerals of all layers. Considering a 25-year rainfall record, the 1.9.2023 to 29.02.2024 rainy-season accumulation of 1,666 mm is near the upper bound of 1,746 mm and clearly above the mean of 1,181 mm. A back analysis taking into account the observed groundwater conditions and assuming a safety factor of approximately 1 yields a cohesion of 4 kPa and a friction angle of 10°, which indicates a smectite-rich shear zone with exceptionally low strength behavior. Our results show that heavily weathered pyroclastic rocks in tropical regions promote the formation of landslides on slopes even with a low angle of inclination. In addition, the slope was exceptionally wet during the rainy season leading up to the failure in February 2024. This factor, combined with changes in land use over time, may have had a negative impact on slope stability. Therefore, hazard mitigation measures should be based on controlling surface runoff through a modern drainage system and on land use planning.
How to cite: Misbahudin, M., Ottner, F., Schneider-Muntau, B., Tohari, A., and Zangerl, C.: Understanding the failure mechanism of a gently inclined earth slide in highly weathered pyroclastic rocks (Java, Indonesia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3782, https://doi.org/10.5194/egusphere-egu26-3782, 2026.
The rockslide Gámanjunni 3, with an estimated sliding volume of 26 Mio. m3 and an annual movement up to 5 cm, is classified as a high-risk object. Hence, the site is subject to continuous monitoring, including global navigation satellite system receivers (GNSS), a ground-based radar (GB InSAR) and a meteorological station. Several studies have analysed the patterns of movement and developed different interpretations of the internal structure of the shear zone and sliding body. The presence of permafrost in the sliding body was investigated in earlier research. Its degradation, togehter with variation in joint water pressure is assumed to be a significant trigger for the rock slope displacement, beginning in mid-Holocene.
Our work aims to investigate the influence of rock mass parameters, geometry of internal structures, ongoing displacement and climate driven triggers on the stability of the slide. This is done by using a model of the universal distinct element code (UDEC).
Prior to the numerical modelling, we analysed the current movement data, visible structures in the backscarp and the results of previous studies. Due to partly contradictory results, several versions for the geometry of the basal shear zone were developed. Based on own field work and earlier investigations, representative sets of rock mass and joint network parameters were estimated.
In the first modelling stage, scenarios with increasing degree of complexity were used to test the sensitivity of the results on the input parameters and the numerical setup. The parameters were calibrated in order to fit the model output with the observed displacement. In the subsequent stage, the stabilisation trend under progressing displacement is investigated for the different versions of shear zone geometry. Additional model scenarios include climate driven triggers, such as groundwater and permafrost.
The results of this case study should improve the understanding of the behavior of rock slope displacements in response to inherent parameters and triggering factors. Further research will focus on the effects of various climate change prediction scenarios on the stability of the slope.
How to cite: Rumerstorfer, F., Grumstad, A., Vick, L., Zangerl, C., and Eriksen, H. Ø.: Geomechanical analysis through distinct element modelling of the rockslide Gámanjunni 3, Troms, Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20462, https://doi.org/10.5194/egusphere-egu26-20462, 2026.
Landslides represent complex geohazards that can significantly impact ecosystems, infrastructure, and human safety. A comprehensive understanding of landslide evolution requires an integration of both micro-scale mechanisms and macro-scale processes. This study synthesizes recent advancements in the field, examining the interplay between geological, hydrological, and climatic factors that influence landslide dynamics.
Utilizing a multi-scale approach, we analyze the mechanisms driving landslide initiation and progression, incorporating field observations, laboratory experiments, and numerical modeling. Our findings highlight the critical role of pore pressure fluctuations in slope stability and the impact of climatic events on landslide frequency. We also discuss the implications of these mechanisms for risk assessment and management in vulnerable regions.
