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 article
| 
06 Aug 2021
Data description article |
| 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
Related authors
Nyree Campion, Keirnan Fowler, Margot Turner, and Joel Hall
EGUsphere, https://doi.org/10.5194/egusphere-2026-378, https://doi.org/10.5194/egusphere-2026-378, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Globally, many river systems have seen less flow for the same rainfall after periods of long drought. This study investigates this behaviour in over 300 catchments across the south-east and south-west of Australia. Through comparison we infer possible underlying causes, expanding on current studies limited to single locations. Over half the catchments studied displayed a drop in flow. We suggest the influence of pre-existing trends in groundwater and highlight the importance of land use history.
Vivek Kumar Yadav, Murray Peel, Keirnan Fowler, Dongryeol Ryu, and Bramha Dutt Vishwakarma
EGUsphere, https://doi.org/10.5194/egusphere-2025-4650, https://doi.org/10.5194/egusphere-2025-4650, 2025
Short summary
Short summary
Identifying drivers is crucial for process understanding and predictions. In Hydrometeorological systems, many variables are closely related, and common methods often rely on correlation. We describe theoretically distinct methods of discovering cause-effect relations from data. We evaluate them in a large simulated environment. Results show that finding cause-effect relations provides a parsimonious picture and to obtain robust predictions, especially under changing environmental conditions.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
Hydrol. Earth Syst. Sci., 29, 5555–5573, https://doi.org/10.5194/hess-29-5555-2025, https://doi.org/10.5194/hess-29-5555-2025, 2025
Short summary
Short summary
Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that although there have been widespread decreases in droughts since the beginning of the 20th century, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data, 17, 4079–4095, https://doi.org/10.5194/essd-17-4079-2025, https://doi.org/10.5194/essd-17-4079-2025, 2025
Short summary
Short summary
This paper presents version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) v2 comprises data for an increased number (561) of catchments, each with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
Gabrielle Burns, Keirnan Fowler, Murray Peel, and Clare Stephens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3122, https://doi.org/10.5194/egusphere-2025-3122, 2025
Short summary
Short summary
Improving how rainfall-runoff models estimate evapotranspiration is key to better reproducing water partitioning under current conditions, and will increase model realism under future changing conditions. We tested how well different conceptual rainfall-runoff model equations simulate evapotranspiration using Australian catchment and flux tower data. We found one equation consistently worked better than the others. However, even this equation had flaws, pointing to missing vegetation processes.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3373, https://doi.org/10.5194/egusphere-2025-3373, 2025
Short summary
Short summary
This study adopts actual evapotranspiration (AET) signatures to diagnose deficiencies in simulation of AET within conceptual rainfall-runoff models. Five models are assessed using flux tower data at 14 Australian sites. Even when AET is included in the calibration, the models struggle to represent aspects of AET dynamics, including interannual variability and timing on seasonal and event scales. The approach shows promise for more insightful critique of model simulations.
Sandra Pool, Keirnan Fowler, Hansini Gardiya Weligamage, and Murray Peel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1598, https://doi.org/10.5194/egusphere-2025-1598, 2025
Short summary
Short summary
Multivariate calibration has become a widely used method to improve model realism. We found that multivariate calibration can lead to less constrained flux maps and more uncertain hydrographs relative to univariate calibration. These symptoms could be caused by non-overlapping behavioural parameter distributions for the individual calibration variables. The results emphasize that the value of non-discharge data in calibration is contingent on the suitability of the model structure.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-373, https://doi.org/10.5194/hess-2024-373, 2025
Preprint under review for HESS
Short summary
Short summary
This study is the first to propose actual evapotranspiration (AET) signatures, which can be used to assess multiple aspects of AET dynamics across various temporal scales. As a demonstration, we applied AET signatures to evaluate two remotely sensed (RS) AET products against flux tower AET. The results reveal specific deficiencies in RS AET and provide guidance for selecting appropriate RS AET, including for modelling studies.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
Short summary
Short summary
Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Nyree Campion, Keirnan Fowler, Margot Turner, and Joel Hall
EGUsphere, https://doi.org/10.5194/egusphere-2026-378, https://doi.org/10.5194/egusphere-2026-378, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Globally, many river systems have seen less flow for the same rainfall after periods of long drought. This study investigates this behaviour in over 300 catchments across the south-east and south-west of Australia. Through comparison we infer possible underlying causes, expanding on current studies limited to single locations. Over half the catchments studied displayed a drop in flow. We suggest the influence of pre-existing trends in groundwater and highlight the importance of land use history.
