High-resolution topographical information improves tree-level storm damage models

Storms cause major forest disturbances in Europe. The aim of this study was to model tree-level storm damage probability based on the properties of tree and its environment and to examine whether fine-scale topographic information is connected to the damage probability. We used data documenting effects of two autumn storms on over 17000 trees on permanent Finnish National Forest Inventory plots. The first storm was associated with wet snow fall that damaged trees, while exceptionally strong winds and gusts characterized the second storm. During the storms soils were unfrozen and deciduous trees without leaves. Generalized linear mixed models were used to study how topographical variables calculated from digital elevation models (DEM) with resolutions of 2 and 10 m (TOPO2 and TOPO10) were related to damage probability, in addition to variable groups for tree (TREE) and stand (STAND) characteristics. We compared models containing different variable groups with Akaike Information Criteria. The best model contained variable groups TREE, STAND and TOPO2. Increase in slope steepness calculated from the high-resolution DEM decreased tree-level damage probability significantly in the model. This suggests that the local topography affects the tree-level damage probability and that high-resolution topographical data improves the tree-level damage probability models.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author