During earthquakes, landslides are triggered both co-sesmically and post-seismically over several weeks or years. The triggering mechanisms of these two phases encompass a combination of dynamic loading during the shaking, fluid migration from sediment contraction, bulk damage inside the landslide mass, fluidization of clay layers, and subtle interplays between landslide units. These different mechanisms are still poorly quantified, due in particular to the paucity of dynamic parameters acquired on landslides during earthquakes. Slow-moving landslides, with their persistent motion through time, provide a unique opportunity for monitoring different physical parameters, including displacements and material mechanical properties. As a consequence, they provide a strong interest for studying the mechanisms of landslides during seismic forcings.
The Maca and Madrigal landslides, located in southern Peru at an altitude of 3,400 m, are two nearby slow-moving landslides (~1m/year) located in a highly seismic environment (Colca valley). For this reason, the two sites have been instrumented since 2012 and 2017 respectively, with permanent GNSSs and broadband seismometers. In October 2021, March 2022 and June 2023, the instruments recorded the response of the landslides to 3 major shallow earthquakes (Ml5.2, 5.5, 5.2 at distance between 3-15 km). Both landslides displayed a co- and a post-seismic motion of different magnitudes and characteristics. In particular, the post-seismic motion is systematically delayed by 2 days on the Madrigal landslide, and the October 2021 earthquake reactivated the whole landslide mass, inactive for at least 6 years. We analyze this dataset, together with seismic, InSAR, Pléiades satellite images and weather data to decipher the mechanisms at play during the co- and post-seismic phases.
How to cite: Lacroix, P., Taipe, E., Albinez, L., Bièvre, G., Pousse, L., and Sanchez, H.: Mechanisms of co- and post-seismic landslides in the Colca valley, Peru, following M5+ earthquakes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11193, https://doi.org/10.5194/egusphere-egu25-11193, 2025.
The reactivation of ancient landslides has become a significant issue in the Nepal Himalayas in recent years. Many people have lived on the historical accumulation of landslide debris for a long period and now face the risk of reactivation. This study examines the deformation mechanism of the Kodari reactivated landslide in Sindhupalchok District of Nepal, which began to reactivate in July 2015 and remains active. Soil samples from twenty distinct locations within the reactivated area were collected. A multistage direct shear test on unsaturated soil was performed to determine shear strength characteristics. The plasticity index and soil composition were derived from laboratory analysis. A comprehensive field examination elucidated the intricate details of the landslide mechanism.
The soil samples demonstrate a cohesion value between 0.7 and 5.5, whereas the angle of internal friction ranges from 26 to 33.3. The soil displayed a plasticity index between 1.7 and 5.5. Of all the samples, seventeen are loose and non-compact, whereas three are dense. The results of the gradation research reveal that the soils are classified as sandy loam, exhibiting a relatively low plasticity index and low cohesion. The cumulative rainfall throughout the monsoon season from 2015 to 2023 varied between 2400 mm and 2900 mm. The precipitation levels during the early monsoon season surged significantly since 2019. The severe rainfall during the pre-monsoon period, following an extended dry season, resulted in surface deformation in the studied area. Moreover, unconsolidated soil exhibiting low cohesion and a low plasticity index underwent deformation due to the 7.8 magnitude Gorkha earthquake and subsequent aftershocks. 118 dwellings and 220 individuals are at risk of Kodari landslide reactivation.
Keywords: Landslide reactivation, settlement threat, Nepal Himalaya, soil investigation
How to cite: Bhandari, B. P.: Mechanism of paleo landslide reactivation in the Himalayas: Insight into Kodari landslide of Sindhupalchwok District, Nepal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14357, https://doi.org/10.5194/egusphere-egu25-14357, 2025.
For decades, regional-scale landslide prediction has predominantly relied on data-driven models, which are inherently detached from the underlying physics of the failure mechanisms. The widespread adoption of these models is due to their ability to utilize proxy variables instead of geotechnical parameters, which are often difficult to obtain across regional scale. In this study, we introduce a Physics-Informed Neural Network (PINN) approach that incorporates physical constraints into a conventional data-driven framework to predict the permanent deformations associated with Newmark slope stability methods. Specifically, the neural network is designed to extract geotechnical parameters from globally available proxy variables and optimize a loss function based on observed coseismic landslide inventories. The results demonstrate that this approach not only achieves high predictive accuracy in terms of traditional susceptibility outputs but also generates spatially resolved maps of inferred geotechnical properties at a regional scale. Consequently, this architecture presents a novel avenue for addressing coseismic landslide prediction and, if validated by further studies, holds the potential for enabling near-real-time PINN-based predictions.
How to cite: Dahal, A. and Lombardo, L.: Towards Physics-Informed Neural Network for Earthquake Induced Landslide Modelling., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5633, https://doi.org/10.5194/egusphere-egu25-5633, 2025.
