A novel dynamic rockfall susceptibility model including precipitation, temperature and snowmelt predictors: a case study in Aosta Valley (northern Italy)

Abstract

AbstractThe overarching goal of the study was the development of a potentially dynamic rockfall susceptibility model by including climate predictors. The work is based on previously defined critical thresholds relating three climate indices — effective water inputs (EWI), wet-dry cycles (WD) and freeze–thaw cycles (FT) — and rockfall occurrence. The pilot area is located in the Aosta Valley region (Italian Western Alps). The susceptibility model settings were optimized through a stepwise procedure, carried out by means of generalized additive models (GAM). Predictors included topographic, climatic and additional snow-related variables. As climatic predictors, the mean annual threshold exceedance frequency was calculated for each index. All models were developed including an automatic penalization of statistically non-significant variables (i.e. shrinkage). The initial susceptibility model was set without considering potential inventory bias. Secondly, a “visibility mask” was produced to limit the modelling domain according to the rockfall event census procedures. Thirdly, GAMs functional relationships were analysed to verify the physical plausibility of predictors. Finally, to reduce concurvity, a principal component analysis (PCA) including climatic and snow-related predictors was carried out. Key findings were as follows: (i) ignoring inventory bias led to excellent model performance but to physically implausible outputs; (ii) the selection of non-rockfall points inside a “visibility mask” is effective in managing inventory bias influence on outputs; (iii) the inclusion of climate predictors resulted in an improvement of the physical interpretability of the associated models and susceptibility maps, being EWI, WD and the maximum cumulated snow melting the most important physically plausible climate predictors; (iv) the PCA strategy can efficiently reduce model concurvity.