Rainfall-induced shallow landslides represent a critical natural hazard in mountainous regions, with their frequency controlled by hydrological processes. Climate change is expected to alter both precipitation patterns and soil moisture dynamics but quantifying these impacts on landslide susceptibility remains challenging.

In this study, we integrate physically-based stability thresholds with distributed hydrological modeling to assess future landslide hazard evolution under multiple climate scenarios. The study is conducted for a small basin (~28 km²) located in the north-eastern Friuli Venezia Giulia (Italy).

Spatially explicit Critical Soil Moisture (CSM) and Critical Wetness Index (CWI) thresholds at 50 m resolution were derived in a previous effort for multiple failure depths (0.75 to 2.00 m) by inverting the infinite slope stability analysis. The thresholds represent hydrological conditions at which slope failure may initiate through either unsaturated zone processes or groundwater table rise. These thresholds were coupled with a calibrated distributed and physically-based hydrological model, the Triangulated Irregular Network‐based real‐time integrated basin simulator (tRIBS), which simulates hourly soil moisture and groundwater dynamics, to assess the occurrence of failure over 100-year periods for three synthetically generated climate scenarios: current conditions, moderate emissions (RCP4.5, 2050), and high emissions (RCP8.5, 2050). The synthetic series of meteorological variables, and particularly precipitation, were generated by combining the AWE-GEN (Advanced WEather GENerator) model with a procedure to correct the distribution of extreme events.

We quantify exceedance frequencies, i.e., the proportion of time during which CSM and CWI thresholds are exceeded, as a measure of temporal exposure to landslide-conducive conditions. Results reveal that, under RCP4.5, exceedance frequencies decrease by up to 14.6% (CWI) and 10.9% (CSM), due to a reduction in annual precipitation despite an increase in mean intensity per event. In contrast, RCP8.5 shows bidirectional patterns, with maximum increases reaching 5.1% (CWI) and 3.6% (CSM), indicating that precipitation intensification begins to overcome the reduction in annual precipitation. Critically, climate impacts amplify with failure depth; the 2.00 m failure depth exhibits changes in magnitude up to three times greater than those at 0.75 m, suggesting that deeper failures become disproportionately more sensitive to climate change.

This research received funding from European Union NextGenerationEU – National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investment 1.1 -PRIN 2022 – 2022ZC2522 - CUP G53D23001400006.

How to cite: Arnone, E., Thomas, J., Ciriminna, D., and Francipane, A.: Assessing Climate-Driven Changes in Rainfall-Induced Landslide Probability Using Distributed Hydrological Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17967, https://doi.org/10.5194/egusphere-egu26-17967, 2026.

Exploring the internal structure of large landslides is crucial for understanding their deformation mechanisms and conducting stability assessments. However, traditional exploration methods, such as drilling, provide only localized information and fail to reflect the spatial continuity of subsurface structures. Single geophysical methods also face challenges in accurately characterizing deep-seated structures due to inversion non-uniqueness and interpretative ambiguity. Multi-source geophysical data fusion is considered an important approach to reduce ambiguity and improve modeling reliability, but existing research largely focuses on shallow landslides, lacking effective methods for the three-dimensional reconstruction of large deep-seated rock landslides. Taking the Tizicao deep-seated toppling on the eastern edge of the Tibetan Plateau as an example, this study proposes a multi-source geophysical data fusion modeling method based on the Gaussian mixture model (GMM). This method comprehensively utilizes electrical resistivity tomography (ERT), multi-channel surface wave exploration (MASW), the horizontal and vertical spectral ratio method (HVSR) for ambient noise, and UAV photogrammetry to achieve the fusion and classification of multiple parameters such as resistivity, shear wave velocity, and structural depth. By automatically partitioning the geophysical feature space using GMM, a three-dimensional model of the Tizicao toppling is constructed. The three-dimensional model is highly consistent with the borehole results, verifying the reliability of the fusion modeling method. In addition, the deep-seated structure revealed by the three-dimensional model plays a key controlling role in the initiation of slope instability. Overall, the proposed GMM-based multi-source geophysical fusion method not only enables accurate reconstruction of the internal structure of large deep-seated rock landslides but also provides a new technical pathway for mechanism analysis and hazard prediction of large deep-seated landslides.

Keywords: Deep-seated toppling; Multi-source geophysical integration; Gaussian Mixture Model (GMM); 3D structural modeling; Deformation evolution.

