Jānis Donis (email), Māra Kitenberga, Guntars Šņepsts, Edgars Dubrovskis, Āris Jansons

Factors affecting windstorm damage at the stand level in hemiboreal forests in Latvia: case study of 2005 winter storm

Donis J., Kitenberga M., Šņepsts G., Dubrovskis E., Jansons Ā. (2018). Factors affecting windstorm damage at the stand level in hemiboreal forests in Latvia: case study of 2005 winter storm. Silva Fennica vol. 52 no. 4 article id 10009. https://doi.org/10.14214/sf.10009


  • In hemiboreal forests in Latvia, dominant tree species, admixture of spruce in canopy-layer, mean height, timing of thinnings, upwind forest edges and wind gusts had significant effect on windstorm damage occurrence at stand-level
  • Stands on peat soils were more damaged than stands on mineral soils
  • Tree species composition of canopy-layer was not statistically significant in the model.


In managed European hemiboreal forests, windstorms have a notable ecological and socio-economic impact. In this study, stand properties affecting windstorm damage occurrence at the stand-level were assessed using a Generalized Linear Mixed model. After 2005 windstorm, 5959 stands dominated by birch (Betula spp.), Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.), with mean height > 10 m were inventoried. Windstorm damage was positively associated with spruce and pine-dominated stands, increasing mean height, fresh forest edges, decreasing time since the last thinning and stronger wind gusts. Tree species composition – mixed or monodominant – was not statistically significant in the model; while, the admixture of spruce in the canopy layer was positively associated with higher windstorm damage. Stands on peat soils were more damaged than stands on mineral soils. Birch stands were more damaged than pine stands. This information could be used in forest management planning, selection of silvicultural treatments to increase forest resilience to natural disturbances.

Norway spruce; natural disturbance; windstorm; hemiboreal

Author Info
  • Donis, Latvian State Forest Research Institute “Silava”, Rigas 111, Salaspils, Latvia, LV-2169 E-mail janis.donis@silava.lv (email)
  • Kitenberga, Latvian State Forest Research Institute “Silava”, Rigas 111, Salaspils, Latvia, LV-2169 E-mail mara.kitenberga@gmail.com
  • Šņepsts, Latvian State Forest Research Institute “Silava”, Rigas 111, Salaspils, Latvia, LV-2169 E-mail guntars.snepsts@silava.lv
  • Dubrovskis, Latvia University of Life Sciences and Technologies, Liela iela 2, Jelgava, Latvia, LV-3001 E-mail edgars.dubrovskis@llu.lv
  • Jansons, Latvian State Forest Research Institute “Silava”, Rigas 111, Salaspils, Latvia, LV-2169 E-mail aris.jansons@silava.lv

Received 30 May 2018 Accepted 2 October 2018 Published 5 October 2018

Views 29687

Available at https://doi.org/10.14214/sf.10009 | Download PDF

Creative Commons License full-model-article10009

1 Introduction

In a hemiboreal zone, natural forest disturbances substantially affect forest stand dynamics (Jõgiste et al. 2017), carbon cycling (Lindroth et al. 2009) and other ecosystem services (Millar and Stephenson 2015). However, in managed forests, natural disturbances have considerable negative economic impacts (Dale et al. 2001).

Between 1950 and 2000, wind damage accounted for more than half of all natural disturbances causing damage to European forests (Schelhaas et al. 2003). The amount of damage caused might increase due to increasing large-scale windstorm intensity, which has been observed over the last three decades, likely driven by climate change (Gregow et al. 2017). To mitigate the negative impacts of wind disturbance, forest ecosystem resilience should be increased by implementing sustainable forest management practices (Suvanto et al. 2016).

The probability of wind damage has regional differences, shaped by complex interactions between multiple factors e.g., wind climate, soil type, topography and silviculture practices (Albrecht et al. 2010; Lagergren et al. 2012; Díaz-Yáñez et al. 2017). Studies investigating such effects have been conducted in Fennoscandia (Valinger and Fridman 2011; Zubizarreta-Gerendiain et al. 2012) and Germany (Albrecht et al. 2010); in Baltic region such information is lacking. In Latvia, annual wind disturbance is the reason for 40 to 60% of all sanitary clearcuts, according to Latvian State Forest Service statistics (LSFS 2018). Therefore, the aim of this study was to identify significant effects of stand properties on windstorm damage occurrence.

