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 2 October 2020. The analysis is based on an inventory of 5383 shallow landslides, including 1656 landslides triggered by Storm Alex. Cumulative rainfall and rainfall duration associated with each landslide were computed following the process of the CTRL-T algorithm. Landslides sharing identical cumulative rainfall and rainfall durations were aggregated into a single point to avoid over-representing rainfall events that triggered many spatially clustered landslides. Then, a 5 % quantile regression 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 susceptibility maps under the same two configurations, which were subsequently compared. Results show that: (a) Including Storm Alex landslides increased the rainfall–duration thresholds by a factor of 1.1 to 1.5, depending on the assumed landslide occurrence time; (b) the exceptional rainfall intensity triggered landslides in areas having an initial lower susceptibility; and (c) including these events in susceptibility modelling 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.