Presenter Bio:
Stephen Lee started his PhD journey with Charles Darwin University in 2022 after working in consulting as a hydrogeologist. Through compiling and analysing publicly available datasets (e.g., chloride and groundwater levels), he is looking to better understand how groundwater recharge varies spatially and temporally across the Australian continent. He is also looking to help facilitate better data access and sharing within the hydrogeology community.
Abstract:
Estimating groundwater recharge rates is essential to sustainably managing groundwater. Numerous studies have used collated recharge datasets produced from a combination of different recharge estimation methods 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. Utilising collated recharge datasets produced from a combination of different recharge estimation methods can introduce significant errors and bias into groundwater models.
We use over 200,000 groundwater chloride measurements to estimate diffuse groundwater recharge rates using a stochastic chloride mass balance method across Australia. Recharge rates were filtered out where the assumptions of the method may have been compromised. The resulting 98,568 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.
In this talk, I will share important components of the automated approach that is based on freely available groundwater chemistry and spatial datasets. I will illustrate the spatial variability in recharge rates across the Australian continent and present some of the important controls on recharge. Finally, I will explore the value behind utilising the gridded recharge map, and the implications of using collated recharge datasets on a large scale.
