T34. Landslide monitoring, modelling, and prediction: bridging new tools and data to the 'slope-failure model' perspective

Landslide monitoring, modeling, and prediction are complex tasks that need different types of good data and reliable analytical tools. Slope-scale, physically-based landslide modeling requires detailed morphological, hydrogeological, and geotechnical measurements; regional-scale, statistical analyses rely on a significant amount of typically proxied data, with high spatial and temporal accuracy.
Recently, emerging modelling methods and survey technologies solve all these requirements with more and more complex/specific applications. However, in front of the incredible easiness with which direct access to sophisticated experimental and analytical tools is nowadays given to any researcher, sometimes the "geological facts" run the risk of being blurred up to a disconnection between field reality and representation.
Field and remote surveys are often affected by a weak knowledge of their real physical coupling to the studied slope failure phenomena. At the same time, few efforts are devoted to critically investigate the adopted stochastic approach (mapping units, proxied data and validation strategies).
Contributions addressing data collection and management at different scales are welcome, as well as discussions on the definition of standards for data collection and storage. At the same time, contributions are encouraged investigating the key issues for landslide hazard modelling (where, why, when, how big and how fast and far).