ProfessorThomas Bishop

Project Opportunities

Title: Modelling the ionic composition of streams for improved management of salinity

Summary of opportunity:

Stream salinity is a major environmental problem in Australia however much of the existing research is based on measurements of stream electrical conductivity (EC) which provides little information on the type of salts present.  This is a problem as different salts have different solubility.  For example chloride salts are highly soluble and can concentrate in streams whereas bicarbonate salts will precipitate out at lower concentrations and pose less of a threat to stream water quality and aquatic health.  Most stream salinity models are based on EC measurements with the assumption that the values are based on the presence of chloride salts.  In this project we will extend these models to account for the different types of salts found in streams.  This is crucial for better targeting our salinity management to streams that are dominated by chloride salts.

Opportunity synopsis:

The project will have a field and modelling component.  The field work will be based in the Muttama creek catchment in southern NSW where we have been monitoring stream salinity since 2010 which augments a network of 6 flow meters.  The field work will involve maintaining the network and collecting new data.  This will be used to test a stream salinity model (e.g. CATSALT) in 3 types of sub-catchments in terms of salts (i) chloride-dominant (ii) bicarbonate-dominant (iii) mixed chloride-bicarbonate signal.  A mineral weathering (source of bicarbonate salts) component will be incorporated into the stream salinity model to see if it can improve the predictions for streams that are dominated by bicarbonate salts or a have a mixed signal.  The student will gain valuable experience in hydrological field work and modelling.

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Project Opportunities

Title: How can we perform experiments in real landscapes?

Summary of opportunity:

This project addresses the fundamental environmental issue of land degradation in Australia.  Current management practices are unsustainable as evidenced by the decline in our soil and water resources.  The project will develop an approach to designing and analysing experiments that allows us to estimate production functions as they vary across a landscape.  The functions will incorporate both economic and environmental concerns.  The approach will enable us to make more informed decisions about how best to manage the environment in both an economically and environmentally sustainable manner.

Opportunity synopsis:

This aim of this project is to develop a new approach for performing experiments in real landscapes, where we have no control over the natural variation.  Rather than seeing natural variation as something to be neutralised when designing and analysing experiments, this project starts from the premise that this natural variation should be managed.  In particular the aims are to:

• develop new statistical approaches to enable prediction of local treatment effects as they vary across an experimental area. -develop an algorithm for designing experiments based on statistical AND environmental-economic criteria. 
• illustrate the approach with two case studies; an agricultural and a forestry experiment. The PhD student will gain expertise in advanced statistical methods and undertake field work in regional Australia.

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Project Opportunities

Title: Development of an observation system for profile soil moisture

Summary of opportunity:

An understanding of the variation in time and space of soil moisture is crucial for the modelling and management of Australian landscapes.  In terms of management, information on soil moisture could be used to decide whether there is enough soil moisture to grow a crop, or when and where to irrigate.  In terms of modelling, soil moisture is needed as inputs to crop growth, hydrological and hydrodynamic models.  Despite this need, there are no readily available estimates of soil moisture in time and space.  This project will directly tackle this issue by combining field observations, a water balance model and readily available geospatial data to develop an approach to predict soil moisture in time and space.

Opportunity synopsis:

The project will have a field and modelling component.  The field work will be based in the Muttama creek catchment in southern NSW where we are establishing a soil moisture monitoring network.  The aim of this project will be to predict soil moisture in the entire profile in space and time at resolutions of 250m and 7 days.  This will be achieved by modelling components of the soil water balance with geospatial data such as remotely-sensed imagery, and updating the model predictions of soil moisture as field observations become available. The student will gain valuable experience in soil field work and modelling.  In particular they will gain experience in state-of-the-art spatial modelling methods.

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Project Opportunities

Title: Change detection in hydrology

Summary of opportunity:

The quantity and quality of water in our streams is dependent on many factors including climate, land use and management.  This leads to questions about how much impact have these factors have had on the stream water, particularly when these factors change through time.  For example, does afforestation impact on how much water is in our streams? Did the 2013 bushfires around Sydney have an impact on quality of water flowing into Lake Burrajorang?  These questions are difficult to answer given the large natural variability present in water data making it difficult to disentangle variation due to a factor causing the change and the natural background variation.  This project will explore approaches to detect change in hydrological time series data

Opportunity synopsis:

The project will be computer based and use datasets from around Australia to compare existing methods for detecting change in time series data.  From this a generic and robust approach will be developed for detecting change.  Both water quality and quantity datasets will be considered, as will statistical and mechanistic modelling approaches.  The student will gain valuable experience in analysing hydrological data using statistical and mechanistic models.  Experience in statistical modelling and the R software package is preferred