Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-2075-2020
© Author(s) 2020. 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-12-2075-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Sep 2020
Data description paper |
| 08 Sep 2020
| 08 Sep 2020
CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil
Vinícius B. P. Chagas
Department of Sanitary and Environmental Engineering, Graduate Program
of Environmental Engineering, Federal University of Santa Catarina–UFSC,
Florianopolis, Brazil
Department of Sanitary and Environmental Engineering, Federal
University of Santa Catarina–UFSC, Florianopolis, Brazil
Nans Addor
Department of Geography, College of Life and Environmental Sciences, University of
Exeter, Exeter, UK
Fernando M. Fan
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Ayan S. Fleischmann
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Rodrigo C. D. Paiva
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Vinícius A. Siqueira
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
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- MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
- A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
- Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
- A Simple Model of Flood Peak Attenuation R. Paiva & S. Lima 10.1029/2023WR034692
- Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil G. Anzolin et al. 10.1080/02626667.2023.2182212
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Discussed (final revised paper)
Latest update: 24 Apr 2024
Short summary
We present a new dataset for large-sample hydrological studies in Brazil. The dataset encompasses daily observed streamflow from 3679 gauges, as well as meteorological forcing for 897 selected catchments. It also includes 65 attributes covering topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables. CAMELS-BR is publicly available and will enable new insights into the hydrological behavior of catchments in Brazil.
We present a new dataset for large-sample hydrological studies in Brazil. The dataset...
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