Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment

Aaron James Neill, Doerthe Tetzlaff, Norval James Colin Strachan, Rupert Lloyd Hough, Lisa Marie Avery, Helen Watson, Chris Soulsby

Research output: Contribution to journalArticle

6 Citations (Scopus)
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Abstract

An 11 year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52 km2) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significant
predictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing
“point sources” of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential “hot spots” of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding public
health. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible “hot spots” of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.
Original languageEnglish
Pages (from-to)840-851
Number of pages13
JournalScience of the Total Environment
Volume612
Early online date25 Sep 2017
DOIs
Publication statusPublished - 15 Jan 2018

Fingerprint

Land use
Catchments
Contamination
Escherichia coli
catchment
land use
point source
Agriculture
livestock
defecation
index method
prediction
contamination
Surface waters
Linear regression
headwater
seasonality
connectivity
pasture
land cover

Keywords

  • E. coli
  • Faecal indicator organism
  • Microbial pollution
  • Spatio-temporal dynamics
  • Surface water
  • Water quality

Cite this

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title = "Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment",
abstract = "An 11 year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52 km2) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significantpredictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing“point sources” of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential “hot spots” of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding publichealth. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible “hot spots” of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.",
keywords = "E. coli, Faecal indicator organism, Microbial pollution, Spatio-temporal dynamics, Surface water, Water quality",
author = "Neill, {Aaron James} and Doerthe Tetzlaff and Strachan, {Norval James Colin} and Hough, {Rupert Lloyd} and Avery, {Lisa Marie} and Helen Watson and Chris Soulsby",
note = "Thanks to the Scottish Government's Hydro Nation Scholars Programme for funding AJN to do this research. Thanks to the Drinking Water Quality Regulator (DWQR) for Scotland for providing information about private water supplies and septic tanks in the Tarland.",
year = "2018",
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doi = "10.1016/j.scitotenv.2017.08.151",
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journal = "Science of the Total Environment",
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TY - JOUR

T1 - Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment

AU - Neill, Aaron James

AU - Tetzlaff, Doerthe

AU - Strachan, Norval James Colin

AU - Hough, Rupert Lloyd

AU - Avery, Lisa Marie

AU - Watson, Helen

AU - Soulsby, Chris

N1 - Thanks to the Scottish Government's Hydro Nation Scholars Programme for funding AJN to do this research. Thanks to the Drinking Water Quality Regulator (DWQR) for Scotland for providing information about private water supplies and septic tanks in the Tarland.

PY - 2018/1/15

Y1 - 2018/1/15

N2 - An 11 year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52 km2) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significantpredictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing“point sources” of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential “hot spots” of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding publichealth. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible “hot spots” of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.

AB - An 11 year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52 km2) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significantpredictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing“point sources” of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential “hot spots” of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding publichealth. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible “hot spots” of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.

KW - E. coli

KW - Faecal indicator organism

KW - Microbial pollution

KW - Spatio-temporal dynamics

KW - Surface water

KW - Water quality

U2 - 10.1016/j.scitotenv.2017.08.151

DO - 10.1016/j.scitotenv.2017.08.151

M3 - Article

VL - 612

SP - 840

EP - 851

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -