Evaluating tracer selection for catchment sediment fingerprinting
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Recent sediment fingerprinting research has shown the sensitivity of source apportionment results to data treatments, tracer number, and mixing model type. In light of these developments, there is a need to revisit procedures associated with tracer selection in sediment fingerprinting studies. Here, we evaluate the accuracy and precision of different procedures to select tracers for un-mixing sediment sources.
Materials and methods
We present a new approach to tracer selection based on identifying and removing tracers that exhibit non-conservative behaviour during sediment transport. This removes tracers on the basis of non-conservative behaviour identified using (1) tracer-particle size relationships and (2) source mixing polygons. We test source apportionment results using six sets of tracers with three different synthetic mixtures comprising one, five, and ten mixture samples. Source tracer data was obtained from an agricultural catchment in northwest England where time-integrated suspended sediment samples were also collected over a 12-month period. Source un-mixing used MixSIAR, a Bayesian mixing model developed for ecological food web studies, which is increasingly being applied in catchment sediment fingerprinting research.
Results and discussion
We found that the most accurate source apportionment results were achieved by the selection procedure that only removed tracers on the basis of non-conservative behaviour. Furthermore, accuracy and precision were improved with five or ten mixture samples compared to the use of a single mixture sample. Combining this approach with a further step to exclude additional tracers based on source group non-normality reduced accuracy, which supports relaxation of the assumption of source normality in MixSIAR. Source apportionment based on the widely used Kruskal-Wallis H test and discriminant function analysis approach was less accurate and had larger uncertainty that the procedure focused on excluding non-conservative tracers.
Source apportionment results are sensitive to tracer selection. Our findings show that prioritising tracer exclusion due to non-conservative behaviour produces more accurate results than selection based on the minimum number of tracers that maximise source discrimination. Future sediment fingerprinting studies should aim to maximise the number of tracers used in source un-mixing constrained only by the need to ensure conservative behaviour. Our procedure provides a quantitative approach for identifying and excluding those non-conservative tracers.
KeywordsMixSIAR Sediment fingerprinting Sediment tracing Tracer selection
Field and laboratory assistance from Poonperm Vardhanabindu, Chris Feeney, Sión Regan, and Chris Cakebread is much appreciated. We also gratefully acknowledge the support of Gareth Jones and Paul Peters from the Ribble Rivers Trust for collecting and maintaining the suspended sediment samplers. We thank the three anonymous reviewers for their comments that helped improve the manuscript.
We acknowledge funding from the Malaysian Government Ministry of Higher Education (MOHE) for the visit by DSK to Liverpool and support for HGS and ATL from the European Commission Horizon 2020 programme under the Marie Skłodowska-Curie Research and Innovation Staff Exchange programme (IMIXSED project: Integrating isotopic techniques with Bayesian modelling for improved assessment and management of global sedimentation problems Project ID 644320).
- Franks SW, Rowan JS (2000) Multi-parameter fingerprinting of sediment sources: uncertainty estimation and tracer selection. In: Bentley LR (ed) Computational methods in water resources XIII. Balkema, Rotterdam, pp 1067–1074Google Scholar
- Martínez-Carreras N, Udelhoven T, Krein A, Gallart F, Iffly JF, Ziebel J, Hoffmann L, Pfister L, Walling DE (2010) The use of sediment colour measured by diffuse reflectance spectrometry to determine sediment sources: application to the Attert River catchment (Luxembourg). J Hydrol 382:49–63CrossRefGoogle Scholar
- Owens PN, Blake WH, Gaspar L, Gateuille D, Koiter AJ, Lobb DA, Petticrew EL, Reiffarth DG, Smith HG, Woodward JC (2016) Fingerprinting and tracing the sources of soils and sediments: earth and ocean science, geoarchaeological, forensic, and human health applications. Earth-Sci Rev 162:1–23CrossRefGoogle Scholar
- Palazón L, Latorre B, Gaspar L, Blake WH, Smith HG, Navas A (2015) Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample mixtures. Sci Total Environ 532:456–466Google Scholar
- Parnell AC, Phillips DL, Bearhop S, Semmens BX, Ward EJ, Moore JW, Jackson AL, Grey J, Kelly DJ, Inger R (2013) Bayesian stable isotope mixing models. Environmetrics 24:387–399Google Scholar
- Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R News 5(2). https://cran.r-project.org/doc/Rnews/
- Rowland CS, Morton RD, Carrasco L, McShane G, O’Neil AW, Wood CM (2017) Land cover map 2015 (vector, GB). NERC Environmental Information Data Centre https://doi.org/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73
- Upadhayhay HR, Bodé S, Griepentrog M, Huygens D, Bajracharya RM, Blake WH, Dercon G, Mabit L, Gibbs M, Semmens BX, Stock BC, Cornelis W, Boeckx P (2017) Methodological perspectives on the application of compound-specific stable isotope fingerprinting for sediment source apportionment. J Soils Sediments 17:1537–1553CrossRefGoogle Scholar