Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1771-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1771-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2024
Research article |
| 17 Apr 2024
| 17 Apr 2024
A high-resolution map of diffuse groundwater recharge rates for Australia
Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, 8000, Australia
National Centre for Groundwater Research and Training, Adelaide, 5000, Australia
Dylan J. Irvine
Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, 8000, Australia
National Centre for Groundwater Research and Training, Adelaide, 5000, Australia
Clément Duvert
Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, 8000, Australia
National Centre for Groundwater Research and Training, Adelaide, 5000, Australia
Gabriel C. Rau
National Centre for Groundwater Research and Training, Adelaide, 5000, Australia
School of Environmental and Life Sciences, the University of Newcastle, Callaghan, 2308, Australia
Ian Cartwright
National Centre for Groundwater Research and Training, Adelaide, 5000, Australia
School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Australia
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Francesco Ulloa-Cedamanos, Adam T. Rexroade, Yihan Li, Lindsay B. Hutley, Wei Wen Wong, Marcus B. Wallin, Josep G. Canadell, Anna Lintern, and Clement Duvert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-233, https://doi.org/10.5194/essd-2025-233, 2025
Preprint under review for ESSD
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Rivers and streams play a key role in how carbon moves through the environment, but we know little about this in Australia. To help close this gap, we compile the first national database of carbon data from rivers and streams, combining past studies, government records, and new data. The data show where and when carbon was measured and reveal major gaps in long-term monitoring. This new resource will help scientists understand carbon and water systems across Australia.
Olaleye Babatunde, Meenakshi Arora, Siva Naga Venkat Nara, Danlu Guo, Ian Cartwright, and Andrew W. Western
EGUsphere, https://doi.org/10.5194/egusphere-2025-2456, https://doi.org/10.5194/egusphere-2025-2456, 2025
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Nitrogen inputs can pollute streams and degrade water quality. We estimated fertiliser nitrogen inputs across different land uses and assessed their relationship with stream nitrogen concentrations. Only a small fraction of the applied nitrogen was exported, with most retained within the landscape. Land use, rainfall, and flow patterns strongly influenced nitrogen dynamics and export. These findings support strategies to reduce stream pollution and protect water quality in agricultural areas.
Clément Duvert, Vanessa Solano, Dioni I. Cendón, Francesco Ulloa-Cedamanos, Liza K. McDonough, Robert G. M. Spencer, Niels C. Munksgaard, Lindsay B. Hutley, Jean-Sébastien Moquet, and David E. Butman
EGUsphere, https://doi.org/10.5194/egusphere-2025-1600, https://doi.org/10.5194/egusphere-2025-1600, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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This study examines the age and composition of carbon in tropical streams. We find that dissolved organic carbon (DOC) is centuries to millennia old, while dissolved inorganic carbon (DIC) is consistently younger, indicating a decoupling between the two. DOC age varies seasonally, with rainforest streams exporting younger DOC during high flow, while agricultural streams mobilise older DOC. Our results suggest land conversion alters carbon export, potentially worsening with climate change.
Haegyeong Lee, Manuel Gossler, Kai Zosseder, Philipp Blum, Peter Bayer, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 29, 1359–1378, https://doi.org/10.5194/hess-29-1359-2025, https://doi.org/10.5194/hess-29-1359-2025, 2025
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A systematic laboratory experiment elucidates two-phase heat transport due to water flow in saturated porous media to understand thermal propagation in aquifers. Results reveal delayed thermal arrival in the solid phase, depending on grain size and flow velocity. Analytical modeling using standard local thermal equilibrium (LTE) and advanced local thermal non-equilibrium (LTNE) theory fails to describe temperature breakthrough curves, highlighting the need for more advanced numerical approaches.
Patrick Haehnel, Todd C. Rasmussen, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 28, 2767–2784, https://doi.org/10.5194/hess-28-2767-2024, https://doi.org/10.5194/hess-28-2767-2024, 2024
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While groundwater recharge is important for water resources management, nearshore sea levels can obscure this signal. Regression deconvolution has previously been used to remove other influences from groundwater levels (e.g., barometric pressure, Earth tides) by accounting for time-delayed responses from these influences. We demonstrate that it can also remove sea-level influences from measured groundwater levels.