2 Methods and materials

2.1 Study area

Latvia is located in the hemiboreal vegetation zone, which is characterized by a mixture of pure and mixed stands of deciduous and conifer tree species (Ahti et al. 1968). According to the National Forest Inventory, the dominant tree species are birch (Betula spp.), Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.), which occupy 28.2%, 26.9% and 18.3% of the total forest area, respectively (NFI 2017). The climatic conditions in Latvia are strongly influenced by the Baltic Sea, intensity of prevailing westerlies and large-scale weather systems, e.g., North Atlantic Oscillation (NAO) (Jaagus et al. 2010, 2014). NAO activity have been linked to winter storm frequency and intensity (Gregow et al. 2017).

In January 2005, the winter cyclonic windstorm Erwin (Gudrun) severely affected the territory of Latvia. It was the third severest windstorm since the 1960s, with maximal average wind speed of 29 m s–1 and with gusts of up to 40 m s–1, according to the Latvian Environment, Geology and Meteorology Centre (LEGMC 2013). Approximately 11.1–15.6 million m3 of wood were damaged (Donis et al. 2007).

2.2 Data Sampling

In total, 169 study plots of 1×1 km2 (Fig. 1) were randomly scattered over the territory of Latvia (1–75, with the mean of 35 stands per plot). All forest stands within each plot were assessed by local LSFS foresters. Stand properties (tree species composition, mean height, relative stand density, mean stem diameter, soil type, timing of thinning) were obtained from an inventory, conducted prior the windstorm (Table 1). After the windstorm, damage (lost proportion of standing stock) and forest edge information was evaluated for each stand. Fieldwork was done in summer 2005. In total 5959 stands dominated by pine, spruce and birch with mean height > 10 m were inventoried.


Fig. 1. Distribution of the study plots in the territory of Latvia. View larger in new window/tab.

Table 1. Description of explanatory variables tested in the model. Number of observations for categorical variables; mean ± standard deviation (SD) for continuous variables.
Abbreviation Description Classes Number of observations / mean ± SD
st10 Dominant tree species ( > 50 % of stand volume) 1 – birch 2010
2 – pine 2854
3 – spruce 1095
stand_str Composition of canopy-layer 1 – mixed 3635
2 – monodominant ( ≥ 75% of standing volume) 2324
mix_spruce Admixture of spruce in canopy-layer 0.07 ± 0.11
storey_spruce Admixture of spruce in understory 0 – no spruce 4918
1 – spruce admixture 1041
dens_group Relative stand density (calculated as ratio between basal area per hectare and the maximum stand basal area for a species and mean height) 1 – ( ≤ 0.4) 163
2 – (0.5–0.8) 3544
3 – (0.8–1.0) 2252
h10 Mean height of the dominant canopy trees (m) 21.27 ± 4.76
d10 Mean diameter of the dominant canopy trees (cm) 23.56 ± 6.73
h2d Height2/diameter ratio 19.58 ± 4.59
soil_group Soil type (based on forest type group) 1 – dry mineral soil 1353
2 – wet mineral soil 2752
3 – peat soil 201
4 – drained mineral soil 903
5 – drained peat soil 750
thin_befk Timing of thinning 1 – thinned more than 10 years ago or no information on thinning 4888
2 – thinned within last 6 to 10 years 475
3 – thinned within last 5 years 596
wind_dir Adjacent upwind zone of the forest edge BL – new forest edge (fresh clearcut/ young stand < 5 years) 190
GL – old forest edge (young stand with mean tree height < 5 m / wetland / agriculture land) 840
H – high-forest edge (forest stand with mean height > 5 m) 4929
gust_group Wind gusts region 1 – (17–19 m s–1) 2830
2 – (20–21 m s–1) 1502
3 – (22–24 m s–1) 980
4 – (25–30 m s–1) 647

Map of maximal wind gust (m s–1) at 10 m height were obtained from the Swedish Meteorological and Hydrological Institute (personal communication).

2.3 Data analysis

Effects of stand properties on windstorm damage (lost proportion of standing stock) were assessed by random intercept Generalized Linear Mixed model (GLMM) using maximum likelihood (Laplace Approximation) approach (Bates et al. 2015) and Gamma distribution with “log” link function. The study plot was used as a random factor. Dependant variables were added 0.1 to fit the Gamma distribution. All continuous variables (e.g. diameter) were scaled.

Multicollinearity between explanatory variables were assessed using Generalized Variance Inflation Factor (GVIF), variables with GVIF > 5 were excluded from the model, in our case – mean stem diameter.

In the analysis, we compared two models: Model_1, which contained all factors retained after GVIF analysis and Model_2, adding to Model_1 interaction between the factors “dominant tree species” and “wind gust region” and “dominant tree species” and “timing of thinning”.

