Ecohydrology of flood water and groundwater dependent terrestrial vegetation systems


Hydrology impacts the structure and pattern of vegetation in a landscape, and conversely, vegetation influences water resources through storage of water, feedback of moisture and filtering. Surprisingly, little is known about these interlinked dynamics and their function across multiple scales. Here we aim to develop and verify a new simplified analytical landscape model based on earlier ecohydrological work that incorporates the important link between groundwater, flooding and vegetation growth in semi-arid inland Australia. Using available field and satellite data for verification we will predict responses in vegetation structure and density under changing climate, changing flood frequency and groundwater levels. The outcomes will highlight opportunities for vegetation management in relation to salinity and water sharing.


Associate Professor Willem Vervoort

Research Location

School of Life and Environmental Sciences

Program Type



This project will combine satellite interpretation and ecohydrological modelling to answer questions related to the groundwater dependency and flooding dependency of vegetation and the interaction between climate, landscape, vegetation and hydrology at different spatial scales. This work builds on previous research in the area of ecohydrology (Vervoort and van der Zee 2008; submitted).
There are strong opportunities to interact with international researchers in Europe (particularly the Netherlands). The project involves a significant focus on analytical modelling using R ( in both space and time. MODIS and Landsat satellite data would be used to develop understanding of variations in leaf area index and evapotranspiration over large spatial areas.

Vervoort RW, Van der Zee SEATM (2008) Simulating the effect of capillary flux on the soil water balance in a stochastic ecohydrological framework. Water Resources Research 44, W08425.
Vervoort RW, Van der Zee SEATM (submitted) Stochastic soil water dynamics of phreatophyte vegetation with dimorphic root systems. Water Resources Research.

Additional Information

We are seeking a PhD candidate to work on this project in the area of ecohydrology – competitive scholarships are available from the Faculty of Agriculture, Food & Natural Resources and the University. International students would require an EIPRS or USYDIS or similar scholarship. The ideal student should have a background in engineering, science or agriculture with strengths in mathematics or physics and experience in computer programming.
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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Stochastic hydrology, water-limited native vegetation, semi-arid landscapes, Environmental Management, environmental science, flood, groundwater

Opportunity ID

The opportunity ID for this research opportunity is: 1077

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