ABSTRACT:

We present a gridded dataset for rainfall streamflow modeling that is fully spatially resolved and covers five complete river basins in central Europe: upper Danube, Elbe, Oder, Rhine, and Weser. We compiled meteorological forcings and a variety of ancillary information on soil, rock, land cover, and orography. The data is harmonized to a regular $9km \times 9km$ grid, temporal resolution is daily from 1980 to 2024. We also provide code to further combine our dataset with publicly available river discharge data for end-to-end rainfall streamflow modeling. We have used this data to demonstrate how neural network-driven hydrological modeling can be taken beyond lumped catchments, and want to facilitate direct comparisons between different model types.

ABSTRACT:

We present a dataset for rainfall streamflow modeling that is fully spatially resolved with the aim of taking neural network-driven hydrological modeling beyond lumped catchments. To this end, we compiled data covering five river basins in central Europe: upper Danube, Elbe, Oder, Rhine, and Weser. The dataset contains meteorological forcings, as well as ancillary information on soil, rock, land cover, and orography. The data is harmonized to a regular 9km * 9km grid and contains daily values that span from October 1981 to September 2011. We also provide code (https://gitlab.hhi.fraunhofer.de/vischer/spatial_streamflow_dataprep) to further combine our dataset with publicly available river discharge data for end-to-end rainfall streamflow modeling. A data descriptor is currently under review.