In recent years an increasing number of research projects focused on natural hazards (NH) and climate change impacts, providing a variety of information to end user or to scientists working on related topics.

The session aims at promoting new and innovative studies, experiences, approaches and models to improve risk management and communication about natural hazards to different end users.

End users such as decision and policy makers or the general public, need information to be easy and quickly interpretable, properly contextualized, and therefore specifically tailored to their needs. On the other hand, scientists coming from different disciplines related to natural hazards and climate change ( e.g., economists, sociologists), need more complete dataset to be integrated in their analysis. The latter are not only represented by big/comprehensive dataset and geo-information architectures and services, but also by indicators that can summarize key information.
However, terminology, data and approaches are often fragmented across literature and among natural hazard/climate communities, , with an evident lack of coherence. Furthermore, there is not a unique approach of communicating information to the different audiences. Rather, several interdisciplinary techniques and efforts can be applied.

We encourage all scientists – especially Early Career Scientists – to submit abstracts describing their solutions, standardization efforts, new ways of presenting and communicating scientific data (preferably not yet published) both via traditional (e.g. production of static maps, graphs, dataset and architectures, and so on) and unconventional methods (e.g. games, system of symbols, paintings and so on).

A short presentation of each contribution will open the session and it will be possible to continue interaction and discussion with each presenter afterwards. All presentations or demo will be accessible during the conference through touch screen videos.

In cooperation with NhET (Natural hazard Early career scientists Team).