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Earth System Science Data
Earth System Science Data
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Preprints
https://doi.org/10.5194/essd-2025-556
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2025-556
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Preprints
11 Nov 2025
 | 11 Nov 2025
Status: this preprint is currently under review for the journal ESSD.

Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling

Marc Aurel Vischer, Noelia Otero, and Jackie Ma

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 9 km×9 km 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.

How to cite. Vischer, M. A., Otero, N., and Ma, J.: Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-556, in review, 2025.
Received: 12 Sep 2025 – Discussion started: 11 Nov 2025
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Marc Aurel Vischer, Noelia Otero, and Jackie Ma

Status: open (until 18 Dec 2025)

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Marc Aurel Vischer, Noelia Otero, and Jackie Ma

Data sets

Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling Marc Vischer, Noelia Otero, Jackie Ma https://www.hydroshare.org/resource/d7f2cbb587ab4a75ac7987854e8f62ca/

Interactive computing environment

spatial_streamflow_dataprep Marc Vischer https://gitlab.hhi.fraunhofer.de/vischer/spatial_streamflow_dataprep

Marc Aurel Vischer, Noelia Otero, and Jackie Ma
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Latest update: 11 Nov 2025
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Short summary
We combined meteorological data with additional information (soil, rock, land cover and elevation) on a single grid that spans five river basins in central Europe over 45 years. Data was not aggregated within river catchments so as to retain the full spatial covariance. It can be efficiently processed in parallel and has been used in our recent study on end-to-end rainfall streamflow modeling with neural networks. We hope to promote further development of spatially resolved modeling approaches.
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