The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs

Simon Pulley; Ian Foster; Adrian L Collins

doi:10.1016/j.jenvman.2016.04.048

The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs

J Environ Manage. 2017 Jun 1:194:16-26. doi: 10.1016/j.jenvman.2016.04.048. Epub 2016 May 6.

Authors

Simon Pulley¹, Ian Foster², Adrian L Collins³

Affiliations

¹ Rhodes University: Geography Department, Rhodes University, Eastern Cape 6140, South Africa; The University of Northampton: School of Science and Technology, University of Northampton, Northampton, NN2 6JD, UK. Electronic address: s.pulley@ru.ac.za.
² Rhodes University: Geography Department, Rhodes University, Eastern Cape 6140, South Africa; The University of Northampton: School of Science and Technology, University of Northampton, Northampton, NN2 6JD, UK.
³ Sustainable Soils and Grassland Systems Department, Rothamsted Research, North Wyke, Okehampton EX20 2 SB, UK.

PMID: 27160761
DOI: 10.1016/j.jenvman.2016.04.048

Abstract

The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes. The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes. The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0-100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty (a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies.

Keywords: Discrimination; Sediment fingerprinting; Sediment sources; Tracing; Uncertainty.

MeSH terms

Environmental Monitoring*
Geologic Sediments*

Grants and funding

BBS/E/C/000I0330/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom