A long-term scenario analysis of snow damage risk: effects of reduced stand density management

• GAYA 2.0 is used to simulate snow damage scenarios. • An adaptation of silviculture by reducing stand density can reduce the risk of snow damages. • The adaptation does not have a significant impact on long-term production and income. Climate change is expected to increase the frequency and severity of natural disturbances. In Nordic conifer forests, damage caused by snow accumulation in the canopy is one of the most significant disturbance agents. This study investigates whether adaptive forest management can enhance resistance to snow damage, using a large forest property in southeastern Norway as a case study. To achieve this, we extended the existing scenario analysis tool, GAYA 2.0, integrating new functionality to analyze the risk of snow damage. We performed scenario simulations using a mechanistic critical snow load model to compare two alternative management strategies: standard management and an adapted management approach that reduces stand density in regeneration and tending phases. We analyzed and compared the management effects on snow damage resistance and probability, and on long-term forest production and income. The results indicate that reduced density management leads to a 2.02 % increase in critical snow load (from 74.19 Kg m -2 to 75.68 Kg m -2 ), and a 10.42 % reduction in yearly damage probability (from 0.345 % to 0.308 %). These findings suggest that adaptive management practices by reducing stand density can effectively enhance resistance and mitigate risks associated with snow damage in Nordic boreal forest ecosystems. The reduced stand density management does not have a significant impact on long-term production and income levels.