
Description CAMELS-DE provides a comprehensive collection of hydro-meteorological and catchment attributes data for 1555 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. Note that this is an early version of CAMELS-DE, which may still be subject to changes as the corresponding manuscript is still in review.The preprint of the CAMELS-DE paper can be found here: https://essd.copernicus.org/preprints/essd-2024-318/ We are also currently working on a version of CAMELS-DE for the global dataset CARAVAN, which will be released soon. Also note that the state agencies do not guarantee the accuracy or completeness of the data. Additionally, all hydrological data may be subject to future revisions, including adjustments to rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state agencies for projects that require water law permits. Additionally, the regulations of the respective federal state apply, and specific inquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any guarantees regarding data accuracy or completeness, thus excluding any liability claims against any of the federal states.
Human influence, Climatology, Soil, Topography, Land cover, Meteorology, Machine learning, Hydrogeology, Streamflow, Deep learning, Hydrology, Rainfall-Runoff Modelling, Benchmark dataset
Human influence, Climatology, Soil, Topography, Land cover, Meteorology, Machine learning, Hydrogeology, Streamflow, Deep learning, Hydrology, Rainfall-Runoff Modelling, Benchmark dataset
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