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Developing Risk-Based Approaches to Modelling Phosphorus Contamination in Agricultural Catchments

Project Description

Phosphorus (P) pollution remains a major cause of surface water quality failures under the Water Framework Directive. Abating P pollution in agricultural catchments requires informed decisions about the likely effectiveness of land management mitigation measures and their spatial targeting. User-friendly transparent decision-support tools are required that allow the integration of uncertain information on potential effects and outcomes.

This project will apply Bayesian modelling approaches to develop a risk-based decision-support tool to facilitate the understanding of the effects of land use on P pollution risk in six experimental catchments in the Irish Agricultural Catchments Program. The project will benefit from state-of-the-art high-resolution monitoring data collected since 2007 at this internationally-recognized research platform that is at the forefront of understanding of agricultural impacts on water and soil quality at a catchment scale.

The project will extend an existing modelling approach, based on a static Bayesian Belief Network (BBN) to develop a dynamic model based on continuous data, linking probabilistic spatial prediction of high-risk critical source areas of pollution on land with point-based predictions of in-stream water quality. The modelling approach will facilitate co-construction of research outcomes by academic and stakeholder communities and will inform targeting of water quality mitigation measures in high risk areas. Scenarios including potential climate change impacts and evaluating the cost-effectiveness of different measures may also be explored. Thus, the project offers an opportunity to develop cross-disciplinary science with a direct real-world impact.

• First or Second Class Honours Degree in an appropriate discipline – e.g. Agriculture, Hydrology, Environmental Science, Environmental Engineering, Chemistry, Mathematics, Physics etc.
• Strong interest in cross-disciplinary environmental science.
• Numerate background with a desire to develop statistical and computational modelling skills.
• Comfortable with moving between Teagasc office in Wexford, Ireland and the James Hutton Institute in Aberdeen, Scotland (year-long blocks in each place are envisaged although final details will be agreed with the student and flexibility can be accommodated).

• MSc in an appropriate discipline.
• An understanding of agricultural impacts on water quality and the physical and chemical processes contributing to pollutant export.
• An understanding of European agri-environmental regulations related to water quality.
• An interest in probabilistic statistical models utilizing Bayesian approaches

The student will have a creative opportunity to shape the project direction and outcomes. While the majority of the work will be computer-based, there will be opportunities for occasional field work or visits to field study sites and frequent interaction with multi-disciplinary scientists and with stakeholders. The student will have access to research seminars and general scientific training through the Hutton Postgraduate Training Scheme and that available at Reading, and opportunity to attend project meetings, and national and international conferences and workshops. The project provides an exciting opportunity for development of numerical modelling skills through creation and coding of probabilistic statistical models using Bayesian approaches, coupled with a holistic understanding of catchment processes within a multi-disciplinary context.

Funding Notes

The PhD Fellowship is a joint research project between Teagasc, Johnstown Castle, Co. Wexford, Ireland (working with the Agricultural Catchments Programme – View Website), University of Reading and the James Hutton Institute, including Biomathematics and Statistics Scotland, in Aberdeen, UK. Short visits for research and post-graduate training to Reading University, where the student will be registered, may also be undertaken. The Fellowship will lead to the awards of MRes and PhD and will start in September 2019.

The fellowship provides a stipend over four years from which University fees are paid. The maintenance grant starts at £15,000 per year.

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