Regional risks of wind damage in boreal forests under changing management and climate projections

Abstract

We employed simulations by forest ecosystem (SIMA) and mechanistic wind damage (HWIND) models in upland boreal forests throughout Finland to study regional risks of wind damage under changing management preferences and climates (current and RCP4.5 and RCP8.5 scenarios) over 2010–2099. We used a critical wind speed for the uprooting of trees as a measure of vulnerability, which together with the probability of such wind speed defined a level of risk. Based on that, we also predicted the stem volume of growing stock at risk and the amount of damage. In this work, medium fertility sites were planted to one of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), or silver birch (Betula pendula Roth) or to the tree species that was dominant before the final clear-felling. The vulnerability to wind damage, the volume of growing stock at risk, and the amount of damage all increased, increasing the most in the south when the proportion of Norway spruce (with shallow rooting) of the growing stock increased. Under a severe climate warming, the proportion of Norway spruce decreased the most in the south, opposite to that of birch. This decreased the risk of damage in autumn (when birch is leafless), unlike in summer. The low risk of damage in the north was due to the large proportion of Scots pine.

Effects of using certain tree species in forest regeneration on regional wind damage risks in Finnish boreal forests under different CMIP5 projections

“…The reasons for this may be many, such as the GCMs being produced by different research institutes (and countries of origin) differing in terms of model parameterization and structure, using different input datasets, spatial resolution and numerical algorithms (see Ruosteenoja et al 2016). For comparison, in this study, we used the multimodel mean values of 28 individual GCMs under RCP4.5 and RCP8.5, which are the same as those used in a study by Ikonen et al (2017).…”

“…Parts of the forest plots from central to northern Finland were also left outside of management, unlike in southern Finland, where currently the total forest conservation area is very small (Finnish Forest Research Institute 2014). This has been done previously by Ikonen et al (2017) and Alrahahleh et al (2018), resulting in more realistic predictions for the growth and volume of growing stock for the first 30-year simulation period (2010-2039) under current climate, compared to the forest statistics for the period (Finnish Forest Research Institute 2014.…”

“…We hypothesized that the use of certain individual GCM runs of CMIP5 could lead to contradictory results for the expected wind damage risks to those predicted based on the multi-model means under RCP4.5 and RCP8.5 by Ikonen et al (2017). In addition, we expected that very severe climate change projections could affect the wind damage risk to a greater degree than tree species preferences in forest regeneration.…”

“…According to Ikonen et al (2017), the increased cultivation of Norway spruce under changing climate will increase the wind damage risk and the amount of damage in Finland, in the long run. Furthermore, tree species preference in forest regeneration may affect the wind damage risk more than the climate change, based on recent-generation global climate model (GCM) predictions (i.e., multi-model means of the Coupled Model Intercomparison Project Phase 5-CMIP5), using the representative concentration pathways RCP4.5 and RCP8.5 over the period 2010-2099 (Ruosteenoja et al 2016).…”

“…The forest ecosystem models, together with up-to-date information on current forest resources, and different climate projections offer a means of predicting forest growth and dynamics under changing management regimes and environmental conditions, respectively (Garcia-Gonzalo et al 2007;Seidl and Lexer 2013;Alrahahleh et al 2017Alrahahleh et al , 2018Reyer et al 2017). The use of the simulated outputs of forest ecosystem models as inputs for mechanistic wind damage models also offers a means of predicting the threshold wind speeds needed for wind damage to forests and, consequently, their probabilities and the amount of damage to expect (Gardiner et al 2008;Peltola et al 2010;Seidl et al 2014;Ikonen et al 2017).…”