How to cite: Wang, H., Pei, X., Quan, Z., Cui, S., Xing, S., and Wang, Y.: 3D structure and deformation evolution of a large deep-seated toppling revealed by GMM-based multi-source geophysical integration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17460, https://doi.org/10.5194/egusphere-egu26-17460, 2026.

Landslide motion spans a continuum from slow, steady creep to rapid catastrophic failure. However, the mechanisms controlling the timing, rate, and nature of sliding, the sensitivity of motion to perturbations driven by precipitation or human activity, and potential transitions from creep to catastrophic failure all remain poorly understood. The response of landslide basal shear zones to rainfall-driven changes in pore pressure and thus effective stress can be interpreted using rate and state friction, a framework that describes the constitutive behavior and sliding stability of frictional shear zones, and is widely applied to earthquake mechanics. Laboratory experiments provide direct constraints on these frictional properties, and thus hold the potential to illuminate the material properties and conditions that control basal slip. We investigate the frictional behavior of Oak Ridge earthflow, a slow-moving landslide in the Coast Ranges of central California hosted within a clay-rich mélange. We conduct a suite of direct shear experiments to characterize its frictional rheology, including both (1) the velocity dependence of friction measured from velocity step tests; and (2) frictional healing, or time-dependent restrengthening between slip events, measured via slide-hold-slide tests. Experiments are conducted across a range of normal stresses approximating the in-situ conditions of the active shear plane (0.3 – 2 MPa) and at sliding velocities that span the range of observed landslide creep (0.001 – 30 𝜇m/s).

The shear plane material exhibits uniformly velocity strengthening behavior, characterized by a positive rate parameter (a-b), indicating that friction increases with increased slip rate, and is consistent with stable sliding. The values of (a-b) from laboratory experiments ranges from 0.001 – 0.015, in agreement with values inferred from coupled field observations of slide motion and pore pressure. Our results suggest that velocity strengthening friction, combined with modulation of effective stress through pore pressure, can generate slip transients, providing a direct mechanistic link between laboratory scale behavior and field observations of landslide motion.

We also find that the clay rich materials entrained along the base of the slide exhibit little to no healing (𝛽 ≈ 0). Near zero healing implies that the slide does not restrengthen during extended periods of low water pressure during the dry California summer. In the absence of healing, slip velocity responds directly and immediately to changes in pore pressure, independent of the duration of dry periods. Taken together, velocity strengthening friction and little to no healing are consistent with the persistent creep observed in the field, where the slip rate is governed by the stress state, pore pressure, and rate dependence of friction. Notably, Oak Ridge earthflow has been active since at least the 1930’s (the date of first air photos). The laboratory derived frictional rheology provides a quantitative framework to explain the observed landslide slip response to changes in pore pressure and suggests that friction laws can be used not only to interpret past slide behavior, but potentially to predict landslide responses to future climate-driven hydrologic forcing or other external perturbations.

How to cite: Schaible, K., Saffer, D., and Finnegan, N.: Experimental constraints on the slip response of a slow-moving landslide to rainfall driven pore pressure changes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14669, https://doi.org/10.5194/egusphere-egu26-14669, 2026.

Relative seismic velocity changes (dv/v) derived from ambient noise interferometry serve as a proxy for the internal rigidity or structural health of landslide materials. Strong ground motion often induces coseismic velocity drops, indicating damage within the shallow crust or the landslide body. This study focuses on the deep-seated, slow-moving Wuhe landslide in eastern Taiwan, which exhibits stable creeping with daily displacement rates ranging from 4 mm to 25 mm(Weng et al., 2025), to investigate its response to the September 2022 earthquake sequence, specifically the ML 6.6 Guanshan and ML 6.8 Chihshang earthquakes.To monitor temporal variations in the landslide's internal state, we applied the single-station cross-component (SC) technique to the Wuhe landslide using continuous ambient noise records. The seismic monitoring network comprises one geophone installed directly on the sliding mass and three reference stations located on stable bedrock outside the landslide area. This configuration aims to differentiate between landslide-specific structural changes and regional reference variations. The preliminary results showed that a clear seismic velocity reduction was found spatially within the landslide area. Through dv/v measurements with in-situ real-time kinematic (RTK) GPS data and strong-motion records, the coseismic velocity drops are in response to the accelerating surface displacement and strong ground shaking, and the spatial relationships between dv/v, surface movement and peak-ground acceleration (PGA) are systematically compared . In fact, the earthquake did not trigger catastrophic landsliding at the Wuhe site, Thus, we further investigate the recovery of landslide material properties following strong ground shaking. The post-seismic recovery duration captured by dv/v observations can help us to better understanding recovery mechanism of landslide material after earthquakes.

