Hydrological impact studies analyse the effects of climate change on hydrological variables, such as changes in soil moisture, streamflow or hydrological extremes. This project aims to investigate the realised added value effect of model bias correction and downscaling methods on hydrological projections for Australia.
This project will explore the use of supervised and unsupervised statistical learning methods (such as neural networks, random forest, clustering) to understand the impact of climate change on hydrological extremes and/or to simulate downstream impacts on affected sectors, such as agriculture, energy, transport, water resources management.
The aim of this project is to investigate the effect of compound hot and dry events on agricultural production in Australia, and to assess the predictability of yield losses due to compound events using seasonal climate and hydrological forecasts. The outcome of the project may inform the development of seasonal forecasts of hydro-climatic risk indicators for agricultural production in Australia.