Cyril Thébault, Wouter J. M. Knoben, Nans Addor, Andrew J. Newman, Diana Spieler, Nicolás A. Vásquez, Yalan Song, Gaby J. Gründemann, Shaun Carney, Mukesh Kumar, Katie van Werkhoven, Chaopeng Shen, Andrew W. Wood, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-6083, https://doi.org/10.5194/egusphere-2025-6083, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Reliable river flow prediction guide water supply planning and flood protection. We tested whether selecting or combining many models improves accuracy compared with single model. 78 models were used and tested in 559 river basins across the United States. A carefully chosen single model nearly matched more complex multi-model approaches, while combining models gave slightly higher accuracy and lower uncertainty. However, no approach worked best everywhere.
Vivek Kumar Yadav, Murray Peel, Keirnan Fowler, Dongryeol Ryu, and Bramha Dutt Vishwakarma
EGUsphere, https://doi.org/10.5194/egusphere-2025-4650, https://doi.org/10.5194/egusphere-2025-4650, 2025
Short summary
Short summary
Identifying drivers is crucial for process understanding and predictions. In Hydrometeorological systems, many variables are closely related, and common methods often rely on correlation. We describe theoretically distinct methods of discovering cause-effect relations from data. We evaluate them in a large simulated environment. Results show that finding cause-effect relations provides a parsimonious picture and to obtain robust predictions, especially under changing environmental conditions.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
Hydrol. Earth Syst. Sci., 29, 5555–5573, https://doi.org/10.5194/hess-29-5555-2025, https://doi.org/10.5194/hess-29-5555-2025, 2025
Short summary
Short summary
Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that although there have been widespread decreases in droughts since the beginning of the 20th century, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Claudia Färber, Henning Plessow, Simon A. Mischel, Frederik Kratzert, Nans Addor, Guy Shalev, and Ulrich Looser
Earth Syst. Sci. Data, 17, 4613–4625, https://doi.org/10.5194/essd-17-4613-2025, https://doi.org/10.5194/essd-17-4613-2025, 2025
Short summary
Short summary
Large-sample datasets are essential in hydrological science to support modelling studies and advance process understanding. Caravan is a community initiative to create a large-sample hydrology dataset of meteorological forcing data, catchment attributes and discharge data for catchments around the world. This dataset is a subset of hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data, 17, 4079–4095, https://doi.org/10.5194/essd-17-4079-2025, https://doi.org/10.5194/essd-17-4079-2025, 2025
Short summary
Short summary
This paper presents version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) v2 comprises data for an increased number (561) of catchments, each with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
Gabrielle Burns, Keirnan Fowler, Murray Peel, and Clare Stephens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3122, https://doi.org/10.5194/egusphere-2025-3122, 2025
Short summary
Short summary
Improving how rainfall-runoff models estimate evapotranspiration is key to better reproducing water partitioning under current conditions, and will increase model realism under future changing conditions. We tested how well different conceptual rainfall-runoff model equations simulate evapotranspiration using Australian catchment and flux tower data. We found one equation consistently worked better than the others. However, even this equation had flaws, pointing to missing vegetation processes.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3373, https://doi.org/10.5194/egusphere-2025-3373, 2025
Short summary
Short summary
This study adopts actual evapotranspiration (AET) signatures to diagnose deficiencies in simulation of AET within conceptual rainfall-runoff models. Five models are assessed using flux tower data at 14 Australian sites. Even when AET is included in the calibration, the models struggle to represent aspects of AET dynamics, including interannual variability and timing on seasonal and event scales. The approach shows promise for more insightful critique of model simulations.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, https://doi.org/10.5194/essd-17-2113-2025, 2025
Short summary
Short summary
We present bias-corrected UK Climate Projections 2018 (UKCP18) regional datasets for temperature, precipitation, and potential evapotranspiration (1981–2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant. Both methods effectively reduce biases in multiple statistics while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment.
