Landslides pose a considerable risk over communities and infrastructure in Iceland. There have been large landslides in the recent years and a least one related to permafrost. Taking into account the changing climate, knowing past and active moving slopes increases preparedness for civil protection purposes. In order to have a better overview of the actual hazard, we mapped and classified all landforms reminiscent of landslides into a database. The mapping was done using aerial orthophotos, digital elevation models (DEM), and satellite interferometry (InSAR) velocity map. To begin with, this allows to extract statistics about the spatial distribution and size of various type of landslides. The largest landslide features mapped covers over 10 km², the smallest below 100 m². The most common type of large landform can be classified as complex: a mix of slide and slow flow. As expected, most landslides are located where there is steep topography: the West Fjords, the Trollaskagi peninsula, and the East Fjords. However, the distribution of landslide landforms is extremely varied within these areas. Some valleys show numerous landslides while other none. To help figuring out this heterogeneity, this database is put into relation with other type of geographical information: digital elevation models, lithology, bedrock geology, volcanic systems, faults, hydrology, permafrost, ground deformation velocities, constructions and infrastructures.

How to cite: Drouin, V. and Stefani, M.: A new landslide database for Iceland: what it tells us., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14887, https://doi.org/10.5194/egusphere-egu23-14887, 2023.

Many theories about processes and conditions of rock avalanches lack field evidence, due to difficulties in monitoring such events and the rarity of accessible locations to study corresponding structures in bedrock outcrops. This study provides a detailed investigation of the basal contact zone (including the rupture/sliding surface) of the Flims rock avalanche at two sites (from the proximal and distal release area) in terms of architecture, microfabric, and formation conditions. In addition, we compare our findings with shallow seismotectonic fault zones and derive indications for processes that have occurred before, during, and after the failure of the Flims rock avalanche.

Field observations document the wide natural variability of the basal contact zone architecture within the rock avalanche source area. The studied contact zone was formed at about 500 m depth as a stepped or undulating structure, few centimeters to several meters thick. It consists of chaotic breccia and locally features an up to 10 cm thick mesocataclasite, granular fault injections, and striated pavements indicating highly localized shear deformation. The pavements represent the main rupture/sliding plane of the rock avalanche and occur either as a sharp boundary to the intact bedrock or as parallel planes within mesocataclasite. In the proximal area of the source zone, a gradual increase of grain comminution towards the rock avalanche basal rupture/sliding surface suggests that most deformation and movement within the rock avalanche was concentrated in this narrow zone. In the more distal area, the deformation and movement were distributed on both the basal rupture surface and internal shear zones.

Microstructural investigations of the contact zone reveal deformations older than the mesocataclasite and pavement, including mylonites and calcite veins related to the previous tectonic history, and an old healed breccia, possibly formed during pre-failure damage in this zone. The architecture of the rock adjacent to the rupture/sliding surface observed in this study shows similarities to observations from shallow seismotectonic fault zones and high-strain and high-speed shear experiments. The analogies help to understand processes that led to the formation of the rupture plane and its increased mobility: Observations of cataclasite at the basal rupture zone suggest that the movement of the rock mass first was slow (< 0.4 m/s) and crushed the rock near the basal rupture surface by constrained comminution, inducing a granular flow. An acceleration of the slip rate to over 1 m/s led to dynamic weakening and the development of a distinct rupture/sliding surface. With the formation of a thin rupture surface, several coupled processes (grain boundary sliding, frictional heating, and thermal decomposition) might have caused a further decrease of the frictional resistance on this plane, resulting in increased mobility of the rock avalanche in the source area. Evidence for these processes is given by the occurrence of rounded nano-grain structures on the pavement of the basal rupture surface, which are possible remains of thermal decarbonation. This decarbonation implies a very local temperature rise due to frictional heating (> 720 °C), less than 10 µm away from the rupture surface.

How to cite: Betschart, S., Loew, S., Mancktelow, N., and Grafulha Morales, L.: Architecture, Microfabric and Formation Conditions of the Basal Contact Zone of the Flims Rock Avalanche (Switzerland), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2084, https://doi.org/10.5194/egusphere-egu23-2084, 2023.