Our review underscores the importance of interdisciplinary research and the need for innovative methodologies that bridge scale gaps in landslide studies. This work contributes to a deeper understanding of landslide processes and emphasizes the necessity for inclusive strategies in mitigating their impact in a changing environment.
How to cite: Fang, K.: Mechanisms of Landslide Evolution: A Multi-scale Perspective on Recent Advances, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1381, https://doi.org/10.5194/egusphere-egu26-1381, 2026.
Earthquakes pose significant threats to mountainous regions, where co-seismic ground shaking and topographic amplification along ridges can trigger hundreds to thousands of landslides, a damaging and widespread impact with implications for relief and reconstruction efforts. While the spatial distribution of co-seismic landslides has been extensively documented from numerous worldwide studies, questions remain regarding long-term landscape preconditioning caused by large earthquakes. Preconditioning occurs when seismic events cause elevated landslide rates above the normal background rate that can persist for up to a decade after the initial trigger. Remote sensing methods utilizing optical satellite imagery enable the development of multi-temporal inventories that characterize slope failures across pre- and post-seismic periods, revealing how peak ground acceleration (PGA) combined with excess topography (landscape zones above a stable threshold slope) can precondition landscapes for future instability. Papua New Guinea (PNG) experiences frequent large earthquakes, with concomitant landsliding and other environmental effects, for example in 2018 the Mw 7.5 Porgera earthquake was reported to have generated ~11,000 co-seismic landslides. This study investigates the sustained effects of PGA on the landscape evolution of PNG. Using false colour composites derived from band-ratio manipulation of high-resolution PlanetLabs imagery, combined with control factors, manual mapping was conducted using systematic visual comparison of pre- and post-event imagery. A new 6-year multi-temporal inventory for PNG is presented, documenting over 6,000 landslides across pre- and post-seismic periods, with a landslide average of ~700/yr pre-earthquake and a maximum of 347 per year post-earthquake. The majority of slope failures occurred during the immediate monsoon season following the 2018 earthquake, exceeding 4,000 events. Analysis of triggering factors revealed non-linear relationships with rainfall and strong negative exponential correlations with stream distance, identifying variables with a greater influence upon landslide susceptibility. Probability-density analysis displayed low rollover thresholds and narrow quantile bands, indicating high inventory completeness and consistent data distribution. Moreover, consistent with previous studies, the landscape exhibits a rapid recovery period, demonstrating short-term preconditioning, with landscape disturbance persisting for only one year before returning to pre-seismic conditions. This multi-temporal dataset for PNG provides significant insights into landslide distribution patterns, enhancing our capacity to forecast post-seismic landslide activity, contributing to more robust susceptibility assessment frameworks for seismically active mountainous regions.
How to cite: Beswick, A., Boulton, S., Jones, J., Stokes, M., Ghoshal, S., Lewin, S., Whitworth, M., Mildon, Z., Bennett, G., Hales, T., and Campforts, B.: Earthquake-induced landscape preconditioning from a 6-year multi-temporal analysis of the 2018 Mw 7.5 Porgera earthquake region, Papua New Guinea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3874, https://doi.org/10.5194/egusphere-egu26-3874, 2026.
Mt.Takabi (869.4m) is underlain by basalt lava, sandstone and mudstone in Miocene. Seismisity around the mountain is low currently; however, intermittent landslide deformation has been observed on site and once an earthquake occur, the deformation may become remarkable. Therefore, continuous monitoring of the deformation is important. In this study, detection of the deformation were compared between airbone LiDAR data observed in 2013 and 2021 and time-series ALOS-2 data observed between April 2016 and January 2025. And the detecting results were evaluated by on-site data. As a result, it was found that the deformation near the ridge was sharply detected by both data; however, though the deformation at the foot of the mountain was detected by the LiDAR, the deformation was not detected by the ALOS-2 data. It is concluded that direction of the deformation near the ridge is E and sensitively detected by the ALOS-2 data but direction of the deformation at the foot of the mountain is S and unsensitively detedted by the ALOS-2 data.
