Coastal cliff collapse across Europe poses an escalating threat to human lives and critical infrastructure due to accelerated erosion driven by climate change and sea-level rise. Coastal cliff response is governed by complex interactions between geological, geomorphological, oceanographic and climatic processes. Environmental forcings such as temperature fluctuations, moisture variations, rainfall, sea-level changes and wave impacts can progressively alter stress conditions within coastal cliffs. These processes can promote cumulative damage, progressive degradation and cliff retreat.

Understanding how these drivers influence the degradation, deformation and progressive failure of coastal rock masses is becoming increasingly important for reliable hazard assessment and long-term risk mitigation in highly vulnerable coastal environments. This session, promoted under the auspices of the AIGA’s (Italian Association of Engineering and Environmental Geology) Working Group on Rocky Coasts, brings together researchers investigating coastal landslides, rockfalls and cliff instability through monitoring, remote sensing, field and laboratory investigations, numerical modelling and data-driven approaches. While focused on coastal environments, the session also welcomes contributions investigating the response of similar slopes to environmental and climatic forcings in analogous settings, where progressive damage accumulation and climate-driven degradation processes play a key role.

We encourage submissions addressing multi-scale monitoring strategies, satellite and ground-based remote sensing, geophysical investigations, laboratory and in-situ testing, and the characterization of material properties controlling cliff response to environmental forcing. Contributions integrating monitoring observations with numerical models (e.g. FEM, DEM, FDEM, CFD and coupled thermo-hydro-mechanical approaches) are particularly encouraged. The session also seeks studies exploring future evolution scenarios through climate projections, digital twins, and applications of machine learning and artificial intelligence. Special emphasis will be given to predictive frameworks combining monitoring data, environmental forcing and climate scenarios to support hazard assessment and coastal adaptation.