The quantification of the effects of external forcings such as seismicity and rainfall on slope destabilization is an open and important question. To investigate the role of these forcings, we analyze an unprecedented 10-year long catalog of the rockfalls occurring in the Piton de la Fournaise volcano crater. Indeed, the dense seismic network operated by the Piton de la Fournaise Volcano Observatory (La Réunion Island) makes it possible to precisely locate the rockfalls and recover the volume of each event. We use statistical tools originally developed for earthquakes to study the spatio-temporal evolution of the rockfall activity and to unravel the impact of the external forcings. Our results highlight the predominant effect of low amplitude seismicity on the slope destabilization, via a progressive damaging of the slopes. Moreover, we show that the efficiency and the time delay of this dynamic triggering depends on the stability state of the slopes, i.e. the distance to failure. To better understand our observations, we compare them with laboratory experiments on granular avalanches triggered by ultrasound.

How to cite: Durand, V., Mangeney, A., Bernard, P., Jia, X., Bonilla, F., Satriano, C., Saurel, J.-M., Aissoui, E. M., Peltier, A., Ferrazzini, V., Kowalski, P., Lauret, F., Brunet, C., and Hibert, C.: The competing roles of seismicity and rainfall in slope destabilization: rockfalls triggered on a metastable volcanic edifice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8130, https://doi.org/10.5194/egusphere-egu23-8130, 2023.

Large slope instability processes result from a complex interaction among different geological, geomorphological and climatic factors. A complex multidisciplinary approach is thus necessary to understand their behavior and develop modeling predictive tools. This work suggests a multi coupling method to predict stability and velocity of a landslide, giving critical values for measurable variables (i.e., piezometric level) up to which remediation actions can be deployed. The aim is to define a time‐dependent stability criterion that links the external forcing of a landslide with its internal response through a thermo-poro-mechanical mathematical model.

The presented model is based on the assumption that most of the landslide deformation is concentrated on a basal shear band representing the sliding surface: the landslide body is deemed as a rigid block sliding on a visco-plastic shear band with thermal softening and velocity hardening. When the landslide moves, it causes friction with mechanical dissipation that raises the basal temperature and reduces the shearing resistance of the shear-band material. This process can continue up to the point when the friction coefficient decreases uncontrollably due to a thermal runaway instability and the system become unstable, even without changes in the external factors.

The model is applied to the Ruinon Landslide located in the Central Italian Alps (upper Valtellina region). It represents one of the most active cases in the alpine region, with a main sliding surface located at a depth of approximately 70 m, for a total estimated volume of about 20 Mm³ threatening the national road SS300 that travels through the valley bottom. On the base of the available in situ monitoring data (Piezometers, Ground-Based Interferometric Radar), velocity–time curves correlate with the piezometric level trend, suggesting a key role of pore pressure as an accelerating factor for the landslide.

The workflow of the analysis involved different steps. A preliminary 3D FEM numerical analysis was performed to provide the stress-strain distribution along the slope. Then, to define the thermo-poro-mechanical behavior of the sliding surface and to calibrate the mathematical model, triaxial compression tests with thermal control were performed on rock samples representative of the shear band. The pore pressure data from in situ piezometers were introduced as input-data and the landslide basal temperature was calculated. Finally, the strain rate was simulated by the model and a process of validation was applied using field displacement histories recorded by the landslide monitoring system.

The outputs of the model well simulate the landslide velocity, reproducing the sliding behavior and its relationship with pore pressure. The developed time dependent stability criterion represents an innovative physics-based tool for analyze landslide evolution leading to early-warning and remediation strategies, that accounts for thermal and velocity sensitivity of shear band materials, as well as the effect of pore pressure in promoting the evolution of different creep stages. The validated model can be also used as a predictive tool, to forecast the behavior of landslides and establish a physically based early warning strategy taking into account future climate scenarios.

