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

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Ralf Loritz - , Karlsruhe Institute of Technology (Author)
  • Alexander Dolich - , Karlsruhe Institute of Technology (Author)
  • Eduardo Acuña Espinoza - , Karlsruhe Institute of Technology (Author)
  • Pia Ebeling - , Helmholtz Centre for Environmental Research (Author)
  • Björn Guse - , Kiel University, Helmholtz Centre Potsdam - German Research Centre for Geosciences (Author)
  • Jonas Götte - , Swiss Federal Institute for Forest, Snow and Landscape Research, Climate Change, Extremes and Natural Hazards in Alpine Regions Research Centre (CERC), ETH Zurich (Author)
  • Sibylle K. Hassler - , Karlsruhe Institute of Technology (Author)
  • Corina Hauffe - , Institute of Hydrology and Meteorology, Chair of Hydrology, TUD Dresden University of Technology (Author)
  • Ingo Heidbüchel - , Helmholtz Centre for Environmental Research, University of Bayreuth (Author)
  • Jens Kiesel - , Kiel University, Stone Environmental, Inc. (Author)
  • Mirko Mälicke - , Karlsruhe Institute of Technology (Author)
  • Hannes Müller-Thomy - , Technical University of Braunschweig (Author)
  • Michael Stölzle - , University of Freiburg (Author)
  • Larisa Tarasova - , Helmholtz Centre for Environmental Research (Author)

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 hydro-meteorological 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 hydro-meteorological 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 1582 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 on human influences. Furthermore, it includes a regionally trained long short-term memory (LSTM) network and a locally trained HBV (Hydrologiska Byråns Vattenbalansavdelning) 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 (636 catchments < 100 km2), and the time series length of up to 70 years (166 catchments with 70 years of discharge data), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends and runoff formation in small catchments and in analysing catchments with strong human influences. This article describes CAMELS-DE version 1.0, which is available at 10.5281/zenodo.13837553 (Dolich et al., 2024).

Details

Original languageEnglish
Pages (from-to)5625-5642
Number of pages18
JournalEarth system science data
Volume16
Issue number12
Publication statusPublished - 9 Dec 2024
Peer-reviewedYes

Keywords

ASJC Scopus subject areas