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Evaluating tracer selection for catchment sediment fingerprinting

  • Sediments, Sec 3 • Hillslope and River Basin Sediment Dynamics • Research Article
  • Published:
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Abstract

Purpose

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.

Conclusions

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.

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References

  • Bini C, Sartori G, Wahsha M, Fontana S (2011) Background levels of trace elements and soil geochemistry at regional level in NE Italy. J Geochem Explor 109:125–133

    Article  CAS  Google Scholar 

  • Bivand RS, Pebesma E, Gomez-Rubio V (2013) Applied spatial data analysis with R, 2nd edn. Springer, New York http://www.asdar-book.org/

    Book  Google Scholar 

  • Blake L, Goulding KWT (2002) Effects of atmospheric deposition, soil pH and acidification on heavy metal content in soils and vegetation of semi-natural ecosystems at Rothamsted Experimental Station, UK. Plant Soil 240:235–251

    Article  CAS  Google Scholar 

  • Blonder B, Lamanna C, Violle C, Enquist BJ (2014) The n-dimensional hypervolume. Glob Ecol Biogeogr 23:595–609

    Article  Google Scholar 

  • Collins AL, Walling DE (2002) Selecting fingerprint properties for discriminating potential suspended sediment sources in river basins. J Hydrol 261:218–244

    Article  CAS  Google Scholar 

  • Collins AL, Walling DE, Leeks GJL (1997) Source type ascription for fluvial suspended sediment based on a quantitative composite fingerprinting technique. Catena 29:1–27

    Article  CAS  Google Scholar 

  • Collins AL, Pulley S, Foster IDL, Gellis A, Porto P, Horowitz AJ (2017) Sediment source fingerprinting as an aid to catchment management: a review of the current state of knowledge and a methodological decision-tree for end-users. J Environ Manag 194:86–108

    Article  CAS  Google Scholar 

  • Cooper RJ, Krueger T (2017) An extended Bayesian sediment fingerprinting mixing model for the full Bayes treatment of geochemical uncertainties. Hydrol Process 31:1900–1912

    Article  CAS  Google Scholar 

  • Cooper RJ, Krueger T, Hiscock KM, Rawlins BG (2014) Sensitivity of fluvial sediment source apportionment to mixing model assumptions. Water Resour Res 50:9031–9047

    Article  Google Scholar 

  • D’Haen K, Verstraeten G, Duser B, Degryse P, Haex J, Waelkens M (2013) Unravelling changing sediment sources in a Mediterranean mountain catchment: a Bayesian fingerprinting approach. Hydrol Process 27:896–910

    Article  Google Scholar 

  • Dawson BSW, Fergusson JE, Campbell AS, Cutler EJB (1991) Depletion of first-row transition metals in a chronosequence of soils in the Reefton area of New Zealand. Geoderma 48:271–296

    Article  CAS  Google Scholar 

  • Fox JF, Papanicolaou AN (2008) An un-mixing model to study watershed erosion processes. Adv Water Resour 31:96–108

    Article  Google Scholar 

  • 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 1067–1074

    Google Scholar 

  • Haddadchi A, Olley J, Laceby P (2014) Accuracy of mixing models in predicting sediment source contributions. Sci Total Environ 497-498:139–152

    Article  CAS  Google Scholar 

  • Hay KL, Dearing JA, Baban SMJ, Loveland P (1997) A preliminary attempt to identify atmospherically-derived pollution particles in English topsoils from magnetic susceptibility measurements. Phys Chem Earth 22:207–210

    Article  Google Scholar 

  • He Q, Walling DE (1996) Interpreting particle size effects in the adsorption of 137Cs and unsupported 210Pb by mineral soils and sediments. J Environ. Radioact 30:117–113

    Article  CAS  Google Scholar 

  • Jobbágy EG, Jackson RB (2001) The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry 53:51–77

    Article  Google Scholar 

  • Kassambara A, Mundt F (2017) factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.4. https://CRAN.R project.org/package=factoextra

  • Koiter AJ, Owens PN, Petticrew EL, Lobb DA (2013) The behavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth-Sci Rev 125:24–42

    Article  CAS  Google Scholar 

  • Kříbek B, Majer V, Veselovský F, Nyambe I (2010) Discrimination of lithogenic and anthropogenic sources of metals and sulphur in soils of the central-northern part of the Zambian Copperbelt Mining District: a topsoil vs. subsurface soil concept. J Geochem Explor 104:69–86

    Article  CAS  Google Scholar 

  • Laceby JP, Evrard O, Smith HG, Blake WH, Olley JM, Minella JPG, Owens PN (2017) The challenges and opportunities of addressing particle size effects in sediment source fingerprinting: a review. Earth-Sci Rev 169:85–103