How to cite: Morcioni, A., Apuani, T., Cecinato, F., and Veveakis, M.: A thermo-poro-mechanical model to simulate and predict landslide evolution: a physics-based method applied to the Ruinon Landslide (Italian Alps), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8771, https://doi.org/10.5194/egusphere-egu23-8771, 2023.

Rock slopes usually exhibit progressive failure phenomena over a long period of time under the active Earth surface environment involving complex geological, mechanical, hydrological, and chemical interactions. Among these processes, weathering has been recognised as a ubiquitous and important factor that drives slope destabilisation. Rock masses in a slope may experience weathering-induced strength degradation of variable degrees depending on the morphology, lithology, depth, fracturing, and time, which can lead to the emergence of various rock slope failure patterns, e.g. planar and rotational slides, slumps, topples, and rock falls. After failure, the slope may transition from slow deformation to catastrophic collapse characterised by rapidly moving material flows of fragmented rocks. These complex processes are driven by various mechanisms operating across different timescales, which pose a great challenge for modelling the entire history of rockslide evolution. In this study, we develop a unified computational framework for simulating the pre- and post-failure behaviour of rock slopes subject to long-term weathering processes. This framework includes the following key features: (i) a coupled weathering-damage model is developed to capture the interplay of weathering-induced strength loss and damage-related strain softening; (ii) pre-existing faults are represented explicitly as thin weakness zones; (iii) an implicit time integration scheme is adopted to simulate the slope evolutionary behaviour across multiple timescales; (iv) a frictional velocity-weakening law is incorporated to capture the development of rapid mass flows; (v) the particle finite element technique is used to track the small to large deformation/motion of rock masses. We show that our model can realistically simulate the pre-failure progressive rock slope destabilisation, the catastrophic rock mass failure, and the post-failure transient runout, demonstrating the capability of our model in realistically capturing the initiation, evolution, and consequence of weathered rock slope failures. Our results provide useful insights into the interplay of natural weathering and brittle damage in rockslide evolution and the control of geological structures on pre- and post-failure patterns of rock slopes.

How to cite: Wang, L., Loew, S., and Lei, Q.: Modelling weathering-induced progressive rock slope failures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4270, https://doi.org/10.5194/egusphere-egu23-4270, 2023.

The study of glacier instabilities can be very useful, particularly when the activation of large ice avalanches can be dangerous for several elements at risk down-valley. This critical condition characterizes a growing number of glaciers in the Alps, where the distance between infrastructures, tourist areas and glaciers are minimal. The tragedy that occurred in Marmolada in 2022 is an example of the impact that an ice avalanche can have on a highly frequented area. In several recent studies, glacier-related instabilities are based on approaches similar to the ones adopted for landslides; in particular, the use of high-rate monitoring systems is fundamental for a characterization of the surficial movement of the glacier and its activity. The presence of an acceleration phase is often a precursor of the fall of the unstable ice chunk, and that is why the use of high-rate monitoring systems can be adopted for early warning purposes. The availability of similar data also allows a deeper knowledge of the processes that characterize the evolution of glaciers. Up to the present, the limited presence of permanent survey systems has prevented a more detailed study of the dynamics that control the evolution of glaciers. Recent monitoring solutions adopted to manage the ice-avalanche-related risk in the Alps represent an excellent opportunity to reduce this gap. The Grand Jorasses (Italian side of the Mont Blanc massif) open-field laboratory for the development of monitoring systems is an interesting example of this recent opportunity. The presence of cold (Whymper serac) and temperate (Planpincieux glacier) monitored glaciers is also important for better evaluating the impact of water at the bedrock-ice interface on the stability of hanging glaciers. The results obtained in the Grand Jorasses open-field laboratory pointed out the high complexity of temperate glaciers due to the variety of triggers that can activate large ice falls. The restricted access to the site for safety reasons limited the direct measurement of important parameters and led to the adoption of proximal remote sensing solutions. Thanks to the acquired data, a conceptual model of the glaciers' dynamics have been developed and adopted for better risk assessment.