Rémi Valois, Agnès Rivière, Jean-Michel Vouillamoz, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 28, 1041–1054, https://doi.org/10.5194/hess-28-1041-2024, https://doi.org/10.5194/hess-28-1041-2024, 2024
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Characterizing aquifer systems is challenging because it is difficult to obtain in situ information. They can, however, be characterized using natural forces such as Earth tides. Models that account for more complex situations are still necessary to extend the use of Earth tides to assess hydromechanical properties of aquifer systems. Such a model is developed in this study and applied to a case study in Cambodia, where a combination of tides was used in order to better constrain the model.
Jose M. Bastias Espejo, Chris Turnadge, Russell S. Crosbie, Philipp Blum, and Gabriel C. Rau
Hydrol. Earth Syst. Sci., 27, 3447–3462, https://doi.org/10.5194/hess-27-3447-2023, https://doi.org/10.5194/hess-27-3447-2023, 2023
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Analytical models estimate subsurface properties from subsurface–tidal load interactions. However, they have limited accuracy in representing subsurface physics and parameter estimation. We derived a new analytical solution which models flow to wells due to atmospheric tides. We applied it to field data and compared our findings with subsurface knowledge. Our results enhance understanding of subsurface systems, providing valuable information on their behavior.
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
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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.
Zibo Zhou, Ian Cartwright, and Uwe Morgenstern
Hydrol. Earth Syst. Sci., 26, 4497–4513, https://doi.org/10.5194/hess-26-4497-2022, https://doi.org/10.5194/hess-26-4497-2022, 2022
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Streams may receive water from different sources in their catchment. There is limited understanding of which water stores intermittent streams are connected to. Using geochemistry we show that the intermittent streams in southeast Australia are connected to younger smaller near-river water stores rather than regional groundwater. This makes these streams more vulnerable to the impacts of climate change and requires management of the riparian zone for their protection.
Gabriel C. Rau, Timothy C. McMillan, Martin S. Andersen, and Wendy A. Timms
Hydrol. Earth Syst. Sci., 26, 4301–4321, https://doi.org/10.5194/hess-26-4301-2022, https://doi.org/10.5194/hess-26-4301-2022, 2022
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This work develops and applies a new method to estimate hydraulic and geomechanical subsurface properties in situ using standard groundwater and atmospheric pressure records. The estimated properties comply with expected values except for the Poisson ratio, which we attribute to the investigated scale and conditions. Our new approach can be used to cost-effectively investigate the subsurface using standard monitoring datasets.
Ian Cartwright
Hydrol. Earth Syst. Sci., 26, 183–195, https://doi.org/10.5194/hess-26-183-2022, https://doi.org/10.5194/hess-26-183-2022, 2022
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Using specific conductivity (SC) to estimate groundwater inflow to rivers is complicated by bank return waters, interflow, and flows off floodplains contributing to baseflow in all but the driest years. Using the maximum SC of the river in dry years to estimate the SC of groundwater produces the best baseflow vs. streamflow trends. The variable composition of baseflow hinders calibration of hydrograph-based techniques to estimate groundwater inflows.
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16, https://doi.org/10.5194/hess-26-1-2022, https://doi.org/10.5194/hess-26-1-2022, 2022
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We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.
Michael Kilgour Stewart, Uwe Morgenstern, and Ian Cartwright
Hydrol. Earth Syst. Sci., 25, 6333–6338, https://doi.org/10.5194/hess-25-6333-2021, https://doi.org/10.5194/hess-25-6333-2021, 2021
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The combined use of deuterium and tritium to determine travel time distributions in streams is an important development in catchment hydrology (Rodriguez et al., 2021). This comment, however, argues that their results do not generally invalidate the truncation hypothesis of Stewart et al. (2010) (i.e. that stable isotopes underestimate travel times through catchments), as they imply, but asserts instead that the hypothesis still applies to many other catchments.