How to cite: Sato, H., Yagi, H., and Hayashi, K.: Monitoring of landslide deformation on the E slope of Mt.Takabi in Yamagata Prefectrue, Japan using ALOS-2 and ariborne LiDAR data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21870, https://doi.org/10.5194/egusphere-egu26-21870, 2026.
Topographic amplification (TA) alters seismic-wave propagation and can intensify ground shaking and earthquake damage. Yet observational assessments of TA remain largely constrained to hillslope and regional scales, and spatially varying TA footprints are still documented primarily through earthquake simulations rather than direct observations. Here, we present an observation-driven approach to estimate TA across contrasting tectonic settings using InSAR-derived coseismic displacement fields from nine earthquakes (Mw 6.0–7.8), with strike-slip, thrust, and normal-fault events equally represented. We quantified TA for each hillslope as the relative increase in coseismic deformation in its upper section compared with its lower section across all sites. These observational patterns were then evaluated with numerical earthquake simulations to address the key limitation of using coseismic displacement as a proxy for amplification in ground shaking. Overall, this work aims to numerically assess how topographic amplification varies across earthquake mechanisms and space.
How to cite: Tanyas, H., Yu, C., Fadel, I., Dahal, A., Li, Y., Li, W., Gorum, T., and Ozacar, A.: Estimating Hillslope-Scale Topographic Amplification from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3988, https://doi.org/10.5194/egusphere-egu26-3988, 2026.
We present a comprehensive coseismic landslide inventory for the 6 February 2023 Türkiye earthquake sequence, together with a companion pre-earthquake geomorphic inventory, covering an area of approximately 80,000 km². The earthquake sequence comprised two major events (Mw 7.8 and Mw 7.5) occurring nine hours apart, affecting 11 provinces and subjecting large mountainous regions to ground shaking levels capable of triggering slope failures (peak ground acceleration > 0.08 g). Given that nearly 15% of the affected terrain exhibits slopes steeper than 20°, extensive landsliding was anticipated, although early satellite observations were hindered by widespread snow cover immediately following the earthquakes. Landslide mapping was conducted through systematic, expert-based visual interpretation of high-resolution pre- and post-event optical imagery, including 29,085 post-earthquake aerial photographs (0.3 m resolution). A pre-event geomorphic inventory was generated using 5 m digital elevation model–based Red Relief Image Maps to identify pre-existing slope instabilities. Multi-temporal post-seismic optical image stacks were employed to overcome cloud and snow limitations and to discriminate coseismic landslides from failures initiated approximately one month later during an intense rainfall event; the latter were excluded from the coseismic inventory. Landslides were mapped as full-footprint polygons and classified according to movement type (fall, avalanche, slide, flow, lateral spread, and complex) and material (earth, debris, rock, and rock–debris). The final coseismic inventory comprises 20,270 landslides, predominantly rock falls and avalanches. Surface rupture through mountainous terrain locally generated large and, in some cases, fatal failures, while incipient landslides and ground cracking are widespread, particularly in northern sectors. Lithology, spatial variability of ground motion, and topographic relief emerge as primary controls on landslide distribution. This study provides one of the most detailed datasets of earthquake-triggered landslides in an arid-to-semiarid landscape, offering valuable insights for hazard assessment and landslide modeling in complex seismic environments.
How to cite: Görüm, T., Yılmaz, A., Tanyas, H., Karabacak, F., and Süzen, M. L.: Landslides triggered by the 2023 Kahramanmaraş Earthquake Doublet, Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14274, https://doi.org/10.5194/egusphere-egu26-14274, 2026.
Earthquake-triggered landslides (ETLs) constitute one of the most hazardous secondary earthquake environmental effects, causing severe impacts on the natural and built environment as well as on human life. Greece is particularly prone to the occurrence of such phenomena due to its complex geotectonic framework and high seismicity. Despite their significance, a comprehensive national-scale assessment of earthquake-triggered landslides in Greece, spanning from antiquity to the present, has been lacking.