How to cite: Giordan, D., Dematteis, N., Troilo, F., and Segor, V.: Dynamics of alpine glaciers large instabilities: results and open problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14471, https://doi.org/10.5194/egusphere-egu23-14471, 2023.

In remote sensing of landslide investigation, the interpretation of optical image is the main method at present. However, when a disaster occurs, it is very difficult to obtain images without cloud coverage. For example, Typhoon Lubi in August 2021 and Typhoon Nissa in 2022 caused many landslides and road interruptions. However, due to the cover of clouds and fog, it was impossible to obtain satellite images in time to judge the scale of the disaster. Unmanned vehicles are also affected by weather factors, which greatly increases the risk of flight. Therefore, it is extremely necessary to develop disaster identification methods that are not affected by weather.

In this study, the long electromagnetic waves of synthetic aperture radar (SAR) are not affected by cloud and fog to develop a landslide detection model for radar images. The reference range of the location and scale of the landslide can be obtained under bad weather conditions to make up for the weather limitations when evaluating the scope of the disaster with optical images.

In this study, the NDSI&RVID method is used as the index for the identification and interpretation of the landslide area, and the analysis and discussion of the landslide area is carried out in combination with multi-time series and different orbital data. The effect of landslide identification is improved by three methods: single-sequence identification and interpretation stacking, multi-time-series index stacking, and multi-time-series image stacking. Among them, better interpretation results can be achieved by stacking multiple time-series images. It is recommended to use the number of 4 images before the disaster and 1 image after the disaster for data interpretation. Although the image pixel classification effect still needs to be improved, the identification rate for landslides of more than 10 hectares can reach more than 90%. In the absence of optical images, it has considerable reference value.

How to cite: Wang, K.-L., Lin, J.-T., and Lee, Y.-H.: The Feasibility Assessment of Quick Landslide Identification Methods After Hazards with Sentinel-1 SAR Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6798, https://doi.org/10.5194/egusphere-egu23-6798, 2023.

A landslide database is of utmost importance for hazard management as well as early warning systems. Historically landslides were manually identified by ground surveys or remote sensing data, but with development in satellite technology open-source satellite imagery has emerged as a preferred data source for landslide identification due to its cost effectiveness. On the other hand, an increase in computing power made computer vision methods especially deep learning popular for satellite image segmentation. Deep learning models require a large amount of data to reach operational performances, however there is very little labelled landslide data present. Labelling satellite imagery is costly and time consuming. Active learning remedies this by optimally selecting the data to label thereby maximizing the performance of the model given the limited data. In this study we present an active learning-based framework to train a segmentation model to identify landslides. The pre- and post-landslide images from sentinel 2 are merged with terrain features to create input data bands. The model is tested on a test database using metrics like IOU. The methodology has been developed with an application in India but can be applied globally.

How to cite: Sharma, N. and Saharia, M.: DL-AISLE: A Deep Learning framework using Active Learning on Satellite imagery for Landslide identification , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7155, https://doi.org/10.5194/egusphere-egu23-7155, 2023.

With the avalanche of satellite remote sensing sensors, significant efforts have been made to develop methods to integrate optical and SAR remote sensing efficiently to quantify the kinematics and lifecycle of landslides. In this study, we design a framework that integrates multi-sensor satellite remote sensing data to investigate post-failure kinematics of the 17 June 2020 Aniangzhai landslide in the Danba County of Southwest China. This ancient landslide was partially reactivated due to rapid river incision and toe erosion during a complex cascading event, which led to an evacuation and relocation of more than 20,000 people.

First, time series of Planet images are exploited using the sub-pixel offset tracking method to generate horizontal deformation. Then advanced Multi-temporal InSAR (MTI) techniques are utilized to analyze the line-of-sight (LOS) displacements for 16 months after the failure. Eventually, the dynamics of the post-failure mechanism are modeled by integrating optical and radar data using an exponential decay model with independent component analysis (ICA) and least squares methods. Besides, the performance of a newly designed corner reflector (CR), consisting of two sets of semi-circular metal plates with a radius of 30-40 cm, is evaluated using both TerraSAR-X (TSX) and Sentinel-1 SAR data.

