Assessing future wind vulnerability of mountain forests using high-resolution remote sensed and climate data: a pilot study in the Italian Alps

Climate change is responsible for the increase in frequency and magnitude of extreme meteorological events, including windstorms, which are expected to cause greater damage to both natural and human resources. In European forests, wind damage is already the first cause of timber loss. Additionally, in the Alps context forests provide protection against gravitational hazards, a function that could be completely or partially compromised in case of wind damage. Thus, identifying the most wind-vulnerable forests is crucial to actively manage them and possibly increase their resistance to such events.

To address this challenge, various physically and statistically based models have been developed to estimate forest vulnerability to windstorms. Such models consider both stand and single tree parameters to derive the critical wind speed (CWS), defined as the wind speed threshold above which damage is likely to occur. While the CWS quantifies the forest wind vulnerability, assessing the probability of forest damages requires the probability of occurrence of a given windstorm event. Moreover, the latter could be influenced by climate change given that the regime of windstorm events is expected to change in the future.

In this study, we assess the forest wind vulnerability of the Rocca Pietore municipality area, using high-resolution LiDAR data to extract detailed stand and individual tree characteristics. These data are input into the semi-mechanistic ForestGALES model to calculate the CWS. The probability and the magnitude of wind damages are calculated using km-scale Convection Permitting Models (CPMs) from CORDEX-FPS on Convective Phenomena over Europe and the Mediterranean (FPS Convection). Specifically, we used wind data from the CPMs ensemble for both historical and future conditions. The study shows the critical maps of likelihood of forest wind damages under current conditions and the future scenario RCP8.5, highlighting changes across the study region and identifying the more exposed areas.

This study underscores the importance of integrating high-resolution forest and climate data to assess the vulnerability of natural resources against windstorms. By combining detailed forest characteristic data with advanced climate projections, the adopted approach provides valuable insights for forest management and climate adaptation planning.