During an earthquake, mountainous and hilly areas can suffer severe additional damage as an effect of co-seismic landsliding favored by the presence of impedance contrasts (e.g. caused by soft slope materials resting on a rigid substratum) that cause seismic shaking amplifications. As a tool to guide actions for the mitigation of such effects, we are testing an expeditious approach to make a rapid reconnaissance of slopes susceptible to seismically induced landsliding. Our approach is based on the estimation of the slope resistance demand posed by seismic shaking with an expected Arias intensity. We exploit expeditious ambient noise analyses to assess the contribution of the dynamic response of slopes to their susceptibility to seismic failure. A technique of instantaneous polarization analysis is used to extract Rayleigh waves from noise recordings with the consequent possibility of (i) inverting the Rayleigh wave ellipticity curves as a function of frequency in terms of S-wave velocity vertical profiles and (ii) calculating the amplification factors in terms of Arias intensity through 1D site response modeling.
To verify the effectiveness of our approach, we use data from a local network of accelerometer stations installed on marginally stable slopes to compare amplification factors estimated from noise recordings with those derived from accelerometer recordings. The results show differences in the amplification factor estimates within 50%, an acceptable level of approximation for a preliminary regional-scale assessment of the slope dynamic response. However, the following limitations in the applicability of the approach should be considered: 1) a careful selection of the seismic inputs for the numerical modeling, with a well-balanced distribution of spectral energy, to avoid strong concentrations over limited frequency bands that may result in a significant underestimation or overestimation of the amplification factor; 2) particular attention to complex slope settings (e.g., the presence of dominant fracturing/fissuring systems resulting in strong anisotropies in the mechanical properties of the slope materials); in such cases, the amplification estimates could be greatly underestimated (up to 300%) by assuming the occurrence of purely stratigraphic site effects.
How to cite: Fredella, F., Del Gaudio, V., Wasowski, J., Venisti, N., and Capone, P.: Ambient noise analysis for expeditious evaluations of slope susceptibility to co-seismic failures: potential and limitations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5946, https://doi.org/10.5194/egusphere-egu25-5946, 2025.
Earthquake-induced landslides (EQIL) account for 4–5% of all landslides worldwide. The most active earthquake hotspots in the terrestrial environment are susceptible to EQIL. Major earthquakes that have triggered EQIL include: 2008 Wenchuan, 2018 Porgera, 2015 Gorkha and 2010 Haiti . These events have caused more than 25000–30000 landslides, in certain cases more than 100000 landslides, and they have been responsible for more than 100000 deaths and property damage worth billions of dollars (Jesse et al., 2020). The events further trigger cascading hazards such as landslide dams for more than two decades (Fan et al., 2019). Current literature in modelling these repercussions of EQIL has evolved to include the temporal effects of such events in the aftermath of large earthquakes (Sridharan et al., 2024; Dahal et al., 2024).
Papua New Guinea (PNG) is one of the many seismically active regions in the world. The Indo-Australian boundary is a major plate boundary that runs through PNG. This fault zone experienced a major earthquake in 2018 near Porgera that is reported to have triggered more than 10,000 landslides in the region (Tanyas et al., 2022). This work explores the prolonged effects of the earthquake in the region. We use the automatically mapped landslide inventory by Bhuyan et al. (2022) to train and validate our model (Bhuyan et al., 2022). To capture the changes caused by the earthquake, we use a longitudinal GAM (Hastie and Tibshirani, 1990) that estimates the variation in log odds with periodic changes in climatic and seismic inputs. Terrain attributes modelled as static covariates also contribute to the changes observed in the terrain.
Our results show that the model performs well with respect to accuracy measures AUC-ROC, Brier score, and the R2 statistic of susceptibility estimates. We observe that the effect of the seismic activity remains for a short period of a few years after the earthquake. We present the longitudinal susceptibility prediction maps for the PNG at a slope unit level for future reference.
References:
Hakan Tanyaş, Kevin Hill, Luke Mahoney, Islam Fadel, Luigi Lombardo, The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake, Engineering Geology, Volume 297, 2022, 106504, ISSN 0013-7952
Sridharan, A., Gutjahr, G., and Gopalan, S., “Markov–Switching Spatio–Temporal Generalized Additive Model for Landslide Susceptibility,” Environ. Model. Softw., vol. 173, no. August, p. 105892, Feb. 2024
Ashok Dahal, Luigi Lombardo, Towards physics-informed neural networks for landslide prediction, Engineering Geology, Volume 344, 2025, 107852, ISSN 0013-7952,
Bhuyan, K., Tanyaş, H., Nava, L. et al. “Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data”. Sci Rep 13, 162, 2023
Hastie, T. J.; Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.