For description of the model performance marginal and conditional pseudo-R2 values were calculated using lognormal method. The selection of the best model was based on AIC (McGullagh and Nelder 1989).

All calculations were performed using R version 3.5.0 (R Core Team 2018) packages “car” (Fox and Weisberg 2011), “lme4” (Douglas et al. 2015).

3 Results

The Model_2 was chosen as the final model, based on lower AIC values, with conditional and marginal pseudo-R2 values of 0.70 and 0.14, respectively. The final model had eleven statistically significant variables (shown in Table 2).

Table 2. Parameter estimates of fitted model. (Df): degree of freedom, * – interaction between factors.
Explanatory variables Chi-squared Df p-value
st10 296.6 2 <0.001
gust_group 44.8 3 <0.001
stand_str 1.4 1 0.22
mix_spruce 131.9 1 <0.001
storey_spruce 0.5 1 0.46
dens_group 44.6 2 <0.001
h10 49.6 1 <0.001
h2d 7.2 1 <0.01
thin_befk 266.7 2 <0.001
wind_dir 302.6 2 <0.001
soil_group 10.7 4 0.03
st10 * gust_group 13.1 6 0.04
st10 * thin_befk 16.4 4 <0.01
Random effects (study plot): variance 2.9; standard deviation 1.72

Windstorm damage was positively associated with spruce and pine-dominated stands, increasing admixture of spruce in canopy-layer, increasing mean stand height, higher wind gusts and fresh forest edges. Windstorm damage was negatively associated with increasing stand relative density, increasing time since the last thinning and increasing h2/d ratio (Table 3). The canopy-layer tree composition (mixed or monodominant) did not have statistically significant influence on damaged stock (Table 3). Stands on peat soils had significantly more damaged stock than stands on mineral soils. Pine-dominated stands in wind gusts region (25–30 m s–1), had significantly less damaged stock than birch-dominated stands in the same wind gust region. Spruce-dominated stands which had been thinned within last 5 years had significantly more damaged stock than birch-dominated stands thinned within the same time period. While, spruce-dominated stands which were thinned in the last 6–10 years, had significantly less damaged stock than birch-dominated stands with the same timing of thinning (Table 3).

Table 3. Fixed effects parameter estimates of fitted model. (SE): standard error, * – interactions between factors.
Explanatory variables Estimate SE t-value p-value
Intercept 1.96 0.18 10.4 <0.001
st10: pine 0.16 0.07 2.1 <0.05
st10: spruce 1.03 0.08 11.7 <0.001
gust_group: 2 1.06 0.18 5.6 <0.001
gust_group: 3 1.06 0.22 4.8 <0.001
gust_group: 4 0.73 0.26 2.7 <0.01
stand_str: monodominant 0.05 0.04 1.2 0.22
mix_spruce 0.26 0.02 11.4 <0.001
storey_spruce: 1 0.04 0.05 0.7 0.45
dens_group: 2 –0.66 0.12 –5.5 <0.001
dens_group: 3 –0.79 0.12 –6.4 <0.001
h10 0.27 0.03 7 <0.001
h2d –0.1 0.03 –2.6 <0.01
thin_befk: 2 0.53 0.13 4.1 <0.001
thin_befk: 4 0.93 0.12 7.3 <0.001
wind_dir: GL –1.4 0.12 –11.6 <0.001
wind_dir: H –1.83 0.11 –16.3 <0.001
soil_group: 2 –0.07 0.06 –1.1 0.25
soil_group: 3 0.2 0.07 2.5 <0.05
soil_group: 4 0.05 0.06 0.7 0.44
soil_group: 5 0.00008 0.07 0 0.99
st10: pine * gust_group: 2 –0.06 0.12 –0.5 0.58
st10: spruce * gust_group: 2 –0.1 0.13 –0.7 0.44
st10: pine * gust_group: 3 –0.21 0.13 –1.5 0.11
st10: spruce * gust_group: 3 0.08 0.16 0.5 0.61
st10: pine * gust_group: 4 –0.44 0.15 –2.8 <0.01
st10: spruce * gust_group: 4 –0.06 0.21 –0.3 0.75
st10: pine * Thin_befk: 2 –0.18 0.15 –1.1 0.24
st10: spruce * Thin_befk: 2 –0.57 0.21 –2.7 <0.01
st10: pine * Thin_befk: 4 0.07 0.15 0.4 0.63
st10: spruce * Thin_befk: 4 0.51 0.19 2.6 <0.01

4 Discussion

This work summarizes the significant effects of stand properties on damaged standing volume, based on data collected throughout territory of Latvia after the windstorm of 2005. Our study results were in alignment with earlier studies carried out in southern Sweden (Valinger and Fridman 2011) and south-western Germany (Albrecht et al. 2010), where similarly spruce admixture, increasing mean stand height, fresh forest edges and recent thinnings increased stand susceptibility to windstorm damage.