Sandra Pool, Keirnan Fowler, Hansini Gardiya Weligamage, and Murray Peel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1598, https://doi.org/10.5194/egusphere-2025-1598, 2025
Short summary
Short summary
Multivariate calibration has become a widely used method to improve model realism. We found that multivariate calibration can lead to less constrained flux maps and more uncertain hydrographs relative to univariate calibration. These symptoms could be caused by non-overlapping behavioural parameter distributions for the individual calibration variables. The results emphasize that the value of non-discharge data in calibration is contingent on the suitability of the model structure.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
Short summary
Short summary
This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Hansini Gardiya Weligamage, Keirnan Fowler, Margarita Saft, Tim Peterson, Dongryeol Ryu, and Murray Peel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-373, https://doi.org/10.5194/hess-2024-373, 2025
Preprint under review for HESS
Short summary
Short summary
This study is the first to propose actual evapotranspiration (AET) signatures, which can be used to assess multiple aspects of AET dynamics across various temporal scales. As a demonstration, we applied AET signatures to evaluate two remotely sensed (RS) AET products against flux tower AET. The results reveal specific deficiencies in RS AET and provide guidance for selecting appropriate RS AET, including for modelling studies.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
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.
Nele Reyniers, Timothy J. Osborn, Nans Addor, and Geoff Darch
Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, https://doi.org/10.5194/hess-27-1151-2023, 2023
Short summary
Short summary
In an analysis of future drought projections for Great Britain based on the Standardised Precipitation Index and the Standardised Precipitation Evapotranspiration Index, we show that the choice of drought indicator has a decisive influence on the resulting projected changes in drought characteristics, although both result in increased drying. This highlights the need to understand the interplay between increasing atmospheric evaporative demand and drought impacts under a changing climate.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
Short summary
Short summary
Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
John P. Bloomfield, Mengyi Gong, Benjamin P. Marchant, Gemma Coxon, and Nans Addor
Hydrol. Earth Syst. Sci., 25, 5355–5379, https://doi.org/10.5194/hess-25-5355-2021, https://doi.org/10.5194/hess-25-5355-2021, 2021
Short summary
Short summary
Groundwater provides flow, known as baseflow, to surface streams and rivers. It is important as it sustains the flow of many rivers at times of water stress. However, it may be affected by water management practices. Statistical models have been used to show that abstraction of groundwater may influence baseflow. Consequently, it is recommended that information on groundwater abstraction is included in future assessments and predictions of baseflow.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
Short summary
Short summary
We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Cited articles
Acharya, S. C., Nathan, R., Wang, Q. J., Su, C.-H., and Eizenberg, N.: An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia, Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, 2019.
Adam, J. C., Clark, E. A., Lettenmaier, D. P., and Wood, E. F.: Correction
of global precipitation products for orographic effects, J. Climate,
19, 15–38, https://doi.org/10.1175/JCLI3604.1, 2006.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and
Mendoza, P. A.: Large sample hydrology: recent progress, guidelines for new
datasets and grand challenges, Hydrol. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2019.
Aghakouchak, A., D. Feldman, M. Stewardson, J. Saphores, Grant S., and
Sanders, B.: Australia's Drought: Lessons for California, Science, 343,
1430–1431, https://doi.org/10.1126/science.343.6178.1430, 2014.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
American Society for Civil Engineering (ASCE): ASCE's Standardized Reference
Evapotranspiration Equation, in: Watershed Management and Operations Management Conference 2000, Fort Collins, Colorado, United States, 20–24 June 2000, 2000.
Andréassian, V., Hall, A., Chahinian, N., and Schaake, J.: Introduction and synthesis: why should
hydrologists work on a large number of basin data sets? Large sample basin
experiments for hydrological model parameterization: results of the Model
Parameter Experiment–MOPEX, vol. 307, CEH Wallingford, IAHS Publ., UK, 307, available at: https://iahs.info/uploads/dms/13599.02-1-6-INTRODUCTION.pdf (last access: 30 July 2021), 2006.
Arsenault, R., Bazile, R., Ouellet Dallaire, C., and Brissette, F.: CANOPEX:
A Canadian hydrometeorological watershed database, Hydrol. Process., 30,
2734–2736, https://doi.org/10.1002/hyp.10880, 2016.
Ashcroft, L., Karoly, D. J., and Gergis, J.: Southeastern Australian
climate variability 1860–2009: a multivariate analysis, Int.