How to cite: Sridharan, A. and Gutjahr, G.: Longitudinal Effects of Earthquake-Induced Landslide Susceptibility in Papua New Guinea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12455, https://doi.org/10.5194/egusphere-egu25-12455, 2025.
Understanding the dynamic response of granular shear zones under cyclic loading is critical for elucidating the mechanisms of earthquake-induced landslides. Existing prediction methods struggle to capture the complexities of landslide dynamics, particularly the transition from slow creep to rapid runout during seismic events. Here, we present results from ring shear experiments that simulate shear zone behavior under dynamic loading. Our study reveals that granular shear zones exhibit post-seismic creep, which increases gradually with each loading cycle. Crucially, we have observed a meta-stable state characterized by a significant increase in post-seismic creep, which precedes shear zone failure. This meta-stable state occurs when the weakened shear resistance approaches the applied shear stress, indicating a phase transition from a solid to a fluid-like state and may serve as a critical precursor for instability. These findings offer a compelling explanation for widespread post-seismic landslide movement and the dynamic triggering of landslides. By incorporating the identified co-seismic weakening and post-seismic healing mechanisms into existing methodologies, such as modified Newmark models, we can potentially greatly improve the accuracy of landslide displacement predictions and advance our understanding of dynamic triggering. This work provides a framework for better assessing seismic landslide hazards.
How to cite: Li, Y., Hu, W., Xu, Q., Luo, H., Chang, C., and Jia, X.: Meta-Stable States in Granular Shear Zones Under Cyclic Loading: Implications for Earthquake-Triggered Landslides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2560, https://doi.org/10.5194/egusphere-egu25-2560, 2025.
Attributing seismic or climatic landslide triggers in retrospect is an unresolved problem in landslide science. Particularly in areas with scarce seismic and rainfall records or in the case of delayed slope response to earthquakes it is often difficult to identify the landslide trigger. Understanding the trigger mechanism is however essential for tailoring landslide risk reduction measures.
We here present an approach coupling the geotechnical Factor of Safety (FS) and Newmark Displacement (ND) methods with the k-means clustering unsupervised machine learning technique to reveal the contributions of seismic and climatic factors in the topographic context to landslide occurrences at the western flank of Mount Oku in Cameroon. The study area is located along the Cameroon Volcanic Line, a seismically active region in Central Africa, where small earthquakes and landslides are observed regularly. Only in a few cases, a clear connection between earthquakes and landslides has been demonstrated, while rainfall is usually considered the main landslide trigger.
Based on geomechanical parameters assessed in fieldwork and laboratory tests, we first calculated the static FS and the ND for the study area for different water saturation and landslide depth scenarios and a magnitude 5.2 earthquake at 10 km distance. For 179 landslide polygons mapped in the study area, we assessed the resulting FS and ND values, as well as some topographic factors such as the slope angle, slope aspect, curvature, distance to ridges, and distance to rivers. In a k-means cluster analysis, different combinations of two and three topographic factors were analyzed regarding their ability to identify clusters of earthquake-triggered landslides.
The combination of the two parameters distance to ridges and distance to rivers turned out to have the best clustering performance and it revealed a cluster of landslides triggered at low distances to ridges and higher distances to rivers with high ND values in the dry case, indicating an influence of seismic acceleration on the formation of these landslides.
How to cite: Braun, A., Djukem, D. L. W., Fan, X., Wouatong, A. S. L., Havenith, H.-B., and Fernandez-Steeger, T. M.: Using Newmark Displacement and cluster analysis of topographic factors to reveal possible seismic landslide triggers at Mount Oku, Cameroon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16182, https://doi.org/10.5194/egusphere-egu25-16182, 2025.
Submarine landslides are significant marine geological hazards, potentially affecting offshore infrastructure anchored to the seafloor, including oil and gas facilities, submarine pipelines and cables, and coastal engineering projects. Given the critical role of offshore areas in current and future energy production, anticipating potential landslide events is essential for effective planning and risk mitigation.
In this context, the present study investigates the landslide potential of the offshore area of Vado Ligure (Northern Italy), a strategically important site for the deployment of a gas pipeline that connects a ship mooring to the coast (https://fsruitalia.it/vado-ligure/). The area is located near the head of a submarine canyon carving the shallow water platform, approximately 2 km east of the harbour of Vado Ligure.
In the absence of detailed studies describing potentially unstable masses along this submarine structure, we evaluated seabed stability along the proposed pipeline route by applying the Minimum Lithostatic Deviation (MLD) method, developed by Tinti and Manucci (2006, 2008) as a reformulation of the classic Limit Equilibrium Method. We took into consideration various potential sliding surfaces along a series of transects intersecting the pipeline route and partially covering the steep slope characterizing the canyon.
