EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

The OMIV service: acquiring and sharing long-period instrumental time series for documenting landslide activity

Jean-Philippe Malet, Catherine Bertrand, Clément Hibert, Mathilde Radiguet, Thomas Lebourg, Stéphanie Gautier, Grégory Bièvre, Maurin Vidal, Xavier Wanner, Candide Lissak, Benjamin Vial, Nicolas Châtelain, Romain Besso, Sandrine Baudin, and Anne Boetsch
Jean-Philippe Malet et al.

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, 24–28 Apr 2023, EGU23-14542,, 2023.