Abstract
Abstract. In recent years, flow-like landslides have extensively affected pyroclastic covers in the Campania region in southern Italy, causing human suffering and conspicuous economic damages. Due to the high criticality of the area, a proper assessment of future variations in event occurrences due to expected climate changes is crucial. The study assesses the temporal variation in flow-like landslide hazard for a section of the A3 “Salerno–Napoli” motorway, which runs across the toe of the Monte Albino relief in the Nocera Inferiore municipality. Hazard is estimated spatially depending on (1) the likelihood of rainfall-induced event occurrence within the study area and (2) the probability that the any specific location in the study area will be affected during the runout. The probability of occurrence of an event is calculated through the application of Bayesian theory. Temporal variations due to climate change are estimated up to the year 2100 through an ensemble of high-resolution climate projections, accounting for current uncertainties in the characterization of variations in rainfall patterns. Reach probability, or defining the probability that a given spatial location is affected by flow-like landslides, is calculated spatially based on a distributed empirical model. The outputs of the study predict substantial increases in occurrence probability over time for two different scenarios of future socioeconomic growth and atmospheric concentration of greenhouse gases.
Highlights
Eminent scholars have debated about the main features of “shallow” and “deep” uncertainties in the assessment of natural hazards (Stein and Stein, 2013; Hallegatte et al, 2012; Cox, 2012)
Shallow uncertainties are associated with reasonably knowing the probabilities of outcomes (Stein and Stein, 2013), while deep uncertainties are associated with (1) several possible future worlds without known relative probabilities, (2) multiple conflicting but reasonable world views, and (3) adaptation strategies with remarkable feedbacks among the sectors (Hallegatte et al, 2012). As stressed in these works, climate change and its impacts can be considered paradigmatic of very deep uncertainty
Given the extent of potential impacts on communities (United Nations, 2015), including their economic dimension (Stern, 2007; Nordhaus, 2007; Chancel and Piketty, 2015), considerable effort has been made in recent years to assess the variations in frequency and magnitude of weatherinduced hazards in a changing climate (Seneviratne et al, 2012)
Summary
Eminent scholars have debated about the main features of “shallow” and “deep” uncertainties in the assessment of natural hazards (Stein and Stein, 2013; Hallegatte et al, 2012; Cox, 2012). Shallow uncertainties are associated with reasonably knowing the probabilities of outcomes (Stein and Stein, 2013), while deep uncertainties are associated with (1) several possible future worlds without known relative probabilities, (2) multiple conflicting but reasonable world views, and (3) adaptation strategies with remarkable feedbacks among the sectors (Hallegatte et al, 2012). As stressed in these works, climate change and its impacts can be considered paradigmatic of very deep uncertainty. The paucity of investigations could be due to the mismatch between the usual scale of analysis for landslide case studies and the much coarser horizontal resolutions of climate projections currently available as well as the difficulty in generalizing findings to other contexts, given the relevance of site-specific geomorphological features
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