    Article  CAS  Google Scholar 

  • Le S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25:1–18

    Article  Google 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 382:49–63

    Article  Google Scholar 

  • Moore JW, Semmens BX (2008) Incorporating uncertainty and prior information into stable isotope mixing models. Ecol Lett 11:470–480

    Article  Google Scholar 

  • Nosrati K, Govers G, Semmens BX, Ward EJ (2014) A mixing model to incorporate uncertainty in sediment fingerprinting. Geoderma 217-218:173–180

    Article  CAS  Google 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 162:1–23

    Article  CAS  Google 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 532:456–466

  • Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source partitioning using stable isotopes: coping with too much variation. PLoS One 5:e9672

    Article  CAS  Google 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 24:387–399

    Google 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/

  • Phillips JM, Russell MA, Walling DE (2000) Time-integrated sampling of fluvial suspended sediment: a simple methodology for small catchments. Hydrol Process 14:2589–2602

    Article  Google Scholar 

  • Pulley S, Foster I, Collins AL (2017) The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs. J Environ Manag 194:16–26

    Article  Google Scholar 

  • R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/

    Google Scholar 

  • 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

  • RStudio Team (2016) RStudio: integrated development for R. RStudio Inc., Boston http://www.rstudio.com/

    Google Scholar 

  • Sherriff SC, Franks SW, Rowan JS, Fenton O, Ó’hUallacháin D (2015) Uncertainty-based assessment of tracer selection, tracer non-conservativeness and multiple solutions in sediment fingerprinting using synthetic and field data. J Soils Sediments 15:2101–2116

    Article  CAS  Google Scholar 

  • Smith HG, Blake WH (2014) Sediment fingerprinting in agricultural catchments: a critical re-examination of source discrimination and data corrections. Geomorphology 204:177–191

    Article  Google Scholar 

  • Stock BC, Semmens BX (2016a) MixSIAR GUI User Manual version 3.1. https://github.com/brianstock/MixSIAR. https://doi.org/10.5281/zenodo.56159

  • Stock BC, Semmens BX (2016b) Unifying error structures in commonly used biotracer mixing models. Ecology 97:2562–2569

    Article  Google Scholar 

  • Taylor A, Blake WH, Smith HG, Mabit L, Keith-Roach MJ (2013) Assumptions and challenges in the use of fallout beryllium-7 as a soil and sediment tracer in river basins. Earth-Sci Rev 126:85–95

    Article  CAS  Google Scholar 

  • Tyler G (2004) Vertical distribution of major, minor and rare elements in a Haplic Podzol. Geoderma 119:277–290

    Article  CAS  Google Scholar 

  • 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 17:1537–1553

    Article  CAS  Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York ISBN 0-387-95457-0

    Book  Google Scholar 

  • Wallbrink PJ, Murray AS, Olley JM (1999) Relating suspended sediment to its original depth using fallout radionuclides. Soil Sci Soc Am J 63:369–378

    Article  CAS  Google Scholar 

  • Walling DE (2013) The evolution of sediment source fingerprinting investigations in fluvial systems. J Soils Sediments 13:1658–1675

    Article  Google Scholar 

  • Ward EJ, Semmens BX, Schindler DE (2010) Including source uncertainty and prior information in the analysis of stable isotope mixing models. Environ Sci Technol 44:4645–4650

    Article  CAS  Google Scholar 

  • Weihs C, Ligges U, Luebke K, Raabe N (2005) klaR analyzing German business cycles. In: Baier D, Decker R, Schmidt-Thieme L (eds) Data analysis and decision support. Springer-Verlag, Berlin, pp 335–343

    Chapter  Google Scholar 

Download references

Acknowledgements

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.

Funding

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).

Author information

Authors and Affiliations

  1. School of Environmental Sciences, University of Liverpool, Liverpool, L69 7ZT, UK

    Hugh G. Smith, Daljit Singh Karam & Amy T. Lennard

  2. Landcare Research, Private Bag 11052, Palmerston North, 4442, New Zealand

    Hugh G. Smith

  3. Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Daljit Singh Karam

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  1. Hugh G. Smith
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  2. Daljit Singh Karam
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Correspondence to Hugh G. Smith.

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Responsible editor: David Allen Lobb

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Smith, H.G., Karam, D.S. & Lennard, A.T. Evaluating tracer selection for catchment sediment fingerprinting. J Soils Sediments 18, 3005–3019 (2018). https://doi.org/10.1007/s11368-018-1990-7

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