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A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments.

Milledge, D.G. and Lane, S.N . and Heathwaite, A.L. and Reaney, S.M. (2012) 'A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments.', Science of the total environment., 433 . pp. 434-449.


The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that are parameterised in such models to ensure generic applicability make them complex and difficult to test on available data. Here, we outline an alternative – data-driven – inverse approach. We apply SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity. We take a Bayesian approach to the inverse problem of determining the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) diffuse pollution risk in eleven UK catchments covering a range of landscapes. The model results show that: 1) some land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. Taken on a case‐by‐case basis, this type of inverse approach may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems.

Item Type:Article
Keywords:Diffuse pollution, Hydrological connectivity, Nutrients, Nitrogen, Phosphorus, Modelling.
Full text:(AM) Accepted Manuscript
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Publisher statement:NOTICE: this is the author’s version of a work that was accepted for publication in Science of The Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of The Total Environment, 433, 1 September 2012, 10.1016/j.scitotenv.2012.06.047.
Date accepted:No date available
Date deposited:02 December 2014
Date of first online publication:September 2012
Date first made open access:No date available

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