International Journal of Biometeorology

, Volume 56, Issue 1, pp 57–69 | Cite as

GIS-based estimation of the winter storm damage probability in forests: a case study from Baden-Wuerttemberg (Southwest Germany)

  • Dirk Schindler
  • Karin Grebhan
  • Axel Albrecht
  • Jochen Schönborn
  • Ulrich Kohnle
Original Paper

Abstract

Data on storm damage attributed to the two high-impact winter storms ‘Wiebke’ (28 February 1990) and ‘Lothar’ (26 December 1999) were used for GIS-based estimation and mapping (in a 50 × 50 m resolution grid) of the winter storm damage probability (PDAM) for the forests of the German federal state of Baden-Wuerttemberg (Southwest Germany). The PDAM-calculation was based on weights of evidence (WofE) methodology. A combination of information on forest type, geology, soil type, soil moisture regime, and topographic exposure, as well as maximum gust wind speed field was used to compute PDAM across the entire study area. Given the condition that maximum gust wind speed during the two storm events exceeded 35 m s-1, the highest PDAM values computed were primarily where coniferous forest grows in severely exposed areas on temporarily moist soils on bunter sandstone formations. Such areas are found mainly in the mountainous ranges of the northern Black Forest, the eastern Forest of Odes, in the Virngrund area, and in the southwestern Alpine Foothills.

Keywords

Winter storm ‘Lothar’ Winter storm ‘Wiebke’ Storm damage Weights of evidence methodology Annual booking records 

