Evaluating the effectiveness of mitigation measures to deliver water quality improvements in agricultural catchments in Ireland

Phosphorus (P) pollution remains a major cause of surface water quality failures. Future climate and land use change are likely to impact P losses to running waters, however the relative balance between these drivers and the scale of future change is uncertain as is the effectiveness of proposed mitigation measures. Bayesian Belief Networks (BBNs) are graphical models that enable representation of these uncertainties through probabilistic modelling. This project builds on recent advances in BBN modelling to develop a risk-based decision-support tool to facilitate the understanding of the effects of land use on P pollution risk in agricultural catchments.

Objectives:

1. Extend existing model capabilities to simulate the effect of water quality mitigation measures under future climate, land use and socio-economic scenarios.

2. Develop a spatial implementation of the model to inform targeting of measures to reduce P losses.

3. Develop a dynamic BBN to examine the role of time delay (e.g. soil P legacy effects or P transfer to groundwater) on observing the effect of measures.

The modelling approach will be co-constructed with stakeholders. The project will draw on knowledge and data from the unique Irish Agricultural Catchments Program experimental platform. The successful candidate would have the intellectual freedom to steer the project direction and will gain numerical skills in data analytics and coding, communication skills, and a detailed understanding of hydrology and catchment science.

The project would suit someone with a First Class or good Upper Second-Class honours degree (or the equivalent), or MSc, in hydrology, geography, environmental science, statistics/mathematics or similar, with an interest in agricultural management. Experience of statistical analysis and coding in the R statistical environment would be an advantage. A willingness to learn coding skills and statistical concepts is essential.

The student will spend the majority of the studentship based at Teagasc in Wexford, Ireland with shorter periods in either Reading or the James Hutton Institute in Aberdeen or both.

This opportunity is available to ‘HOME’ students only. A ‘HOME’ student in this case is defined as a Republic of Ireland or UK national who has been living in the Republic of Ireland or elsewhere in the EU or in the UK for the last three years. Qualification through settled or pre-settled status in the UK should also be possible.

How to Apply: https://www.reading.ac.uk/ges/phd/how-to-apply-for-a-phd

Informal enquiries are welcome and can be made to: Andrew Wade ([email protected]) or Per-Erik Mellander ([email protected]). The fellowship will also be supervised by Miriam Glendell (James Hutton Institute, Aberdeen) and Nick Schurch (Biomathematics and Statistics, Aberdeen)