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The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50% of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66% of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In a forested country, such as Finland, over 50 % of its current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high spatial resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. Coarse spatial resolution estimates of the return levels of maximum wind speed based, e.g., on reanalysed meteorological data or climate scenarios can be downscaled to forest stand levels with the help of land cover and terrain elevation data. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data. These data were downscaled to 26 meteorological station locations to represent very diverse environments: Open Baltic Sea islands, agricultural land, forested areas, and Northern Finland treeless fells. Applying a comparison, the downscaled 10-year return levels explained 77 % of the observed variation among the stations examined. In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsP- model simulated wind. The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences. However, for areas representing a typical Finnish forested landscape with no major topographic variation, both of the methods produced very similar results. Further fine-tuning of wind multipliers could improve the downscaling for the locations with large topographic variation. However, the current results already indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]