This study presents the first extensive temporal, spatial, and statistical analysis of ETLs in Greece, covering an exceptionally long time period from 279 BC to 2023. Through a systematic review and reevaluation of Greek and international scientific literature, historical sources, earthquake catalogues, technical reports, and field survey data, a total of 673 ETLs associated with 144 earthquakes with Mw ranging from 4.0 to 8.3 were identified and documented. The analysis was supported by Geographic Information Systems (GIS), enabling the integration and correlation of ETLs with geological, geomorphological, tectonic, seismological, and environmental parameters.
The results indicate that the highest concentration of ETLs occurs in western Greece, particularly in the Ionian Islands and the Peloponnese, regions characterized by active tectonic structures and intense seismic activity. Most ETLs are associated with geotectonic units belonging to the External Hellenides, while limestone-dominated lithologies and post-alpine deposits were identified as particularly susceptible to landslide occurrence. The majority of documented ETLs were triggered by earthquakes of moderate to strong magnitude (Mw=5.5-7.0), highlighting the importance of such events in generating widespread slope failures. Rockfalls represent the most frequent type of ETLs in Greece, accounting for nearly half of the recorded cases, reflecting the steep topography and widespread exposure of fractured rock masses.
Spatial analysis further revealed that the distribution of the ETLs is not random but predominantly occur within areas classified as high and very high susceptible. Although less frequent, coastal and offshore landslides were also documented and constitute a significant hazard, as they may be associated with secondary effects such as local tsunami and coastal instability. The impact of ETLs on the built environment of Greece is substantial, including damage to buildings, transportation networks, and critical infrastructure, which in turn exacerbates the socio-economic consequences of earthquakes and poses additional public health risks.
The findings of this study emphasize the critical importance of systematically recording and analyzing ETLs as an integral component of seismic risk assessment. The compiled dataset and the derived spatial and statistical insights provide a valuable foundation for improving landslide susceptibility assessment, land-use planning, Civil Protection strategies, and disaster risk reduction policies. In a seismogenic country such as Greece, understanding the patterns and controlling factors of ETLs is essential for enhancing resilience and mitigating the compound and cascading impacts of future seismic events.
How to cite: Mavroulis, S., Sarantopoulou, A., and Lekkas, E.: Patterns and Controls of Earthquake-Triggered Landslides in Greece: Evidence from a Long-Term National Inventory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8379, https://doi.org/10.5194/egusphere-egu26-8379, 2026.
Coseismic landslides, or earthquake-triggered landslides, are a major type of hazard during seismic events and often lead to considerable casualties. The systematic establishment of an accurate landslide inventory that includes both landslide location and area is crucial for understanding the distribution characteristics and mechanisms of coseismic landslides. Furthermore, correlating the inventory with factors such as topographic, geologic, and seismic parameters can help delineate potential landslide zones, thereby enhancing disaster response and improving early warning capabilities and hazard mitigation.
A major earthquake sequence occurred in Taitung, Taiwan, on 17 and 18 September, 2022, with a Mw 6.5 foreshock in Guanshan followed by a Mw 6.9 mainshock in Chihshang. The Guanshan-Chihshang earthquake sequence triggered many coseismic landslides in eastern Taiwan. Since previous landslide inventory studies of this earthquake are mostly incomplete, this study aims to establish a complete, accurate, and detailed landslide inventory for this event.
High-resolution remote-sensing data, including 50-cm resolution Pléiades satellite images and 25-cm resolution aerial photos, were utilized to identify and map the coseismic landslides. The Normalized Difference Vegetation Index (NDVI) was initially employed for rapid detection of landslide areas, followed by visual inspection and mapping. By searching all regions that experienced a seismic intensity of 5-lower or higher from the Central Weather Administration during both earthquakes, this study identified a total of 876 landslides, with a combined area of approximately 1.9×106 m2.
