Retrospective analysis of weather-related extreme events, large-scale disturbances and silvicultural strategies. A lesson for future recommendations for action in the model regions Thuringian Forest and Ore Mountains (RETROWALD)

Forests are currently particularly exposed to weather extremes (e.g. drought, heat, storms, heavy rainfall) as a result of climate change, which lead to disturbances of varying degrees. The functionality of forest ecosystems is crucial for the further development of the climate and for the provision of diverse products and services, the demand for which is constantly increasing in our society. A frequently pursued strategy to mitigate climate-related risks to forest ecosystems consists of modeling and forecasting future development scenarios and adapting existing forest management concepts. In contrast, the RETROWALD project looks to the past and is dedicated to the retrospective analysis of weather-related extreme events in two densely forested model regions. The low mountain regions of the Saxon Ore Mountains and the Thuringian Forest are particularly strongly influenced by weather extremes, both currently and in the past. For these regions, the recorded disturbances in the forests are analyzed on the basis of historical sources and linked to archival climate records. In addition, the forest conditions, the forestry expertise, the silvicultural and silvicultural-technical procedures as well as the social demands and framework conditions at the respective time are included in the analyses. In this way, the causal relationships between climatic conditions, the resulting disturbances in commercial forests and the strategies for coping with them can be established. The project aims to show what influenced solution strategies for coping with damage in commercial forests, the effects of which can extend to the present day. It is also important to shed light on the socio-political demands on forestry stakeholders in order to draw conclusions for the current management of forest ecosystems in the model regions and to be able to draw appropriate lessons from past experience.