Abiotic and biotic factors that cause damage to forest trees also threaten the sustainability of forest resources. Although winter storms can be one of the most damaging forces, very few studies have focused on winter storm damage in Turkey. To prevent or minimize storm damage, we must evaluate the factors that influence the degree of damage and develop storm risk maps for the forested areas. Here, a GIS-based mathematical model (fuzzy logic) was used to develop such a risk map by considering risk factors such as tree species, tree age, crown density, site quality, topographical features (elevation, slope, aspect), climatic variables (wind, precipitation), and soil depth. The Alabarda Forest Enterprise Chief within the borders of Tavşanlı Forest Enterprise Directorate was selected as the study area due to high occurrence of storm damage in the region during winter 2015–2016. For model verification, the risk map of storm damage was compared to the actual areal distribution of storm damage reported by the Enterprise Directorate. The model based on fuzzy logic indicated that slightly more than half of the study area (52.49%) was under very low storm damage risk, 28.12% was under low risk and 19.19% was classified as high risk. A very small portion of the total study area was classified as very high risk. These results indicated a close relation with the storm damage map generated using Tavşanlı FED records. The results revealed that the most effective risk predictors for overall storm damage risk were wind direction and speed, followed by slope and site quality factors.

造成林木损害的非生物和生物因素也威胁着森林资源的可持续性。尽管冬季风暴可能是破坏力最大的力量之一，但很少有研究集中在土耳其的冬季风暴破坏上。为了防止或最大程度地减少暴风雨的破坏，我们必须评估影响破坏程度的因素，并为森林地区建立暴风雨风险图。在这里，基于GIS的数学模型（模糊逻辑）用于通过考虑诸如树种，树龄，树冠密度，站点质量，地形特征（海拔，坡度，坡向），气候变量等风险因素来开发此类风险图（风，降水）和土壤深度。由于在2015-2016年冬季该地区发生暴风雨破坏的发生率很高，因此，在塔夫萨兰森林企业管理局边界内的阿拉巴达州森林企业负责人被选为研究区域。为了进行模型验证，将风暴破坏的风险图与企业总局报告的风暴破坏的实际区域分布进行了比较。基于模糊逻辑的模型表明，研究区域的一半以上（52.49％）处于极低的风暴破坏风险下，28.12％处于低风险下，19.19％被归类为高风险。整个研究领域中只有极小部分被归类为极高风险。这些结果表明与使用TavşanlıFED记录生成的风暴破坏图密切相关。