Hydrological impact studies analyse the effects of climate change on hydrological variables, such as changes in soil moisture, streamflow or hydrological extremes. Such studies are important, for example, for ensuring sustainable water resources management, agriculture or infrastructure development. Hydrological impact assessments are commonly based on hydrological models forced with corrected outputs of general circulation models (GCMs) that simulate future climate conditions, including temperature, precipitation, wind or solar radiation, under a range of possible scenarios for future greenhouse gas concentrations (e.g. CMIP outputs). Due to very high computing requirements of climate simulations, the model outputs are typically available at relatively coarse resolution – coarser than is needed to force hydrological models. In addition, small-scale processes that are below the climate model resolution are approximated using parameterisations, leading to potential biases in some variables or processes. To overcome these issues, bias-correction and downscaling methods have been developed to remove any systemic biases and to increase the resolution of the model output to match the spatial resolution required by the impact models.
The aim of this student project is to investigate the realised added value effect of such bias correction and downscaling methods on hydrological projections for Australia. The Bureau of Meteorology (BoM) is currently developing a National Hydrological Projections Service that will provide estimates of future climate change impacts on Australian water resources, based on four general circulation models (GCM) and a range of statistical and dynamical bias-correction and downscaling methods. The following statistical bias correction and downscaling methods have been applied to raw GCM outputs: 1) a trend-preserving quantile matching approach developed for the Intersectoral Impacts Model Intercomparison Project (ISIMIP) (Hempel, Frieler, Warszawski, Schewe, & Piontek, 2013), 2) a multi-variate bias-correction and spatial disaggregation (MRNBC) method (Mehrotra & Sharma, 2016; Nahar & Sharma, 2017), and 3) a quantile matching empirical statistical downscaling method optimised for preserving extreme events (Dowdy, 2019).
Focusing on selected hydrological indicators (e.g. the frequency and severity of heavy precipitation events, drought frequency, severity or duration) and on key catchments across Australia, the student will investigate two research questions:
1) Which are the spatio-temporal GCM/bias-corrected as well as RCM differences in hydrological change signals across Australia? 2) Applying the ‘realised added value’ methodology (Di Virgilia et al 2020) which additional evidence can be provided of where and when downscaling methods provides new information to hydrological change signals across Australia?
The student will be ideally based at the Bureau of Meteorology in Melbourne (other Bureau offices might be possible). The project would ideally suit a student who is expected to have 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.
To apply: the Undergraduate Scholarship application form can be found here