Optical results show that the landslide underwent large deformation up to around 14.3 meters within 1.5 months after the failure, then the rate of deformation decreased slowly with time. InSAR analysis suggests that the LOS velocity reached a maximum of approximately 300 mm/year, indicating the active status of the ancient landslide body after failure. Using ICA decomposition, we extracted different features with various spatiotemporal patterns from the landslide body, which was then applied in data integration and 4D modeling of landslide kinematics. Our experiment using newly designed CRs indicates improvement in the background intensity in TSX images by around 30 dB, with signal-to-clutter ratio (SCR) exceeding 25 dB. The radar cross-section (RCS) of CRs in both TSX and S1 images remains relatively stable, ranging from 15-23 dB, making them suitable for CR-InSAR analysis.

How to cite: Xia, Z., Motagh, M., Li, T., Peng, M., and Roessner, S.: Characterizing 4D post-failure slope kinematics of the 2020 Aniangzhai landslide combining different remote sensing measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9298, https://doi.org/10.5194/egusphere-egu23-9298, 2023.

The deep-seated landslides often caused severe hazard due to the large area and landslide mass associated with the landslide movement. Thus, monitoring the landslide movement is an important task for landslide hazard management. The Microelectromechanical Systems (MEMS) technique developed rapidly in recent years provides the ability of low-cost sensors and easy installation for monitoring of the landslide movement in field. Typically, the landslide movement monitoring using MEMS is based on the tilt angle determined from the measured ground acceleration variations in three directions, and being subjected to the signal noise. We adopt Moving Window Fast Fourier Transform and other seismic wave analysis in this study to improve resolution of the seismic signals and achieve a sound detection of deep-seated landslide movement. The MEMS was installed at the Lantai deep-seated landslide study area, which measured the ground accelerations mid-slope of the landslide. The seismic signals recorded for eleven earthquake events and three heavy rainfall events are selected for analysis. It was found that the signal frequency can be separated from the system responses and related to the landslide movement. Validations were conducted by comparing the analysis results to the field monitoring data of in-place inclinometer and borehole extensometer while available. It is suggested that the landslide movement can be identified with seismic signal at approximately 17 Hz, and the results are consistent for both earthquake-induced and rainfall-induced events. 

How to cite: Lin, M.-L., Chiu, S.-Y., Wang, K.-L., and Hsieh, Y.-M.: Detecting Deep-seated Landslide Movement Using Seismic Signal Analysis of MEMS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11251, https://doi.org/10.5194/egusphere-egu23-11251, 2023.

Monitoring the creeping indications of landslides could provide valuable information for hazard prevention. The DIC methods allow to measure horizontal ground deformation with optical images. The surface moving information of landslide could offer the necessary data to infer the geometry including landslide sliding surface and volume of landslides.

This study focuses on a deep-seated landslide in Guanghua area which has been creeping since 2006 in northern Taiwan and there were sporadic collapse events in this slope area during recent years. The satellite images from 2016 to 2022 were collected and applied in Sliding Time Master Digital Image Correlation Analyses (STMDA) procedure to obtain the surface deformation of the landslide. The results including surface displacement and moving direction highly coincided with other monitoring data from on-site instruments. The landslide depth derived from surface displacements is about 20 m. The achievements reveal that using DIC method help to understand the landslide creeping process and the geometry distribution of potential landslide

How to cite: Kuo, H.-L., Lin, G.-W., Lin, T.-Y., and Chu, C.: Using Digital Image Correlation (DIC) method to monitor the creeping indication and infer the geometry of landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12150, https://doi.org/10.5194/egusphere-egu23-12150, 2023.

Landslides show various characteristics of spatio-temporal distribution of movement. For example, nearby parts of the same landslide may respond differently to heavy rainfall. We report here on measurements of various episodic and transient movements, using low-cost continuously recording GNSS instruments, in two landslides areas in Iceland.