An analysis of the Parametric Catalog of Italian Earthquakes (CPTI15 v4.0.0) (Rovida et al., 2020) reveals the occurrence of historical earthquakes with moderate magnitudes (4–5) in the Vado Ligure area. At the same time, the Database of Individual Seismogenic Sources -- DISS database (DISS Working Group 2021) identifies seismogenic structures in the region capable of generating earthquakes with magnitudes up to 7.4. These findings emphasize the importance of including seismic loading in seabed stability assessments. Accordingly, a sensitivity analysis was conducted, varying the Peak Ground Acceleration (PGA) between 0.2 g and 0.7 g to account for uncertainties and assess its influence on slope stability.
Furthermore, a second sensitivity analysis focused on key geotechnical parameters, such as cohesion and friction angle, which play a crucial role in slope stability evaluation and which are poorly known for the studied area. The combined results of these analyses indicate that the offshore area under investigation is stable, even under the worst-case assumptions.
References:
DISS Working Group, “DISS, Version 3.3.0: A compilation of potential sources for earthquakes larger than M 5.5 in Italy and surrounding areas. Istituto Nazionale di Geofisica e Vulcanologia (INGV).” https://diss.ingv.it/index.php, 2021.
Rovida A., Locati M., Camassi R., Lolli B., Gasperini P., “The Italian earthquake catalogue CPTI15.” Bulletin of Earthquake Engineering, vol. 18, no. 7, pp. 2953–2984, 2020.
Tinti, S. and Manucci, A., “Gravitational stability computed through the limit equilibrium method revisited”, Geophysical Journal International, vol. 164, no. 1, pp. 1–14, 2006.
Tinti, S. and Manucci, A., “A new computational method based on the minimum lithostatic deviation (MLD) principle to analyse slope stability in the frame of the 2-D limit-equilibrium theory”, Natural Hazards and Earth System Sciences, vol. 8, no. 4, pp. 671–683, 2008.
How to cite: Zanetti, M., Angeli, C., Armigliato, A., Paolucci, E., and Zaniboni, F.: Evaluating Stability in Vado Ligure offshore (Liguria, NW Italy) Through the MLD Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10559, https://doi.org/10.5194/egusphere-egu25-10559, 2025.
Strong earthquakes in mountain landscapes can trigger widespread slope failures, initiating chains of multiple hydro-geomorphic hazards such as channel blockage, instability, flooding, and coarse sedimentation. These impacts disrupting ongoing response operations may be fueled and potentially amplified by extreme post-seismic precipitation delivered by atmospheric rivers (ARs), which can form continent-spanning corridors of concentrated moisture. Yet, such cases of ARs occurring in the aftermath of major earthquakes have remained unreported to the best of our knowledge. Here, we document the combined effects of seismic and precipitation extremes that perturbed the area struck by the February 6, 2023 Türkiye-Syrian earthquakes (Mw 7.8 and 7.6), the largest seismic sequence ever recorded in the region. Strong ground shaking triggered thousands of landslides and was followed, 36 days later, by an exceptionally strong AR bringing severe precipitation with up to 183 mm in 20 hour. This rainfall induced yet more landslides, debris flows, and flooding, disrupting recovery efforts, affecting earthquake victims and temporary settlement areas, and claiming more lives. This unprecedented disaster highlights the need to revise rapid hazard assessment protocols to account better for hazard cascades arising from tightly timed seismic and weather extremes.
How to cite: Tanyas, H., Bozkurt, D., Korup, O., İstanbulluoğlu, E., Lütfi Şen, Ö., Yılmaz, A., Karabacak, F., Lombardo, L., Guan, B., and Görüm, T.: Cascading Disasters: How the 2023 Türkiye-Syria Earthquake Was Amplified by an Atmospheric River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17484, https://doi.org/10.5194/egusphere-egu25-17484, 2025.
Earthquake-induced landslides cause a significant threat to communities living in earthquake-prone areas, as they potentially worsen the destructive impact of an earthquake event physically and socio-economically. This hazard emerges as an aftermath of strong ground motion in mountainous areas, which disturbs the stability of the hillslope material and reduces its shear strength, leading to failure. This earthquake legacy effect is often called shear-strength reduction (RSS). An understanding regarding this matter is important, as it can be used for an immediate post-seismic response and long-term mitigation strategies. However, incorporating RSS for post-seismic landslide predictions remains challenging due to the complex interactions between the hillslope and the ground shaking, making it hard to quantify the RSS degree. Applying the same RSS estimation method used for the 2008 Wenchuan earthquake to the 2023 Turkey earthquake, this study aims to estimate the RSS caused by the earthquake and incorporate it into the post-seismic landslide prediction model.
The study uses a data-driven approach to develop the co-seismic landslides prediction model, utilizing the co-seismic landslide inventories and various predictor variables to see which variable most strongly contributes to the failure. The model was evaluated with random (RCV) and spatial cross-validation (SCV). Simulations will be conducted using a seismic hazard map as a ground-shaking predictor variable to estimate the spatial distribution of earthquake-induced landslides for future events.
