Tracing catchment fine sediment sources using the new SIFT (SedIment Fingerprinting Tool) open source software

A - Papers appearing in refereed journals

Pulley, S. and Collins, A. L. 2018. Tracing catchment fine sediment sources using the new SIFT (SedIment Fingerprinting Tool) open source software. Science of the Total Environment. 635, p. 838–858.

AuthorsPulley, S. and Collins, A. L.
Abstract

The mitigation of diffuse sediment pollution requires reliable provenance information so that measures can be targeted. Sediment source fingerprinting represents one approach for supporting these needs, but recent methodological developments have resulted in an increasing complexity of data processing methods rendering the approach less accessible to non-specialists. A comprehensive new software programme (SIFT; SedIment Fingerprinting Tool) has therefore been developed which guides the user through critical data analysis decisions and automates all calculations. Multiple source group configurations and composite fingerprints are identified and tested using multiple methods of uncertainty analysis. This aims to explore the sediment provenance information provided by the tracers more comprehensively than a single model, and allows for model configurations with high uncertainties to be rejected. This paper provides an overview of its application to an agricultural catchment in the UK to determine if the approach used can provide a reduction in uncertainty and increase in precision. Five source group classifications were used; three formed using a k-means cluster analysis containing 2, 3 and 4 clusters, and two a-priori groups based upon catchment geology. Three different composite fingerprints were used for each classification and bi-plots, range tests, tracer variability ratios and virtual mixtures tested the reliability of each model configuration. Some model configurations performed poorly when apportioning the composition of virtual mixtures, and different model configurations could produce different sediment provenance results despite using composite fingerprints able to discriminate robustly between the source groups. Despite this uncertainty, dominant sediment sources were identified, and those in close proximity to each sediment sampling location were found to be of greatest importance. This new software, by integrating recent methodological developments in tracer data processing, guides users through key steps. Critically, by applying multiple model configurations and uncertainty assessment, it delivers more robust solutions for informing catchment management of the sediment problem than many previously used approaches.

KeywordsSediment; Sediment source tracing; Sediment fingerprinting; Catchment management; Uncertainty
Year of Publication2018
JournalScience of the Total Environment
Journal citation635, p. 838–858
Digital Object Identifier (DOI)doi:10.1016/j.scitotenv.2018.04.126
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0048969718312816
Open accessPublished as ‘gold’ (paid) open access
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
FunderBiotechnology and Biological Sciences Research Council
Publisher's version1-s2.0-S0048969718312816-main SIFT.pdf
Output statusPublished
Publication dates
Online24 Apr 2018
Publication process dates
Accepted08 Apr 2018
Copyright licenseCC BY
PublisherElsevier Science Bv
ISSN0048-9697

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