Bayesian network modelling of phosphorus pollution in agricultural catchments with high-resolution data

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint


Camilla Negri , Per-Erik Mellander, Nicholas Schurch, Andrew J. Wade, Zisis Gagkas, Douglas H. Wardell-Johnson2, Kerr J. Adams, Miriam Glendell


A Bayesian Belief Network was developed to simulate phosphorus (P) loss in an Irish agricultural catchment. Septic tanks and farmyards were included to represent all P sources and assess their effect on model performance. Bayesian priors were defined using daily discharge and turbidity, high-resolution soil P data, expert opinion, and literature. Calibration was done against seven years of daily Total Reactive P concentrations. Model performance was assessed using percentage bias, summary statistics, and visually comparing distributions. Bias was within acceptable ranges, the model predicted mean and median P concentrations within the data error, with simulated distributions wider than the observations. Considering the risk of exceeding regulatory standards, predictions showed lower P losses than observations, likely due to simulated distributions being left-skewed. We discuss model advantages and limitations, the benefits of explicitly representing uncertainty, and priorities for data collection to fill knowledge gaps present even in a highly monitored catchment.



Agriculture, Biochemistry, Environmental Monitoring, Statistical Models


diffuse pollution; point sources; high-resolution water-quality monitoring; participatory model; uncertainty analysis


Published: 2024-01-11 00:24

Last Updated: 2024-01-11 07:24


CC-BY Attribution-NonCommercial 4.0 International

Additional Metadata

Conflict of interest statement:
The Authors declare no conflict of interest.

Data Availability (Reason not available):