Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-5755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Dec 2023
Data description paper |
| 19 Dec 2023
| 19 Dec 2023
CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland
Eawag, Dübendorf, Switzerland
Martina Kauzlaric
Geographisches Insitut, Universität Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Rosi Siber
Eawag, Dübendorf, Switzerland
Ursula Schönenberger
Eawag, Dübendorf, Switzerland
Pascal Horton
Geographisches Insitut, Universität Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Jan Schwanbeck
Geographisches Insitut, Universität Bern, Bern, Switzerland
Marius Günter Floriancic
ETH Zürich, Zurich, Switzerland
Daniel Viviroli
Universität Zürich, Zurich, Switzerland
Sibylle Wilhelm
Geographisches Insitut, Universität Bern, Bern, Switzerland
Anna E. Sikorska-Senoner
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland
Center for Climate Systems Modeling C2SM, ETH Zurich, Zurich, Switzerland
Nans Addor
Fathom, Bristol, UK
University of Exeter, Exeter, UK
Manuela Brunner
ETH Zürich, Zurich, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Sandra Pool
University of Melbourne, Melbourne, Australia
Massimiliano Zappa
WSL, Birmensdorf, Switzerland
Fabrizio Fenicia
Eawag, Dübendorf, Switzerland
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Total article views: 3,640 (including HTML, PDF, and XML)
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Total article views: 1,420 (including HTML, PDF, and XML)
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| Country | # | Views | % |
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- Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
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20 citations as recorded by crossref.
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- BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
- Toward a better understanding of curve number and initial abstraction ratio values from a large sample of watersheds perspective A. Brandão et al. 10.1016/j.jhydrol.2025.132941
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- Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al. 10.5194/essd-17-461-2025
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- Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
- CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. 10.5194/hess-29-1061-2025
- A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. 10.5194/hess-28-5331-2024
- High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland J. Magnusson et al. 10.5194/essd-17-703-2025
- Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
- HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. 10.1016/j.ecoinf.2025.102994
- FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
- CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
- EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
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Latest update: 29 Mar 2025
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in...
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