How to cite: Weng, H.-K. and Chao, W.-A.: Coseismic Seismic Velocity Variations of a Deep-Seated Landslide Caused by Two M6.5+ Earthquakes in Eastern Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15810, https://doi.org/10.5194/egusphere-egu26-15810, 2026.

Earthflows are flow-like landslides involving fine-grained, clay-rich materials that exhibit complex kinematics, long-term activity, and alternating phases of slow movement and sudden acceleration. Although their flow-like behaviour is commonly attributed to distributed internal deformation and plastic rheology, the mechanisms governing the transition from solid-like sliding to fluid-like flowing remain poorly understood, particularly with respect to boundary conditions and material properties. This transition is critical, as it may lead to surging events associated with high mobility and significant hazard.
This study investigates the role of mineralogical, geotechnical, rheological, and geomorphological factors in controlling earthflow mobility and material fluidization. A set of representative earthflows located in the southern Apennines was selected, covering a wide range of geological settings and morphological characteristics. Laboratory analyses were conducted on samples collected from different sectors of the landslides, including grain size distribution, Atterberg limits, mechanical behaviour, quantitative mineralogical composition. Moreover, rheometrical analysis of the fine fractions under controlled shear conditions were also performed. These data were integrated with long-term geomorphological analyses based on satellite imagery and morphometric reconstructions of landslide geometry.
Earthflow behaviour was analysed using a one-dimensional framework based on a Herschel–Bulkley viscoplastic rheological model, aimed at reproducing internal kinematic compartmentalisation in relation to variable water content.
The influence of water content variations, as a function of rainfall-induced infiltration conditions, on rheological parameters and mechanical response was investigated. The results highlight strong correlations between plasticity, occurrence of expandable clay minerals, rheology, and mobility, emphasizing the key role of fine-grained materials in promoting solid–fluid transitions. 
By integrating multi disciplinary datasets, this work advances the understanding and prediction of earthflow fluidization and mobility-processes for which current forecasting capabilities remain notably limited.

How to cite: Annibali Corona, M., Calcaterra, D., Di Spirito, N. A., Izzo, F., Langella, A., Mercurio, M., Pasquino, R., Russo, G., Vitale, E., and Guerriero, L.: From Sliding to Flowing: Integrating Geotechnical, Mineralogical, and Rheological Controls on Earthflow Mobility, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14514, https://doi.org/10.5194/egusphere-egu26-14514, 2026.

Shallow landslides represent a major hazard in western Rwanda, where steep slopes, deeply weathered materials and intense precipitation frequently interact. This study, carried out in the framework of the WALL project (Grant ID: GCRW-CL001, https://www.wallatrwanda.org/), focuses on a landslide-prone area within the Karongi District and presents a proof-of-concept analysis aimed at investigating the sensitivity of slope stability to key geotechnical and pore pressure–related parameters.

Slope stability is analysed using Scoops 3D (Reid et al., 2015), which implements three-dimensional limit-equilibrium methods (LEM) and evaluates slope stability by testing a large number of potential spherical trial failure surfaces. This approach allows for a systematic exploration of potential instability mechanisms while maintaining a computationally efficient framework suitable for regional-scale and data-scarce applications. Due to the limited availability of site-specific geotechnical data, model parameters are defined within plausible ranges derived from published literature and regional information.

Under these conditions, a global sensitivity analysis based on Sobol indices (Saltelli and Sobol, 1995) represents a suitable and robust strategy to investigate model behaviour and uncertainty. The Sobol analysis is applied to investigate the influence of key geotechnical parameters, including cohesion, internal friction angle and unit weight, and additional pore pressure accounting for hydrological conditions on slope stability results. Both first-order effects and higher-order interaction terms are analysed, providing insights into the combined mechanical and hydraulic controls on slope stability.

The proposed workflow identifies the dominant sources of variability on the output and offers a structured basis for prioritizing the quantification of geotechnical parameters in future data acquisition and model refinement, also in connection with specific triggering factors relevant for the studied area, such as rainfall.