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272, https://doi.org/10.5194/gmd-14-6257-2021, https://doi.org/10.5194/gmd-14-6257-2021, 2021
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The hydraulic and mechanical properties of the subsurface are inherently heterogeneous. RHEA is a simulator that can perform couple hydro-geomechanical processes in heterogeneous porous media with steep gradients. RHEA is able to fully integrate spatial heterogeneity, allowing allocation of distributed hydraulic and geomechanical properties at mesh element level. RHEA is a valuable tool that can simulate problems considering realistic heterogeneity inherent to geologic formations.
Dylan J. Irvine, Cameron Wood, Ian Cartwright, and Tanya Oliver
Hydrol. Earth Syst. Sci., 25, 5415–5424, https://doi.org/10.5194/hess-25-5415-2021, https://doi.org/10.5194/hess-25-5415-2021, 2021
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It is widely assumed that 14C is in contact with the atmosphere until recharging water reaches the water table. Unsaturated zone (UZ) studies have shown that 14C decreases with depth below the land surface. We produce a relationship between UZ 14C and depth to the water table to estimate input 14C activities for groundwater age estimation. Application of the new relationship shows that it is important for UZ processes to be considered in groundwater mean residence time estimation.
Shovon Barua, Ian Cartwright, P. Evan Dresel, and Edoardo Daly
Hydrol. Earth Syst. Sci., 25, 89–104, https://doi.org/10.5194/hess-25-89-2021, https://doi.org/10.5194/hess-25-89-2021, 2021
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We evaluate groundwater recharge rates in a semi-arid area that has undergone land-use changes. The widespread presence of old saline groundwater indicates that pre-land-clearing recharge rates were low and present-day recharge rates are still modest. The fluctuations of the water table and tritium activities reflect present-day recharge rates; however, the water table fluctuation estimates are unrealistically high, and this technique may not be suited for estimating recharge in semi-arid areas.
Gabriel C. Rau, Mark O. Cuthbert, R. Ian Acworth, and Philipp Blum
Hydrol. Earth Syst. Sci., 24, 6033–6046, https://doi.org/10.5194/hess-24-6033-2020, https://doi.org/10.5194/hess-24-6033-2020, 2020
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This work provides an important generalisation of a previously developed method that quantifies subsurface barometric efficiency using the groundwater level response to Earth and atmospheric tides. The new approach additionally allows the quantification of hydraulic conductivity and specific storage. This enables improved and rapid assessment of subsurface processes and properties using standard pressure measurements.
Cited articles
Baudron, P., Alonso-Sarría, F., García-Aróstegui, J. L., Cánovas-García, F., Martínez-Vicente, D., and Moreno-Brotóns, J.: Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification, J. Hydrol., 499, 303–315, https://doi.org/10.1016/j.jhydrol.2013.07.009, 2013.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Berghuijs, W. R., Luijendijk, E., Moeck, C., van der Velde, Y., and Allen, S. T.: Global Recharge Data Set Indicates Strengthened Groundwater Connection to Surface Fluxes, Geophys. Res. Lett., 49, e2002GL099010, https://doi.org/10.1029/2022GL099010, 2022.
Bowen, B. B. and Benison, K. C.: Geochemical characteristics of naturally acid and alkaline saline lakes in southern Western Australia, Appl. Geochem., 24, 268–284, https://doi.org/10.1016/j.apgeochem.2008.11.013, 2009.
Broad, M.: Using Groundwater Age to Inform Aquifer Sustainability, Unpublished Honours Thesis, Flinders University, Adelaide, 2020.
Brunke, M. and Gonser, T. O. M.: The ecological significance of exchange processes between rivers and groundwater, Freshwater Biol., 37, 1–33, https://doi.org/10.1046/j.1365-2427.1997.00143.x, 1997.
