The effect of different landslide absence sampling time windows in event-based landslide susceptibility models

Landslide inventories are often incomplete and biased due to limited personnel and financial resources, which constrains the development of high-quality, long-term spatio-temporal landslide datasets. In comparison, event-based landslide inventories, which are typically compiled shortly after triggering storms, can be mapped more comprehensively and tend to be internally consistent. Leveraging such inventories is thus valuable for exploring the interconnections between extreme precipitation events and environmental characteristics on slope instability.

Here, we evaluate the temporal transferability of event-based landslide susceptibility models to another landslide event, and the sensitivity of transferability to the choice of landslide absence sampling time windows. Accounting for spatial landslide collection bias and temporal biases in landslide absence sampling, we trained three Generalized Additive Models (GAMs) on landslides triggered by the September 2024 extreme precipitation event "Boris" to the Pielachtal region, Lower Austria. The models differ only in their temporal windows for landslide absence sampling: (M1) from the onset of the precipitation event until the observation date of the last inventoried landslide (September 12-17, 2024), (M2) from from the start of the month until the observation date of the last inventoried landslide (September 1-17, 2024), and (M3) only on the dates of landslide occurrence (September 16 -17, 2024). The models were then validated against an independent event, the May 2014 precipitation-triggered landslide inventory, to assess temporal generalization.

This research provides insights into how absence sampling design influences event-based, spatio-temporally dynamic landslide susceptibility modelling and its transferability across events. Our findings support cost-effective protocols for inventory compilation and model development, and enhancing readiness for future extreme precipitation events.