REFERENCES

Reid, M. E., Christian, S. B., Brien, D. L., & Henderson, S. T. (2015). Scoops3D: software to analyze 3D slope stability throughout a digital landscape (No. 14-A1). US Geological Survey.

Saltelli, A., Sobol’, I. M. (1995). Sensitivity analysis for nonlinear mathematical models: numerical experience. Matematicheskoe Modelirovanie, 7(11), 16–28.

How to cite: Zanetti, M., Armigliato, A., Angeli, C., Zaniboni, F., Barayagwiza, S., and Meriaux, C.: Exploring the stability of shallow landslides through global sensitivity analysis: a proof of concept from western Rwanda, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11274, https://doi.org/10.5194/egusphere-egu26-11274, 2026.

With intensifying precipitation events, landslides pose increasing environmental hazards. Unsaturated slopes are key monitoring targets due to their rapid, and sometimes severe, response to rainfall. This study investigates how hydrological changes in an unsaturated slope in Eidsvoll (Norway) influence seismic velocities through time and space using a physics-based modelling framework. Vertical effective stress and density fields from hydromechanical simulations in GeoStudio are used as inputs to the Biot-Gassmann relationship to estimate time-varying P- and S-wave velocities. These velocities are used to compute Rayleigh wave phase velocity dispersion curves and sensitivity kernels for selected days throughout a 250-day (September 2019-May 2020) simulation period. The results reveal a strong coupling between infiltration, effective stress, and seismic velocities, especially in the upper part of the unsaturated slope. Rayliegh wave sensitivity is highly frequency- and depth- dependent: high frequencies (above 60 Hz) are sensitive to near-surface changes, while lower frequencies probe deeper layers. A persistent blind zone in an intermediate high-velocity layer limits the surface waves sensitivity to certain depths, underscoring the importance of survey design and the usefulness of surface waves depending on the geologic scenario. This forward modelling approach enables identification of optimal frequency ranges and target depths, providing critical input for future field investigations. These findings contribute to the development of focused site-specific seismic monitoring strategies, including passive surveys using anthropogenic noise sources or active source MASW. By bridging hydromechanical modelling and the associated seismic response using slope-scale physical processes, this approach can support early warning systems and landslide hazard assessment under changing climate conditions.

How to cite: Mikesell, T. D., Lorentzen, E. B., Piciullo, L., and Sørensen, M. B.: Linking Hydrological Forcing to Seismic Sensitivity in an Unsaturated Slope Using Physics-Based Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16829, https://doi.org/10.5194/egusphere-egu26-16829, 2026.

Shallow landslides triggered by intense and prolonged precipitation represent a major geohazard in many soil-dominated landscapes. This study presents the development of an integrated monitoring and modelling framework for the early detection and characterisation of hydraulically induced shallow landslides. The approach is based on the selection of three representative pilot sites and the implementation of comprehensive field investigations (engineering-geological, pedological, geotechnical, hydrological) and laboratory testing to determine the chemical, physical, and mechanical properties of characteristic soil horizons. A real-time monitoring system has been established to continuously record  soil volumetric water content and suction, together with precipitation, providing high-resolution hydro-meteorological and hydrological data. Geoelectrical measurements and field investigations were applied to characterise soil structure and depth, and to establish relationships between geophysical parameters and physico-mechanical soil properties. These analyses enable the development of a non-invasive monitoring approach capable of diagnosing landslide initiation, delineating landslide geometry, and estimating potentially unstable volumes. Based on the monitoring data obtained at pilot sites, hydro-meteorological thresholds and critical soil parameters controlling shallow landslide occurrence are derived for key soil types. Safety factors and probabilistic landslide occurrence models are developed to identify dominant triggering mechanisms. The results contribute to a national-scale framework for shallow landslide susceptibility mapping and provide a transferable methodology for operational landslide early-warning systems. This research is supported by the Slovenian Research and Innovation Agency through research projects: A holistic approach to Earth surface processes driven by extreme weather events (J7-60124) and Geospatial information technologies for a resilient and sustainable society (GC-0006).

How to cite: Jemec Auflič, M., Maček, M., Smolar, J., Kure, K., Peternel, T., Grčman, H., Turniški, R., Zupan, M., Zupanc, V., Žvokelj, L., and Pulko, B.: Framework for early detection and characterisation of hydraulically induced shallow landslides, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9556, https://doi.org/10.5194/egusphere-egu26-9556, 2026.

