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 20 m 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.
The forest-based bioeconomy plays an important role in climate change
mitigation (Kilpeläinen et al., 2016), and in a forested country like
Finland, over 50 % of the current bioeconomy relies on the
sustainable management and utilization of forest resources. In Scandinavia,
forest grows relatively slowly, and it takes typically 50–100 years from
forest cultivation to final harvesting. During this long period the
projected climate change (Ruosteenoja et al., 2016) may largely alter the
growing conditions, thus affecting the survival and productivity of forests
(Kellomäki et al., 2008; Bärring et al., 2017). For example,
according to Bärring et al. (2017) in Scandinavia the vegetative growing
period may extent by around 1 month by 2050 compared to current climate. A
warming climate is expected to increase the volume of growing stock of
Finnish forests due to increasing forest growth (see, e.g., Kellomäki et
al., 2008). However, warming is also expected to increase certain risks to
forests. Drought may have negative impacts especially in southern Finland
for Norway spruce forests (Ruosteenoja et al., 2017; Kellomäki et al.,
2008). Related to drought, forest fire danger will increase (Lehtonen et
al., 2016b). During winter season heavy snow loads will decrease in southern
but increase in northern Finland (Lehtonen et al., 2016a). In the past few
decades, wind storms have damaged a significant amount of timber and caused
large economic and ecological losses in forestry from central to northern
Europe (Schelhaas et al., 2003; Gregow, 2013; Gregow et al., 2011; Reyer et al., 2017). In
Finland, strong winds have damaged over 24 million m
In addition to the properties of wind (e.g., speed, direction, gustiness and their duration), the stand and site characteristics affect largely the vulnerability to wind damage (Peltola et al., 1999b; Gardiner et al., 2016). In Finnish conditions, mature stands adjacent to newly clear-cut areas or recently heavily thinned stands are especially vulnerable to wind damage (e.g., Laiho, 1987; Zubizarreta-Gerendiain et al., 2012). Risks to these forests may be decreased by proper forest management and planning for the spatial and temporal patterns of cuttings in forested areas (Tarp and Helles, 1997; Meilby et al., 2001; Zeng et al., 2004, 2007a; Heinonen et al., 2009; Zubizarreta-Gerendian et al., 2017). Several mechanistic models that have been built in recent decades allow the prediction of threshold wind speeds that can uproot or break trees under alternative forest stand configurations (e.g., Peltola et al., 1999b, 2010; Gardiner et al., 2000, 2008; Byrne and Mitchell, 2013; Seidl et al., 2014; Dupont et al., 2015). Consequently, based on these predicted threshold wind speeds it will be possible to predict the probability (and amount) of wind damage based on local wind characteristics if sufficient knowledge about the local wind climate is available (e.g., Gardiner et al., 2008; Blennow et al., 2010; Zubizarreta-Gerendian et al., 2017).
An estimation of the frequency of extreme weather events, like extreme wind speeds, can be accomplished by utilizing extreme value analysis (EVA) methods. These methods enable to fit their statistical distribution (e.g., Gumbel, Frechet or Weibull distribution) into observations that offer the best estimate of the occurrence probability of the most extreme values of the studied phenomena (e.g., Coles, 2001). The software package, Extremes Toolkit, developed by the National Center of Atmospheric Research (NCAR), is a widely used example of a tool that can be utilized to produce such a statistical distribution (Gilleland and Katz, 2011). For an accurate estimation of the probability of the occurrence of very extreme events with long return periods (e.g., 50 to 100 years), observations over many decades are needed. Additional difficulty to gain an accurate estimation of return levels of extreme wind speeds and wind gusts is caused by a lack of homogeneous wind observation time series due to changes in the measuring conditions and instruments (Laapas and Venäläinen, 2017). One possibility of assessing the return levels of extreme wind speed in coarse resolution is to use reanalyzed datasets (e.g., Dee et al., 2011), which are produced by assimilating all available observations in a systematic way. The benefit of these datasets is that they offer consistent spatial and temporal resolution over several decades (and hundreds of variables). Reanalyzed datasets are also relatively straightforward to handle from a processing standpoint. Although the quality of these data varies from location to location and from variable to variable, the scale of the magnitude of extreme wind for a coarse spatial scale can indeed be estimated based on them (e.g., Brönniman et al., 2012). From the point of local effects, the ERA-Interim dataset has a relatively coarse spatial resolution of about 80 km and detailed spatial variation cannot be taken into account in such a coarse grid. In addition, the continuous change in the availability of reliable observational data creates limitations and must be taken into account, especially if trend analyses of a change in, for example, wind are made (Dee et al., 2011).