A subsequent quantitative analysis was conducted on landslide controlling factors, including distance to the epicenter, distance to surface rupture, seismic intensity, lithology, slope, slope aspect, elevation, and distance to rivers. The results reveal a distinct correlation between landslide frequency and four factors, including seismic intensity, epicentral distance, distance to surface rupture, and distance to rivers. Although the largest number of landslides occurred in sedimentary rocks, the total landslide area was higher in regions with metamorphic and igneous rocks. However, after normalizing for total area, the igneous rock region has the highest landslide area density. Spatially, the majority of landslides exhibit a preferential orientation on south- and southeast-facing slopes, which may be associated with directivity effect of seismic wave propagation. Additionally, the largest number of landslides was observed on slopes of 30˚–45˚ and at elevations of 250–500 meters.
This study successfully established a comprehensive landslide inventory for the 2022 Guanshan-Chihshang earthquake sequence and provides a quantitative analysis of landslide contributing factors. We hope these results will serve as important references for subsequent research on coseismic landslide susceptibility assessment and related geohazard mitigation.
How to cite: Tai, Y.-H. and Shyu, J. B. H.: Coseismic landslides triggered by the 2022 Guanshan-Chihshang earthquake sequence, eastern Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2265, https://doi.org/10.5194/egusphere-egu26-2265, 2026.
This study investigates the kinematic behavior of a deep-seated landslide prone area in Laiyi Village, Pingtung County, Taiwan, through a comprehensive in-situ monitoring system. The instrumentation array includes surface dual-axis tiltmeters, piezometers, Global Navigation Satellite System (GNSS) for surface displacement, borehole inclinometers, and rain gauge, with data transmitted in real-time at 10-minute intervals. Spanning from 2023 to 2025, the monitoring data reveals a significant non-linear coupling between slope displacement and intense rainfall events. Notably, during Typhoon Gaemi in 2024 and the heavy rainfall events in late July 2025, GNSS-derived displacement rates exhibited a stepwise escalation, with peak velocities exceeding 90 mm/day. During the torrential rain event on July 28 (the 0728 event), the maximum cumulative displacement surpassed 600 mm, accompanied by surface tilt variations exceeding 600 arc-seconds.
Conversely, piezometric monitoring indicated only minor fluctuations in groundwater levels (rising 1~ 4 m) across multiple rainfall events, suggesting that the groundwater elevation is not the primary driver of slope instability in this area. Instead, rainfall infiltration serves as the dominant triggering mechanism. The analysis identifies a critical threshold where slope mobility significantly intensifies when the 72-hour cumulative rainfall exceeds 600 mm. Furthermore, such kinematic activity is observed to decelerate and cease within approximately 10 days following the cessation of the rainfall event.
How to cite: Wang, J.-S., Jan, C.-D., Zeng, Y.-C., Ko, T.-T., Lin, J.-J., Ting, W.-C., and Yeh, N.-C.: Kinematic Behavior Analysis of a Deep-Seated Landslide Prone Area: A Case Study of Laiyi Village, Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21247, https://doi.org/10.5194/egusphere-egu26-21247, 2026.
Landslides are widespread geohazards worldwide, influenced by site-specific geological, geomorphological, hydrological, and vegetative conditions. Although rainfall, seismic activity, and human disturbances are the primary triggering factors, local environmental settings can substantially shape both the initiation mechanisms and movement patterns.
This study examines the role of hydrogeological characteristics in driving long-term, continuous slope displacement over several decades at a coastal landslide site. The 30-hectare catchment is mantled by colluvial deposits that vary in thickness from a few meters on the lower slope to several tens of meters on the middle slope, extending along an approximately 500-m-long hillslope. The colluvium overlies a thick mudstone formation, within which subsurface investigations identified a 5–10 m thick highly saturated zone near the coastline. A village is situated on the lower slope close to the coast, while the remainder of the site is covered by undeveloped forest.
