Modelling the wind damage probability in forests in Southwestern Germany for the 1999 winter storm 'Lothar'.
The wind damage probability (P (DAM)) in the forests in the federal state of Baden-Wuerttemberg (Southwestern Germany) was calculated using weights of evidence (WofE) methodology and a logistic regression model (LRM) after the winter storm 'Lothar' in December 1999. A geographic information system (GIS) was used for the area-wide spatial prediction and mapping of P (DAM). The combination of the six evidential themes forest type, soil type, geology, soil moisture, soil acidification, and the 'Lothar' maximum gust field predicted wind damage best and was used to map P (DAM) in a 50 x 50 m resolution grid. GIS software was utilised to produce probability maps, which allowed the identification of areas of low, moderate, and high P (DAM) across the study area. The highest P (DAM) values were calculated for coniferous forest growing on acidic, fresh to moist soils on bunter sandstone formations-provided that 'Lothar' maximum gust speed exceeded 35 m s(-1) in the areas in question. One of the most significant benefits associated with the results of this study is that, for the first time, there is a GIS-based area-wide quantification of P (DAM) in the forests in Southwestern Germany. In combination with the experience and expert knowledge of local foresters, the probability maps produced can be used as an important tool for decision support with respect to future silvicultural activities aimed at reducing wind damage. One limitation of the P (DAM)-predictions is that they are based on only one major storm event. At the moment it is not possible to relate storm event intensity to the amount of wind damage in forests due to the lack of comprehensive long-term tree and stand damage data across the study area.
Schindler
Dirk
D
Meteorological Institute, University of Freiburg, 79085 Freiburg, Germany. dirk.schindler@meteo.uni-freiburg.de
Grebhan
Karin
K
Albrecht
Axel
A
Schönborn
Jochen
J
D003198:Computer Simulation; D015233:Models, Statistical; D018570:Risk Assessment; D012307:Risk Factors; D012621:Seasons; D014197:Trees / Q000145:classification* / Q000254:growth & development*; D014919:Wind