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CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany

Dolich, Alexander ORCID iD icon 1; Espinoza, Eduardo Acuña; Ebeling, Pia; Guse, Björn; Götte, Jonas; Hassler, Sibylle ORCID iD icon 2; Hauffe, Corina; Kiesel, Jens; Heidbüchel, Ingo; Mälicke, Mirko ORCID iD icon 1; Müller-Thomy, Hannes; Stölzle, Michael; Tarasova, Larisa; Loritz, Ralf 3
1 Institut für Wasser und Umwelt (IWU), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung Atmosphärische Spurengase und Fernerkundung (IMKASF), Karlsruher Institut für Technologie (KIT)
3 Institut für Wasser und Gewässerentwicklung (IWG), Karlsruher Institut für Technologie (KIT)

Abstract:

Description

CAMELS-DE provides a comprehensive collection of hydro-meteorological and catchment attributes data for 1582 streamflow gauges across Germany. The time series data is in daily resolution and spans up to 70 years, from January 1951 to December 2020. The static catchment attributes include information about topography, soils, land cover, hydrogeology and human influences in the catchments. Additionally, the dataset includes discharge simulations from a regional Long-Short Term Memory (LSTM) network and a conceptual hydrological model, providing benchmark data for future hydrological modelling studies in Germany.

The accompanying data description gives information on data sources, the structure of the data set and contains extensive information on time series and catchment attribute variables.

Information about the code and methods for generating CAMELS-DE can be found here: CAMELS-DE Processing Pipeline.

The CAMELS-DE data description paper can be found here: https://doi.org/10.5194/essd-16-5625-2024.

CAMELS-DE is also part of the Caravan project, a global hydrological dataset. Due to the use of data products that are available beyond the Germany national boundaries, Caravan-DE includes  305 additional streamflow gauges, resulting in a total of 1887 streamflow gauges: https://doi.org/10.5281/zenodo.13320514.
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Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Atmosphärische Spurengase und Fernerkundung (IMKASF)
Institut für Wasser und Umwelt (IWU)
Publikationstyp Forschungsdaten
Publikationsjahr 2024
Identifikator KITopen-ID: 1000180046
Lizenz Creative Commons Namensnennung 4.0 International
Schlagwörter Hydrology, Machine learning, Meteorology, Hydrogeology, Climatology, Deep learning, Benchmark dataset, Human influence, Land cover, Topography, Soil, Streamflow, Rainfall-Runoff Modelling
Art der Forschungsdaten Dataset

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Originalveröffentlichung
DOI: 10.5281/zenodo.12733967
Seitenaufrufe: 9
seit 19.03.2025
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