Advert ID: 20006616
PhD opportunity: Uncertainty in Predicting Soil Water and Plant Available Water at High-Resolution Scales
University of Southern Queensland
Stipend (living allowance), valued at AUD $30,000 per annum, tax free.
Soil moisture and plant available soil water (PAW) are foundational data to predict crop production and inform tactical and strategic decisions on farm management and system design. There are several methods to monitor or estimate soil water and PAW - e.g. sensors, proximal sensing, remote sensing, modelling (process based physical models artificial intelligence) across field and soil profile - each with their particular limitations and uncertainties.
This project is about the development of a scientific framework for modelling PAW at any point in space and time and in the soil profile, using hybrid approaches from process based physical models and Artificial intelligence. It will explore the different sources of data to build an optimum approach with high spatial and temporal resolution. It will explore the calibration of model parameters using the observation of different data sources using different techniques.
This PhD research will explore issues for uncertainty analysis of soil moisture and PAW at sub-paddock scale and implications of uncertainty from different observations and modelling approaches. How high spatial variabilities will constraint model calibration and accurate estimations.
Soil water, modelling, soil water balance, machine learning, crop, soil physics, hydrology
To be eligible applicants must:
How to apply:View details
If the above eligibility criteria is met, applicants will be required to send a cover letter and a targeted CV to Dr Afshin Ghahramani by email (email@example.com). Relevant qualifications, skills and areas of expertise will be provided in the targeted CV.
Closing Date: Applications will remain open until filled for Semesters 1 or 2 in 2021.
Further information: Dr Afshin Ghahramani, firstname.lastname@example.org