26 Oct 2023
 | 26 Oct 2023
Status: this preprint is open for discussion.

A high-resolution map of diffuse groundwater recharge rates for Australia

Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau, and Ian Cartwright

Abstract. Estimating groundwater recharge rates is important to understand and manage groundwater. Numerous studies have used collated recharge datasets to understand and project regional or global-scale recharge rates. Recharge estimation methods each have distinct assumptions, quantify different recharge components, and operate over different temporal scales. To address these challenges, we use over 200,000 groundwater chloride measurements to estimate groundwater recharge rates using the chloride mass balance (CMB) method across Australia. Recharge rates were produced stochastically using gridded chloride deposition, runoff, and precipitation datasets. After filtering out recharge rates where the assumptions of the method may have been compromised, 98,568 estimates of recharge were produced. The resulting recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality, and potential evapotranspiration, exert the most significant influence on recharge rates, with vegetation (NDVI) also contributing significantly. Importantly, both the mean values of the recharge point dataset (43.5 mm y-1) and of the spatial recharge model (22.7 mm y-1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method and the potential disparities arising from distinct recharge estimation methodologies. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation, and the spatial recharge models collectively provide valuable insights for water resources management across the Australian continent and similar approaches can be applied globally.

Stephen Lee et al.

Status: open (until 27 Dec 2023)

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  • RC1: 'Comment on egusphere-2023-2414', Anonymous Referee #1, 28 Nov 2023 reply

Stephen Lee et al.

Data sets

Supporting information>Datasets Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright

Supporting information>Gridded map output files Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright

Supporting informaion>Supporting_information_20231016.docx Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright

Model code and software

Supporting information>Python scripts Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright

Stephen Lee et al.


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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.