the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A high-resolution map of diffuse groundwater recharge rates for Australia
Stephen Lee
Dylan J. Irvine
Clément Duvert
Gabriel C. Rau
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.
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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
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Review of the paper “A high-resolution map of diffuse groundwater recharge rates for Australia” by Lee et al.
This paper presents an interested method to estimate groundwater recharge rates across Australia using chloride measurements. The text is well written and logically organized so that is easy to follow. The figures are excellent and ready for publication. I am not specialist of geochemistry or chloride but I found this study very relevant for hydrologist like me who is interested in groundwater processes. While I consider my following comments as minors, I think they must be addressed with attention before publication.
I am especially disappointed by the comparison between the presented product (from chloride) to the previous estimates from other studies (for example Moeck et al. 2020). Such previous studies report mean recharge estimates 5 times larger than the present study. You only attribute this drastic difference to “the spatial distribution of recharge point estimates, and the estimation of recharge values using different recharge estimation techniques” (line 470). I am sorry but I am not very convinced by this argument.
- Why you do not compare point estimates from Moeck et al. (2020) with your product at the same locations, exactly? For example, using a squatter plot, or the density function of each product. In other words, I find your comparison with existing previous products is not enough detailed. Please, improve this comparison.
- It seems that, naturally, Australian soils are largely affected by salt (Wicke et al. 2011; https://www.encyclopedie-environnement.org/en/zoom/land-salinization/). Perhaps my understanding is not very good, but it seems that only chloride range of 35-125 mg/L for groundwater is considered as normal. Your Figure 1 shows that at least the half of your data are superior to this range. Could your underestimation (of recharge estimates) thus be due to a natural large groundwater concentration in chloride (Clgw) making your equation 2 obsolete?
- Chloride concentration in groundwater could be significantly impacted by human activities like agriculture or industry. If anthropogenic chloride “flows” into groundwater, its concentration will be larger than in natural systems and then, because equation 2, your estimates will be biased and too low. Are you sure that your measurements are not drastically impacted by these processes?
- Line 550, you clam “Our CMB-based recharge rates are considerably lower than other studies including global water balance models (e.g., Döll and Fiedler, 2008; de Graaf et al., 2015; Müller Schmied et al., 2021). This is likely due to the fact that global water balance models estimate modern recharge.” … Ah ok, but why, I do not understand your explanation? Do you mean that your estimates do not account for modern recharge, so they account for what? In other words, what is the period of validity of your estimates?
So, even if I found this study very relevant and promising, I think that (1) the comparison with other product should be done more in depth, (2) a discussion about the salt-affected soils over Australia is perhaps relevant, and (3) a discussion about the impact of anthropogenic processes on groundwater chloride concentration (and then on your results) must be emphasized.
Biblio:
Wicke B, Smeets E, Dornburg V, Vashev B, Gaiser T, Turkenburg W & Faaij A (2011) The global technical and economic potential of bioenergy from salt-affected soils. Energy Environ Sci 4:2669-2681. https://doi.org/10.1039/C1EE01029H
Citation: https://doi.org/10.5194/egusphere-2023-2414-RC1
Stephen Lee et al.
Data sets
Supporting information>Datasets Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright https://www.hydroshare.org/resource/088b1f35ee1b4c348a44a6cbad21250d/
Supporting information>Gridded map output files Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright https://www.hydroshare.org/resource/088b1f35ee1b4c348a44a6cbad21250d/
Supporting informaion>Supporting_information_20231016.docx Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright https://www.hydroshare.org/resource/088b1f35ee1b4c348a44a6cbad21250d/
Model code and software
Supporting information>Python scripts Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau and Ian Cartwright https://www.hydroshare.org/resource/088b1f35ee1b4c348a44a6cbad21250d/
Stephen Lee et al.
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