Phosphorus (P) pollution is a major cause of surface water quality failures in Scotland. However, process-based modelling of P pollution is often hampered by lack of available data that would allow plausible representation of the complex processes involved in P mobilisation, transport and delivery to receiving waters, as well as gaps in the understanding of in-stream processing and the corresponding ecological impact.
Bayesian Belief Networks (BBNs) are probabilistic graphical models that allow the integration of both quantitative and qualitative information from a range of sources (including data, other model outputs and non-scientific knowledge, such as expert opinion) in one model, whilst making explicit the uncertainties associated with both the model and data. This project aims to develop a risk-based model to facilitate the understanding of the effects of land use on P pollution risk within an uncertainty framework for river catchments across Scotland.
Links:
[1] https://www.hutton.ac.uk/staff/miriam-glendell
[2] https://www.hutton.ac.uk/staff/marc-stutter
[3] https://www.hutton.ac.uk/staff/samia-richards
[4] https://www.hutton.ac.uk/staff/nikki-baggaley
[5] https://www.hutton.ac.uk/staff/alessandro-gimona
[6] https://www.hutton.ac.uk/staff/mads-troldborg
[7] https://www.hutton.ac.uk/research/srp2016-21/wp122-impacts-change-water
[8] https://www.hutton.ac.uk/research/srp2016-21/wp12-water