J. Climatol., 34, 1928–1944, https://doi.org/10.1002/joc.3812, 2014.
Australian Bureau of Statistics (ABS): Australian Census 2006 Population
Statistics, raw data available at: https://www.abs.gov.au/websitedbs/censushome.nsf/home/historicaldata2006?opendocument&navpos280 (last access: 30 July 2021), 2006.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Bureau of Meteorology, Australia (BOM): Hydrologic Reference Stations, Website, available at: http://www.bom.gov.au/water/hrs/update_2015.shtml (last access: 1 June 2020), 2015.
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.
Court, A.: Measures of streamflow timing, J. Geophys. Res., 67, 4335–4339,
https://doi.org/10.1029/JZ067i011p04335, 1962.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
CSIRO: AUS SRTM 1sec MRVBF mosaic v01, Bioregional Assessment Source
Dataset [data set], available at: https://data.gov.au/data/dataset/79975b4a-1204-4ab1-b02b-0c6fbbbbbcb5 (last access: 30 July 2021),
2016.
Department of the Environment and Water Resources, Australia (DEWR):
Estimated Pre-1750 Major Vegetation Subgroups – NVIS Stage 1, Version 3.1, available at: https://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system (last access: 30 July 2021),
2008.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Falkenmark, M. and Chapman, T.: Comparative hydrology: An ecological approach to land and water resources, Unesco, Paris, 1989.
Food and Agriculture Organization of the United Nations (FAO): Irrigation
and drainage paper 56: Crop evapotranspiration – Guidelines for computing
crop water requirements, FAO, Rome, Italy, available at: http://www.fao.org/3/x0490e/x0490e00.htm (last access: 30 July 2021), ISBN 92-5-104219-5, 1998.
Fowler, K., Peel, M., Western, A., Zhang, L., and Peterson, T. J.:
Simulating runoff under changing climatic conditions: Revisiting an apparent
deficiency of conceptual rainfall-runoff models, Water Resour. Res.,
52, 1820–1846, https://doi.org/10.1002/2015WR018068, 2016.
Fowler, K., Peel, M., Western, A., and Zhang, L. Improved rainfall-runoff
calibration for drying climate: Choice of objective function, Water
Resour. Res., 54, 3392–3408, https://doi.org/10.1029/2017WR022466, 2018.
Fowler, K., Acharya, S. C., Addor, N., Chou, C., and Peel, M.: CAMELS-AUS v1:
Hydrometeorological time series and landscape attributes for 222 catchments
in Australia, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.921850, 2020a.
Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., Seo, K.,
Western, A. Many commonly used rainfall-runoff models lack long, slow
dynamics: implications for runoff projections, Water Resour. Res., 56, e2019WR025286,
https://doi.org/10.1029/2019WR025286, 2020b.
Gallant, J. and Austin, J.: Slope derived from 1” SRTM DEM-S. v4, CSIRO,
Data Collection, https://doi.org/10.4225/08/5689DA774564A, 2012.
Gallant, J., Wilson, N., Tickle, P. K., Dowling, T., and Read, A.: 3 second SRTM
Derived Digital Elevation Model (DEM) Version 1.0. Record 1.0, Geoscience
Australia, Canberra, available at: http://pid.geoscience.gov.au/dataset/ga/69888 (last access: 30 July 2021), 2009.
Gallant, J. C. and Dowling, T. I.: A multiresolution index of valley bottom
flatness for mapping depositional areas, Water Resour. Res., 39, 1347,
https://doi.org/10.1029/2002WR001426, 2003.
Geoscience Australia: Dams and Water Storages 1990, Geoscience Australia,
Canberrra, later versions available at: https://data.gov.au/data/dataset/ce5b77bf-5a02-4cf8-9cf2-be4a2cee2677 (last access: 30 July 2021),
2004.
Geoscience Australia: Surface Geology of Australia 1:1 million scale
dataset, latest version is available at: https://data.gov.au/dataset/ds-dga-48fe9c9d-2f10-49d2-bd24-ac546662c4ec/details (last access: 30 July 2021),
2008.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019.
Gordon, N. D., McMahon, T. A., Finlayson, B. L., and Christopher, J.:
Stream Hydrology: an Introduction for Ecologists, John Wiley & Sons, Ltd., Chichester, UK,
1992.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochem. Geophy. Geosy., 13, Q12004, https://doi.org/10.1029/2012GC004370, 2012.
