EGU22-11120, updated on 24 Apr 2023
EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Snow melt triggering of shallow landslides under climate change. The case study of Tartano valley, Italian Alps.

Davide Danilo Chiarelli, Giovanni Martino Bombelli, Daniele Bocchiola, Renzo Rosso, and Maria Cristina Rulli
Davide Danilo Chiarelli et al.
  • Politecnico di MIlano, Scienza e Ingegneria dell'Acqua SIA, Dipartimento di Ingegneria Civile e Ambientale, Milano, Italy (

Shallow landslides (SLs) imply downhill movements of soil, rocks, debris. These typically occur on steep terrains, in mountainous, and hilly areas, representing a major risk for people and infrastructures. Properly mapping of shallow landslides in space and time is fundamental for prediction, forecast, and setting up of countermeasures. However, modelling of shallow landslides is complex, given (very) local nature of the phenomenon. Recently investigation started about the role of snow melt in triggering shallow landslides, displaying increasing evidence of catastrophic events at thaw. Little was done hitherto in modelling snow melt triggered SLs, especially in terms of physically based modeling. Under the umbrella of the recent project MHYCONOS, funded by Fondazione CARIPLO of Italy, we developed a robust, and parameter-wise parsimonious model, able to mimic triggering of SLs accounting for the combined effect of precipitation duration and intensity, and snowmelt at thaw. In our model, when temperature is below 0 °C, precipitation is stored as snowpack on the soil surface, and released later in thaw season. Storage of melting water during springtime increases soil moisture, so creating potential for SLs. The model is demonstratively applied to the Tartano river valley, in the Alps of Lombardia region of Italy. In this region mass movements and flash-floods in the wake of intense storms are common. Currently from our model about 26% of the Tartano valley displays (permanent) unstable conditions, more than 40% of it influenced by soil moisture changes. Conversely, by applying a traditional rainfall-based analysis, only 19% of the basin is predicted as potentially unstable, mainly in fall, when intense rainfall occurs. When including snowmelt as a cause of SLS triggering, one finds anticipation of the (modeled) peak of instability to springtime, during April and May. Forcing the model under 6 different climate change scenarios of IPCC at 2050, and 2100, an increase is expected in temperature (i.e. with rapider snow melt), and extreme precipitation events, further aggravating SLs hazard. Mapping zones prone to instability in space and time under present conditions, and future scenarios, will help to prevent casualties, and damages in the short-term, while providing base for structural mitigation measures in the long term, during periods of potential instability, even at low to medium rainfall rates.

How to cite: Chiarelli, D. D., Bombelli, G. M., Bocchiola, D., Rosso, R., and Rulli, M. C.: Snow melt triggering of shallow landslides under climate change. The case study of Tartano valley, Italian Alps., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11120,, 2022.


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