Soil-landscape dynamic modelling

Summary

Soil is the largest terrestrial store of carbon. Understanding causes and controls of spatial and temporal variation of this carbon pool is crucial for managing climate change, food water and energy security and for maintaining biodiversity.  Knowledge of how the spatial variability of soil C behaves dynamically is the key in understanding the impact of climate change and land management.  This project will combine the empirical data with process models in an optimal way to give a better prediction and understanding of the C distribution along with its confidence in prediction.

Supervisor(s)

Professor Budiman Minasny

Research Location

Sydney Institute of Agriculture

Program Type

PHD

Synopsis

There is a global demand for soil data and information for food security and global environmental management. As a consequence, there is now a large and growing interest in knowing the size of soil carbon pool and its sequestration potential . Digital soil mapping technology has progressed rapidly in the past decade, making it operational for routine mapping over large areas. While this empirical model has been successfully used to predict the spatial distribution of soil, the relationships are non-transferrable and no mechanism is involved. Furthermore, the dynamics of soil carbon with time means that we need to consider the time dimension in our maps. Meanwhile mechanistic or process-based models of soil C have been used effectively to predict the state of change in soil C. However, most C models are just an austere representation of a topsoil layer with no spatial component; it needs calibration, and the state (initial condition) of the soil need to be known first. We need to reverse the role, to use our process-based understanding in a mechanistic model to help us make a better prediction of the spatial distribution of soil properties, including carbon. This project will build a novel soil-landscape model which will predict the 4-dimensional space-time distribution of soil carbon. This innovative dynamic model will combine a process-based model with an empirical spatial data model under a Bayesian approach.  This project involves a considerable amount of modelling work, thus advanced skills in computer modelling is required. The student will acquire valuable knowledge and skills in the areas of soil carbon modelling. The student will also be expected to further develop skills in modern statistical and spatial data analysis and scientific publication.

Additional Information

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

Bayesian technique, Soil carbon, carbon models, RothC, DNDC, Bayesian hierarchical modelling

Opportunity ID

The opportunity ID for this research opportunity is: 1709

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