Finite element analyses were performed to simulate hydrological evolution within the geological strata under rainfall conditions from 2013 to 2024. The results indicate that hydraulic head near the coast remains substantially higher than that in the upslope area, while within the mudstone layer it gradually decreases landward. This configuration restricts subsurface flow from the slope toward the sea during rainfall events, suggesting the presence of a hydraulic barrier along the coastal boundary. Precipitation in the catchment also appears to drive an upslope migration of the high-moisture zone in the shallow colluvium and mudstone layer, forming a freshwater aquifer. The gradual landward expansion of this moisture zone may induce long-term creep deformation within the highly saturated mudstone near the coastline, contributing to progressive slope destabilization—consistent with site instrumentation data showing horizontal displacements exceeding 25 meters into the underlying mudstone.
These findings highlight the critical role of site-specific interactions among geomorphological, geological, and hydrological processes in governing landslide mechanisms. They further underscore the importance of an integrated hydrogeological investigation and understanding for improving landslide assessment, prediction, and long-term hazard management.
How to cite: Fan, C.-C., Yang, C.-J., and Lin, S.-F.: Coupled Hydrogeological Controls on Long-Term Slope Displacement in a Coastal Landslide Site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1769, https://doi.org/10.5194/egusphere-egu26-1769, 2026.
Earthquakes in mountainous regions can trigger tens of thousands of shallow landslides, reshaping hillslopes and amplifying disaster impacts. Predicting their spatial distribution remains challenging because most existing models are empirical, event-specific, and lack physical interpretability. We present ShallowLandslider, a new component within the open-source Landlab framework that integrates deterministic mechanics with probabilistic and empirical elements for regional-scale prediction of coseismic landslides. The model extends the classical Newmark sliding block approach to three dimensions, incorporating transient seismic accelerations, slope geometry, and variable properties of mobile regolith, such as cohesion, internal friction, and moisture content, on structured grids. Instability is assessed using critical acceleration thresholds, complemented by a probabilistic selection scheme to represent natural variability in failure occurrence. To improve geometric realism, the component partitions unstable regions using an empirical distribution of observed landslide length-width ratios from the study area.
We validate ShallowLandslider against landslide inventories from two subregions affected by the 2015 Mw 7.8 Gorkha earthquake in Nepal. Performance is evaluated using distributional metrics across landslide area, elevation, slope, and aspect. Results highlight that mobile regolith depth, parameterised by local elevation and planform curvature, strongly controls predicted landslide distributions and size. In addition, moderate cohesion values (10–15 kPa) proved critical for matching observed clustering of landslides on hillslopes and limiting unrealistically large failures. While pixel-level prediction remains impractical, the model captures first-order spatial and statistical patterns of coseismic landsliding, offering a reproducible, physically grounded tool for hazard assessment. Its modular design enables coupling with other Earth-surface process models, paving the way for integrated simulations of landscape response to seismic forcing and cascading hazards. We are extending ShallowLandslider beyond earthquake-specific triggers to support rainfall-induced failures, creating a multi-trigger framework that also links fault mechanics and slope stability through coupling with a dynamic rupture model. These developments aim to enable more holistic simulations of shallow landslide distributions and support next-generation approaches for regional and global landslide risk assessment.
How to cite: Ghoshal, S., Boulton, S. J., Hales, T. C., Bennett, G. E., Beswick, A., Jones, J. N., Lewin, S., Mildon, Z. K., Stokes, M., Whitworth, M. R. Z., and Campforts, B.: ShallowLandslider: a hybrid Landlab component for predicting regional distributions of coseismic landslides, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1992, https://doi.org/10.5194/egusphere-egu26-1992, 2026.