The high-resolution spatial variation in extreme wind speed that is affected
by topography and surface characteristics (e.g., Wieringa, 1986) can be
considered by applying spatial statistical tools (e.g., Etienne et al., 2010;
Jung and Schindler, 2015). Additionally, complex airflow models like WAsP
(Mortensen, 2015) and WindSim (
In this study, we evaluated the applicability of the wind multiplier
approach for an estimation of the high-resolution (20 m) variability of
extreme wind speeds in Finnish forested landscapes, employing CORINE land-use and high-resolution digital elevation data. First we calculated the
return levels of extreme wind speeds using the ERA-Interim reanalyses
dataset to each coarse resolution grid box and for eight wind directions
(cardinal and sub-cardinal). Based on the elevation data, the wind
multiplier depicting the effect of orography on wind speed was processed.
Likewise, the multiplier depicting the effect of terrain properties on wind
speed was processed for each 20 m grid square. Thereafter, wind multipliers
were used to provide quantitative estimates of local wind conditions
relative to the regional wind speeds in our 20 km
The wind multiplier approach used here follows the one presented in AS/NZS
1170.2 (2011) and Yang et al. (2014), where terrain properties are taken
into account when assessing local maximum wind speeds (see Eq. 1). The
return level of regional maximum wind speed (
The regional-scale return levels of maximum wind speeds were calculated
using the ERA-Interim dataset (Dee et al., 2011) and the generalized extreme
value (GEV) method (e.g., Coles, 2001). This method estimated the 10-year
return level of maximum wind speed as, for example, in inland Finland at
below 12 m s
Ten-year return level of maximum wind speed calculated using ERA-Interim 1979–2015 data and the GEV analysis approach.
In AS/NZS 1170.2 (2011) for elevations below 50 m, 1000 m fetch was used
when the surface roughness impact was estimated. In this study, we applied a
somewhat different approach. First, each CORINE land-use class was
interpreted to roughness lengths following the technique applied in the
production of the Finnish Wind Atlas (Tammelin et al., 2013). We were
interested in this work in very high-resolution spatial variation in wind
speed in typically highly variable terrain mosaic composed of forests,
fields, lakes, clear-cut areas etc. The detailed structure of wind flow in
this kind of heterogeneous terrain is very complex (e.g., Dupont and Brunet,
2008). One dominant feature is rapid deceleration of wind when wind
encounters a forest edge. In Finnish conditions the main wind damages are found
typically within one to two mean stand heights from the upwind forest edge
(Peltola et al., 1999b). When estimating the impacts of upwind conditions on
wind speed in the location that was of interest, we used 500 m fetch to
calculate the effective roughness (
In ERA-Interim analyses, a roughness length for each grid cell is presumed.
To normalize the roughness length of the ERA-Interim data into a reference
roughness, we multiplied the ERA-Interim wind speed values by
The values of
The value of surface roughness lengths
The topographic multiplier
An example of the change in topographic multiplier in the case of a
transection reaching over the roughly 500 m high Pyhätunturi Fell (Fig. 5)
in northern Finland in the case of northwesterly wind is given in Figs. 4 and A5. As this place is located at a relatively high elevation, the
purely on elevation-dependent
A visualization of the calculation of the topographic multiplier
Variations in elevation (
The meteorological stations used in the analyses (see Table 1, Fig. 6) and the topography of the Pyhätunturi area located in northern Finland. The black northwest direction line in the Pyhätunturi figure indicates the transection analyzed in Fig. 4; the black square indicates the border of the WAsP simulation.
The first verification tests were done by utilizing wind measurements made
at 26 observing stations in Finland; 23 of these stations belong to the
observation network maintained by the Finnish Meteorological Institute (FMI)
and represent conditions ranging from open sea to agricultural land,
forests, and open hill areas. More detailed analyses were made in northern
Finland (67.02204
Based on the measurements made at the observing stations, 10-year return levels of maximum wind speeds were calculated for each location and compared with return period values obtained for the station locations using the wind multiplier approach and the ERA-Interim maximum wind speed estimates. The return levels were calculated using the same GEV approach as in the case of ERA-Interim data. The observations were 10 min winds speed measured after every 3 h and the annual maximum values was filtered from these data. In this sense the observational values are not exactly the same as reanalyzed data, and this may create some systematic difference. However, when using the annual maximum values as the bases for fitting the distribution this may reduce the bias. For stations MM1, MM2 and MM3, there was only 2 years of data available, a short period to estimate even 10-year return levels. Therefore, to have the extreme value analysis be as robust as possible, for these stations, we applied the block maxima approach (e.g., Coles, 2001) to the monthly maximum values, using the R package extRemes (Gilleland and Katz, 2016). For most of the other station locations, the data used for the extreme value analyses covered the years 1979–2015, which is the same period as used in the case of ERA-Interim data.
