Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-3847-2021
© Author(s) 2021. 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-13-3847-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Aug 2021
Data description paper |
| 06 Aug 2021
| 06 Aug 2021
CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia
Keirnan J. A. Fowler
CORRESPONDING AUTHOR
Department of Infrastructure Engineering, University of Melbourne,
Parkville, Victoria, Australia
Suwash Chandra Acharya
Department of Infrastructure Engineering, University of Melbourne,
Parkville, Victoria, Australia
Nans Addor
Department of Geography, University of Exeter, Exeter, UK
Chihchung Chou
Department of Infrastructure Engineering, University of Melbourne,
Parkville, Victoria, Australia
now at: Department of Earth Sciences, Barcelona Supercomputing
Centre, Barcelona, Spain
Murray C. Peel
Department of Infrastructure Engineering, University of Melbourne,
Parkville, Victoria, Australia
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32 citations as recorded by crossref.
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- Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
- Integrated framework for rapid climate stress testing on a monthly timestep K. Fowler et al. 10.1016/j.envsoft.2022.105339
- Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia D. Kim et al. 10.5194/hess-26-5955-2022
- Hydrological Shifts Threaten Water Resources K. Fowler et al. 10.1029/2021WR031210
- Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
- Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
- Neglecting hydrological errors can severely impact predictions of water resource system performance D. McInerney et al. 10.1016/j.jhydrol.2024.130853
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- 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
- Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl 10.1088/1748-9326/acb8ca
- AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
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- Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
- A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
- rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
- Power‐Function Expansion of the Polynomial Complementary Relationship of Evaporation J. Szilagyi et al. 10.1029/2022WR033095
- 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
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
- 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
- CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
- DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
- Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
- Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective K. Fowler et al. 10.5194/hess-26-6073-2022
- Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship X. Lei et al. 10.1029/2023WR035567
- Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
- 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
- Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning S. Cheng et al. 10.1029/2021WR031412
30 citations as recorded by crossref.
- PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. 10.1038/s41597-023-02828-2
- A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
- Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
- The time of emergence of climate-induced hydrologic change in Australian rivers A. John et al. 10.1016/j.jhydrol.2023.129371
- Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
- Integrated framework for rapid climate stress testing on a monthly timestep K. Fowler et al. 10.1016/j.envsoft.2022.105339
- Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia D. Kim et al. 10.5194/hess-26-5955-2022
- Hydrological Shifts Threaten Water Resources K. Fowler et al. 10.1029/2021WR031210
- Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
- Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
- Neglecting hydrological errors can severely impact predictions of water resource system performance D. McInerney et al. 10.1016/j.jhydrol.2024.130853
- FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
- 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
- Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl 10.1088/1748-9326/acb8ca
- AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
- 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
- Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
- A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
- rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
- Power‐Function Expansion of the Polynomial Complementary Relationship of Evaporation J. Szilagyi et al. 10.1029/2022WR033095
- 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
- QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
- 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
- CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
- DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
- Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
- Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective K. Fowler et al. 10.5194/hess-26-6073-2022
- Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship X. Lei et al. 10.1029/2023WR035567
- Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
2 citations as recorded by crossref.
- 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
- Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning S. Cheng et al. 10.1029/2021WR031412
Discussed (final revised paper)
Latest update: 25 Apr 2024
Short summary
This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments with long-term monitoring, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://doi.pangaea.de/10.1594/PANGAEA.921850.
This paper presents the Australian edition of the Catchment Attributes and Meteorology for...
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