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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Total article views: 5,317 (including HTML, PDF, and XML)
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37 citations as recorded by crossref.
- GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
- Climate and land management accelerate the Brazilian water cycle V. Chagas et al. 10.1038/s41467-022-32580-x
- How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
- Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al. 10.1016/j.jhydrol.2021.126773
- CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
- Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
- Spatial and temporal patterns of propagation from meteorological to hydrological droughts in Brazil A. Bevacqua et al. 10.1016/j.jhydrol.2021.126902
- CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
- MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
- Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
- Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil G. Anzolin et al. 10.1080/02626667.2023.2182212
- Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
- LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
- PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
- Geostatistical modeling and traditional approaches for streamflow regionalization in a Brazilian Southeast watershed R. Ferreira et al. 10.1016/j.jsames.2021.103355
- Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
- A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
- Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
- Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
- rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
- A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
- Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
- Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
- A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
- LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
- A Continental Assessment of Reservoir Storage and Water Availability in South America B. Paredes-Beltran et al. 10.3390/w13141992
- Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
- National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
- Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
- Process Controls on Flood Seasonality in Brazil V. Chagas et al. 10.1029/2021GL096754
- Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. 10.1029/2020WR029377
- Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
- CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. 10.5194/hess-25-3105-2021
- Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model A. Meyer Oliveira et al. 10.1029/2023WR034875
- Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 01 Dec 2023
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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