References

  1. Adrian G, Fiedler F (1991) Simulation of unstationary wind and temperature fields over complex terrain and comparison with observations. Beitr Phys Atmos 64:27–48Google Scholar
  2. Agterberg FP, Bonham-Carter GF, Cheng Q, Wright D (1993) Weights of evidence modeling and weighted logistic regression for mineral potential mapping. In: Davis JC, Herzfeld UC (eds) Computers in geology—25 years of progress. Oxford University Press, New York, pp 13–32Google Scholar
  3. Albrecht A (2009) Sturmschadensanalysen langfristiger waldwachstumskundlicher Versuchsflächendaten in Baden-Württemberg. Schriftenreihe Freiburger Forstliche Forschung, No. 42Google Scholar
  4. Baker SG, Kramer BS (2007) Peirce, Youden, and receiver operating characteristic curves. Am Stat 61:343–346CrossRefGoogle Scholar
  5. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon, OxfordGoogle Scholar
  6. Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF(eds) Statistical Applications in the Earth Sciences. Geological Survey of Canada Paper 89-9, pp 171–183Google Scholar
  7. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sensing 65:1388–1399Google Scholar
  8. Cremer KW, Borough CJ, McKinnel FH, Carter PR (1982) Effects of stocking and thinning on wind damage in plantations. N Z J For Sci 12:244–268Google Scholar
  9. Daneshfar B, Desrochers A, Budkewitsch P (2006) Mineral-potential mapping for MVT deposits with limited data sets using Landsat data and geological evidence in the Borden Basin, Northern Baffin Island, Nunavut, Canada. Nat Resour Res 15:129–149CrossRefGoogle Scholar
  10. de Langre E (2008) Effects of wind on plants. Annu Rev Fluid Mech 40:141–168CrossRefGoogle Scholar
  11. Dilts TE, Sibold JS, Biondi F (2009) A weights-of-evidence model for mapping the probability of fire occurrence in Lincoln County, Nevada. Ann Assoc Am Geogr 99:1–15CrossRefGoogle Scholar
  12. Dobbertin M (2002) Influence of stand structure and site factors on wind damage comparing the storms Vivian and Lothar. For Snow Landsc Res 77:187–205Google Scholar
  13. Donat MG, Leckebusch GC, Pinto JG, Ulbrich U (2010a) Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases. Int J Climatol 30:1289–1300Google Scholar
  14. Donat MG, Leckebusch GC, Pinto JG, Ulbrich U (2010b) European storminess and associated circulation weather types: future changes deduced from a multi-model ensemble of GCM simulations. Clim Res 42:27–43CrossRefGoogle Scholar
  15. Gardiner BA (1995) The interactions of wind and tree movement in forest canopies. In: Coutts MP, Grace J (eds) Wind and trees. Cambridge Univ Press, Cambridge, pp 41–59CrossRefGoogle Scholar
  16. Gardiner B, Byrne K, Hale S, Kamimura K, Mitchell SJ, Peltola H, Ruel J-C (2008) A review of mechanistic modelling of wind damage risk to forests. Forestry 81:447–463CrossRefGoogle Scholar
  17. Hanewinkel M, Zhou W, Schill C (2004) A neural network approach to identify forest stands susceptible to wind damage. For Ecol Manage 196:227–243CrossRefGoogle Scholar
  18. Hartebrodt C (2004) The impact of storm damage on small-scale forest enterprises in the southwest of Germany. Small Scale For Econ Manage Policy 3:203–222Google Scholar
  19. Heneka P, Hofherr T, Ruck B, Kottmeier C (2006) Winter storm risk of residential structures—model development and application to the German state of Baden-Württemberg. Nat Hazard Earth Syst Sci 6:721–733CrossRefGoogle Scholar
  20. Hurrell JW, Kushnir Y, Visbeck M (2001) The North Atlantic Oscillation. Science 291:603–605CrossRefGoogle Scholar
  21. Jalkanen A, Mattila U (2000) Logistic regression model for wind and snow damage in northern Finland based on the National Forest Inventory data. For Ecol Manage 135:315–330CrossRefGoogle Scholar
  22. Kändler G, Bösch B, Schmidt M (2005) Wesentliche Ergebnisse der zweiten Bundeswaldinventur in Baden-Württemberg—Rückblick und Ausblick. Forstarchiv 60:45–49Google Scholar
  23. Keil M, Kiefl R, Strunz G (2005) CORINE Land Cover 2000—Germany. Final Report, WesslingGoogle Scholar
  24. Kohnle U, Gauckler S, Risse FJ, Stahl S (2003) Orkan Lothar im Spiegel von Betriebsinventur und Einschlagsbuchführung: Auswirkungen auf einen Forstbezirk im Randbereich des Sturms. Allg Forstz Wald 58:1203–1207Google Scholar
  25. Kraus H, Ebel U (2003) Risiko Wetter. Die Entstehung von Stürmen und anderen atmosphärischen Gefahren. Springer, BerlinGoogle Scholar
  26. Lohmander P, Helles F (1987) Windthrow probability as a function of stand characteristics and shelter. Scand J For Res 2:227–238CrossRefGoogle Scholar
  27. Masetti M, Poli S, Sterlacchini S (2007) The use of weights-of-evidence modelling technique to estimate the vulnerability of groundwater to Nitrate contamination. Nat Resour Res 16:109–119CrossRefGoogle Scholar
  28. Mayer H (1987) Wind-induced tree sways. Trees 1:195–206CrossRefGoogle Scholar
  29. Mayer H (1989) Windthrow. Phil Trans R Soc Lond B 324:267–281CrossRefGoogle Scholar
  30. Mayer P, Brang P, Dobbertin M, Hallenbarter D, Renaud J-P, Walthert L, Zimmermann S (2005) Forest storm damage is more frequent on acidic soils. Ann For Sci 62:303–311CrossRefGoogle Scholar