Ten-year return levels of maximum wind speed (m s
For the Pyhätunturi Fell area, we also compared a spatial variation in
high wind speed as simulated by the WAsP package with a wind multiplier
downscaled wind. The area was slightly smaller (Fig. 5) due to the
availability of terrain information needed for a WAsP simulation. In the
WAsP simulation, the geostrophic wind speed was expected to be 39 m s
A comparison of the ERA-Interim and wind-multiplier-based assessment of
10-year return levels of wind speed to the estimates based on measurements
for the test locations (Table 1, Fig. 6) revealed that for these locations,
and representing different kinds of terrain and elevations, the wind
multiplier approach improved the local wind speed return level estimates
remarkably (
Comparison of 10-year return levels of maximum wind speeds, as calculated, based on observations and by utilizing the wind multiplier method (Eq. 1) and the ERA-Interim dataset for the 26 measuring sites (Fig. 5). Return levels taken directly from the ERA-Interim dataset with no wind multiplier correction are included in the visualization. The shaded areas are 95 % confidence levels for the linear trend lines depicting the dependence between the datasets. The diamond shape symbols indicate that the station is located on small Baltic Sea islands.
At the four Pyhätunturi Fell stations, the wind multiplier estimates
were close to the measurement-based estimates with the exception of station
MM1. The estimate based on measurements made at MM1 (29.6 m s
The spatial variation in 10-year return levels of wind speeds within the
roughly 4000 km
Ten-year return levels of maximum wind speed (A) calculated using
the wind multiplier method (Eq. 1) and the ERA-Interim dataset for the
Pyhätunturi test area (Fig. 5). The values where wind speed exceeded 12 m s
Comparison of the spatial variation in wind speed as estimated,
using the wind multiplier approach, calculated using the WAsP program.
The last figure depicts the difference between the two methods. Wind
direction is from the northwest and in the case of the wind multiplier it is
12.7 m s
In a qualitative comparison, the wind multiplier approach and a WAsP
simulation produced the same dominant features of spatial variation in
maximum wind speed; maximum values were found at treeless fell-top areas
(Fig. 8). One interesting feature was the case of the WAsP simulation for
the acceleration of wind at the forest–lake edge; it was immediate, and so
was the deceleration on the opposite shore. In such a case of wind
multiplier simulation, the impact of roughness change is reflected over a longer
distance, as can be seen in the case of the Lake Pyhäjärvi. On top
of the fell, the wind speed was adjusted to approximately the same 26 m s
The wind multiplier method has been used earlier to estimate the design values of buildings and other constructions (AS/NZS 1170.2, 2011) and assessment of wind damage risk (Yang et al., 2014). Based on our study, the wind multiplier method is very capable of identifying the locations having the highest extreme wind speeds in Finnish conditions. This is true despite the fact that this approach is much simpler than the dynamical models. The method seem to underestimate wind speeds at small islands located on the open sea and this issue has to be taken into account if high-spatial-resolution assessment of extreme wind speeds is calculated to such conditions. The wind multiplier approach is also easily transferable to any location with needed terrain information and is an interesting and easily applicable alternative to use to assess the exposure of terrain.
How precise each grid square estimate is depends on several external factors. First, we must have an estimate of the coarse-scale return levels of the extreme wind speeds. Reanalyzed data give such a coarse estimate. If the reanalyzed data are compared to in situ measurements in certain wind storm event, it is easy to find large differences between them. In addition, the return levels of wind speed calculated using ERA-Interim grid values can be quite different from the value based on point measurements, but downscaling the grid value to the point using the wind multiplier approach improves the estimate substantially, as we demonstrated in Fig. 6. It is also good to remember that, although the wind measurements made at meteorological stations can go through several quality control steps, they may still contain erroneous values. FMI has a three-stage quality control system. The first check is done at the observation station site checking the main instrument malfunctions. The next check is done before storing the data in the database. This check includes, for example, comparison with the extreme values and temporal and spatial consistency. The final step is the manual quality control for those values that did not pass earlier steps. The quality control ensures that the values stored in database are realistic and can have occurred. However, quality control does not guarantee that the measurements are exactly correct. In addition, quality control does not ensure the homogeneity of observations. The changes at measuring site and changes in instrumentation as well as the changes in the height of anemometer installation can lead to discontinuations, i.e., break points, in observational time series. These break points are relatively common also in wind observational time series like studied by Laapas and Venäläinen (2017). In that sense the return periods based on measured values (Table 1, Fig. 6) contain uncertainties that are wise to remember when the comparison is fully valued.
