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
Viewed
Total article views: 6,194 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Jan 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,411 | 1,660 | 123 | 6,194 | 116 | 135 |
- HTML: 4,411
- PDF: 1,660
- XML: 123
- Total: 6,194
- BibTeX: 116
- EndNote: 135
Total article views: 4,693 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Aug 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,592 | 999 | 102 | 4,693 | 105 | 123 |
- HTML: 3,592
- PDF: 999
- XML: 102
- Total: 4,693
- BibTeX: 105
- EndNote: 123
Total article views: 1,501 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Jan 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 819 | 661 | 21 | 1,501 | 11 | 12 |
- HTML: 819
- PDF: 661
- XML: 21
- Total: 1,501
- BibTeX: 11
- EndNote: 12
Viewed (geographical distribution)
Total article views: 6,194 (including HTML, PDF, and XML)
Thereof 5,824 with geography defined
and 370 with unknown origin.
Total article views: 4,693 (including HTML, PDF, and XML)
Thereof 4,496 with geography defined
and 197 with unknown origin.
Total article views: 1,501 (including HTML, PDF, and XML)
Thereof 1,328 with geography defined
and 173 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
48 citations as recorded by crossref.
- LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
- Assessing the Influence of Zero‐Flow Threshold Choice for Characterising Intermittent Stream Hydrology S. Yu et al. 10.1002/hyp.15300
- 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
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- 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
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
- Hydrological Shifts Threaten Water Resources K. Fowler et al. 10.1029/2021WR031210
- Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns 10.1002/rra.4336
- 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
- Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
- BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
- 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
- Influence of irrigation on root zone storage capacity estimation F. van Oorschot et al. 10.5194/hess-28-2313-2024
- 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
- Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
- Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
- 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
- Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
- 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
- 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
- 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
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment F. Helfer et al. 10.1016/j.jhydrol.2024.132073
- 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
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al. 10.1088/1748-9326/ad8805
- 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
46 citations as recorded by crossref.
- LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
- Assessing the Influence of Zero‐Flow Threshold Choice for Characterising Intermittent Stream Hydrology S. Yu et al. 10.1002/hyp.15300
- 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
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- 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
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
- Hydrological Shifts Threaten Water Resources K. Fowler et al. 10.1029/2021WR031210
- Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns 10.1002/rra.4336
- 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
- Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
- BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
- 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
- Influence of irrigation on root zone storage capacity estimation F. van Oorschot et al. 10.5194/hess-28-2313-2024
- 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
- Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
- Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
- 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
- Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
- 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
- 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
- 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
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment F. Helfer et al. 10.1016/j.jhydrol.2024.132073
- 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
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al. 10.1088/1748-9326/ad8805
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: 22 Nov 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...
Altmetrics
Final-revised paper
Preprint