Bureau of Meteorology: NDVI (Normalised Difference Vegetation Index) – High resolution gridded monthly NDVI dataset (1992 onwards), http://www.bom.gov.au/metadata/catalogue/19115/ANZCW0503900404 (last access: 12 January 2022), 2022a.
Bureau of Meteorology: Australian Groundwater Explorer, http://www.bom.gov.au/water/groundwater/explorer/map.shtml (last access: 9 June 2022), 2022b.
Bureau of Meteorology: Australian Water Outlook, https://awo.bom.gov.au/ (last access: 13 December 2022), 2022c.
Bureau of Meteorology: Climate classification maps – Seasonal rainfall – all zones, http://www.bom.gov.au/climate/maps/averages/climate-classification/?maptype=seasb (last access: 13 December 2022), 2022d.
Bureau of Meteorology: Decadal and multi-decadal rainfall averages maps, http://www.bom.gov.au/climate/maps/averages/decadal-rainfall/ (last access: 9 May 2023), 2023.
Cartwright, I., Weaver, T. R., Stone, D., and Reid, M.: Constraining modern and historical recharge from bore hydrographs, 3H, 14C, and chloride concentrations: Applications to dual-porosity aquifers in dryland salinity areas, Murray Basin, Australia, J. Hydrol., 332, 69–92, https://doi.org/10.1016/j.jhydrol.2006.06.034, 2007.
Cartwright, I., Cendón, D., Currell, M., and Meredith, K.: A review of radioactive isotopes and other residence time tracers in understanding groundwater recharge: Possibilities, challenges, and limitations, J. Hydrol., 555, 797–811, https://doi.org/10.1016/j.jhydrol.2017.10.053, 2017.
Cartwright, I., Morgenstern, U., Hofmann, H., and Gilfedder, B.: Comparisons and uncertainties of recharge estimates in a temperate alpine catchment, J. Hydrol., 590, 125558, https://doi.org/10.1016/j.jhydrol.2020.125558, 2020.
Crameri, F.: Scientific colour maps, Zenodo [data set], https://doi.org/10.5281/zenodo.1243862, 2018.
Crosbie, R. S., McCallum, J. L., and Harrington, G. A.: Diffuse groundwater recharge modelling across northern Australia. A report to the Australian Government from the CSIRO Northern Australia Sustainable Yields Project. CSIRO Water for a Healthy Country Flagship, Australia, 56 pp., https://publications.csiro.au/rpr/download?pid=changeme:394&dsid=DS1 (last access: 5 January 2024), 2009.
Crosbie, R., Jolly, I. D., Leaney, F. W., and Petheram, C.: Can the dataset of field based recharge estimates in Australia be used to predict recharge in data-poor areas?, Hydrol. Earth Syst. Sci., 14, 2023–2038, https://doi.org/10.5194/hess-14-2023-2010, 2010a.
Crosbie, R., Jolly, I., Leaney, F., Petheram, C., and Wohling, D.: Review of Australian groundwater recharge studies, CSIRO, 72 pp., https://doi.org/10.4225/08/58503a7f5aad4, 2010b.
Crosbie, R., Raiber, M., Wilkins, A., Dawes, W., Louth-Robins, T., and Gao, L.: Quantifying diffuse recharge to the Great Artesian Basin groundwater system, CSIRO, 52 pp., https://doi.org/10.25919/fwyj-cp80, 2022.
Crosbie, R. S. and Rachakonda, P. K.: Constraining probabilistic chloride mass-balance recharge estimates using baseflow and remotely sensed evapotranspiration: the Cambrian Limestone Aquifer in northern Australia, Hydrogeol. J., 29, 1399–1419, https://doi.org/10.1007/s10040-021-02323-1, 2021.
Crosbie, R. S., Peeters, L. J. M., Herron, N., McVicar, T. R., and Herr, A.: Estimating groundwater recharge and its associated uncertainty: Use of regression kriging and the chloride mass balance method, J. Hydrol., 561, 1063–1080, https://doi.org/10.1016/j.jhydrol.2017.08.003, 2018.