The simple visualization and comparison of the spatial variation in wind speed at Pyhätunturi Fell was done by applying WAsP and, in addition, by applying wind multipliers. These demonstrate that the main features of spatial variation in an extreme wind field produced by these two different methods are very similar. A profound analysis on the exact accuracy of the simulations is not possible, however, based on the available measurements; it would require much more detailed and reliable wind measurement data. However, by fine tuning the wind multipliers, it is possible to achieve results that are closer to the WAsP simulation. Pyhätunturi is not a typical Finnish forested landscape due to its high topographic variation. In those parts of the test area that exemplify a more typical landscape with only relatively small topographic features and found at elevations below 300 m, these two methods give quite similar results. It is also good to remember that, as we are summarizing all wind directions (Fig. 7), the importance of lee-side wind simulation accuracy is not as crucial as having accuracy for the windward size having the highest wind speeds.
The wind multiplier method itself is also relatively easy to apply. The calculation of surface roughness and topographic multipliers can be done using routine GIS tools, and these calculations can be done for large areas, such as the whole country. Similarly, this method could be used to assess the risks to forests that are related to forest management and planning with relatively little extra effort. Further, climate change impact assessments can be done with high spatial resolution when the return levels of maximum wind speed are calculated using climate scenarios instead of only reanalyzed data.
One challenge of the method is the accuracy of surface roughness information
in the CORINE dataset; it is updated approximately every 6 years and thus
does not represent real-time land-use conditions for all locations. For
example, forest clear-cutting changes the roughness conditions very
dramatically. Thinning affects it less. More frequent updates to surface
conditions could be obtained from satellite measurements. As an example, the
European Space Agency's (ESA) satellite Sentinel-2
(
The rapidly growing, forest-based bioeconomy calls for increasing wood harvesting intensity, which means an increase in thinning and a final felling area. This will increase the wind damage risks to forests, especially at the upwind edges of new clear-felling areas and in recently thinned stands that have not yet been acclimated to increasing wind loading. Thus, proper risk assessment is a clear pre-condition for a sustainable forest-based bioeconomy. This study demonstrated a useful tool to use for forest management and planning.
The tested wind multiplier method is very capable of identifying the locations (at high resolution) having the highest extreme wind speeds and could well support the precise assessment of wind damage risks to forests. It can also be used to provide needed wind climate information for wind damage risk model calculations. Thus, it would make it possible to estimate the probability of predicted threshold wind speeds for wind damage, and consequently the probability (and amount) of wind damage under certain forest stand configurations. Accurate estimations of the spatial variation in the return levels of extreme wind speed with very high spatial resolution over the whole country or even over larger areas like Fennoscandia are possible in the future using this approach. A high-resolution estimation of climate change impacts on wind damage risks to forests is also feasible using this approach.
Wind measurements at station locations are available through
the Finnish Meteorological Institute's open data service (
Box plot depicting measured 10 min wind speed values at the
Pyhätunturi telecom mast station during years 1997–2016 and as taken from
the ERA-Interim. The values measured at an elevation of 61 m were corrected
to represent 10 m by applying logarithmic wind law. Only values that are
11.4 m s
The weight of each grid square's roughness on the effective
roughness (
Land-use map for the Pyhätunturi Fell area based on the CORINE dataset.
Effective roughness (
Topographic wind multipliers (Eq. 6) calculated for the Pyhätunturi Fell area for northwesterly winds.
Histogram depicting the difference between the wind speed as estimated using the wind multiplier approach and as calculated using the WAsP programme (see Fig. 8).
The authors declare that they have no conflict of interest.
This article is part of the special issue “Multiple drivers for Earth system changes in the Baltic Sea region”. It is a result of the 1st Baltic Earth Conference, Nida, Lithuania, 13–17 June 2016.
This work was supported by the Strategic Research Council at the Academy of Finland, project FORBIO (grant number 293380). Edited by: Marcus Reckermann Reviewed by: two anonymous referees