Hutchinson, M. F., Stein, J. L., Stein, J. A., Anderson, H., and Tickle, P. K.:
GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow
Direction Grid 2008, Record DEM-9S.v3, Geoscience Australia, Canberra, available at:
http://pid.geoscience.gov.au/dataset/ga/66006 (last access: 30 July 2021), 2008.
Isbell, R. F.: The Australian Soil Classification, revised edn., CSIRO
Publishing, Melbourne, available at: https://www.asris.csiro.au/themes/Atlas.html#Atlas_Digital (last access: 30 July 2021), 2002.
Jeffrey, S. J., Carter, J. O., Moodie, K. B., and Beswick, A. R.: Using
spatial interpolation to construct a comprehensive archive of Australian
climate data. Environ. Modell. Softw., 16, 309–330,
https://doi.org/10.1016/S1364-8152(01)00008-1, 2001.
Jian, J., Costelloe, J., Ryu, D., and Wang, Q. J.: Does a fifteen-hour shift make
much difference? – Influence of time lag between rainfall and discharge
data on model calibration, 22nd International Congress on Modelling and
Simulation, Hobart, Tasmania, 3–8 December 2017, available at: https://www.mssanz.org.au/modsim2017/H3/jian.pdf (last access: 30 July 2021), 2017.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate
data-sets for Australia, Aust. Meteorol. Ocean., 58, 233–248, https://doi.org/10.22499/2.5804.003,
2009.
Knoben, W. J. M., Woods, R. A., and Freer, J. E.: A quantitative hydrological climate classification evaluated with independent streamflow data, Water Resour. Res., 54, 5088–5109, https://doi.org/10.1029/2018WR022913, 2018
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and
Nearing, G. S.: Toward Improved Predictions in Ungauged Basins: Exploiting
the Power of Machine Learning, Water Resour. Res., 55, 11344–11354,
https://doi.org/10.1029/2019WR026065, 2019a.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019b.
Kuentz, A., Arheimer, B., Hundecha, Y., and Wagener, T.: Understanding hydrologic variability across Europe through catchment classification, Hydrol. Earth Syst. Sci., 21, 2863–2879, https://doi.org/10.5194/hess-21-2863-2017, 2017.
Ladson, A., Brown, R., Neal, B., and Nathan, R.: A standard approach to
baseflow separation using the Lyne and Hollick filter, Aust. J. Water
Resour., 17, 25–34, https://doi.org/10.7158/W12-028.2013.17.1, 2013.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood,
E. F.: Global reconstruction of naturalized river flows at 2.94 million
reaches, Water Resour. Res., 2019WR025287,
https://doi.org/10.1029/2019WR025287, 2019.
Linke, S., Lehner, B., Dallaire, C. O., Ariwi, J., Grill, G., Anand, M.,
Beames, P., Burchard-levine, V., Moidu, H., Tan, F., and Thieme, M.:
HydroATLAS: global hydro-environmental sub-basin and river reach
characteristics at high spatial resolution, Sci. Data, 6, 283,
https://doi.org/10.1038/s41597-019-0300-6, 2019.
Liu, S. F., Raymond, O. L., Stewart, A. J., Sweet, I. P., Duggan, M.,
Charlick, C., Phillips, D., and Retter, A. J.: Surface geology of Australia
1:1,000,000 scale, Northern Territory [Digital Dataset]. The Commonwealth of
Australia, Geoscience Australia, Canberra, available at: http://www.ga.gov.au (last access: 30 July 2021), 2006.
Lymburner, L., Tan, P., McIntyre, A., Thankappan, M., and Sixsmith, J.: Dynamic
Land Cover Dataset Version 2.1, Geoscience Australia, Canberra, available at: http://pid.geoscience.gov.au/dataset/ga/83868 (last access: 30 July 2021), 2015.
Mathevet, T., Gupta, H., Perrin, C., Andréassian, V., and Le Moine, N.:
Assessing the performance and robustness of two conceptual rainfall-runoff
models on a worldwide sample of watersheds, J. Hydrol., 585, 124698, https://doi.org/10.1016/j.jhydrol.2020.124698, 2020.
