ABSTRACT:

The rate at which groundwater is replenished (groundwater recharge) varies across space and time. The estimation of groundwater recharge rates (GRRs) is important to ensure sustainable water use. We estimate annual GRRs using the water table fluctuation (WTF) method for over 400 bores across Australia. Specific yield values are estimated using lithological information linked to literature values. Comparisons were made between mean inter-annual GRRs from 224 bores and long-term GRRs derived from the chloride mass balance (CMB) method. Mean inter-annual WTF-based GRRs were 365.5 mm/y for humid, 248 mm/y for dry subhumid, 128.6 mm/y for semi-arid and 50.3 mm/y for arid zones. Inter-annual recharge variability is higher in arid and semi-arid climate zones relative to wetter climates. WTF and CMB-based GRR estimates exhibited low agreement in arid and semi-arid zones, where most WTF-derived GRRs exceeded CMB values by over an order of magnitude. While this can be explained by differing dominance of focused vs diffusive recharge, we show influence from other factors including the inability of the WTF method to quantify low GRRs, impacts of land use change, and non-ideal conditions like river-aquifer connections. Major differences between the WTF and CMB methods are attributed to CMB reflecting pre-land clearing GRRs in many instances. This study serves as a comparative framework for evaluating the appropriateness and differences between the WTF and CMB methods which can be applied to groundwater recharge studies globally. If you use the datasets or Python/R scripts, we would appreciate it if you could cite this resource as well as the research article submitted to Water Resources Research that is yet to be accepted/published. Details of the journal article will be made available upon final publication. For any further information, please do not hesitate to contact Stephen Lee on stephen.lee@cdu.edu.au.

ABSTRACT:

Estimating groundwater recharge rates is vitally important to understanding and managing groundwater. Numerous studies have used collated recharge datasets to understand and project regional or global-scale recharge rates. However, a key challenge stems from the inherent variability in recharge estimation methods utilised across these collations. Recharge estimation methods each carry distinct assumptions, address different recharge components, and operate over varied temporal scales. To address these challenges, this study uses a comprehensive dataset of over 200,000 groundwater chloride measurements to estimate groundwater recharge rates using the chloride mass balance (CMB) method throughout Australia. Recharge rates were produced stochastically using the groundwater chloride dataset and supplemented by gridded chloride deposition, runoff, and precipitation datasets within a Python framework. After QA/QC and data filtering, the resulting recharge rates and 17 spatial datasets are integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of recharge rates across Australia. 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 vast and dry Australian continent and similar approaches can be applied globally. If you use the datasets, gridded map output files, or Python scripts, we would appreciate it if you could cite the associated publication in Hydrology and Earth System Sciences here: https://hess.copernicus.org/articles/28/1771/2024/. For any further information, please do not hesitate to contact Stephen Lee on stephen.lee@cdu.edu.au.

ABSTRACT:

Estimating groundwater recharge rates is vitally important to understanding and managing groundwater. Numerous studies have used collated recharge datasets to understand and project regional or global-scale recharge rates. However, a key challenge stems from the inherent variability in recharge estimation methods utilised across these collations. Recharge estimation methods each carry distinct assumptions, address different recharge components, and operate over varied temporal scales. To address these challenges, this study uses a comprehensive dataset of over 200,000 groundwater chloride measurements to estimate groundwater recharge rates using the chloride mass balance (CMB) method throughout Australia. Recharge rates were produced stochastically using the groundwater chloride dataset and supplemented by gridded chloride deposition, runoff, and precipitation datasets within a Python framework. After QA/QC and data filtering, the resulting recharge rates and 17 spatial datasets are integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of recharge rates across Australia. 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 vast and dry Australian continent and similar approaches can be applied globally. If you use the datasets, gridded map output files, or Python scripts, we would appreciate it if you could cite the associated publication in Hydrology and Earth System Sciences. Details of the journal article will be made available upon final publication. For any further information, please do not hesitate to contact Stephen Lee on stephen.lee@cdu.edu.au.

ABSTRACT:

Estimating groundwater recharge rates is vitally important to understanding and managing groundwater. Numerous studies have used collated recharge datasets to understand and project regional or global-scale recharge rates. However, a key challenge stems from the inherent variability in recharge estimation methods utilised across these collations. Recharge estimation methods each carry distinct assumptions, address different recharge components, and operate over varied temporal scales. To address these challenges, this study uses a comprehensive dataset of over 200,000 groundwater chloride measurements to estimate groundwater recharge rates using the chloride mass balance (CMB) method throughout Australia. Recharge rates were produced stochastically using the groundwater chloride dataset and supplemented by gridded chloride deposition, runoff, and precipitation datasets within a Python framework. After QA/QC and data filtering, the resulting recharge rates and 17 spatial datasets are integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of recharge rates across Australia. 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 vast and dry Australian continent and similar approaches can be applied globally. If you use the datasets, gridded map output files, or Python scripts, we would appreciate it if you could cite the associated publication in Hydrology and Earth System Sciences. Details of the journal article will be made available upon final publication. For any further information, please do not hesitate to contact Stephen Lee on stephen.lee@cdu.edu.au.