Upscaling riverine GHG fluxes in Germany with machine learning | Submission 2026 v1.3

This River GHG emission Dataset for Germany is based on the publication of Mwanake et al. 2025, which provides GHG emissions per day and m² of river surface within a catchment. To use these upscaled data, they must be scaled to the specific waterbody. The dataset consists of catchment-based GHG emissions, combined with geodata provided by the Thünen Institute, which calculated a first estimate of GHG emissions from aquatic natural and anthropogenic sources within the framework of the ITMS Germany project (Submission 2025). References: Mwanake, R.M., Wangari, E.G., Winkler, K., Gettel, G.M., Butterbach-Bahl, K., Kiese, R. (2025) From data to insights: Upscaling riverine GHG fluxes in Germany with machine learning. Science of the Total Environment 958: 177984. https://doi.org/10.1016/j.scitotenv.2024.177984 NFR Sectors (according to the IPCC guidelines) Gases Temporal resolution Spatial resolution Uncertainties wet 2 rivers including artificial and natural running waters CO2, CH4, N2O Yearly (currently only the year 2020 is available) Vector line and surface layers plus aggregated UBA Grid at 1 km × 1 km resolution Yes, from the original publication with global numbers Update Submission 2026 v1.2 Updated the geographical extent to match the CoastGEm dataset outlines. More specifically, the extent of the estuaries provided from the ITMS-Q&S_II project CoastGEm. Update Submission 2026 v1.3: Generation of a 1 x 1 km² gridded layer aggregating Channels, Ditches and Rivers. General Remarks Rivers < 3 m are currently not included to allow comparison to TI estimates from Submission 2025. Some country line problems occur when a river crosses the German border, for example the Rhine (border of Baden-Württemberg & France). Differences to Submission 2025 Not applicable. Planned modifications for 2027 Replace ditch emissions by ditch surface and IPCC emission factor. Include rivers < 3 m. Improve country borders splitting rivers. Extend to further years. Long-term goal: increase temporal resolution to months. Uncertainties Based on original publication: CO2 CH4 N2O Uncertainty total based on error propagation +-34.35% +-38.98% +-36.05% License The line and polygon features are ITMS-only and provided to UBA for the Gridded National Dataset. The gridded data to the UBA Grid (1 x 1 km²) is OpenData. Documentation Original Publication: Mwanake, R.M., Wangari, E.G., Winkler, K., Gettel, G.M., Butterbach-Bahl, K., Kiese, R. (2025) From data to insights: Upscaling riverine GHG fluxes in Germany with machine learning. Science of the Total Environment 958: 177984. https://doi.org/10.1016/j.scitotenv.2024.177984