| 04 Feb 2026
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.
The manuscript presents a solid and well-structured analysis of the impact of extreme rainfall events on landslide rainfall thresholds and susceptibility modelling. The dataset is comprehensive, and the methodological framework (CTRL-T and Random Forest) is appropriate and carefully implemented. The results are clear and provide meaningful insights into how extreme events can alter statistical models used for landslide prediction. Overall, the manuscript is suitable for publication after moderate revisions. However, several aspects could be further strengthened to improve clarity, robustness, and broader applicability. First, the discussion section should be slightly expanded to better generalize the findings beyond the study area, particularly addressing whether similar effects of extreme rainfall events on thresholds and susceptibility can be expected in other climatic and geomorphological contexts. Second, the limitations of the study should be more explicitly acknowledged, especially regarding the spatial representativeness of the Storm Alex landslides, the temporal inconsistency of the inventory, and the assumptions made in rainfall–landslide matching (e.g., fixed occurrence time). Third, a brief perspective on future research directions would be valuable, for example the need for non-stationary modelling frameworks, event-based calibration strategies, or hybrid approaches distinguishing between ordinary and extreme triggering conditions. Finally, moderate clarifications could be added regarding the influence of non-landslide sampling strategy and the potential uncertainty introduced by converting landslide polygons into points. With these moderate improvements, the manuscript will be further strengthened and make a valuable contribution to the field.