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Visualization of data for Figure 2 from Dittrich, Andreas, Ralf Seppelt, Tomáš Václavík, and Anna F. Cord. 2017. “Integrating Ecosystem Service Bundles and Socio-Environmental Conditions – A National Scale Analysis from Germany.” Ecosystem Services 28: 273–82. doi.org/10.1016/j.ecoser.2017.08.007
This dataset contains the variable Qrouted from the representative concentration pathway rcp8p5 forcing the hydrological model mhm with meteorological data from the Global Circulation Model GFDL-ESM2M. The warming levels 1.5, 2 and 3 °C were either reached in different time periods or not reached by the respective RCP (see global attribute warming_periods_[1.5, 2.0, 3.0]K). This study has been mainly funded within the scope of the HOKLIM project (www.ufz.de/hoklim) by the German Ministry for Education and Research (grant number 01LS1611A). This study has been partially funded by the Copernicus Climate Change Service. The European Centre for Medium Range Weather Forecasts implements this service and the Copernicus Atmosphere Monitoring Service on behalf of the European Commission. We would like to thank all the colleagues who contributed to the EDgE project (http://edge.climate.copernicus.eu/).
THIS METADATA-RECORD IS CURRENTLY UNDER DEVELOPMENT
Based on Sentinel-2A imageries from 2016, the map shows a detailed classification of Germany's arable land, distinguishing 19 land-use classes. "Forests", "Waters", "Urban" and "Other Vegetation" are predefined classes and were not classified in this study. The map is the result of a highly automated adaptable approach for pixel-based compositing and classification, called APiC. The novel APiC concept is designed for the use of high-resolution spatio-temporal space-borne data. Within the scope of extensive land-use mapping we define composite periods purely data-driven based on cloud-free training pixels. Since these adapted periods (APs) are not static nor dependent on expert knowledge, they support the creation of training pixel composites (TPCs) in regions with different weather conditions, species composition and phenological behaviour. We aimed to maximize the AP number within the classification year in order to best represent land-use phenology. To account for the cloud cover distribution of all pixels to be classified, over 10,000 predictive models were needed to create this map. Multiple classification models allow to contrast the classified land-use with the associated model performance at pixel level. Due to different climatic and geomorphological gradients in Germany that have an impact on plant phenology, six landscape regions have been independently classified. The individual classification results have been combined for the map shown.