Abstract
In mountain regions, the impact of areas on the sediment conveyance can not only be described by their susceptibility to debris flow release, but also by their structural connectivity to the rivers. This generates the need to combine susceptibility and connectivity for accurate analyses of sediment transport. Our study exploits an approach developed by [Steger, er al. 2022; https://doi.org/10.1002/esp.5421] and upscales it to the South Tyrolean Dolomites region. The approach comprised the modeling of debris flow release susceptibility using an interpretable machine learning algorithm, the training of a logistic regression model, and the combination of the resultant classified maps to create a joint susceptibility-connectivity map. The results show the quantitative thresholds for the susceptibility probability and the Index of Connectivity (IC) that allow to discriminate between susceptible and not susceptible, as well as connected and disconnected areas, which are represented via a variety of maps.