Effects of bark beetle attacks on forest snowpack and avalanche formation – Implications for protection forest management
•Snow penetration resistance was measured repeatedly below bark beetle killed trees.•Variability in snow stratigraphy was quantified with a new layer matching algorithm.•Spatial variability in snow stratigraphy is driven by percentage of canopy cover.•Leaving dead trees in protection forests contrib...
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Veröffentlicht in: | Forest ecology and management 2019-04, Vol.438, p.186-203 |
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Sprache: | eng |
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Zusammenfassung: | •Snow penetration resistance was measured repeatedly below bark beetle killed trees.•Variability in snow stratigraphy was quantified with a new layer matching algorithm.•Spatial variability in snow stratigraphy is driven by percentage of canopy cover.•Leaving dead trees in protection forests contributes to avalanche protection.•Salvage logging can result in snow stratigraphy more prone to avalanche release.
Healthy, dense forests growing in avalanche terrain reduce the likelihood of slab avalanche release by inhibiting the formation of continuous snow layers and weaknesses in the snowpack. Driven by climate change, trends towards more frequent and severe bark beetle disturbances have already resulted in the death of millions of hectares of forest in North America and central Europe, affecting snowpack in mountain forests and potentially reducing their protective capacity against avalanches. We examined the spatial variability in snow stratigraphy, i.e., the characteristic layering of the snowpack, by repeatedly measuring vertical profiles of snow penetration resistance with a digital snow micro penetrometer (SMP) along 10- and 20-m transects in a spruce beetle-infested Engelmann spruce forest in Utah, USA. Three study plots were selected characterizing different stages within a spruce beetle outbreak cycle: non-infested/green, infested > 3 years ago/gray stage, and salvage-logged. A fourth plot was installed in a non-forested meadow as the control. Based on our SMP measurements and a layer matching algorithm, we quantified the spatial variability in snow stratigraphy, and tested which forest, snow and/or meteorological conditions influenced differences between our plots using linear mixed effects models. Our results showed that spatial variability in snow stratigraphy was best explained by the percentage of canopy covering a transect (R2 = 0.71, p |
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ISSN: | 0378-1127 1872-7042 |
DOI: | 10.1016/j.foreco.2019.01.052 |