Applications open: 8/07/2022
Applications close: 18/08/2022
About this scholarship
This project, which will involve an internship at the Department of Biodiversity, Conservation and Attractions (DBCA) aims at downscaling Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) satellite data using artificial neural network trained on high-quality borehole and climatic data to characterize southwest of Western Australia (SWWA)’s groundwater for quantifying variability, resilience to climate change, and potential use in adaptation strategies to climate change. The availability of spatially and temporally cohesive groundwater information is of crucial importance for managing Australia’s precious groundwater resources at local, regional, and continent-wide scales. Currently, monitoring relies heavily on in-situ borehole data, which, due to considerable limitations in spatial and temporal coverage, are not suited for modelling over large areas such as SWWA. The availability and accessibility of groundwater data is a major constraint in developing groundwater resources across Australia. A versatile source of groundwater storage data is from the GRACE and GRACE-FO twin satellites. However, their spatial resolutions are too coarse (>300-400km) to accurately inform local policy, regulatory and investment decisions on groundwater of SWWA, which is only covered by a few data pixels. This shortcoming will be rectified by developing a novel framework to characterize localized groundwater changes over SWWA combining the strengths and mitigating the weaknesses of large-scale satellite remotely sensed products and local in-situ data (rainfall, runoff data, and evapotranspiration estimates in a selection of meso-scale catchments).
The framework intends to lay a foundation for a new hybrid groundwater product with unprecedented high spatial resolution and large-scale coverage that will significantly improve spatio-temporal groundwater characterization and modelling. The new framework will be based on the use of statistical artificial neural network (ANN) downscaling method applied to large-scale terrestrial water storage (TWS) changes obtained from the GRACE/GRACE-FO satellite missions. Combined with readily available climatic/physical data (e.g., precipitation, topography) and SWWA’s existing but limited in-situ groundwater monitoring stations, this has the capability to generate a hybrid groundwater product with high spatial resolution and continuous temporal coverage over almost the last two decades. The novelty of the proposed project lies in (i) developing SWWA’s first groundwater framework based on statistical downscaling techniques and the newly released GRACE-FO data, (ii) developing SWWA’s first hybrid model, (iii) pioneering the application of multi-mission and supporting data, e.g., maximum benefit from all data sources, and (iv), the first spatio-temporal analysis of SWWA’s groundwater based on almost two decades of GRACE/GRACE-FO data.
This will be highly beneficial to decision makers and other studies that assess groundwater sustainability, resilience to climate change, and use in adaptation strategies to climate and other environmental changes. In so doing, the project will offer novel ANN-method that could be further developed in future for Australia-wide coverage incorporating the new GRACE-FO products never used before for data fusion for the regions within Australia that suffer from insufficient in-situ groundwater monitoring stations. DBCA guarantee an unpaid internship and once a student has started on a DBCA project, they can apply (with high success rate) to get a paid summer internship.
An Internship opportunity may also be available with this project.Student type
- Future Students
- Faculty of Science & Engineering
- Science courses
- Engineering courses
- Western Australian School of Mines (WASM)
- Higher Degree by Research
- Australian Citizen
- Australian Permanent Resident
- New Zealand Citizen
- Permanent Humanitarian Visa
- Merit Based
The annual scholarship package (stipend and tuition fees) is approx. $60,000 – $70,000 p.a.
Successful HDR applicants for admission will receive a 100% fee offset for up to 4 years, stipend scholarships, valued at $28,854 p.a. for up to a maximum of 3.5 years, are determined via a competitive selection process. Applicants will be notified of the scholarship outcome in November 2022.
For detailed information, visit: Research Training Program (RTP) Scholarships | Curtin University, Perth, Australia.
Maximum number awarded
All applicable HDR coursesEligibility criteria
The PhD student will be under the direct supervision of Awange and Kuhn. A suitable candidate who must have adequate statistical background will explore statistical downscaling techniques such as based on multivariate regression (MR) and use the already established and tested ANN technique to downscale GRACE TWS data to localized groundwater storage changes on the one hand and provide an in-depth comparison between ANN and MR techniques on the other hand, thus adding significant scientific value to the project.
If this project excites you, and your research skills and experience are a good fit for this specific project, you should contact the Project Lead (listed below in the enquires section) via the Expression of Interest (EOI) form.
Eligible to enrol in a Higher Degree by Research Course at Curtin University by March 2023
To enquire about this project opportunity that includes a scholarship application, contact the Project lead, Joseph Awange via the EOI form above.