CSIRO: National Soil Grids – Australian Soil Classification, https://www.asris.csiro.au/themes/NationalGrids.html (last access: 21 June 2023), 2023.
Cuthbert, M. O., Acworth, R. I., Andersen, M. S., Larsen, J. R., McCallum, A. M., Rau, G. C., and Tellam, J. H.: Understanding and quantifying focused, indirect groundwater recharge from ephemeral streams using water table fluctuations, Water Resour. Res., 52, 827–840, https://doi.org/10.1002/2015WR017503, 2016.
Cutler, A., Cutler, D. R., and Stevens, J. R.: Random forests, in: Ensemble machine learning: Methods and applications, Springer, New York, NY, 157–175, https://doi.org/10.1007/978-1-4419-9326-7_5, 2012.
Davies, P. J. and Crosbie, R. S.: Mapping the spatial distribution of chloride deposition across Australia, J. Hydrol., 561, 76–88, https://doi.org/10.1016/j.jhydrol.2018.03.051, 2018.
de Graaf, I., Sutanudjaja, E. H., Van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823—837, https://doi.org/10.5194/hess-19-823-2015, 2015.
Department of Climate Change, Energy, the Environment and Water: Australia – Present Major Vegetation Groups – NVIS Version 6.0 (Albers 100 m analysis product), https://fed.dcceew.gov.au/maps/erin::australia-present-major-vegetation-groups-nvis-version-6-0 (last access: 12 January 2022), 2022.
de Vries, J. J. and Simmers, I.: Groundwater recharge: an overview of processes and challenges, Hydrogeol. J., 10, 5–17, https://doi.org/10.1007/s10040-001-0171-7, 2002.
Döll, P.: Vulnerability to the impact of climate change on renewable groundwater resources: a global-scale assessment, Environ. Res. Lett., 4, 035006, https://doi.org/10.1088/1748-9326/4/3/035006, 2009.
Döll, P. and Fiedler, K.: Global-scale modeling of groundwater recharge, Hydrol. Earth Syst. Sci., 12, 863–885, https://doi.org/10.5194/hess-12-863-2008, 2008.
Eamus, D.: Ecohydrology vegetation function, water and resource management, CSIRO Pub., Collingwood, Vic, 361 pp., https://doi.org/10.1071/9780643094093, 2006.
Eamus, D., Fu, B., Springer, A. E., and Stevens, L. E.: Groundwater Dependent Ecosystems: Classification, Identification Techniques and Threats, in: Integrated Groundwater Management: Concepts, Approaches and Challenges, edited by: Jakeman, A. J., Barreteau, O., Hunt, R. J., Rinaudo, J.-D., and Ross, A., Springer International Publishing, Cham, 313–346, https://doi.org/10.1007/978-3-319-23576-9_13, 2016.
Famiglietti, J. S.: The global groundwater crisis, Nat. Clim. Change, 4, 945–948, https://doi.org/10.1038/nclimate2425, 2014.
Fan, Y., Li, H., and Miguez-Macho, G.: Global patterns of groundwater table depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013.
FedUni: VVG – Visualising Victoria's Groundwater, https://www.vvg.org.au (last access: 20 August 2022), 2022.
Feitz, A. J., Tenthorey, E., and Coghlan, R. A.: Prospective hydrogen production regions of Australia, Geoscience Australia, 64 pp., https://doi.org/10.11636/Record.2019.015, 2019.
Ferguson, G., McIntosh, J. C., Jasechko, S., Kim, J.-H., Famiglietti, J. S., and McDonnell, J. J.: Groundwater deeper than 500 m contributes less than 0.1 % of global river discharge, Commun. Earth Environ., 4, 48, https://doi.org/10.1038/s43247-023-00697-6, 2023.
Frost, A. J. and Shokri, A.: The Australian Landscape Water Balance model (AWRA-L v7), Technical Report, Bureau of Meteorology, 58 pp., https://awo.bom.gov.au/assets/notes/publications/AWRA-Lv7_Model_Description_Report.pdf (last access: 21 September 2023), 2021.
