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

Abstract

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 groundwater recharge rates. However, recharge estimation methods all have distinct assumptions, quantify different recharge components and operate over different temporal scales. We use over 200 000 groundwater chloride measurements to estimate groundwater recharge rates using an improved chloride mass balance (CMB) method across Australia. Groundwater recharge rates were produced stochastically using gridded chloride deposition, runoff and precipitation datasets. After filtering out groundwater recharge rates where the assumptions of the method may have been compromised, 98 568 estimates of recharge were produced. The resulting groundwater recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of groundwater recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality and potential evapotranspiration, exert the most significant influence on groundwater recharge rates, with vegetation (the normalised difference vegetation index or NDVI) also contributing significantly. Importantly, the mean values of both the recharge point dataset (43.5 mm yr−1) and the spatial recharge model (22.7 mm yr−1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method, the potential disparities arising from distinct recharge estimation methodologies and limited averaging across climate zones. 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 resource management across the Australian continent, and similar approaches can be applied globally.