From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)

Abstract. Rockfall modelling is a common topic in the landslide literature, but a comprehensive workflow for rockfall susceptibility zonation remains a challenge. Several aspects of the modelling, such as rockfall runout simulation, are consolidated, but others still show inconsistencies and ambiguities, such as the source area identification or the criteria to obtain probabilistic susceptibility zonation. This study proposes a workflow for rockfall susceptibility zonation at the regional scale that integrates (i) source area identification criteria, (ii) deterministic runout modelling, (iii) approaches for the runout classification, and (iv) robust procedures for validation and comparison. The workflow is tested on El Hierro Island (Canary Islands, Spain) and considers the effect of different methods to identify the rockfall source areas that are used as input data for rockfall runout modelling. The runout outputs are classified to derive rockfall susceptibility zonation considering different types of classification (i.e. supervised versus unsupervised methods). The source area identification reflects situations with limited data availability and scenarios with a large amount of topographic, geological, and geomorphological information. The first approach is based on slope angle thresholding, the second uses a statistical method based on empirical cumulative distribution functions (ECDFs) of slope angle values, and the third involves the combination of multiple multivariate statistical classification models where the source area is the dependent variable and thematic information is the independent variables. The source area maps obtained from the three methods are utilized as inputs for a rockfall runout model (STONE) to derive rockfall trajectory count maps. Two classification approaches are applied to generate probabilistic susceptibility maps from the trajectory counts: unsupervised and supervised statistical methods using distribution functions. The unsupervised classification employs only the rockfall trajectory counts as input, whereas the supervised classification requires additional data on the areas already affected by rockfalls. To complement the workflow, statistical methods and metrics are proposed to verify, validate, and compare the susceptibility zonation.