McInerney, D., Thyer, M., Kavetski, D., Lerat, J., and Kuczera, G.:
Improving probabilistic prediction of daily streamflow by identifying Pareto
optimal approaches for modeling heteroscedastic residual errors, Water
Resour. Res., 53, 2199–2239, https:/doi.org/10.1002/2016WR019168,
2017.
McKenzie, N. J., Jacquier, D. W., Ashton L. J., and Cresswell, H. P.: Estimation
of Soil Properties Using the Atlas of Australian Soils, CSIRO Land and Water,
Technical Report, 11/00, available at: https://www.asris.csiro.au/themes/Atlas.html#Atlas_Digital (last access: 30 July 2021), 2000.
McMahon T. and Peel M.: Uncertainty in stage–discharge rating curves:
application to Australian Hydrologic Reference Stations data, Hydrolog.
Sci. J., 64, 255–275, https://doi.org/10.1080/02626667.2019.1577555, 2019.
McMahon, T. A., Finlayson, B. L., Haines, A. T., and Srikanthan, R:. Global runoff: continental comparisons of annual flows and peak discharges, Catena
Verlag, Germany, 1992.
Morton, F. I.: Operational estimates of areal evapotranspiration and their
significance to the science and practice of hydrology, J. Hydrol.,
66, 1–76, 1983.
National Land and Water Resources Audit: Gridded soil information layers,
Canberra, available at: http://www.asris.csiro.au/mapping/viewer.htm (last access: 30 July 2021), 2001.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B., Auler, A., Bailly-Comte, V., Barberá, J. A., Batiot-Guilhe, C., Bechtel, T., Binet, S., Bittner, D., Blatnik, M., Bolger, T., Brunet, P., Charlier, J.-B., Chen, Z., Chiogna, G., Coxon, G., De Vita, P., Doummar, J., Epting, J., Fleury, P., Fournier, M., Goldscheider, N., Gunn, J., Guo, F., Guyot, J. L., Howden, N., Huggenberger, P., Hunt, B., Jeannin, P.-Y., Jiang, G., Jones, G., Jourde, H., Karmann, I., Koit, O., Kordilla, J., Labat, D., Ladouche, B., Liso, I. S., Liu, Z., Maréchal, J.-C., Massei, N., Mazzilli, N., Mudarra, M., Parise, M., Pu, J., Ravbar, N., Hidalgo Sanchez, L., Santo, A., Sauter, M., Seidel, J.-L., Sivelle, V., Skoglund, R. Ø., Stevanovic, Z., Wood, C., Worthington, S., and Hartmann, A.:
Global karst springs hydrograph dataset for research and management of the
world's fastest-flowing groundwater, Sci. Data, 7, 59,
https://doi.org/10.1038/s41597-019-0346-5, 2020.
Open Data Charter: International open data charter, available
at: https://opendatacharter.net/wp-content/uploads/2015/10/opendatacharter-charter_F.pdf (last access: 30 July 2021), 2015
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Variability of annual
precipitation and its relationship to the El Niño–Southern Oscillation,
J. Climate, 15, 545–551, 2002.
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Continental differences in
the variability of annual runoff-update and reassessment, J.
Hydrol., 295, 185–197, 2004.
Peel, M. C., Pegram, G. G. S., and McMahon, T. A.: Global analysis of runs of
annual precipitation and runoff equal to or below the median: Run magnitude
and severity, Int. J. Climatol., 24, 549–568,
https://doi.org/10.1002/joc.1147, 2005.
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Vegetation impact on
mean annual evapotranspiration at a global catchment scale, Water Resour.
Res., 46, W09508, https://doi.org/10.1029/2009WR008233, 2010.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Peterson, T. J., Wasko, C., Saft, M., and Peel, M. C.: AWAPer: An R package
for area weighted catchment daily meteorological data anywhere within
Australia, Hydrol. Process., 34, 1301–1306, https://doi.org/10.1002/hyp.13637, 2020
Peterson, T. J., Saft, M., Peel, M. C., and John, A.: Watersheds may not
recover from drought, Science, 372, 745–749,
https://doi.org/10.1126/science.abd5085, 2021.
Pool, S., Viviroli, D., and
Seibert, J.: Value of a limited number of discharge observations for
improving regionalization: A large-sample study across the United States,
Water Resour. Res., 55, 363–377,
https://doi.org/10.1029/2018WR023855, 2019.
