Preprints
https://doi.org/10.5194/essd-2024-318
https://doi.org/10.5194/essd-2024-318
30 Jul 2024
 | 30 Jul 2024
Status: this preprint is currently under review for the journal ESSD.

CAMELS-DE: hydro-meteorological time series and attributes for 1555 catchments in Germany

Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova

Abstract. Comprehensive large sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and Meteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydrometeorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydrometeorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1555 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15,000 km2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties and data about human influences. Furthermore, it includes a regionally trained Long-Short Term Memory (LSTM) network and a locally trained conceptual model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (617 catchments < 100 km2), and the time series length of up to 70 years (156 catchments), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends, runoff formation in small catchments and in analysing catchments with strong human influences.

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Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova

Status: open (until 05 Sep 2024)

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Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova

Data sets

CAMELS-DE: hydrometeorological time series and attributes for 1555 catchments in Germany A. Dolich, E. A. Espinoza, P. Ebeling, B. Guse, J. Götte, S. Hassler, C. Hauffe, J. Kiesel, I. Heidbüchel, M. Mälicke, H. Müller-Thomy, M. Stölzle, L. Tarasova, and R. Loritz https://doi.org/10.5281/zenodo.12733968

Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova

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Short summary
The CAMELS-DE dataset features data from 1555 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends, and supports the development of hydrological models.
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