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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• We synthesise interdisciplinary knowledge about Australian non-perennial rivers. • Current research strengths include salinity, dryland ecology, droughts, and floods. • Lessons are relevant globally and four high-level research challenges are identified. Non-perennial rivers are valuable water resources that support millions of humans globally, as well as unique riparian ecosystems. In Australia, the Earth's driest inhabited continent, over 70% of rivers are non-perennial due to a combination of ancient landscape, dry climates, highly variable rainfall regimes, and human interventions that have altered riverine environments. Here, we review Australian non-perennial river research incorporating geomorphology, hydrology, biogeochemistry, ecology, and Indigenous knowledges. The dominant research themes in Australia were drought, floods, salinity, dryland ecology, and water management. Future research will likely follow these themes but must address emerging threats to river systems due to climate change and other anthropogenic impacts. Four high level opportunities for future research are identified, namely: (1) integrating Indigenous and western scientific knowledge; (2) quantifying climate change impacts on hydrological and biological function; (3) clarifying the meaning and measurement of "restoration" of non-perennial systems; and (4) understanding the role of groundwater. These challenges will require inter- and multi-disciplinary efforts supported by technological advances. The evolving body of knowledge about Australian rivers provides a foundation for comparison with other dryland areas globally where recognition of the importance of non-perennial rivers is expanding. [ABSTRACT FROM AUTHOR]