Preliminary results of the developed co-seismic landslide model showed that most of the morphometric variables significantly contributed to the failure, as well as the seismic factor, where only the sediment and metamorphic lithology gave a positive contribution to the failure. The Area Under the Curve (AUC) value from the RCV and SCV showed a strong correlation between observed and predicted landslide areas. The RSS will be integrated into the simulation output to evaluate its impact on the post-seismic landslide estimation, which is expected to provide valuable insight into the earthquake-induced landslide predictions.
How to cite: Rizqilana, A., Tanyas, H., and Lombardo, L.: A Data-Driven Approach to Post-Seismic Landslide Hazard Assessment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19643, https://doi.org/10.5194/egusphere-egu25-19643, 2025.
Slow-moving (5x10-5 (mm/s)), deep-seated (>5 m) landslides exhibit persistent but non-uniform motion at low velocities, and deformation rates can increase abruptly with a triggering factor such as earthquakes. Although it is generally reported that such landslides become reactive in areas close to earthquake epicenters depending on the attenuation relations, the Meydandere (Siirt) landslide is approximately 560 km away from the February 06, Kahramanmaraş earthquake epicenters, and due to the increased movement on the hillslopes a couple of days after the earthquake, local people reported the incident to the local authorities. Meydandere paleo landslide complex contains many secondary landslides and is developed in the Paleocene–Early Eocene sedimentary rocks. Here, we utilize four years of Interferometric Synthetic Aperture Radar, accumulated precipitation, and volumetric soil water layer data to explore a slow-moving landslide's kinematics and causal linkage with far-field seismic effects. Based on the InSAR results, we have determined that the deformation rate of different secondary landslide bodies in the main landslide complex is slow but continuous with time, yet this rate has doubled after the earthquakes. We have revealed that cumulative precipitation and volumetric soil water layer changes may also play a profound role in this rate. On the other hand, we have statistically shown that the February 6, 2023, earthquake doublet has a primary control on landslide acceleration because there is no significant increasing trend in the velocities, although the peak values of precipitation-induced changes were higher in the previous period. We conclude that understanding the earthquake response not only of co-seismic landslides in earthquake-affected areas but also of existing large bedrock landslides in far-field areas relative to the earthquake epicenter will provide a comprehensive understanding of the hazard chain of large earthquakes.
How to cite: Gorum, T., Coskun, S., Akbas, A., Bayık, C., Abdikan, S., Balik Sanli, F., and Tanyas, H.: Increased motion of a slow-moving landslide following 2023 Kahramanmaraş Earthquake Doublet: Insights from Meydandere (Siirt) Landslide Complex, Türkiye, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5820, https://doi.org/10.5194/egusphere-egu25-5820, 2025.
On February 6, 2023, two earthquakes of magnitudes 7.8 Mw and 7.6 Mw occurred on the East Anatolian Fault zone, centered in Kahramanmaraş, Türkiye. These earthquakes caused widespread destruction, particularly in residential areas, and triggered lateral spreads that caused significant economic damage to agricultural lands. Within the earthquake-effective area, such landslides are more frequent along the Asi River in the south of Hatay. The banks of the Asi River have been a region where lateral spreading events are frequent due to morphological and sedimentological factors such as loose sandy soil and gentle slopes. This study aims to understand the distribution of lateral spreads triggered by the February 6 Türkiye earthquake sequence and the geomorphological conditions that affected this distribution along the 86 km section of the Asi River within Türkiye's borders. For this purpose, high-resolution satellite imagery, aerial photographs, and optical data collected via UAVs during fieldwork were processed using remote sensing software to produce mapping bases and conduct analyses. We mapped 328 lateral spreads in the earthquake-affected area. Along the Asi (Orontes) River, 238 lateral spreads were identified, constituting 72.6% of the total inventory of lateral spreads triggered by the earthquake doublet. The mapped lateral spreads in this area ranged from a minimum of 200 m² to a maximum of 100,000 m², with a total surface area of 3.5 km² exclusively along the Asi River. These earthquake-triggered lateral spreads demonstrated varying crack densities and deformation characteristics influenced by the meandering structure of the Asi River, which has a sinuosity index reaching up to 3.76. Lateral spreads were predominantly observed at point bars with higher sand content, where they moved horizontally towards the Asi River channel by a minimum of 1 meter and a maximum of 35 meters. We conclude that a deeper understanding of the frequency-magnitude relationships and spatial distribution patterns of lateral spreads enhances the development of regional susceptibility models and improves insight into the long-term geomorphic impacts of earthquakes, particularly concerning riverbank erosion.
How to cite: Çetinkaya, A. and Görüm, T.: Geomorphological Controls on The Distribution of Lateral Spreading Triggered by The February 6, 2023, Kahramanmaraş (Türkiye) Earthquake Sequence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6319, https://doi.org/10.5194/egusphere-egu25-6319, 2025.