In the Tungnakvíslarjökull landslide, which lies on the west flank of the Katla Volcano in south Iceland, two GNSS instruments were installed in 2019 and 2020, at 830 and 650 m a.s.l. height, respectively. This landslide mass has subsided gradually by approximately 200 m in the past 70 years and has a scarp approximately 1.5 km long. The GNSS stations show movements of several decimeters per year, with most movement confined to late summer and fall each year. The lower station of the two shows distinct "jerky" motion, with instantaneous movements of 5-15 cm each time. These offsets are sometimes accompanied by regionally located seismic events occurring within seconds of the offsets. The upper station, however, moves more continuously. The landslide region experiences heavy rain in the fall season, however, also in the spring when little movement is observed. One possibility explaining the lack of motion in the spring time that frozen surface layers in spring to mid-summer may hinder precipitation from entering the landslide mass.

The Almenningar landslide region in north Iceland is composed of three main landslides spanning ~5 km distance. The fastest moving part is ~0.3 km wide and moves by ~1 m per year. A main road traverses the landslide area and needs frequent repairs because of differential motion. In the summer of 2022, nine continuously running GNSS stations were installed along the main road in the landslide region at 50 – 60 m a.s.l. height, with eight stations located in active parts, and one acting as a local reference station for monitoring purposes. Since the installation, three distinct movement episodes have been recorded, all following heavy rain, recorded by local and regional meteorological stations. However, different segments of the landslide area respond differently to the rain forcing, starting and stopping at different times, with some stations showing abrupt start with near-exponential decay, while some show gradual acceleration, followed by deceleration. We suggest that hydrological pressure inside the landslide governs much of its behavior.

In summary, the continuous low-cost GNSS observations complement spatially dense deformation techniques, such as using InSAR, differential DEM, or feature tracking. The continuous GNSS monitoring allows for great potential in understanding the time-dependent mechanics of landslides, and contributing to early warning of excessive motion.

How to cite: Geirsson, H., Sæmundsson, T., Skúladóttir, J. M., and Jónasson, N.: Seasonal and precipitation-triggered movements of the Almenningar and Tungnakvíslarjökull landslides, Iceland, monitored by low-cost GNSS observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13439, https://doi.org/10.5194/egusphere-egu23-13439, 2023.

Landslides are a major geohazard in hilly and mountainous environments. We focus on slow-moving, deep-seated landslides that are characterized by gradual, non-catastrophic deformations of millimeters to decimeters per year and cause extensive economic damage. Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. Recent studies have demonstrated the effective application of machine learning for deformation forecasting to specific cases of slow-moving, non-catastrophic, deep-seated landslides. To test to what extent a combination of data-driven machine learning techniques and remote sensing observations can be used for landslide deformation forecasting, we developed a machine learning based nowcasting model on the multi-sensor monitored, deep-seated, Vögelsberg landslide, near Innsbruck, Austria. Our goal was to link the landslide deformation pattern to the conditions on the slope, and to produce a four-day, short-term forecast, a nowcast, of deformation accelerations.

Precipitation, snowmelt, soil moisture, evaporation, and temperature were identified as hydro-meteorological variables with high potential for forecasting deformation acceleration. Time series of those variables were obtained from remote sensing sources where possible, and otherwise from reanalysis sources as surrogate for data that is likely to be available in the near future. Deformation, the result of slope instability, was monitored daily by a local, automated total station of the Division of Geoinformation of the Federal State of Tyrol.

The five years of daily deformation and hydro-meteorological observations at the Vögelsberg landslide is quite limited for a machine learning model. To limit the complexity of the model, and the number of parameters to be optimized, the model was designed to mimic a bucket model, a simple hydrological model. A shallow neural network based on Long-Short Term Memory, was implemented in TensorFlow, as custom sequence of existing building blocks. In addition, a traditional neural network and recurrent neural network were tested for comparison.

