The work presented here is part of the socioeconomic analysis that is carried out within the RADOST project. It has been the starting point of developing a dynamic regionalized Input-Output (IO) model that is used to assess the effects of climate change and adaptation strategies on the regional economy. In a first step the model has been set up for the tourist sector in Mecklenburg-Western Pomerania. The possible developments of the tourism demand – influenced by climate change and other factors – were represented in three scenarios, which in turn were used as input data for the IO model.
Based on concepts for innovation processes and co-production of knowledge, approaches are investigated that address the urgent and complex problems related to climate change, because especially the participation of, and close collaboration with, practice partners is needed. The paper presents the agricultural knowledge management approach in the organic agriculture module of the R&D project INKA BB (Innovation Network for Climate Change Adaptation Brandenburg Berlin) in north-eastern Germany (Knierim et al. 2009). The methodology for the science-practice collaboration follows an action research approach that supports the communication and cooperation of researchers and practitioners. The framework is the action research cycle with iterative stages of planning, action, and reflection. The organic agriculture module, which addresses individual research questions on several farms, is presented as a good practice example for close transdisciplinary network cooperation. The workshop contribution will provide reflections on the innovation development process over two project years.
Vorstellung von Projektergebnissen aus KLIMZUG-NORD bezüglich jährliche und saisonale Temperatur- und Niederschlagsänderungen zur Mitte und Ende des 21. Jahrhunderts, sowie Ergebnisse aus dem Projekt Hamburg 2K. In Hamburg 2K wird analysiert, was eine Begrenzung auf eine Temperaturänderung von 2K für Hamburg bedeutet. Ausgewertet wurden Temperatur- und Niederschlagsänderungen sowie ausgewählte Indices.
The precipitation data of the Regional Climate Model CLM are used for the water management impact models within the dynaklim networking and research project. For this purpose, it is necessary to apply a bias correction to the CLM
precipitation data. First, the bias assessed for varying temporal resolutions and precipitation characteristics is described. Subsequently, a method for the bias correction is introduced. The developed methodology is a modified form of the socalled
quantile mapping. The focus lies on the corrections of the dry days and the heavy rainfall events. They are considered separately, deviating from other quantile mapping procedures.