• Elisabeth Vogel (BOM – ),
  • Lisa Alexander (UNSW – )

Australian wheat production is highly water-limited and wheat yields correlate strongly with precipitation amounts during the growing season. Australia is one of the top wheat producers in the world; therefore, impacts on Australian wheat production from hydrological extremes are not only felt locally, but can potentially have effects on global wheat trade.

Under certain conditions, periods of below-average rainfall may not immediately lead to negative yield impacts, as crops source water from the soil, leading to reduced or lagged impacts. Other indicators, capturing soil moisture drought, may therefore be better predictors of yield losses. One potential advantage is that, due to soil memory effects, seasonal forecasts of soil moisture can have higher skill compared to precipitation forecasts, especially during dry periods, and may therefore offer promising potential for informing seasonal forecasts of wheat yields in Australia.

The Bureau of Meteorology (BoM) is currently developing a seasonal forecasting system of hydrological variables for Australia, using the AWRA-L land surface water balance model, forced with seasonal climate forecasts of precipitation, temperature, solar radiation and wind from the ACCESS-S model. The aim of this student project is to investigate the relationships between hydrological extremes (especially soil moisture drought) and wheat production in Australia. The outcome of the project may inform the development seasonal forecasts of hydrological indicators for agricultural production in Australia.

The project is divided into two parts:

  1. The first part investigates the upper limit of predictability of wheat yields using soil moisture, precipitation, temperature and solar radiation. It aims to investigate to which degree variations in wheat yield and production in Australia are explained by variations in soil moisture, temperature and solar radiation (using historical, observed data). Does using soil moisture data improve the statistical predictions compared to using precipitation data?
  2. In the second part, the student may use retrospective seasonal forecasts (called hindcasts) of soil moisture, as well as climate variables, to assess the usefulness of hydrological forecasts for predicting yield losses in Australia at varying lead times.

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.