EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modelling phosphorus pollution risk in agricultural catchments using a spatially distributed Bayesian Belief Network

Camilla Negri1,2,3, Miriam Glendell1, Nick Schurch4, Andrew J. Wade3, and Per-Erik Mellander2
Camilla Negri et al.
  • 1The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK (
  • 2Teagasc, Environment Research Centre, Johnstown Castle, Co. Wexford Y35 Y521, Ireland
  • 3University of Reading, School of Archaelogy, Geography and Environmental Science, Whiteknights, Reading RG6 6AB, UK
  • 4Biomathematics and Statistics Scotland, Craigiebuckler, Aberdeen AB15 8QH, UK

Diffuse pollution of phosphorus (P) from agriculture is a major pressure on water quality in Ireland. The Agricultural Catchments Programme (ACP) was initiated to evaluate the Good Agricultural Practice measures implemented under the EU Nitrates Directive. Within the ACP, extensive monitoring and research has been made to understand the drivers and controls on nutrient loss in the agricultural landscape. However, tapering P pollution in agricultural catchments also requires informed decisions about the likely effectiveness of measures as well as their spatial targeting.  There is a need to develop Decision Support Tools (DST) that can account for the uncertainty inherently present in both data and water quality models.

Bayesian Belief Networks (BBNs) are probabilistic graphical models that allow the integration of both quantitative and qualitative information from different sources (experimental data, model outputs and expert opinion) all in one model. Moreover, these models can be easily updated with new knowledge and can be applied with scarce datasets. BBNs have previously been used in multiple decision-making settings to understand causal relationships in different contexts. Recently, BBNs were used to support ecological risk-based decision making.

In this study, a prototype BBN was implemented with the Genie software to develop a DST for understanding the influence of land management and P pollution risk in four ACP catchments dominated by intensively farmed land with contrasting hydrology and land use. In the fist stage of the study, the spatial BBN was constructed visualising the ‘source-mobilisation-transport-continuum’, identifying the main drivers of P pollution based on previous findings from the ACP catchments. A second step involved the consultation of experts and stakeholders through a series of workshops aimed at eliciting their input. These stakeholders have expertise ranging from hydrology and hydrochemistry, land management and farm consulting, to policy and environmental modelling.

At present, the BBN is being parameterized for a 12km2 catchment with mostly grassland on poorly drained soils, using a high temporal and spatial resolution dataset that includes hydro-chemo-metrics, mapped soil properties (drainage class and Soil Morgan P), landscape characteristics (i.e. land use and management, presence of mitigation measures and presence of point pollution sources). Preliminary results show that the model captures the difference in P loss risk between catchments, probably caused by contrasting hydrological characteristics and soil P sources.

Future research will be focussed on parameterizing and testing the BBN in three other ACP catchments. Such parametrization will be pivotal to testing the model in data sparse catchments and possibly upscaling the tool to regional and national scale. Moreover, climate change and land use change modelled scenarios will be crucial to inform targeting of mitigation measures.  

How to cite: Negri, C., Glendell, M., Schurch, N., Wade, A. J., and Mellander, P.-E.: Modelling phosphorus pollution risk in agricultural catchments using a spatially distributed Bayesian Belief Network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-555,, 2020.


Display file