- Elisabeth Vogel (BOM – Elisabeth.Vogel@bom.gov.au )
- Louise Wilson (BOM – Louise.Wilson@bom.gov.au ),
- Anna Ukkola (ANU – firstname.lastname@example.org),
- Margot Bador (UNSW – email@example.com)
Climate change affects the frequency and severity of certain hydrological extremes, such as the risk of flooding events or soil moisture drought. These changes in hydrological extremes are a concern for many sectors that are highly dependent on hydrological conditions, such as water resources management, infrastructure or agriculture. In order to prepare for these changes, it is crucial to gain a better understanding of the spatial and temporal pattern of climate change impacts on hydrological extremes.
The aim of this student project is to investigate the impacts of climate change on hydrological extremes, such as high runoff events, hydrological or agricultural drought. It uses outputs of the AWRA-L hydrological model, which underpins the BoM’s Australian Landscape Water Balance website. The model simulates the land surface water balance and outputs hydrological stores and fluxes, including run-off, evapotranspiration and soil moisture in three soil layers (0m–0.1m, 0.1m–1.0m, 1.0m–6.0m).
As part of the Bureau’s Hydrological Projections project, AWRA-L was forced with an ensemble of climate data based on:
- two scenarios for future greenhouse gas concentrations,
- four general circulation models (GCM) that have been assessed to be skilful for the Australian domain, and
- a range of statistical and dynamical bias-correction and downscaling methods.
Using the data, the student will
- investigate changes in selected hydrological extreme indicators between the past and future, and
- analyse uncertainties in the projections related to GCM selection, bias-correction and downscaling method, and emission scenarios.
The student will be based at the Bureau of Meteorology in Melbourne (other Bureau offices might be possible). The project would ideally suit a student with some experience in programming and data visualisation (e.g. using Python, R, Matlab). Experience in working with large datasets (e.g. on the NCI) would be preferable. The timing of the project can be arranged flexibly with the student.