In the context of global climate change, avalanche disasters frequently occur in the Kanas-Hemu scenic area of Xinjiang, China, posing continuous threats to regional disaster prevention and mitigation, transportation safety, and tourism. To improve the accuracy of avalanche susceptibility assessment, this study aims to construct and compare coupled models that integrate frequency ratio (FR) and machine learning (ML) methods, systematically evaluate avalanche susceptibility along key transportation routes in the study area, and identify the key influencing factors and their spatial distribution characteristics. A total of 11 factors from three categories, including topography, meteorology, and snow cover, were selected. Six susceptibility assessment models were constructed by combining FR with various ML algorithms (SVM, MLP, XGBoost). The SHAP method was employed to interpret the contribution of each factor. The results indicate that the coupled models (FR-SVM, FR-MLP, FR-XGBoost) outperformed their corresponding single ML models. Among them, the FR-XGBoost model achieved the best overall performance, with an AUC of 0.897. Slope gradient and NDVI were identified as the most important influencing factors across all models. Besides, spatial distribution analysis reveals that high and very high susceptibility zones are primarily distributed in a strip-like pattern along gullies and major transportation routes with significant topographic relief in the northwestern and southwestern parts of the study area. This study demonstrates the superiority and applicability of coupled FR-ML models in avalanche susceptibility assessment. The findings can provide a scientific basis for local avalanche risk prevention and control, transportation safety assurance, and the development of a tourism early warning system.
How to cite: Liu, L. and Wang, K.: Avalanche Susceptibility Assessment in the Kanas-Hemu Scenic Area of Xinjiang Using Coupled Frequency Ratio and Machine Learning Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15761, https://doi.org/10.5194/egusphere-egu26-15761, 2026.
The study focuses on integrating multiple UAV surveys conducted at critical hotspots with significant stakeholder density and pressures. The objective is to develop and validate a government-operable workflow that fuses cm-scale UAV models with mm-scale X-band A-DInSAR and in-situ measurements for cliff-instability risk assessment. Case studies include St Peter’s Pool, which experienced a major collapse, and Il‑Madonna tal‑Aħrax, a culturally significant area. High-resolution UAV photogrammetry provides precise visual evidence of the collapse zone, enabling accurate mapping of geomorphological changes. To complement UAV data, tilt plate measurements are used, which have already detected significant ground displacement, confirming ongoing instability in the affected areas, particularly during a significant storm event. These in-situ observations are combined with archived and contemporary satellite radar datasets for a multi-temporal analysis consisting of in-situ measurements, cartographic resources (both historic (Tranchant et al., 2024) and contemporary) and state of the art A-DInSAR interferometric analysis, which is a novel approach for Government. The end goal is to produce ground deformation maps that can detect ground movement, verified with in-situ measurements. The methodology applied to these case studies can, if successful, be transposed into other applications critical to governance, such as monitoring of critical infrastructures, for example roadways. The overarching goal of Malta’s Public Works Department, through the application of these methodologies, is to assist policy makers in acting based upon best available technology and practices. The outputs will be hosted as georeferenced hosted layers, accessible to all relevant government or academic stakeholders.
How to cite: Fenech, D., Gauci, C., Belaama, A., Vassallo, J., Vella, M., Piscopo, M., and Buhagiar, G.: Integration of UAV photogrammetry, in-situ measurements and X-band-based A-DInSAR for risk assessment in Malta, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19935, https://doi.org/10.5194/egusphere-egu26-19935, 2026.
Austria is highly exposed to complex gravitational processes, including rockfalls, rockslides, rock avalanches, and landslides, driven by steep alpine topography, heterogeneous lithology, and strong hydro-meteorological forcing. While satellite radar interferometry is well established for detecting and monitoring slow-moving slope instabilities, a major challenge remains the understanding and anticipation of failure mechanisms that lead to rapid mass movements and cascading process chains with long runout distances.
