Abstract
Rainfall-induced shallow landslides are expected to change in frequency and distribution as a result of altered patterns and intensity of rainfall. Yet, linking climate change effects to past occurrences is challenging due to the lack of long-term, systematic, and reliable datasets of landslide events. However, the widely observed increase in the number of recorded landslides over time may also be indicative in the extent of exposed assets and their vulnerability, as well as the more comprehensive event documentation carried out in recent years, rather than reflecting the actual impacts of climate change.
To decipher such a conundrum, a high-resolution space-time data-driven model recently developed and trained for well-observed time periods within the territory of South Tyrol (Italian Alps) was used to create a continuous dataset of daily landslide hindcasts (i.e. modelled probabilities) to be used as a proxy for critical conditions of landslide occurrence in space and time. High landslide probabilities in the dataset can be linked to recorded landslides, but could also represent nearly-missed events, landslides that occurred but were not recorded (for example, those that happened in remote areas away from infrastructures), or to model errors.
Daily landslide probability predictions were obtained on a 30mx30m grid for the years 1980-2020, using both static (topography, geologicy and vegetation) and dynamic factors (antecedent and triggering precipitation, and seasonal effects). The results were aggregated over 5261 slope units identified for South Tyrol, which better reflect the hydrological and geomorphological processes shaping the landscape providing, at the same time, consistent geographical boundaries to manage the aleatory uncertainty of the model.
This new enriched dataset has been used to explore changing trends and patterns in landslide probability predictions and investigate underlying causes, such as the role of the Jenkinson and Collison weather types in shaping the spatial patterns of probability predictions.
Our results could improve the ability to predict critical conditions for landslide occurrences in the future, thereby offering new tools for mitigation and adaptation strategies, and specifically supporting the elaboration of efficient early warning systems.