Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments

Oliver, D.M.; Porter, K.D.H.; Pachepsky, Y.A.; Muirhead, R.W.; Reaney, S.M.; Coffey, R.; Kay, D.; Milledge, D.M.; Hong, E.; Anthony, S.G.; Page, T.; Bloodworth, J.W.; Mellander, P-E.; Carbonneau, P.; McGrane, S.J.; Quilliam, R.S.

Abstract

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics.

Citation

Oliver, D., Porter, K., Pachepsky, Y., Muirhead, R., Reaney, S., Coffey, R., …Quilliam, R. (2016). Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments. Science of the Total Environment, 544, 39-47. https://doi.org/10.1016/j.scitotenv.2015.11.086

• Journal Article Type: Article
• Acceptance Date: Nov 17, 2015
• Online Publication Date: Dec 3, 2015
• Publication Date: Feb 15, 2016
• Deposit Date: Aug 5, 2016
• Publicly Available Date: Aug 5, 2016
• Journal: Science of the Total Environment
• Print ISSN: 0048-9697
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 544
• Pages: 39-47
• DOI: https://doi.org/10.1016/j.scitotenv.2015.11.086
• Public URL: https://durham-repository.worktribe.com/output/1378401