Fu, G., Crosbie, R. S., Barron, O., Charles, S. P., Dawes, W., Shi, X., Van Niel, T., and Li, C.: Attributing variations of temporal and spatial groundwater recharge: A statistical analysis of climatic and non-climatic factors, J. Hydrol., 568, 816–834, https://doi.org/10.1016/j.jhydrol.2018.11.022, 2019.
Gallant, J. and Austin, J.: Slope derived from 1′′ SRTM DEM-S, v4, CSIRO, 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, https://pid.geoscience.gov.au/dataset/ga/69888 (last access: 12 January 2022), 2009.
Gaur, M. K. and Squires, V. R.: Geographic extent and characteristics of the world's arid zones and their peoples, in: Climate variability impacts on land use and livelihoods in drylands, Springer, Cham, 3–20, https://doi.org/10.1007/978-3-319-56681-8_1, 2018.
Geoscience Australia: Geodata Coast 100K 2004, https://pid.geoscience.gov.au/dataset/ga/61395 (last access: 31 January 2022), 2004.
Geoscience Australia: Geoscience Australia Portal, https://portal.ga.gov.au/ (last access: 9 January 2022), 2022.
Gray, D. and Bardwell, N.: Hydrogeochemistry of New South Wales: Data Release, v1, CSIRO, https://doi.org/10.4225/08/5756B395C68B0, 2016a.
Gray, D. and Bardwell, N.: Hydrogeochemistry of Northern Territory: Data Release, v2, CSIRO, https://doi.org/10.4225/08/5987D4BA86FF7, 2016b.
Gray, D. and Bardwell, N.: Hydrogeochemistry of Queensland: Data Release, v1, CSIRO, https://doi.org/10.4225/08/575A453145914, 2016c.
Gray, D. and Bardwell, N.: Hydrogeochemistry of South Australia: Data Release, v1, CSIRO, https://doi.org/10.4225/08/5756B3BF09204, 2016d.
Gray, D. and Bardwell, N.: Hydrogeochemistry of Victoria: Data Release, v2, CSIRO, https://doi.org/10.4225/08/5987D4751859E, 2016e.
Gray, D. and Bardwell, N.: Hydrogeochemistry of Western Australia: Data Release, v1, CSIRO, https://doi.org/10.4225/08/575CDF378054A, 2016f.
Gray, D., Reid, N., Noble, R., and Giblin, A.: Hydrogeochemical Mapping of the Australian Continent, CSIRO, 109 pp., https://doi.org/10.25919/5d8bb939ef2f2, 2019.
Henne, A. and Reid, N.: Hydrogeochemistry of Tasmania: Data Release, v1, CSIRO, https://doi.org/10.25919/1P8B-G702, 2021.
Huang, X., Gao, L., Crosbie, R. S., Zhang, N., Fu, G., and Doble, R.: Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning, Water, 11, 1879, https://doi.org/10.3390/w11091879, 2019.
Huang, X., Gao, L., Zhang, N., Crosbie, R. S., Ye, L., Liu, J., Guo, Z., Meng, Q., Fu, G., and Bryan, B. A.: A top-down deep learning model for predicting spatiotemporal dynamics of groundwater recharge, Environ. Model. Softw., 167, 105778, https://doi.org/10.1016/j.envsoft.2023.105778, 2023.
Irvine, D. J. and Cartwright, I.: CMBEAR: Python-Based Recharge Estimator Using the Chloride Mass Balance Method in Australia, Groundwater, 60, 418–425, https://doi.org/10.1111/gwat.13161, 2022.
King, A. C., Raiber, M., Cox, M. E., and Cendón, D. I.: Comparison of groundwater recharge estimation techniques in an alluvial aquifer system with an intermittent/ephemeral stream (Queensland, Australia), Hydrogeol. J., 25, 1759, https://doi.org/10.1007/s10040-017-1565-5, 2017.
