Large mass movements in rock, debris, and ice in glacial masses, represent enormous risks. These complex systems are difficult to describe, investigate, monitor, and model. Hence a reliable model of these phenomena requires acquisition and analysis of all available data to support successive steps up to the management of Early Warning systems.
Large instabilities affect all materials (rock, weak rocks, debris, ice), from low to high altitudes, evolving as slow or fast complex mass movements. This and the complex dependency on forcing factors result in different types and degrees of hazard and risk. Some aspects of these instabilities are still understudied and debated, because of difficult characterization and few cases thoroughly studied. Regional and temporal distribution, relationships with controlling and triggering factors are poorly understood resulting in poor predictions of behavior and evolution under present and future climates. How will it change their state of activity under future climatic changes? How this will impact on existing structures and infrastructures? How can we improve our predictions? Relationships among geological and hydrological boundary conditions and displacements are associated with the evolution in space and time of thermo-hydro-mechanical controls as well as the properties of the unstable mass. Even for well-studied and active phenomena warning thresholds are mostly qualitative, based on semi-empirical approaches. Hence a multidisciplinary approach and robust monitoring data are needed. Many modeling approaches can be applied to evaluate instability and failure, considering triggerings, and failure propagation, leading to rapid mass movements. Nevertheless, these approaches are still phenomenological and have difficulty explaining the observed behavior. The impacts of such instabilities on structures represent a relevant risk and an opportunity in terms of investigations and quantitative measurements of the effects on tunnels, dams, and roads. The design of these structures and knowledge of their expected performance are fundamental.
We invite to present case studies, share views and data, discuss monitoring and modeling approaches and tools, to introduce new approaches for threshold definition, including advanced numerical modeling, Machine Learning for streamline and offline data analyses, development of monitoring tools, and dating or investigation techniques.