Marc Vischer
Fraunhofer Heinrich Hertz Institute
| Subject Areas: | Machine Learning,Hydrological Modeling,Rainfall Runoff Modeling |
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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.
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Created: Nov. 20, 2024, 2:57 p.m.
Authors: Vischer, Marc · Noelia Otero Felipe · Jackie Ma
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.