INTRODUCTION

Underground mining frequently leads to surface instability such as subsidence, sinkholes, and landslides. In the Bulqiza chrome mine in Albania, decades of extraction and the transition from cut-and-fill to sublevel stoping have increased rock-mass deformation, resulting in fissures, caving, and surface failures. This study focuses on Profile XIV, where both continuous subsidence and a sinkhole are present, in order to evaluate the accuracy of predictive methods used to assess mining-induced deformation.

AIM

This study aims to assess the surface impacts of underground mining in the Bulqiza district by applying both empirical subsidence modelling and numerical simulations using Finite Element Methods. The study compares predicted results with observed deformation, evaluates the influence of caved zones (goaf) and tectonic structures, and verifies the suitability of using a combined empirical and numerical approach for deformation assessment.

METHODS

Geological and mechanical properties were defined through field investigations and archived mine data. An empirical model with a subsidence coefficient of K = 0.9 was used to calculate the critical collapse depth (Hcal) and compare it with the effective mining depth (Hfac). Numerical simulations were then performed with the Rocscience FEM software for two surface-deformation profiles: one exhibiting continuous subsidence and the other featuring a surface sinkhole. Each profile was modelled under different conditions, including the presence or absence of goaf and the inclusion or exclusion of tectonic influence. Surface displacement was used as the main indicator for assessing deformation.

RESULTS

The empirical model indicated a low likelihood of funnel formation in the subsidence profile, where Hcal was smaller than Hfac, while in the sinkhole profile, Hcal exceeded Hfac, confirming a high probability of collapse consistent with field observations. Numerical modelling supported these findings. In the subsidence profile, vertical displacement remained small around 14 mm regardless of whether the goaf was included, and no funnel formation was predicted. In the sinkhole profile, displacement increased to 24.3 mm when the goaf was considered without tectonics. When tectonic effects were included, displacement increased substantially to values between 40.4 and 61 mm, closely reproducing the actual sinkhole conditions. These results show that tectonics strongly amplifies surface deformation.

CONCLUSIONS

This study demonstrates that both empirical and numerical methods effectively reproduce the types and magnitudes of surface deformation observed in the Bulqiza mine. Numerical modelling closely matched actual conditions, particularly when tectonic effects were incorporated. While goaf conditions had little effect in the subsidence zone, they significantly increased deformation in the sinkhole area. The findings confirm that tectonic structures are a major factor controlling surface collapse and that a combined empirical and numerical approach provides a reliable method for assessing mining-induced surface impacts in Bulqiza and comparable underground mining environments.

How to cite: Belba, P.: Surface Deformation Assessment in the Bulqiza Chrome Mine Using Empirical and Numerical Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-798, https://doi.org/10.5194/egusphere-egu26-798, 2026.

How to cite: Agustina, L., Arnhardt, C., Van Wyk De Vries, M., Hussain, E., Large, D., and Turnbull, B.: Understanding and Zoning Rainfall-Induced Landslide Hazards in Indonesia: Insights from Observation to Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22967, https://doi.org/10.5194/egusphere-egu26-22967, 2026.

Understanding landslide (LS) distribution in deglaciated mountains is key to landscape evolution and geohazard risk. We present an orogen-scale assessment of 1,691 Post-Little Ice Age (LIA) LSs (91% shallow) along the South Patagonian Icefield (SPI, 48–52°S) margins. Mapped via high-resolution multitemporal imagery (2010–2025) and multi-operator validated, kernel densities (10 km bandwidth) show clustering in western and southern SPI—central peak, northwest secondary—amid ~20% ice loss (since the end of the LIA) and uplift >40 mm/yr.

Environmental variables from LS/non-LS areas fed Bayesian horseshoe variable selection. Sparse Gaussian process regression (R²=0.96, SPAEF ≥0.85) identified precipitation, fault density, and uplift as dominant controls. Precipitation destabilizes slopes via pore pressures, triggering shallow LSs (positive correlation); fault density signals structural weakness/seismic facilitation; uplift shows complex negative LS correlation, as active deformation/steep slopes favor erosion over accumulation, reducing LS buildup. Lithology, permafrost, retreat rates exert weaker, context-dependent influences. LS versus non-LS distinctions underscore the value of integrating correlation-based and predictive approaches. Coupled climate-deglaciation-tectonics govern landslide distribution in the SPI.

