Researchers have long attempted to determine the amount of rainfall needed to trigger slope failures, yet relatively little progress has been reported on the effects of climate change on landslide initiation. Indeed, some relationships between landslides and climate change have been highlighted, but sign and magnitude of this correlation remain uncertain and influenced by the spatial and temporal horizon considered. This work makes use of statistically adjusted high-resolution regional climate model simulations, to study the expected changes of landslides frequency in the eastern Esino river basin (Central Italy). Simulated rainfall was used in comparison with rainfall thresholds for landslide occurrence derived by two observation-based statistical models (1) the cumulative event rainfall–rainfall duration model, and (2) the Bayesian probabilistic model. Results show an overall increase in projected landslide occurrence over the twenty-first century. This is especially confirmed in the high-emission scenario representative concentration pathway 8.5, where according to the first model, the events above rainfall thresholds frequency shift from * 0.025 to * 0.05 in the mountainous sector of the study area. Moreover, Bayesian analysis revealed the possible occurrence of landslide-triggering rainfall with a magnitude never occurred over the historical period. Landslides frequency change signal presents also considerable seasonal patterns, with summer displaying the steepest positive trend coupled to the highest inter-model spread. The methodological chain here proposed aims at representing a flexible tool for future landslide-hazard assessment, applicable over different areas and time horizons (e.g., short-term climate projections or seasonal forecasts).

Introduction

Over the next decades, societies will face massive environmental changes potentially able to substantially alter life styles and priorities (Lenton et al. 2008). Climate change will possibly be the major driver of such changes. However, if on the one hand the scientific community unequivocally recognized over these past decades the connection between the increase in greenhouse gases (GHGs) concentration and the increase in global temperature (Bindoff et al. 2013), on the other hand, changes in the rainfall patterns as a second-order effect of the increased temperature is connoted by higher uncertainty. Such reservations concern the magnitude and frequency of extreme events, how they will scale across the globe in response to the increase in temperature (Seneviratne et al. 2012; Scoccimarro et al. 2013; Drobinski et al. 2016). Notwithstanding these uncertainties, a broad consensus prevails on the acceleration of the hydrological cycle in a warmer atmosphere (Trenberth 1999; Mariotti et al. 2002; Lorenz and DeWeaver 2007; Volosciuk et al. 2016); acceleration appears strongly affected by the local-scale morphological and orographic peculiarities. (Xoplaki et al. 2004; Walsh et al. 2014). Rainfall is a major trigger of landslides (e.g., De Vita and Reichenbach 1998; Guzzetti et al. 2007), one of the most widespread geohydrological hazards over the world, and an increase in rainfall frequency and intensity may directly affect landslides frequency and magnitude thus increasing ensuing damages and fatalities. Indeed, good progresses have been made in linking the amount of rainfall needed to trigger slope failures (e.g., Crozier 1996; Aleotti 2004; Guzzetti et al. 2007; Martinotti et al. 2017), yet the nexus between climate change and landslide initiation appears more complicated and requires further study (Dikau and Schrott 1999; McInnes et al. 2007; Crozier 2010; Coe and Godt 2012). The available literature indicates a relationship between landslides and climate change, but the sign and strength of this correlation remain uncertain and extremely influenced by the spatial and temporal horizon considered (Seneviratne et al. 2012; Gariano and Guzzetti 2016). Landslides, strictly depending on geological, geomorphological, and land cover contexts, represent a category of climate change-related impacts (Pisano et al. 2017), which understanding and management require specific considerations. Moreover, the implementation of effective landslide adaptation measures depends on the availability of plausible multi-scale information about future climate trends. Regional climate model (RCM) simulations are considered primary tools for climate impact studies focusing on rainfall and other key climate variables (Giorgi 1990; Giorgi and Mearns 1999; Giorgi et al. 2009; Hawkins and Sutton 2009; Jacob et al. 2014).