Comparison of landslide forecasting services in Piedmont (Italy) and Norway, illustrated by events in late spring 2013

Abstract

Abstract. Only few countries operate systematically national and regional forecasting services for rainfall-induced landslides (i.e

Reply to comments by referee #1

“…Probabilistic approaches allow for a more thorough consideration of uncertainties and inherent variability in model-specific parameters. Spatially varying parameters (both geotechnical and hydraulic) are usually represented as univariate distributions of random variables based on an underlying pdf and statistical characteristics (Fan et al, 2016). Friction angle and cohesion are commonly considered to be such varying variables that are treated in a probabilistic way for model parameterization (e.g., Park et al, 2013;Chen and Zhang, 2014;Raia et al, 2014;Salciarini et al, 2017).…”

“…For larger areas (e.g., scales > 1 : 25 000), there are several factors that cause spatial variation in, for example, soil water content; topography; differences in soil depth, soil type, and soil texture; vegetation characteristics; and rainfall patterns. Additionally, spatially varying soil and hydraulic properties are influenced by interrelated soil formation processes (such as weathering processes, biological perturbations, atmospheric interactions) (Fan et al, 2016), and thus making selective in situ soil sampling a tricky task when performed at a larger scale. Small-area (e.g., scales < 1 : 10 000) variability usually lacks a spatial organization, hence its representation as a stochastic process.…”

“…The forecasting of rainfall-triggered natural hazards with a rapid onset such as landslides and flash floods greatly benefits from numerical weather predictions (NWPs), in particular from rainfall nowcasting and short-term rainfall forecast-ing that offer rainfall predictions several hours ahead. While it is evident that processes with a potentially very short response time require more efforts for timely early warning than just real-time measurement of rainfall, real forecasting initiatives are scarce, especially in the landslide community (Devoli et al, 2018). At present, only a few countries operate nationwide landslide early warning systems, and Italy (Rossi et al, 2012) and Norway (Devoli et al, 2015(Devoli et al, , 2018 are probably the most prominent examples.…”

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

“…While it is evident that processes with a potentially very short response time require more efforts for timely early warning than just real-time measurement of rainfall, real forecasting initiatives are scarce, especially in the landslide community (Devoli et al, 2018). At present, only a few countries operate nationwide landslide early warning systems, and Italy (Rossi et al, 2012) and Norway (Devoli et al, 2015(Devoli et al, , 2018 are probably the most prominent examples.…”

Editorial: Landslide Hazard in a Changing Environment

“…There are many operational EWSs currently implemented for various types of natural hazards, including landslides. Overview and classification of existing landslide EWSs are presented in Thiebes et al (2012), Bazin (2012), Stähli et al (2015), Devoli et al (2018), and Segoni et al (2018). In this research topic, a methodology to couple rainfall thresholds and susceptibility maps for hazard assessment at a regional scale is presented by Segoni et al: this new approach has been tested in northern Tuscany (central Italy) where an appropriate calibration and validation of the hazard matrix has been implemented to meet stakeholder requirements.…”