Thanks to the limited complexity of the model, the major contributors could be determined by trial-and-error of nearly 150 000 model variations. Models including soil moisture information are more likely to generate high quality nowcasts, followed by models based solely on precipitation or snowmelt. Although none of the shallow neural network configurations produced a convincing nowcast deformation, they provide important context for future attempts. The machine learning model was poorly constrained as only five years of observations were available in combination with the four acceleration events that occurred in these five years. Furthermore, standard error metrics, like mean squared error, are unsuitable for model optimization for landslide nowcasting.

We showed that landslide deformation nowcasting is not a straightforward application of machine learning. The complexity of the machine learning model formulation at the Vögelsberg illustrates the necessity of expert judgement in the design and evaluation of a data-driven nowcast of slow deforming slopes. A future, successful nowcasting system will require a simple, robust model and frequent, high quality and event-rich data to train upon.

How to cite: van Natijne, A., Bogaard, T., and Lindenbergh, R.: Challenges for satellite-based deep-seated landslide nowcasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14398, https://doi.org/10.5194/egusphere-egu23-14398, 2023.

Documenting landslide activity over long periods and monitoring standards (sensors, acquisition rates, quality-control) is critical for understanding the landslide forcing factors, develop process-based models, identify the effect of climate change on their behavior, and ultimately define warning thresholds.

The French Landslide Observatory (Observatoire Multi-Disciplinaire des Instabilités de Versants) OMIV is the service of the French Research Institute (CNRS) in charge of deploying, acquiring, exploiting and disseminating multi-parametric sensor data over several large landslides in France. OMIV has developed, since more than 15 years, standards in terms of sensor types, using both high-grade and low-cost sensing in order to construct reference and spatially dense monitoring time series. The service provides open access to records of landslide kinematics, landslide micro-seismicity, landslide hydro-meteorology and landslide hydro-geophysics. Combined, these four categories of observations are unique worldwide for long-term landslide observations. OMIV is currently supervisizing the acquisition and dissemination of sensor data on 8 permanent unstable slopes (Avignonet/Harmallière, La Clapière, Séchilienne, Super-Sauze/La Valette, St-Eynard, Pégairolles, Vence, Villerville) and on unstable slopes currently experiencing gravitational crises (La Clape, Viella, Marie-sur-Tinée, Aiguilles). The service is organized around the dissemination of qualified data (in international reference file format) and products for 5 categories of observation (Geodesy, Seismology, Hydrology, Meteorology, Hydrogeophysics). For each categories of observation, specific FAIR data repository and access portals have been developed and automated processing methods have been proposed to meet the needs of the landslide research community. The products being generated are time series of GNSS and total station positions, catalogue of endogeneous landslide micro-seismicity, resistivity tomography datasets, and hydro-meterological parameters).

OMIV provides consistent and harmonized landslide monitoring data in order to identify the physical processes that control the landslide dynamics, both for slopes affected by slow-moving slides and cliffs affected by rockfalls, use these datasets to develop and validate landslide deformation/propagation models, extract (from the long-term observations) the patterns that may characterize changes in the landslide dynamics (annual, seasonal, event) and propose possible forerunners. The OMIV observations aim at contributing at identifying the key controlling parameters of different landslide types (e.g. soft/hard rock, cohesion/friction, slip/fracture, localized/diffuse damage) and at monitoring their evolution in time and space (deceleration or acceleration according to the triggering factors, sliding- flowing transition).

The objectives are to present the OMIV datasets, sensing standards and automated processing methods that has been developed, both for the science community and for operational partners in charge of landslide risk management (ONF-RTM, BRGM, CEREMA), for some of the monitored landslides. The objectives are also to present the future directions of the service with a focus on the modelling of the landslide processes using both process-based and machine learning approaches.

How to cite: Malet, J.-P., Bertrand, C., Hibert, C., Radiguet, M., Lebourg, T., Gautier, S., Bièvre, G., Vidal, M., Wanner, X., Lissak, C., Vial, B., Châtelain, N., Besso, R., Baudin, S., and Boetsch, A.: The OMIV service: acquiring and sharing long-period instrumental time series for documenting landslide activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14542, https://doi.org/10.5194/egusphere-egu23-14542, 2023.