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Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling
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| Type: | Resource | |
| Storage: | The size of this resource is 8.0 GB | |
| Created: | Sep 12, 2025 at 9:28 a.m. (UTC) | |
| Last updated: | Sep 23, 2025 at 8:47 a.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Private (Accessible via direct link sharing) |
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| Views: | 61 |
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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.
Subject Keywords
Coverage
Spatial
Temporal
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Content
readme.md
Descriptor
The data is described in detail in our data descriptor publication, referenced in the abstract.
Files
The files forcings_pub.nc and forcings_paper.nc contain spatiotemporal meteorological data. The files ancillary_pub.nc and ancillary_paper.nc contain spatial ancillary data, matching the grid of the forcings. Ancillary data includes geology, soil, land use and orography. The files ending with "_paper.nc" were used in our modeling publication, but we offer an alternative format more suited for most use cases with the files ending in "_pub.nc". The data in both versions is identical, the main difference is that the data in publication format has dimension (latitude, longitude, time), with NaN-values for cells outside our study area. The data in paper format has (cell_id, time), where "cell_id" is a unique identifier for each grid cell. This flattens the grid and makes it dense, so it can be conveniently fed into a neural network (see code linked below for a PyTorch DataLoader tailored to this data).
Code
The code to preprocess the data from the raw sources, test it and load it as PyTorch dataloader can be found in our repository.
Data Sources and Disclaimer
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Meteorological data was derived from the ERA5-Land dataset. The data was downloaded from the Copernicus Climate Change Service. The results contain modified Copernicus Climate Change Service information (2022). Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
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Hydrogeological information was derived from the International Hydrogeological Map of Europe (IHME), version 1.2, © Bundesanstalt für Geowissenschaft und Rohstoffe, 2022.
- Land Cover information was obtained from the Corine Land Cover Map (CLC), version 2012. Generated using European Union’s Copernicus Land Monitoring Service information; https://doi.org/10.2909/916c0ee7-9711-4996-9876-95ea45ce1d27. The Corine Land Cover Map data was created with funding by the European union. It was adapted and modified by the authors. The authors’ activities are not officially endorsed by the Union.
- Soil type information was obtained from the dataset European Soil Database Derived Data, created by the European Soil Data Centre with funding by the European union. It was adapted and modified by the authors. The authors’ activities are not officially endorsed by the Union.
- Orographic information was derived from the European Union Digital Elevation Map (EU-DEM). Generated using European Union’s Copernicus Land Monitoring Service information. The European Union Digital Elevation Map created with funding by the European union. It was adapted and modified by the authors. The authors’ activities are not officially endorsed by the Union.
Related Resources
| This resource updates and replaces a previous version | Vischer, M., N. O. Felipe, J. Ma (2025). Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling, HydroShare, http://www.hydroshare.org/resource/05d5633a413b4aec93b08a7e61a2abbb |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| German Federal Ministry for Economic Affairs and Climate Action (BMWK) | DAKI-FWS | 01MK21009A |
| European Union Horizon Europe research and innovation program | MedEWSa | 101121192 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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