  31. Mitchell SJ, Hailemariam T, Kulis Y (2001) Empirical modeling of cutblock edge windthrow risk on Vancouver Island, Canada, using stand level information. For Ecol Manage 154:117–130CrossRefGoogle Scholar
  32. Mitchell SJ, Lanquaye-Opoku N, Modzelewski H, Shen Y, Stull R, Jackson P, Murphy B, Ruel J-C (2008) Comparison of wind speeds obtained using numerical weather prediction models and topographic exposure indices for predicting windthrow in mountainous terrain. For Ecol Manage 254:193–204CrossRefGoogle Scholar
  33. MLR (ed) (1994) Dokumentation der Sturmschäden 1990. Schriftenreihe Landesforstverwaltung Baden-Württemberg, No. 75, pp 9–61Google Scholar
  34. Peltola H (1996) Swaying of trees in response to wind and thinning in a stand of Scots pine. Boundary Layer Meteorol 77:285–304CrossRefGoogle Scholar
  35. Pfister C (1999) Wetternachhersage. 500 Jahre Klimavariationen und Naturkatastrophen. Haupt, Bern, pp 246–255Google Scholar
  36. Quine C, Gardiner B (2007) Understanding how the interaction of wind and trees results in windthrow, stem breakage, and canopy gap formation. In: Johnson EA, Miyanishi K (eds) Plant disturbance ecology—the process and the response. Elsevier, Amsterdam, pp 103–155Google Scholar
  37. Quine CP, White IMS (1998) The potential of distance-limited topex in the prediction of site windiness. Forestry 71:325–332CrossRefGoogle Scholar
  38. Raines GL, Bonham-Carter GF, Kemp L (2000) Predictive probabilistic modelling using ArcView GIS. http://www.esri.com/news/arcuser/0400/files/wofe.pdf
  39. Rauthe M, Kunz M, Kottmeier C (2010) Changes in wind gust extremes over Central Europe derived from a small ensemble of high resolution regional climate models. Meteorol Z 19:299–312CrossRefGoogle Scholar
  40. Romero-Calcerrada R, Luque S (2006) Habitat quality assessment using weights of evidence based GIS modelling: The case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest. Ecol Model 196:62–76CrossRefGoogle Scholar
  41. Romero-Calcerrada R, Novello CJ, Millington JDA, Gomez-Jimenez I (2008) GIS analysis of spatial patterns of human-caused wildfire ignition in the SW of Madrid (Central Spain). Landscape Ecol 23:341–354CrossRefGoogle Scholar
  42. Rudnicki M, Silins U, Lieffers VJ, Josi G (2001) Measure of simultaneous tree sways and estimation of crown interactions among a group of trees. Trees 15:83–90CrossRefGoogle Scholar
  43. Sawatzky DL, Raines GL, Bonham-Carter GF, Looney CG (2008) Spatial Data Modeller (SDM): ArcMAP 9.2 geoprocessing tools for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural networks. http://www.arcscripts.esri.com/details.asp?dbid=15341
  44. Schelhaas M-J, Nabuurs G-J, Schuck A (2003) Natural disturbances in the European forests in the 19th and 20th centuries. Glob Chang Biol 9:1620–1633CrossRefGoogle Scholar
  45. Schindler D (2008) Responses of Scots pine trees to dynamic wind loading. Agric For Meteorol 148:1733–1742CrossRefGoogle Scholar
  46. Schindler D, Grebhan K, Albrecht A, Schönborn J (2009) Modelling the wind damage probability in forests in Southwestern Germany for the 1999 winter storm ‘Lothar’. Int J Biometeorol 53:543–554CrossRefGoogle Scholar
  47. Schindler D, Vogt R, Fugmann H, Rodriguez M, Schönborn J, Mayer H (2010) Vibration behavior of plantation-grown Scots pine trees in response to wind excitation. Agric For Meteorol 150:984–993CrossRefGoogle Scholar
  48. Scott RE, Mitchell SJ (2005) Empirical modelling of windthrow risk in partially harvested stands using tree, neighbourhood, and stand attributes. For Ecol Manage 218:193–209CrossRefGoogle Scholar
  49. Sellier D, Brunet Y, Fourcaud T (2008) A numerical model of tree aerodynamic response to a turbulent airflow. Forestry 81:279–297CrossRefGoogle Scholar
  50. Sinton DS, Jones JA, Ohmann JL, Swanson FJ (2000) Windthrow disturbance, forest composition, and structure in the Bull Run Basin, Oregon. Ecology 81:2539–2556CrossRefGoogle Scholar
  51. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Da Costa BV, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Van De Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  52. Valinger E, Lundquist L, Bondesson L (1993) Assessing the risk of snow and wind damage from tree physical characteristics. Forestry 66:249–260CrossRefGoogle Scholar
  53. von Teuffel K (2001) Waldbauliche Erfahrungen mit der Bewältigung der Sturmschäden von 1990 in Baden-Württemberg. In: J Huss, M Hehn (eds) Wiederbewaldung von Sturmschadensflächen. Ber Freiburg Forstl Forsch, No. 25, pp 79–87Google Scholar
  54. Wang XL, Zwiers FW, Swail VR, Feng Y (2009) Trends and variability of storminess in the Northeast Atlantic region, 1874–2007. Clim Dyn 33:1179–1195CrossRefGoogle Scholar

Copyright information

© ISB 2011

Authors and Affiliations

  • Dirk Schindler
    • 1
  • Karin Grebhan
    • 1
  • Axel Albrecht
    • 2
  • Jochen Schönborn
    • 1
  • Ulrich Kohnle
    • 2
  1. 1.Meteorological InstituteAlbert-Ludwigs-University of FreiburgFreiburgGermany
  2. 2.Forest Research Institute of Baden-WuerttembergFreiburgGermany

Personalised recommendations