Raupach, M. R., Kirby, J. M., Barrett, D. J., and Briggs, P. R.: Balances of Water, Carbon,
Nitrogen and Phosphorus in Australian Landscapes version 2.04, CSIRO Land
and Water, Canberra, available at: http://www.clw.csiro.au/publications/technical2001/tr40-01.pdf (last access: 30 July 2021), 2002.
Raymond, O. L., Liu, S. F., and Kilgour, P.: Surface geology of Australia
1:1,000,000 scale, Tasmania – 3rd edn., Digital Dataset, The
Commonwealth of Australia, Geoscience Australia, Canberra, available at:
http://www.ga.gov.au (last access: 30 July 2021), 2007a.
Raymond, O. L., Liu, S. F., Kilgour, P. L., Retter, A. J., Stewart, A. J.,
and Stewart, G.: Surface geology of Australia 1:1,000,000 scale, New South
Wales – 2nd edn., Digital Dataset, The Commonwealth of Australia,
Geoscience Australia, Canberra, available at: http://www.ga.gov.au (last access: 30 July 2021), 2007b.
Raymond, O. L., Liu, S. F., Kilgour, P., Retter, A. J., and Connolly, D. P.:
Surface geology of Australia 1:1,000,000 scale, Victoria – 3rd edn.,
Digital Dataset, The Commonwealth of Australia, Geoscience Australia,
Canberra, available at: http://www.ga.gov.au (last access: 30 July 2021), 2007c.
Rayner, D.: Australian synthetic daily Class A pan evaporation, Technical
Report December, Queensland Department of Natural Resources and Mines,
Indooroopilly, Qld., Australia, 40 pp., available at: https://data.longpaddock.qld.gov.au/static/silo/pdf/AustralianSyntheticDailyClassAPanEvaporation.pdf (last access: 30 July 2021),
2005.
Reynolds, J. E., Halldin, S., Seibert, J., and Xu, C. Y.: Definitions of
climatological and discharge days: do they matter in hydrological
modelling?, Hydrolog. Sci. J., 63, 836–844,
https://doi.org/10.1080/02626667.2018.1451646, 2018.
Saft, M., Peel, M. C., Western, A. W., Perraud, J. M., and Zhang, L.: Bias in streamflow projections due to climate‐induced shifts in catchment response, Geophys. Res. Lett., 43, 1574–1581. https://doi.org/10.1002/2015GL067326, 2016.
Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate
elasticity of streamflow in the United States, Water Resour. Res., 37,
1771–1781, https://doi.org/10.1029/2000WR900330, 2001.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Shen, C.: A Transdisciplinary Review of Deep Learning Research and Its
Relevance for Water Resources Scientists, Water Resour. Res., 54,
8558–8593, https://doi.org/10.1029/2018WR022643, 2018.
Skinner, D. and Langford, J.: Legislating for sustainable basin management:
the story of Australia's Water Act (2007), Water Policy, 15, 871–894,
2013.
Stein, J. L., Stein, J. A., and Nix, H. A.: Spatial analysis of anthropogenic
river disturbance at regional and continental scales: identifying the wild
rivers of Australia, Landscape Urban Plan., 60, 1–25,
https://doi.org/10.1016/S0169-2046(02)00048-8, 2002.
Stein, J. L., Hutchinson, M. F., and Stein, J. A.: National Catchment and
Stream Environment Database version 1.1.4, available at: http://pid.geoscience.gov.au/dataset/ga/73045 (last access: 30 July 2021), 2011.
Stewart, A. J., Sweet, I. P., Needham, R. S., Raymond, O. L., Whitaker, A.
J., Liu, S. F., Phillips, D., Retter, A. J., Connolly, D. P., and Stewart,
G.: Surface geology of Australia 1:1,000,000 scale, Western Australia,
Digital Dataset, The Commonwealth of Australia, Geoscience Australia,
Canberra, available at: http://www.ga.gov.au (last access: 30 July 2021), 2008.
Strahler, A. N.: Quantitative analysis of watershed geomorphology, Eos,
Transactions American Geophysical Union, 38, 913–920,
https://doi.org/10.1029/TR038i006p00913, 1957.
Tozer, C. R., Kiem, A. S., and Verdon-Kidd, D. C.: On the uncertainties associated with using gridded rainfall data as a proxy for observed, Hydrol. Earth Syst. Sci., 16, 1481–1499, https://doi.org/10.5194/hess-16-1481-2012, 2012.