On December 18, 2023, a Ms 6.2 earthquake struck Jishishan County, Gansu Province, China, triggering a catastrophic loess flowslide in Zhongchuan Town, Qinghai Province. The flowslide covered a total area of 508,200 m² and initiated a hazard chain that eroded an 8-meter-high earth dam, destroyed 51 residential buildings, and caused over 20 fatalities. Notably, the flowslide showed extraordinary mobility, traveling 3160 meters across gentle terrain with an overall travel angle of just 1.5°, surpassing the mobility of other known landslide types. To explore the underlying mechanisms behind its hypermobility, we conducted field surveys, UAV-based photogrammetry, LiDAR analysis, and numerical simulations. Three primary causes of its hypermobility were identified: (1) liquefaction of water-saturated silty loess, induced by irrigation activities and seismic loading, which significantly reduced basal resistance; (2) the macro-pore structure of loess, promoting the fluidization of displaced material; and (3) channelized topography combined with a low-friction icy channel bed, which enhanced flow momentum. Numerical simulations further demonstrated that variations in degrees of liquefaction strongly influenced the flowslide’s mobility and destructive potential.
How to cite: Xiao, S., Zhang, L., He, J., Peng, M., Jiang, R., and Lu, W.: A Highly Mobile Loess Landslide Induced by the 2023 Ms 6.2 Jishishan Earthquake in Northwest China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6581, https://doi.org/10.5194/egusphere-egu25-6581, 2025.
Earthquakes can induce a legacy effect on hillslopes by reducing rock mass shear strength through fracture development and cohesion loss, thereby increasing landslide susceptibility. While methods for modeling shear strength reduction exist, post-seismic landslide susceptibility assessments that account for this earthquake legacy effect remain unexplored. In this study, we address this gap by performing regional-scale slope stability analyses using Finite Element Methods (FEM). Focusing on the area affected by the 2023 Türkiye earthquake, we conduct back-analyses to estimate the shear modulus of hillslopes that failed during the event. Using an empirical relationship for shear strength reduction, we estimate post-seismic shear strength and integrate these parameters into FEM simulations to evaluate hillslope deformation. The deformation results are subsequently used to assess landslide susceptibility across the region.
This study represents the first application of FEM for regional-scale landslide susceptibility analysis, systematically accounting for changes in rock mass strength due to seismic events. The proposed framework enhances the accuracy and reliability of post-seismic slope stability assessments, providing a step forward in regional-scale, post-seismic landslide hazard evaluation.
How to cite: Wang, Y., Lombardo, L., Westen, C. J. V., Gorum, T., Yılmaz, A., and Tanyaş, H.: Assessing Post-Seismic Hillslope Stability with Finite Element Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11165, https://doi.org/10.5194/egusphere-egu25-11165, 2025.
On April 3, 2024, a strong earthquake with amagnitude ML 7.2 struck Shoufeng Township in Hualien County, Taiwan, causing 18 fatalities due to coseismic landslides. Coseismic landslides have long been a key topic in global geohazard research. To understand their characteristics and causes, it is essential to establish an objective and comprehensive inventory of landslides. Moreover, comparing coseismic landslide inventories from different earthquakes could understand how topographic, seismic, and geological characteristicsinfluence the spatial distribution of these landslides. The 2024 Hualien earthquake induced 3,232 coseismiclandslides with total area of 41.74 square kilometers.These landslides mostly occurred in regions where the peak ground acceleration exceeded 250 gal and the peak ground velocity surpassed 30 cm/s. They were primarily concentrated in areas composed of marble and schist, and predominantly faced east or southeast, with slope angles mainly ranging from 40°-60°.
This study further compares the coseismic landslide inventories from the 2024 Hualien earthquake (ML7.2), the 2022 Taitung earthquake (ML6.8), and the 1999 Chi-Chi earthquake (ML 7.6). Except for the 2024 Hualien earthquake, the numbers of coseismic landslides in the other events are 306, and 9,272, respectively, with total landslide areas of 0.93, and 127.8 square kilometers. The slope aspects of the other two earthquakes are southeast and south mainly. The slope gradients range from 40°-60° and 40°-50° for the 2022 Taitung earthquake and the 1999 Chi-Chi earthquake. In terms of lithology, the coseismic landslides associated with the 2022 Taitung earthquake and the 1999 Chi-Chi earthquake occurred in areas characterized by schist and epiclastics, andsandstone and conglomerate, respectively. Therefore, these three coseismic landslide inventories show the influence of different geologic background and topographic relief. This study presented the topographic, geologic, seismic diffrences of coseimic landslide inventories with the regions between metamorphic rock and sedimentary rock in Taiwan.