Increasing windstorm damage with decreasing relative stand density is difficult to explain, as relative density did not correlate with timing of thinning, suggesting that vulnerability to wind damage might be related to historical forest stand management.

In our study, tree species composition was not statistically significant in the model. Although pure Norway spruce stands are considered the most vulnerable to windstorm damage (Valinger and Fridman 2011), also mixed stands are often damaged by wind and snow, and in some cases, even more than monodominant stands (Díaz-Yáñez et al. 2017).

Higher damages in stands on peat soils mostly likely is linked to shallow rooting, which affects tree stability (Nicoll and Ray 1996). Similar results have been obtained in Finland, where stands on peat soils had severe windstorm caused damages (Zubizarreta-Gerendiain et al. 2012). In 2005 windstorm, soils were unfrozen, under these conditions trees commonly are uprooted and birch has weaker resistance to uprooting than pine (Peltola et al. 2000), which might be the reason of larger damages in birch and in pine stands in wind zone (25–30 m s–1).

Our results suggest that the vulnerability to windstorm damage could be addressed through silvicultural treatments. Wind resistance of individual trees could be improved by decreasing initial stand density (Gardiner and Quine 2000). In this way, the number of thinnings could be reduced. Lower initial stand density could benefit growth of a radial increment (Gardiner and Quine 2000), which might reduce the rotation period as target diameter could be reached faster, therefore minimizing the probability of windstorm damage. However, many uncertainties are linked to climate change and its various impacts on regional wind climate and forest growth (Blennow et al. 2010). General conclusions based on single storm events must be treated with caution (Albrecht et al. 2010; Valinger and Fridman 2011). Therefore, further studies could combine and analyse data of several windstorm events, to assess forest stand susceptibility to windstorm damages on broader perspective.


We thank Didzis Elferts and Roberts Matisons for assistance with statistical analysis. We are very grateful to the two anonymous reviewers, for their suggestions and comments which helped us to improve the manuscript.

This work was supported by the European Regional Development Fund project Development of decision support tool for prognosis of storm damages in forest stands on peat soils (No


Ahti T., Hämet-ahti L., Jalas J., Annales S., Fennici B. (1968). Vegetation zones and their sections in northwestern Europe. Annales Botanici Fennici 5: 169–211.

Albrecht A.T., Hanewinkel M., Bauhus J., Kohnle U. (2010). How does silviculture affect storm damage in forests of south-western Germany? Results from empirical modeling based on long-term observations. European Journal of Forest Research 131(1): 229–247. https://doi.org/10.1007/s10342-010-0432-x.

Bates D., Maechler M., Bolker B., Walker S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67(1): 1–48. https://doi.org/10.18637/jss.v067.i01.

Blennow K., Andersson M., Sallnäs O., Olofsson E. (2010). Climate change and the probability of wind damage in two Swedish forests. Forest Ecology and Management 259(4): 818–830. https://doi.org/10.1016/j.foreco.2009.07.004.

Dale V.H., Joyce L.A., Mcnulty S., Neilson R.P., Ayres M.P., Flannigan M.D., Hanson P.J., Irland L.C., Lugo A.E., Peterson C.J., Simberloff D., Swanson F.J., Stocks B.J., Wotton M. (2001). Climate change and forest disturbances. BioScience 51(9): 723–734. https://doi.org/10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2.

Díaz-Yáñez O., Mola-Yudego B., González-Olabarria J.R., Pukkala T. (2017). How does forest composition and structure affect the stability against wind and snow? Forest Ecology and Management 401: 215–222. https://doi.org/10.1016/j.foreco.2017.06.054.

Donis J., Zarins J., Rokpelnis M. (2007). Ekstrēmu vēju ātrumu ietekmes uz kokaudzes noturību novērtējums. [Assessment of extreme wind influence on forest stands]. http://www.silava.lv/userfiles/file/Projektu%20parskati/2007_Donis_MAF_s259.pdf. [Cited 21 May 2018].

Fox J., Weisberg S. (2011). An {R} companion to applied regression. Second edition. Sage, Thousand Oaks, CA. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion.

Gardiner B.A., Quine C.P. (2000). Management of forests to reduce the risk of abiotic damage – a review with particular reference to the effects of strong winds. Forest Ecology and Management 135(1–3): 261–277. https://doi.org/10.1016/S0378-1127(00)00285-1.