Turner, M., Bari, M., Amirthanathan, G., and Ahmad, Z.: Australian network
of hydrologic reference stations-advances in design, development and
implementation, in: Hydrology and Water Resources Symposium,
Sydney, Australia, 19–22 November 2012, Engineers Australia, available at: http://www.bom.gov.au/water/hrs/media/static/papers/Turner2012.pdf (last access: 30 July 2021), p. 1555, 2012.
Van Dijk, A. I., Beck, H. E., Crosbie, R. S., de Jeu, R. A., Liu, Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040–1057, https://doi.org/10.1002/wrcr.20123, 2013.
Verdon-Kidd, D. C. and Kiem, A. S.: Nature and causes of protracted droughts
in southeast Australia: Comparison between the Federation, WWII, and Big Dry
droughts, Geophys. Res. Lett., 36, L22707, https://doi.org/10.1029/2009GL041067, 2009.
Vertessy, R. A.: Water information services for Australians, Australasian Journal of Water Resources, 16, 91–105, available at: https://www.tandfonline.com/doi/abs/10.7158/13241583.2013.11465407 (last access: 30 July 2021), 2013.
Viglione, A., Borga, M., Balabanis, P., and Blöschl, G.: Barriers to the
exchange of hydrometeorological data in Europe: Results from a survey and
implications for data policy, J. Hydrol., 394, 63–77, 2010.
Western, A. and McKenzie, N.: Soil hydrological properties of Australia
Version 1.0.1, CRC for Catchment Hydrology, Melbourne, 2004.
Western, A. W., Matic, V., and Peel, M. C.: Justin Costelloe: a champion of
arid-zone water research, Hydrogeol. J., 28, 37–41, https://doi.org/10.1007/s10040-019-02051-7, 2020.
Whitaker, A. J., Champion, D. C., Sweet, I. P., Kilgour, P., and Connolly, D.
P.: Surface geology of Australia 1:1,000,000 scale, Queensland 2nd edn.,
Digital Dataset, The Commonwealth of Australia, Geoscience Australia,
Canberra, available at: http://www.ga.gov.au (last access: 30 July 2021), 2007.
Whitaker, A. J., Glanville, D. H., English, P. M., Stewart, A. J., Retter,
A. J., Connolly, D. P., Stewart, G. A., and Fisher, C. L.: Surface geology of
Australia 1:1,000,000 scale, South Australia, Digital Dataset, The
Commonwealth of Australia, Geoscience Australia, Canberra, available at:
http://www.ga.gov.au (last access: 30 July 2021), 2008.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., Hoen, P. A. C ’t, Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR guiding principles for scientific
data management and stewardship, Scientific Data, 3, 160018,
https://doi.org/10.1038/sdata.2016.18, 2016
Woods, R. A.: Analytical model of seasonal climate impacts on snow
hydrology: Continuous snowpacks, Adv. Water Resour., 32, 1465–1481,
https://doi.org/10.1016/j.advwatres.2009.06.011, 2009.
Wright, D. P., Thyer, M., and Westra, S.: Influential point detection
diagnostics in the context of hydrological model calibration, J.
Hydrol., 527, 1161–1172, https://doi.org/10.1016/j.jhydrol.2018.01.036,
2018.
Xu, T. and Hutchinson, M.: ANUCLIM version 6.1 user guide, The Australian
National University, Fenner School of Environment and Society, Canberra, available at:
https://fennerschool.anu.edu.au/files/anuclim61.pdf (last access: 30 July 2021), 2011.
Zhang, S. X., Bari, M., Amirthanathan, G., Kent, D., MacDonald, A., and
Shin, D.: Hydrologic reference stations to monitor climate-driven streamflow
variability and trends, in: Hydrology and Water Resources Symposium, Perth, Western Australia, 24–27 February 2014, Engineers Australia, p.
1048, 2014.
Zhang, X. S., Amirthanathan, G. E., Bari, M. A., Laugesen, R. M., Shin, D., Kent, D. M., MacDonald, A. M., Turner, M. E., and Tuteja, N. K.: How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations, Hydrol. Earth Syst. Sci., 20, 3947–3965, https://doi.org/10.5194/hess-20-3947-2016, 2016.
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