Keywords: 2024 Hualien Earthquake, 2022 Taitung Earthquake, 1999 Chi-Chi Earthquake, Coseismiclandslide inventory.
How to cite: Huang, Z. S., Yang, C.-M., and Chao, W.-A.: Comparison of coseismic landslide inventories from the 2024 Hualien Earthquake, the 2022 Taitung Earthquake, and the 1999 Chi-Chi Earthquake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16942, https://doi.org/10.5194/egusphere-egu25-16942, 2025.
National Research Institute of Earth Science and Disaster Resilience (NIED) revealed many landslide landform at NE Noto Peninsula, Japan. M7.6 main shock occurred there on 1 Jan 2024, and Geospatial Information Authority of Japan (GSI) revealed that many landslides were triggered by the 2024 earthquake. Previous study also revealed the landslide was reactivated by the 2024 earthquake at W Yataro Pass located ca. 10km SW from the epicenter. We calculated 3D displacement at 12m in resolution by pixel offset using ALOS-2 ascending data, measured on 26 Sep 2022 and 1 Jan 2024, and ALOS-2 descending data, measured on 6 Jun 2023 and 2 Jan 2024. Because remarkable crustal deformation, e.g., uplift more than 4m occurred along the northern shore of the peninsula, the 3D crustal-deformation amount was estimated, and it was deducted from the calculated 3D displacement, and resulting 3D landslide displacement amount was obtained. The estimation was performed as follows; the calculated 3D displacement data were resampled into 120m, then the resampled displacement amount was clipped by the non-landslide landform produced from the database and the non-landslide triggered by the 2024 earthquake. Then, the displacement amount was estimated for all over the peninsula including the non-landslide landform and the non-landslide triggered by the 2024 earthquake at 12m in resolution, as applying Kriging method. Finally, we obtained NS, EW, and up-down (UD) component of the landslide displacement amounts at 12m in resolution. The previous study revealed that the large reactivated by the 2024 earthquake at W Yataro Pass moved from N to S, we expressed NS and UD displacement profile vectorially along the measurement lines. As a result, it was found that surface landslide body moved not only synclinal dip direction from N to S but also synclinal anti-dip direction from N to S, and at the S end of the edge of the synclinal structure disrupted landslides occurred along the landslide body. At the N end of the edge of the synclinal structure, continuous cracks appeared on the landslide body and we sampled charcoal at the outcrop of the crack. According to the carbon 14 dating of the charcoal indicated 2,500-2,100 BP year, we think that the previous iterated earthquake occurred in 2,500-2,100 BP year at the same magnitude of the 2024 earthquake and induced large landslide such as the landslide in this case. This study used KAKEN 23K00972. The ALOS-2 data used in this study was given by JAXA, through the support of ERI JURP 2024-B-02 in Earthquake Research Institute, the University of Tokyo.
How to cite: Sato, H. P., Yagi, H., and Sato, M.: Large landslide at W Yataro Pass detected by ALOS-2 data pixel offset analysis, triggered by the 2024 Noto Hanto Earthquake (M7.6), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20091, https://doi.org/10.5194/egusphere-egu25-20091, 2025.
In the last decade the interest in understanding or assessing the susceptibility of earthquake induced landslides has considerably increased in the scientific literature. In this context rockfalls present a challenge due to the difficulty to collect information on fallen boulders following an earthquake emergency. The data obtained is often biased, as it is usually recorded only when it causes damage to buildings or road networks. Important aspects for future studies, such as the identification of source areas, are generally overlooked.
This study aims to develop a coseismic rockfall analysis based on the Turkish scenario, where two massive earthquakes of magnitude 7.8 and 7.5 struck on February 6, 2023, triggering approximately 3,673 landslides, the majority of which were rockfalls. The approach combines data collection and statistical analysis to obtain the key input data needed for forecasting rockfall trajectories caused by future earthquakes.
The well-timed collection of post-event high resolution ortho-images allowed to realize a thorough coseismic rockfall inventory of the area affected by the earthquakes. This inventory will serve to train both the source area susceptibility model and the trajectories simulation model.
This information allows the application of an occurrence probability model based on ground motion predictive equations and the estimated peak ground acceleration for a potential earthquake along the left-lateral East Anatolian Fault, focusing on the identification of future source areas susceptible to rockfall. From these sources, some rockfall trajectories will be simulated to assess the hazard zonation along the main infrastructures like road, pipelines and villages.