Many catastrophic events are preceded by slow deformation phases and evolve through a combination of rock mass detachment, rock avalanche propagation, and subsequent transformation into rapid flow-like landslides when interacting with saturated soils, specific soil types, or glacial and periglacial environments. These coupled processes are not widely studied due to limitations in space and time, which limit the effectiveness of current hazard assessment and early warning strategies.
This contribution presents a conceptual and methodological framework that examines SAR time series and Copernicus European Ground Motion Service (EGMS) products to study failure mechanisms and process transitions in alpine terrain. EGMS serves as a baseline for identifying millimeter-scale precursory ground deformations linked to slow-moving instabilities, rock mass creep, and potential detachment zones. Deformation signals are combined with topographic, geological, and geomorphological data, as well as hydro-meteorological indicators such as precipitation, snowmelt, soil moisture proxies, and glacier presence, to evaluate conditions that could promote rapid failure and runout amplification. Plus, the use of simple process models for runout estimation.
Instead of focusing only on deformation detection, the proposed approach aims to connect observed ground motion patterns with environmental factors that influence detachment, mobility, flow transformation, and their reach. The framework supports analyses at multiple scales, from national screening to detailed studies of specific processes affecting infrastructure and settlements. Designed as a foundation for future PhD research on EO-based monitoring, failure mechanisms, and early warning of complex mass movement processes.
How to cite: Garcia Boadas, E. and Preh, A.: From slow deformation to rapid mass movements: investigating detachment mechanism, runout, and process chains in the Austrian Alps using remote sensing (satellite data, RS, and GIS) and conventional methods., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10083, https://doi.org/10.5194/egusphere-egu26-10083, 2026.
Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot 3
EGU26-9527 | ECS | Posters virtual | VPS14
Atmospheric Rivers as Triggers of Slope Instability and Landslides in the HimalayasFri, 08 May, 14:27–14:30 (CEST) vPoster spot 3
Landslides are among the most destructive natural hazards in the Himalayan region, where steep terrain, complex lithology, heterogeneous soil cover, and intense hydro-meteorological forcing collectively govern slope instability. Despite growing recognition of ARs as major contributors to extreme rainfall, their explicit integration into physically informed slope stability assessments in the Himalayas remains limited. This research aims to investigate the impact of atmospheric-river-driven precipitation on slope stability across the Himalayan region by coupling landslide inventory data, soil characteristics, topographic controls, and slope stability theory. landslide occurrences are analyzed with respect to topographic parameters derived from digital elevation models, such as slope angle, elevation, and terrain morphology. Given the limited availability of site-specific geotechnical data over large mountainous regions, soil mechanical properties specifically cohesion and angle of internal friction are inferred from soil type and texture classes obtained from global soil databases. Representative ranges of shear strength parameters are assigned based on established values reported in the literature.
Temporal characteristics of AR events, including shape, movement, intensity, duration, and antecedent moisture conditions, are linked with observed landslide occurrences to identify critical thresholds associated with slope failure. Slope stability is evaluated using the factor of safety (FOS) concept derived from limit equilibrium principles for infinite and shallow slope conditions. The influence of atmospheric rivers is incorporated through rainfall-induced changes in pore-water pressure and effective stress, enabling assessment of strength reduction and progressive destabilization under extreme precipitation scenarios. The outcomes of this research are expected to quantify the degradation of slope stability associated with atmospheric-river-driven rainfall, identify soil and terrain combinations most susceptible to AR-induced failures, and provide a physically meaningful explanation for observed landslide spatial clustering during extreme precipitation events. By integrating atmospheric processes with geotechnical slope stability analysis, this study advances the understanding of hydro-geomorphic hazards in the Himalayas and contributes to improved landslide susceptibility assessment, risk mitigation, and climate-resilient land-use planning in mountainous regions.
How to cite: Raina, B. A. and Nayak, M. A.: Atmospheric Rivers as Triggers of Slope Instability and Landslides in the Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9527, https://doi.org/10.5194/egusphere-egu26-9527, 2026.
