The session focuses on landslide early warning systems (LEWSs) at both regional and local scale, particularly landslides induced by rainfall and/or snowmelt. Landslide warning systems at regional scale are used to assess the probability of landslide occurrence over a priori defined warning zones, typically through forecasting and monitoring of meteorological variables, in order to give generalized warnings to communities and institutions working with hazard mitigation measures towards buildings and infrastructure. Conversely, the main aim of local LEWSs is the temporary evacuation of people from areas where, at specific times, the risk level to which they are exposed is considered to be intolerably high.

The structure of LEWSs can be schematized as an interrelation of different components, such as: monitoring, forecasting, thresholds, actors involved, alert issuing, information and communication procedures. The definition of these components and the aims of the alert issued may vary as a function of the scale at which the system is employed.

The session wish to highlight approaches and developments regarding: rainfall threshold definitions, monitoring systems, models for issuing warning levels, risk management, landslide and warning databases, weather prediction models, emergency phases, communication strategies and other activities necessary for designing and operating LEWSs.

Contributions addressing the following topics are welcome:
• operational and prototype landslide early warning systems at regional and local scale;
• correlation models for early warning purposes;
• landslide warning models;
• performance analysis of landslide warning models;
• risk management
• landslide risk perception.

We are also preparing a special issue in an impacted Journal (probably, Natural Hazard Earth System Sciences), with the most relevant contributions to the session. To whom it may be of interest to contribute with a full paper, please let the conveners know in advance fulfilling this brief form (https://goo.gl/forms/5UN4QVyki5mMbDty1).