How to cite: Gallo, I. G., Gorum, T., Lombardo, L., Tanyas, H., Robustelli, G., and Sarro, R.: From ground motion simulations to rockfall hazard scenarios: A look into cascading effects from the 2023 Turkish earthquakes , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12308, https://doi.org/10.5194/egusphere-egu25-12308, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-13226 | ECS | Posters virtual | VPS12
Evaluating the Efficiency and Predictive Accuracy of Temporal Susceptibility Models for Co-Seismic Landslides Using Real-Time Validation: A Case Study from the NW HimalayasMon, 28 Apr, 14:00–15:45 (CEST) | vP3.11
The Himalayan region, characterized by its rugged terrain, distinctive geography, and active tectonics, ranks among the most landslide-prone zones globally. Landslide susceptibility and hazard mapping are critical tools to mitigate future risks and devise effective management strategies. This study uses data-driven statistical approaches to evaluate co-seismic landslide susceptibility in District Hattian, NW Himalayas, Pakistan. A comprehensive co-seismic landslide inventory comprising 349, 393, and 735 landslide events from 2005, 2007, and 2012, respectively, was utilized to train, test and validate predictive models.
Thirteen landslide causative factors (LCFs), including topographic, environmental, geologic, and anthropogenic variables, were analyzed to determine their influence on landslide occurrence. Three data-driven statistical models i.e., Weight of Evidence (WoE), Information Value (IV), and Frequency Ratio (FR) were employed to develop landslide susceptibility maps (LSMs). Model training used 70% of the landslide inventory, while 30% was reserved for validation. Model performance was evaluated using Receiver Operating Characteristic-Area Under Curve (ROC-AUC) metrics and predictive accuracy assessments. Among the models, the WoE approach outperformed well among the other models as ROC-AUC SRC scores of 84.4, 84.2, and 85.3 for 2005, 90.4, 86.4, and 87.2 for 2007, and 81.9, 86.7, and 85.9 for 2012 for WoE, FR, and IV models, respectively. PRC scores of the WoE, FR, and IV models were recorded as 85.7, 89.4, and 82.5 for 2005, 87.5, 77.5, and 80.4 for 2007, and 80.7, 88.3, and 87.7 for 2012. For the validation of long-term predictivity, efficiency models are checked by comparing the generated LSMs with newly recorded landslide events. The 2005 model was validated using 2007 data, the 2007 model with 2012 data, and the 2012 model with 2024 data. Results revealed a gradual decline in the predictive accuracy of the LSMs model of all three approaches over time; however, WoE consistently outperformed from the IV and FR models, maintaining robust predictive capabilities even after 12 years.
This study highlights that landslide-prone zones in District Hattian exhibit persistent mass movement activity and underscores the urgent need for proactive landslide management to minimize life loss and economic damage in this tectonically active region. The integration of advanced susceptibility modelling techniques with real-time validation offers a reliable framework for hazard assessment and risk mitigation. Policymakers and stakeholders are encouraged to implement targeted interventions, such as optimized land-use planning, the establishment of early warning systems, and increased community awareness programs, to enhance resilience against landslide hazards in the NW Himalayas.
How to cite: Riaz, M. T., Wani, S., Basharat, M., Riaz, M. T., and Manocha, A. R.: Evaluating the Efficiency and Predictive Accuracy of Temporal Susceptibility Models for Co-Seismic Landslides Using Real-Time Validation: A Case Study from the NW Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13226, https://doi.org/10.5194/egusphere-egu25-13226, 2025.
EGU25-13829 | Posters virtual | VPS12
Performance and Future Directions of the USGS Near-real-time Earthquake-triggered Ground Failure ProductMon, 28 Apr, 14:00–15:45 (CEST) | vP3.31
Within minutes of any major global earthquake, the U.S. Geological Survey (USGS) Ground Failure product (GFP) provides summary alert levels and spatial estimates of landslide and liquefaction hazard and population exposure. Since the GFP went live in September 2018, 187 events have had an elevated alert level (yellow, orange, or red), indicating limited to extensive hazard and exposure. These events include well-known ground-failure triggering earthquakes such as the 2023 Türkiye-Syria earthquake sequence, the 2021 Nippes, Haiti earthquake, as well as numerous other events. In many cases, the GFP proved to be valuable by estimating the potential extent of these hazards and their overlap with the local population. In this presentation, we discuss how the product has performed since it was deployed and how it has been used for situational awareness, planning, and reconnaissance. Significant users of the GFP include scientists, the media, emergency responders, and the public. We also discuss operational considerations, such as how moving the GFP to the cloud has improved speed and reliability. We conclude with an overview of enhancements under development, such as model regionalization, road obstruction estimation, fatality estimation, ongoing hazard information, model updating, and integration into other USGS impact products, such as Prompt Assessment of Global Earthquakes for Response (PAGER) and ShakeCast.
How to cite: Allstadt, K. E., Thompson, E. M., Wald, D. J., Hunsinger, H. E., Haynie, K. L., Hearne, M., Bürgi, P. M., Ellison, S. M., Engler, D. T., Jaiswal, K. S., Marano, K., and Lin, K.: Performance and Future Directions of the USGS Near-real-time Earthquake-triggered Ground Failure Product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13829, https://doi.org/10.5194/egusphere-egu25-13829, 2025.
