Predicting microbial water quality with models

over-arching questions for managing risk in agricultural catchments

David M. Oliver, Kenneth D.H. Porter, Yakov A. Pachepsky, Richard A. Muirhead, Sim M. Reaney, Rory Coffey, David Kay, David G. Milledge, Eunmi Hong, Steven G. Anthony, Trevor Page, Jack W. Bloodworth, Per-Erik Mellander, Patrice E. Carbonneau, Scott J. McGrane, Richard S. Quilliam

Research output: Contribution to journalArticle

31 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)39-47
Number of pages9
JournalScience of the Total Environment
Volume544
Early online date3 Dec 2015
DOIs
Publication statusPublished - 15 Feb 2016

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agricultural catchment
arching
Catchments
Water quality
water quality
catchment
Nutrients
modeling
Decision making
decision making
indicator
organism
Health
Availability
pollutant
nutrient

Keywords

  • catchment management
  • diffuse pollution
  • faecal indicator organism
  • human health
  • pathogens

Cite this

Oliver, D. M., Porter, K. D. H., Pachepsky, Y. A., Muirhead, R. A., Reaney, S. M., Coffey, R., ... Quilliam, R. S. (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
Oliver, David M. ; Porter, Kenneth D.H. ; Pachepsky, Yakov A. ; Muirhead, Richard A. ; Reaney, Sim M. ; Coffey, Rory ; Kay, David ; Milledge, David G. ; Hong, Eunmi ; Anthony, Steven G. ; Page, Trevor ; Bloodworth, Jack W. ; Mellander, Per-Erik ; Carbonneau, Patrice E. ; McGrane, Scott J. ; Quilliam, Richard S. / Predicting microbial water quality with models : over-arching questions for managing risk in agricultural catchments. In: Science of the Total Environment. 2016 ; Vol. 544. pp. 39-47.
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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.",
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Oliver, DM, Porter, KDH, Pachepsky, YA, Muirhead, RA, Reaney, SM, Coffey, R, Kay, D, Milledge, DG, Hong, E, Anthony, SG, Page, T, Bloodworth, JW, Mellander, P-E, Carbonneau, PE, McGrane, SJ & Quilliam, RS 2016, 'Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments', Science of the Total Environment, vol. 544, pp. 39-47. https://doi.org/10.1016/j.scitotenv.2015.11.086

Predicting microbial water quality with models : over-arching questions for managing risk in agricultural catchments. / Oliver, David M.; Porter, Kenneth D.H.; Pachepsky, Yakov A.; Muirhead, Richard A.; Reaney, Sim M.; Coffey, Rory; Kay, David; Milledge, David G.; Hong, Eunmi; Anthony, Steven G.; Page, Trevor ; Bloodworth, Jack W.; Mellander, Per-Erik; Carbonneau, Patrice E.; McGrane, Scott J.; Quilliam, Richard S.

In: Science of the Total Environment, Vol. 544, 15.02.2016, p. 39-47.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Predicting microbial water quality with models

T2 - over-arching questions for managing risk in agricultural catchments

AU - Oliver, David M.

AU - Porter, Kenneth D.H.

AU - Pachepsky, Yakov A.

AU - Muirhead, Richard A.

AU - Reaney, Sim M.

AU - Coffey, Rory

AU - Kay, David

AU - Milledge, David G.

AU - Hong, Eunmi

AU - Anthony, Steven G.

AU - Page, Trevor

AU - Bloodworth, Jack W.

AU - Mellander, Per-Erik

AU - Carbonneau, Patrice E.

AU - McGrane, Scott J.

AU - Quilliam, Richard S.

PY - 2016/2/15

Y1 - 2016/2/15

N2 - 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.

AB - 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.

KW - catchment management

KW - diffuse pollution

KW - faecal indicator organism

KW - human health

KW - pathogens

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U2 - 10.1016/j.scitotenv.2015.11.086

DO - 10.1016/j.scitotenv.2015.11.086

M3 - Article

VL - 544

SP - 39

EP - 47

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -