Upgrade long-term monitoring of Passo della Morte Landslide

Detecting large, slow-moving landslides can be a complex task, as it necessitates identifying geomorphological evidence and conducting a thorough analysis of potential geological factors contributing to their formation and movement. Long-term monitoring is crucial in creating a temporal database and implementing effective mitigation measures. Analysing datasets makes it possible to identify potential correlations among the monitored parameters, which may also include the potential triggering factors. Near-real-time monitoring enhances the level of detail by offering high-quality data and immediate notifications of landslide activity. This
facilitates a better understanding of active landslide dynamics and the development of more efficient mitigation strategies. This study introduces the large (24 × 106 m3) landslide of Passo della Morte affecting the "Carnica" national road 52 and the eastern entrance of a 2 km road tunnel called San Lorenzo. The area is located in the eastern part of the Italian Alps, near Forni di Sotto and Ampezzo, in the province of Udine, on the left bank of the Tagliamento river valley. A landslide monitoring system has been deployed in the area since 2002; it consists of several different sensors, such as quasi-real-time piezometers, inclinometers, In-Place Inclinometers (IPI),
GNSS surveys, a thin plate weir to continuously measure the flow of the Rio Verde, a rain gauge and temperature probes. The data set made it possible to define the type of movement and the triggering factors. In order to validate the hypothesis of landslide block-type movement behaviour assessed through extensive field data collected with standard geotechnical instrumentation and know the absolute displacements of the landslide body, the new permanent GNSS monitoring system was installed in the Passo della Morte landslide area. This new installation complements the existing periodic GNSS monitoring network, which has been in operation
since 2002, with stations on both the moving slope and a stable area. While the previous system was valuable in understanding the instability process, it could not assess displacement trends. Furthermore, the continuous network will enable tracking seasonal movement trends and their correlation with rainfall; it will facilitate the estimation of trigger thresholds and potentially assess the feasibility of configuring the network with an early warning system.