Wind disturbances are one of the main drivers of forest dynamics in Europe, shaping forest stands and modifying the ecosystem services provisioning. Salvage logging is often most common strategy adopted after a high-severity disturbance in managed stands. Understanding natural regeneration dynamics including their interaction with the logging operations, is crucial to understand how forests will be changing under a climate with increasing variability and to design adequate adaptive post-disturbance management strategies. In this study, we focused on 148 stands damaged by storm Vaia (2018). The aim was to analyze natural regeneration dynamics under different logging systems and to investigate influences of site characteristics and disturbance legacies on sapling growth and seedling emergence. The sampling protocol consisted of one transect per stand, perpendicular to one of the intact forest edges, and with a length of 80 m. Along the transect, we collected soil cover, natural seedling and sapling stem density, and deadwood quantity in four sample plots of 3 m radius each at distances of 0, 20, 40, and 80 meters from the edge (592 plots in total). Regeneration species composition was mainly driven by previous stand composition, with some exceptions depending on seed dispersal strategy. Distance from the edge significantly influenced seedlings and saplings occurrence in large gaps and affected the browsing damage percentage, together with deadwood presence. According to GLM's models, distance from the edge, elevation, and logging methods influenced seedling establishment. At the same time, species characteristics, edge structure, deadwood and logging damages significantly influenced pre-storm seedlings and saplings presence and health. In conclusion site factors, disturbance legacies, and logging strategies are key points to consider in post-disturbance management for a fast forest recovery.

Western Siberia is exposed to extreme wind events caused by severe convective storms. However, our knowledge on such storms in Siberia is still fragmentary compared to other parts of the world primarily due to the lack of weather radar data. These storms cause substantial damage, which signifies the need for comprehensive assessment of their characteristics and predictability even on the basis of existing data. In this paper, we present a case study analysis of a severe weather outbreak that occurred on 25-26 May 2020 in Western Siberia, during a record six-month heatwave that lasted in Siberia from January to June. The outbreak resulted in six fatalities and substantial economic losses. Using various satellite data and damage reports we found that two consecutive mesoscale convective systems (MCSs) developed within the outbreak having an exceptionally long total track about 2000 km and causing large-scale forest damage with a total area of 64.5 km(2). Such an exceptionally long path was supported by a strong mid-tropospheric jet, which settled extremely high values of wind shear that fostered the development of the outbreak. To analyze the accuracy of the forecast of the MCS and three asso-ciated windstorms on 26 May, we performed a set of simulations with the COSMO and ICON numerical weather prediction models launched with convection-permitting resolution (2.2 km) with different forecast lead times. Both models successfully predicted the most severe windstorm with the 24 h lead time, this emphasized the predominant role of large-scale dynamics and the minor role of local factors in the outbreak formation and development. In particular, the intrusion of the upper tropospheric high potential vorticity streamer along the blocking periphery induced strong deep convection and determined the severe character of the outbreak. Specifically, the studied outbreak had an exceptional longevity compared to other long-lived windstorms observed in Northern Eurasia at the blocking periphery.