Search

In this report, we summarise the activities of the Research Institute for Geo-Hydrological Protection (IRPI, http://www.irpi.cnr.it/en/ ), of the Italian National Research Council (CNR, http://www.cnr.it/en/ ), as a World Centre of Excellence (WCoE) on Landslide Risk, of the International Programme on Landslides (IPL, http://iplhq.org ). The report is organised as follows. After a brief description of CNR IRPI (“The Research Institute for Geo-Hydrological Protection”), we present recent developments related to landslide early warning systems (“Landslide early warning systems”), climate change and landslides interactions (“Landslides in a changing climate”), and landslide risk assessment, mitigation and dissemination efforts (“Landslide risk assessment, mitigation and dissemination efforts”). We conclude listing relevant papers published between 2017 and 2018 by CNR IRPI scientists (“References”).

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence.