Droughts have far-reaching impacts on water security, health of natural systems, and profitability of agriculture, livestock businesses. Droughts affect the surface water cycle on various spatiotemporal scales. The deficits in soil moisture (soil moisture droughts) are of particular interest to the agriculture and livestock sectors. Once a drought has begun the important question is: when will the drought end?
In Australia, the evolution of soil moisture during drought events are influenced by large scale changes in global oceans. Information about the ocean states can thus be utilised to estimate the probability that an ongoing drought will end. We have worked on a statistical method (coded in python) that uses historical data of past drought events to estimate the soil moisture drought breaking probabilities at sub-seasonal timescales (4 to 12 weeks). The student will work on operationalising the method to provide near real-time estimates of probabilities that an ongoing drought will end through an online web interface.
Supervisors: Dr Anjana Devanand, Prof Jason Evans
Required skills: Experience programming in Python
Desirable skills: Experience with APIs, Experience with data visualisation tools such as Tableau or Power Bi, Basic knowledge of probability and statistics
Prior climate science knowledge is not required for this project. Data Science, computer science, and software engineering students are particularly encouraged to apply.
This project will be based at UNSW.