Critically, ~17% of LSs overlap glacial lake upslope areas (30 m buffer), preconditioning glacier lake outburst flood risks at, e.g. Torre Glacier's ~8 Mm³ failure—shallow dominance may temper severity, sea-proximal cases extend threats. Findings illuminate paraglacial responses to glacier retreat, offering predictive hazard frameworks for warming cryosphere.

How to cite: Seier, G., Slíva, M., Pánek, T., and Winocur, D.: Environmental Controls on Post-Little Ice Age Landslide Distribution Around the South Patagonian Icefield, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3757, https://doi.org/10.5194/egusphere-egu26-3757, 2026.

Road construction on hillslopes has increased explosively due to the rapid socioeconomic development in China’s mountainous areas. The exposure of steep and rapidly weathering slopes caused by road construction accelerates slope movements, especially roads building on residual soil. Residual soil slopes are prone to slow movements and may evolve to failure in response to infiltration of rainwater. Engineering works on residual soil (e.g., excavation, filling for buildings and roads) exacerbate these problems through altering the internal and external stress of slopes. Yet our understanding of the interactive effects of rainfall and road construction on slope dynamics or even failure in subtropical residual soils remains elusive. Here, we used three-decadal radar remote sensing data to quantify the time series deformation before a catastrophic slope failure, occurring at Meida Highway in China that caused 52 fatalities. Physics-based decomposition of the time series movements over the past 8 years reveals that there is a constant seasonal movement related to rainfall and a precursory accelerated movement triggered by slope reinforced measures before failure occurrence in May 2024. Emergency mitigations of reinforced measures modified the infiltrates and routes of surface and subsurface water, leading to an adverse impact of reducing slope failure risk. Analysis of numerical simulation indicates that rainfall-induced pore water pressure reduced the shear strength of granite residual soils, ultimately triggering slope failure. This improved understanding of the slope dynamics in response to different forces will be important to avoid economic and life loss, strengthen emergency planning and identify potential risks.

How to cite: Huang, X. and Ma, P.: Satellite images reveal progressive slope deformation triggered by mountainous road construction in subtropical South China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4787, https://doi.org/10.5194/egusphere-egu26-4787, 2026.

Landslides are a global phenomenon occurring in several climatic and geomorphologic contexts, generating billions in economic losses and causing thousand of causalities each year. This phenomenon is often characterized as a local problem, but its effect and cost frequently cross local jurisdiction and may become a national problem [1]. Landslides, resulting from disturbance in slope equilibrium induced by the movement of a mass of rock, debris or earth down a slope and pose a significant threat to landscapes, infrastructure and human life [2]. Landslides can be labelled into different categories depending on the type of movement and the type of material involved. They may be triggered by several phenomena; the primary are seismic activities and heavy rainfall. More precisely, rainfall-induced landslides typically occur in regions prone to heavy precipitation, with steep slopes and poorly consolidated soil or rock [2]. In Italy, the most recent case study, is the Cormons (Gorizia, Italy) landslide occurred on November 17^th 2025. Here, intense rainfalls caused a mud-flow inducing the collapse of several buildings and two casualties. In this area, landslides are the most frequent type of instability. These are mostly small and medium-sized landslides, located on flyschoid hills, affecting vineyards and only locally affecting roads and rural settlements [3]. Identifying these phenomena through satellite-based remote sensing techniques offers essential data and insight for landslide studies. Information regarding timing, location and spatial extent of detected landslides, along with changes in surface materials, plays a key role in risk and susceptibility assessments as well as in effective disaster management, monitoring and response activities. For the purpose of this work, optical satellite images provided by Sentinel-2, together with the Lidar provided by the Italian Civil Defense have been used with the aim to identifying the Cormons landslide and its characteristics in terms of dimensions, shape and amount of material moved during the event. The use of optical imagery from Sentinel-2 it’s been used to evaluate spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Bare Soil Index (BSI). Instead Lidar and DEM have been used to define the ground changes in terms of elevation and also the amount of material involved in the event. From the GIS analysis, the results confirm the presence of a mudflow within a watershed located in the Cormons area. Additionally, from the Lidar other small collapse features have been highlighted in the surrounding area.