Koch, J., Berger, H., Henriksen, H. J., and Sonnenborg, T. O.: Modelling of the shallow water table at high spatial resolution using random forests, Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, 2019.
Leaney, F., Crosbie, R., O'Grady, A., Jolly, I., Gow, L., Davies, P., Wilford, J., and Kilgour, P.: Recharge and discharge estimation in data poor areas, Scientific reference guide, CSIRO, 70 pp., https://doi.org/10.4225/08/59b19769af701, 2011.
Lee, S.: A high-resolution map of diffuse groundwater recharge rates for Australia, HydroShare [code and data set], http://www.hydroshare.org/resource/5e7b8bfcc1514680902f8ff43cc254b8 (last access: 12 April 2024), 2024.
Lerner, D. N., Issar, A. S., and Simmers, I.: Groundwater recharge: A Guide to Understanding and Estimating Natural Recharge, International Contributions to Hydrogeology, 8, Verlag Heinz Heise, Germany, 345 pp., ISBN 392270591X, 1990.
MacDonald, A. M., Lark, R. M., Taylor, R. G., Abiye, T., Fallas, H. C., Favreau, G., Goni, I. B., Kebede, S., Scanlon, B., and Sorensen, J. P.: Mapping groundwater recharge in Africa from ground observations and implications for water security, Environ. Res. Lett., 16, 034012, https://doi.org/10.1088/1748-9326/abd661, 2021.
Malone, B. and Searle, R.: Soil and Landscape Grid National Soil Attribute Maps – Clay (3′′ resolution) – Release 2 (4), CSIRO, https://doi.org/10.25919/hc4s-3130, 2022a.
Malone, B. and Searle, R.: Soil and Landscape Grid National Soil Attribute Maps – Sand (3′′ resolution) – Release 2 (3), CSIRO, https://doi.org/10.25919/rjmy-pa10, 2022b.
Malone, B. and Searle, R.: Soil and Landscape Grid National Soil Attribute Maps – Silt (3′′ resolution) – Release 2, CSIRO, https://doi.org/10.25919/2ew1-0w57, 2022c.
Moeck, C., Grech-Cumbo, N., Podgorski, J., Bretzler, A., Gurdak, J. J., Berg, M., and Schirmer, M.: A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships, Sci. Total Environ., 717, 137042, https://doi.org/10.1016/j.scitotenv.2020.137042, 2020.
Mohan, C., Western, A. W., Wei, Y., and Saft, M.: Predicting groundwater recharge for varying land cover and climate conditions – a global meta-study, Hydrol. Earth Syst. Sci., 22, 2689–2703, https://doi.org/10.5194/hess-22-2689-2018, 2018.
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., and Reinecke, R.: The global water resources and use model WaterGAP v2.2d: Model description https://doi.org/10.5194/gmd-14-1037-2021, 2021.
National Land and Water Resources Audit: Australian dryland salinity assessment 2000: extent, impacts, processes, monitoring and management options, National Land & Water Resources Audit, Turner, ACT, 129 pp., ISBN 0642371067, 2001.
Ouedraogo, I., Defourny, P., and Vanclooster, M.: Validating a continental-scale groundwater diffuse pollution model using regional datasets, Environ. Sci. Pollut. Res., 26, 2105–2119, https://doi.org/10.1007/s11356-017-0899-9, 2019.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Petheram, C., Walker, G., Grayson, R., Thierfelder, T., and Zhang, L.: Towards a framework for predicting impacts of land-use on recharge: 1. A review of recharge studies in Australia, Soil Res., 40, 397–417, https://doi.org/10.1071/SR00057, 2002.
Rahmati, O., Pourghasemi, H. R., and Melesse, A. M.: Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran, Catena, 137, 360–372, https://doi.org/10.1016/j.catena.2015.10.010, 2016.
