Impact of Extreme Rainfall on Triggering Conditions and Susceptibility for Shallow landslides: a case study in the Alpes-Maritimes region (France)

Abstract. Prediction of shallow landslides at the regional scale generally relies on statistical analyses of landslide inventories. Rainfall-duration thresholds and susceptibility maps are among the most common approaches to anticipate future landslide occurrences. However, the outputs and reliability of these approaches can be strongly affected by the representativeness of the landslides included in the inventory. This study specifically investigates the impact of landslides triggered by an extreme rainfall event on the determination of rainfall-duration thresholds and susceptibility maps. We consider the case of Storm Alex, a millennial return period rainfall event, which hit the Alpes-Maritimes region (France) on October 2, 2020. The analysis is based on an inventory of 5,383 shallow landslides, including 1,656 landslides triggered by Storm Alex. The CTRL-T algorithm was used to compute statistical rainfall-duration thresholds with and without the inclusion of Storm Alex landslides. A Random Forest approach was used to produce and compare susceptibility maps under the same two configurations. Results show that: (a) rainfall-duration thresholds derived from datasets including Storm Alex landslides are significantly higher; (b) the exceptional rainfall intensity triggered landslides in areas having an initial lower susceptibility; and (c) including these events in susceptibility modeling alters the spatial distribution of susceptibility values. This study provides a quantitative analysis of the impact of landslides triggered by extreme rainfall events on statistical prediction models.