Mauro Rossi (1), Ivan Marchesini (1), Maria Elena Martinotti (1), Maria Teresa Brunetti (1), Silvia Peruccacci (1), Vinicio Balducci (1), Fausto Guzzetti (1), 2019, Landslide early warning_ lessons learned after 10-year experience in Italy, AOGS 2019, Singapore, 28/07/2019,
URL: http://www.cnr.it/prodotto/i/429743

As prioritized by the Sendai Framework, enhancing disaster preparedness is fundamental for the effective response, for taking actions in anticipation of events, and to ensure that the appropriate capacities are in place for effective response and recovery at all levels. Under this view early warning systems can be seen as irreplaceable tools to supporting the Civil Protection authorities in the preparedness and response phases. This is particularly relevant for the case of rainfall-induced slope failures that occur worldwide every year, claiming lives and causing severe economic disruption. Implementing early warning systems to forecast the occurrence of such geo-hydrological phenomena is difficult and challenging both from the scientific and technological side. Here we present a framework developed in Italy for the operation forecasting of rainfall induce landslides over large areas, which includes (i) criteria, tools and technological supports for the collection of rainfall induced landslide occurrences; (ii) scientific methods and tools for the analysis of the relation between rainfall and landslides occurrences and for the landslide rainfall threshold definition; (iii) operational early warning system procedures and technological supports for the rainfall induced landslide forecasting; (iv) interfaces for the query and analysis of the early warning system outputs; (v) criteria, tools and technological supports for the validation of the early warning system outputs. The main lessons learned in the last decade and the most critical issues experienced implementing such framework for the entire Italian territory (SANF system) and for different regions both in Italy and India (SARF systems, LANDSLIP LEWS system) are highlighted and discussed.

Data from https://intranet.cnr.it/people/