Raymond, O. L., Liu, S., Gallagher, R., Zhang, W., and Highet, L. M.: Surface Geology of Australia 1:1 million scale dataset 2012 edition, Geoscience Australia, Canberra, https://doi.org/10.26186/74619, 2012.
Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., and Ribeiro, L.: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain), Sci. Total Environ., 476, 189–206, https://doi.org/10.1016/j.scitotenv.2014.01.001, 2014.
Scanlon, B. R., Healy, R. W., and Cook, P. G.: Choosing appropriate techniques for quantifying groundwater recharge, Hydrogeol. J., 10, 18–39, https://doi.org/10.1007/s10040-001-0176-2, 2002.
Scanlon, B. R., Keese, K. E., Flint, A. L., Flint, L. E., Gaye, C. B., Edmunds, W. M., and Simmers, I.: Global synthesis of groundwater recharge in semiarid and arid regions, Hydrol. Process., 20, 3335–3370, https://doi.org/10.1002/hyp.6335, 2006.
Shah, T.: Groundwater and human development: challenges and opportunities in livelihoods and environment, Water Sci. Technol., 51, 27–37, https://doi.org/10.2166/wst.2005.0217, 2005.
Sihag, P., Angelaki, A., and Chaplot, B.: Estimation of the recharging rate of groundwater using random forest technique, Appl. Water Sci., 10, 1–11, https://doi.org/10.1007/s13201-020-01267-3, 2020.
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinform., 8, 1–21, https://doi.org/10.1186/1471-2105-8-25, 2007.
Toloşi, L. and Lengauer, T.: Classification with correlated features: unreliability of feature ranking and solutions, Bioinformatics, 27, 1986–1994, https://doi.org/10.1093/bioinformatics/btr300, 2011.
United Nations Environment Programme: World Atlas of Desertification, 2nd Edn., https://wedocs.unep.org/20.500.11822/30300 (last access: 17 January 2024), 1997.
Wada, Y., van Beek, L. P. H., van Kempen, C. M., Reckman, J. W. T. M., Vasak, S., and Bierkens, M. F. P.: Global depletion of groundwater resources, Geophys. Res. Lett., 37, L20402, https://doi.org/10.1029/2010GL044571, 2010.
Walker, D., Parkin, G., Schmitter, P., Gowing, J., Tilahun, S. A., Haile, A. T., and Yimam, A. Y.: Insights from a multi-method recharge estimation comparison study, Groundwater, 57, 245–258, https://doi.org/10.1111/gwat.12801, 2019.
West, C., Reinecke, R., Rosolem, R., MacDonald, A. M., Cuthbert, M. O., and Wagener, T.: Ground truthing global-scale model estimates of groundwater recharge across Africa, Sci. Total Environ., 858, 159765, https://doi.org/10.1016/j.scitotenv.2022.159765, 2023.
Wilford, J., Searle, R., Thomas, M., and Grundy, M.: Soil and Landscape Grid National Soil Attribute Maps – Depth of Regolith (3′′ resolution) – Release 2 (6), CSIRO, https://doi.org/10.4225/08/55C9472F05295, 2018.
Wilkins, A., Crosbie, R., Louth-Robins, T., Davies, P., Raiber, M., Dawes, W., and Gao, L.: Australian gridded chloride deposition-rate dataset, Data Brief, 42, 108189, https://doi.org/10.1016/j.dib.2022.108189, 2022.
Wood, W. W.: Use and Misuse of the Chloride-Mass Balance Method in Estimating Ground Water Recharge, Ground Water, 37, 2–3, https://doi.org/10.1111/j.1745-6584.1999.tb00949.x, 1999.
Short summary
Global groundwater recharge studies collate recharge values estimated using different methods that apply to different timescales. We develop a recharge prediction model, based solely on chloride, to produce a recharge map for Australia. We reveal that climate and vegetation have the most significant influence on recharge variability in Australia. Our recharge rates were lower than other models due to the long timescale of chloride in groundwater. Our method can similarly be applied globally.
Global groundwater recharge studies collate recharge values estimated using different methods...
