Abstract
Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km2 grid cell (Ayr: r=0.57; p<0.001, Lunan: r=0.32; p<0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P<0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments.
Highlights
The modelled distributions of E. coli burden across the River Ayr and Lunan Water catchments are shown in Figs. 4 and 5, respectively, and represent some of the first catchment wide spatially distributed maps of predicted E. coli burden to land contributed from grazing livestock
It should be noted that the maximum occurrence of the total E. coli burden within each grid cell is likely to vary temporally depending on the composition of livestock grazing activity
This study reports on the successful deployment of the Visualising Pathogen & Environmental Risk (ViPER) model to two contrasting catchments in Scotland, providing spatially distributed predictions of E. coli burden through time
Summary
Targeted decision-making and deployment of mitigation is critical for effective and efficient water resource management, helping to reduce agricultural impacts on surface waters (Vinten et al, 2017; Greene et al, 2015). Developments in nutrient management planning and efforts to limit nitrogen (N) and phosphorus (P) export from land to water have highlighted the importance of critical source areas (CSAs), defined as zones in the landscape where high sources of nutrients coincide with high potential for hydrological transfer (Heathwaite et al, 2000). A significant proportion of surface water contamination with faecal indicator organisms (FIOs), of which Escherichia coli is one of the most common, can be attributed to CSAs of microbial pollution (Oliver et al, 2012)
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