Definition of 3D rainfall thresholds to increase operative landslide early warning system performances
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Ascanio Rosi I Samuele Segoni I Vanessa Canavesi I Antonio Monni I Angela Gallucci I Nicola Casagli
Definition of 3D rainfall thresholds to increase operative landslide early warning system performances
Abstract Intensity–duration rainfall thresholds are commonly used in regional-scale landslide warning systems. In this manuscript, 3D thresholds are defined also considering the mean rainfall amount fallen in each alert zone (MeAR, mean areal rainfall) in Emilia Romagna region (Northern Italy). In the proposed 3D approach, thresholds are represented by a plane instead of a line, and the third dimension allows to indirectly account for the influence of complex rainfall patterns. MeAR values are calculated according to different time periods ranging from 7 to 30 days, and all threshold parameters are calibrated independently for the 8 alert zones in which the region is divided. The approach was validated and compared with classical intensity–duration thresholds, finding that the 3D threshold may be used to get better performances, especially in terms of a consistent reduction of false alarms:− 20 to − 86%, depending on the alert zone and the selected MeAR duration. These results open new encouraging perspectives for the development of the regional warning system that is operated in the study area. Keywords Landslide . Early warning system (EWS) . 3D . Rainfall threshold Introduction Landslides are one of the most widespread natural hazards in the word, responsible every year for casualties and huge economic losses (Froude and Petley, 2018). As a consequence, landslides are intensely studied natural phenomena: in the scientific literature, several papers addressing landslide hazard or risk management and prevention can be found (Aleotti and Chowdhury, 1999; Guzzetti et al., 1999; Fell et al. 2005; Hungr et al., 2005; Chae et al., 2017; Maes et al., 2017, Devoli et al., 2018; Hungr, 2018, Rosi et al. 2018; Salvatici et al. 2018; Dikshit et al., 2020). Several global-scale works highlighted that China, Japan, India, and Italy are among the countries most exposed to landslide risk (Petley, 2012; Dowling and Santi, 2014; Haque et al., 2016; Kirschbaum et al., 2015). In Italy, in particular, landslides along with floods are a very recurring phenomenon: over 15,000 landslide events news can be found in online newspapers from 2011 according to results of the semantic engine developed by Battistini et al. (2013); from 2000 to 2018, landslides have been responsible for over 250 fatalities and for about 5.6 billion € of damages (Rossi et al., 2019). As a consequence, several attempts of reducing their impact have been made, both by the public authorities and the scientific community, sometimes also working in strict collaboration (Melillo et al., 2016; Segoni et al. 2018a; Tiranti et al., 2019). For instance, in Italy, the establishment of landslide warning systems for all the 20 regions is compulsory by law, and today, all the regional public authorities are expending efforts to define reliable and functional early warning sys
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