Modelling the wind damage probability in forests in Southwestern Germany for the 1999 winter storm ‘Lothar’

Abstract

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.

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

“…A predictor variable nearly equally important for RF-model accuracy as GS stat,Dec was FOR (PI = 18.3) which is in good agreement with findings reported for the study area in previous investigations [17,19]. Also important for the RF-modeling performance was MOIST (PI = 11.9).…”

“…In moist soils, root development is often hampered [42,43] and root anchorage is lower as compared with drier soils [44,45]. In this study, all other predictor variables that have been shown to be of importance for storm damage occurrence like soil type [17,19] or soil acidification [46] are of minor importance (PI < 10) for classification of storm damage probability.…”

“…Multicollinearity among the predictor variables was investigated following [17] by assessing the variance inflation and the condition index in combination with variance-decomposition proportions according to [28]. However, no collinearity was detected among the predictor variables.…”

“…The proportions of the forest types are very similar between state forests and non-state forests. A map showing the distribution of forest types in the study area can be found in [17]. The largest contiguous forest area is found in the low mountain range Black Forest with the highest elevations Feldberg (1496 m) in the south and Hornisgrinde (1164 m) in the north.…”

Modelling natural disturbances in forest ecosystems: a review

“…Spatial and neighbourhood aspects were also included as explanatory variables in such statistical approaches (e.g., Scott and Mitchell, 2005;Schindler et al, 2009). While most of these studies generally achieved satisfactory explanatory power, a high level of stochasticity was documented, e.g., in the analysis by Schütz et al (2006).…”