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ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany

Authors: Dolich, Alexander; Espinoza, Eduardo Acuña; Ebeling, Pia; Guse, Björn; Götte, Jonas; Hassler, Sibylle; Hauffe, Corina; +7 Authors

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

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. Disclaimer for discharge and water level data provided by the German federal state agencies: english:The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded. german:Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind. Changelog v1.0.0 CAMELS-DE v1.0.0 is the version of the dataset that is described by the CAMELS-DE data description paper. Addition of the federal state of Saarland, resulting in 27 additional catchments and coverage of all federal states except the city states of Berlin, Bremen and Hamburg. This also leads to a change in the title of the dataset from 1555 catchments to 1582 catchments. Addition of HBV model parameters and LSTM model training period epochs. Catchment DE911970: Removal of erroneous zero discharge values at the beginning of the measurement period. Minor fixes such as the elimination of discrepancies between the variable names in the dataset and in the data description. We were able to identify and fix some of these problems based on the feedback from the community, thank you very much!

Keywords

Climatology, Human influence, Land cover, Topography, Soil, Meteorology, Machine learning, Hydrogeology, Deep learning, Streamflow, Hydrology, Rainfall-Runoff Modelling, Benchmark dataset

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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