Gregow H., Laaksonen A., Alper M.E. (2017). Increasing large scale windstorm damage in western, central and northern European forests, 1951–2010. Scientific Reports 7(46397): 1–7. https://doi.org/10.1038/srep46397.

Jaagus J., Briede A., Rimkus E., Remm K. (2010). Precipitation pattern in the Baltic countries under the influence of large-scale atmospheric circulation and local. International Journal of Climatology 30(5): 705–720. https://doi.org/10.1002/joc.1929.

Jaagus J., Briede A., Rimkus E., Remm K. (2014). Variability and trends in daily minimum and maximum temperatures and in the diurnal temperature range in Lithuania, Latvia and Estonia in 1951–2010. Theoretical and Applied Climatology 118(1–2): 57–68. https://doi.org/10.1007/s00704-013-1041-7.

Jõgiste K., Korjus H., Stanturf J.A., Frelich L.E., Baders E., Donis J., Jansons A., Kangur A., Laarmann D., Maaten T., Marozas V., Metslaid M., Nigul K., Polyachenko O., Randveer T., Vodde F. (2017). Hemiboreal forest: natural disturbances and the importance of ecosystem legacies to management. Ecosphere 8(2): 1–20. https://doi.org/10.1002/ecs2.1706.

Lagergren F., Jönsson A.M., Blennow K., Smith B. (2012). Implementing storm damage in a dynamic vegetation model for regional applications in Sweden. Ecological Modelling 247: 71–82. https://doi.org/10.1016/j.ecolmodel.2012.08.011.

LEGMC (2013). Visstiprākie vēji Latvijā. [The highest wind speeds in Latvia]. https://www.meteo.lv/lapas/visstiprakie-veji-latvija?&id=1844. [Cited 21 May 2018].

Lindroth A., Lagergren F., Grelle A., Klemedtsson L., Langvall O., Weslien P., Tuulik J. (2009). Storms can cause Europe-wide reduction in forest carbon sink. Global Change Biology 15(2): 346–355. https://doi.org/10.1111/j.1365-2486.2008.01719.x.

LSFS (2018). 2017 Gada publiskais. [2017 Annual report]. https://www.zm.gov.lv/public/files/CMS_Static_Page_Doc/00/00/01/28/25/Publiskais_parskats_2017.pdf. [Cited 21 May 2018].

McGullagh P., Nelder J.A. (1989). Generalized linear models. 2nd ed. CRC Press, New York. 532 p.

Millar C.I., Stephenson N.L. (2015). Temperate forest health in an era of emerging megadisturbance. Science 349(6250): 823–826. https://doi.org/10.1126/science.aaa9933.

NFI (2017). Meža statistiska inventarizācija. [National Forest Inventory]. http://www.silava.lv/petijumi/nacionlais-mea-monitorings.aspx. [Cited 21 May 2018].

Nicoll B.C., Ray D. (1996). Adaptive growth of tree root systems in response to wind action and site conditions. Tree physiology 16(11–12): 891–898. https://doi.org/10.1093/treephys/16.11-12.891.

Peltola H., Kellomäki S., Hassinen A., Granander M., (2000). Mechanical stability of Scots pine, Norway spruce and birch: an analysis of tree-pulling experiments in Finland. Forest Ecology and Management 135(1–3): 143–153. https://doi.org/10.1016/S0378-1127(00)00306-6.

Schelhaas M.-J., Nabuurs G.-J., Schuck A. (2003). Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology 9(11): 1620–1633. https://doi.org/10.1046/j.1365-2486.2003.00684.x.

Suvanto S., Henttonen H.M., Nöjd P., Mäkinen H. (2016). Forest susceptibility to storm damage is affected by similar factors regardless of storm type: comparison of thunder storms and autumn extra-tropical cyclones in Finland. Forest Ecology and Management 381: 17–28. https://doi.org/10.1016/j.foreco.2016.09.005.

Valinger E., Fridman J. (2011). Forest ecology and management factors affecting the probability of windthrow at stand level as a result of Gudrun winter storm in southern Sweden. Forest Ecology and Management 262(3): 398–403. https://doi.org/10.1016/j.foreco.2011.04.004.

Zubizarreta-Gerendiain A., Pellikka P., Garcia-Gonzalo J., Ikonen V.P., Peltola H. (2012). Factors affecting wind and snow damage of individual trees in a small management unit in Finland: assessment based on inventoried damage and mechanistic modelling. Silva Fennica 46(2): 181–196. https://doi.org/10.14214/sf.441.

Total of 26 references.

Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content

Your selected articles
Your search results