Long-Term In-Situ Monitoring for the Analysis of Landslides Acceleration vs Precipitation Relationships (Northern Apennines and Eastern Alps, Italy)

Nowadays, the correlation between precipitation and changes in displacement rates of suspended or reactivated landslides, especially for deep-seated phenomena, is still poorly defined on a quantitative basis. This study, exploits long-term in-situ monitoring time series to propose new rainfall intensity-duration (ID) thresholds that can discriminate the acceleration of complex deep-seated landslides, including earthslides-earthflows (ES-EF), rockslides-earthslides (RS-ES), and deep-seated gravitational slope deformations-rockslides (DSGSD-RS).

The analysis focuses on 15 landslides in the Northern Apennines and Eastern Alps of Italy, which have been monitored in the period from 2001 to 2024. Monitoring was conducted using Robotic Total Stations (RTS), periodic, and continuous GNSS networks, leading to the documentation of 100 acceleration events. These events were analysed in relation to rainfall and temperature data from nearby meteorological stations, enabling the retrieval of intensity (mm/h) and duration (h) values regarding the antecedent triggering rainfall. This association was conducted considering both total rainfall (TR) and effective rainfall (ER). ER represents the amount of water potentially infiltrating in the ground having accounted for the aliquot lost due to evapotranspiration (ET) and snowfall and for the aliquot gained due to snowmelt processes.

Simultaneously, rainfall events not resulting in landslide accelerations were identified by examining the complete meteorological records for each landslide within the monitoring period. Both sets of intensity-duration records – i.e. those linked to and those independent from acceleration events – were analysed using a Receiver Operating Characteristics (ROC) approach. This method allowed to identify optimal rainfall thresholds and to compare their predictive capability with that of thresholds established by other authors for landslides occurrences.

The findings reveal that the proposed new thresholds tailored to a landslide’s accelerations dataset offer higher predictive accuracy compared to the established ones. Moreover, the study emphasizes the enhanced predictive performance achieved by incorporating effective rainfall, especially in scenarios where snowmelt contributes to landslide acceleration. These results underscore the importance of long-term in-situ monitoring and of introducing effective rainfall computations in the analysis, so to better account for various hydrological processes influencing landslide behaviour, ultimately improving early warning systems and risk management strategies for complex landslides in mountainous regions.