REFERENCES:

• Highland, L. M., & Bobrowsky, P. (2008). The landslide handbook-A guide to understanding landslides(No. 1325). US Geological Survey.
• Peters, S., Liu, J., Keppel, G., Wendleder, A., & Xu, P. (2024). Detecting coseismic landslides in GEE using machine learning algorithms on combined optical and radar imagery. Remote Sensing, 16(10), 1722.
• https://www.isprambiente.gov.it/files/pubblicazioni/rapporti/rapporto-frane-2007/Capitolo_11_Friuli_Venezia_Giulia.pdf

How to cite: scalabrini, A., fornasari, S. F., and costa, G.: Cormons landslide characterization using Lidar and remote sensed data., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6955, https://doi.org/10.5194/egusphere-egu26-6955, 2026.

An increase in soil water content (SWC) from rainfall infiltration reduces the matric suction and shear strength; hence, rainfall is a primary trigger of shallow landslides. While accurate SWC monitoring is critical for predicting slope failure, traditional point-based sensors lack the spatial resolution required for effective field-scale assessment. This study aims to bridge this gap by integrating hyperspectral and multispectral imaging technologies with advanced machine learning (ML) models. Based on 114 in-situ soil samples collected from landslide-affected areas across South Korea, correlations between physical soil properties (e.g., void ratio, soil color) and hyperspectral data in the visible and near-infrared (Vis-NIR) regions were analyzed. Two ML algorithms, Random Forest (RF) and Multilayer Perceptron (MLP), were employed to develop predictive models for SWC. In this study, statistical evaluation indicated that the RF model demonstrated superior accuracy and robustness in handling high-dimensional spectral data compared to the MLP model. To validate the method's applicability for landslide monitoring, field tests were conducted in the mountainous region of Pyeongchang, South Korea, using a multispectral camera mounted on an unmanned aerial vehicle (UAV). The RF model successfully predicted the spatial distribution of SWC using spectral reflectance and geotechnical parameters. Although the model showed limitations in extrapolating beyond the training data range, it effectively captured critical variations in soil moisture relevant to slope stability. These results suggest that integrating UAV-based remote sensing with ML offers a promising, non-contact approach for high-resolution monitoring of shallow landslides, contributing to more proactive disaster prevention strategies.

How to cite: Lim, H.-H., Cheon, E., and Lee, S.-R.: UAV-Based Multispectral Assessment of Soil Water Content for Shallow Landslide Monitoring: A Machine Learning Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2879, https://doi.org/10.5194/egusphere-egu26-2879, 2026.

Slope creep refers to the imperceptibly slow and gradual downslope movement of soil and rock driven by gravity. It is mainly driven by moisture-induced expansion of clay-rich materials and the resulting decrease in shear strength. Although subsurface conditions can influence slope creep vulnerability, identifying their effects remains challenging. In recent years, electrical resistivity and seismic surveys have been widely used to characterize the spatial and temporal variability of subsurface soil properties. These geophysical methods provide a non-destructive means of investigating subsurface physical characteristics. In this study, electrical resistivity and seismic surveys were conducted to assess slope creep vulnerability associated with subsurface conditions. Geophysical survey data were obtained from 124 slope sites, and their slope creep vulnerability was classified into two groups (low and high) based on field investigations. Cross-plot analysis was applied to integrate electrical resistivity and seismic velocity, and the resulting data points were classified into four quadrants according to threshold values of seismic velocity and electrical resistivity. The threshold values were statistically determined using a t-test. The composition ratios of the four quadrants were used as input variables for deep learning training, and the bedrock proportion based on seismic velocity included as an additional input. As a result, a total of five input variables were used, and deep learning training was performed by classifying slope creep vulnerability into two groups. As a result, a total of five input variables were used to train a deep learning model for classification of slope creep vulnerability into two groups. Due to the limited dataset size, five-fold cross-validation was applied for model evaluation. As a result, the deep learning model achieved an accuracy of 81.5% and a recall of 83.0% in classifying slope creep vulnerability, indicating its effectiveness in identifying slope creep–prone areas.

Acknowledgments: This study was carried out with the support of ´R&D Program for Forest Science Technology (RS-2025-02213490)´ provided by Korea Forest Service (Korea Forestry Promotion Institute).

How to cite: Bong, T., Jeon, J., Jeong, E., Lee, S., Heo, J., and Seo, J.: Deep Learning-Based Assessment of Slope Creep Vulnerability Using Geophysical Survey Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10608, https://doi.org/10.5194/egusphere-egu26-10608, 2026.