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

Negri, C., Mellander, P.-E., Schurch, N., Wade, A. J. ORCID: https://orcid.org/0000-0002-5296-8350, Gagkas, Z., Wardell-Johnson, D. H., Adams, K. and Glendell, M. (2024) Bayesian network modelling of phosphorus pollution in agricultural catchments with high-resolution data. Environmental Modelling and Software, 178. 106073. ISSN 1873-6726 doi: 10.1016/j.envsoft.2024.106073

Abstract/Summary

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 more variable 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/116468
Identification Number/DOI	10.1016/j.envsoft.2024.106073
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Publisher	Elsevier
Download/View statistics	View download statistics for this item