Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Water Resources Research

Volumn 50, Issue 11, 2014, Pages 9031-9047

  Cooper, Richard J ^a   Krueger, Tobias ^b   Hiscock, Kevin M ^a   Rawlins, Barry G ^c  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

^b HUMBOLDT UNIVERSITY   (Germany)

^c BRITISH GEOLOGICAL SURVEY   (United Kingdom)

Author keywords

Bayesian; fingerprinting; mixing model; optimization; sediment; sensitivity analysis

Indexed keywords

BAYESIAN NETWORKS; CATCHMENTS; COLOR IMAGE PROCESSING; MIXING; OPTIMIZATION; SALINITY MEASUREMENT; SEDIMENTS; SUSPENDED SEDIMENTS;

BAYESIAN; FINGERPRINTING; MIXING MODELS; OPTIMIZATION APPROACH; SEDIMENT GEOCHEMISTRIES; SOURCE APPORTIONMENT; SUB-SURFACE MATERIALS; SUSPENDED PARTICULATE MATTERS;

SENSITIVITY ANALYSIS;

ALLUVIAL DEPOSIT; BAYESIAN ANALYSIS; CATCHMENT; COVARIANCE ANALYSIS; DATA SET; GEOCHEMISTRY; MIXING; OPTIMIZATION; SENSITIVITY ANALYSIS; SUSPENDED PARTICULATE MATTER;

BLACKWATER RIVER [ESSEX]; ENGLAND; ESSEX [ENGLAND]; UNITED KINGDOM;

EID: 84919628821     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR016194     Document Type: Article

References (65)

1. Aitchison, J., (1986), The Statistical Analysis of Compositional Data: Monographs on Statistics and Applied Probability, Chapman & Hall Ltd., London, U. K.
2. Barnard, J., R. McCulloch, and, X.-L. Meng, (2000), Modelling covariance matrices in terms of standard deviations and correlations, with application to shrinkage, Stat. Sin., 10, 1281-1311.
3. Brewer, M. J., D. Tetzlaff, I. A. Malcolm, and, C. Soulsby, (2011), Source distribution modelling for end-member mixing in hydrology, Environmetrics, 22, 921-932, doi: 10.1002/env.1110.
4. Carlin, B. P., and, T. A. Louis, (1996), Bayes and Empirical Bayes Methods for Data Analysis, Chapman and Hall, London, U. K.
5. Christensen, W. F., and, R. F. Gunst, (2004), Measurement error models in chemical mass balance analysis of air quality data, Atmos. Environ., 38, 733-744, doi: 10.1016/j.atmosenv.2003.10.018.
6. Collins, A. L., D. E. Walling, and, G. J. L. Leeks, (1997), Source type ascription for fluvial suspended sediment based on a quantitative composite fingerprinting technique, Catena, 29, 1-27, doi: 10.1016/S0341-8162(96)00064-1.
7. Collins, A. L., Y. Zhang, D. E. Walling, S. E. Grenfell, P. Smith, J. Grischeff, A. Locke, A. Sweetapple, and, D. Brogden, (2012), Quantifying fine-grained sediment sources in the River Axe catchment, southwest England: Application of a Monte Carlo numerical modelling framework incorporating local and genetic algorithm optimization, Hydrol. Processes, 26, 1962-1983.
8. Collins, A. L., Y.S. Zhang, R. Hickinbotham, G. Bailey, S. Darlington, S. E. Grenfell, R. Evans, and, M. Blackwell, (2013), Contemporary fine-grained bed sediment sources across the River Wensum Demonstration Test Catchment, UK, Hydrol. Processes, 27, 857-884, doi: 10.1002/hyp.9654.
9. Cooper, R. J., B. G. Rawlins, B. Lézé, T. Krueger, and, K. M. Hiscock, (2014a), Combining two filter paper-based analytical methods to monitor temporal variations in the geochemical properties of fluvial suspended particulate matter, Hydrol. Processes, 28, 4042-4056, doi: 10.1002/hyp.9945.
10. Cooper, R. J., T. Krueger, K. M. Hiscock, and, B. G. Rawlins, (2014b) High-temporal resolution fluvial sediment source fingerprinting with uncertainty: A Bayesian approach, Earth Surf. Processes Landforms, doi: 10.1002/esp.3621, in press.
11. Darimont, C. T., P. C. Paquet, and, T. E. Relmchen, (2009), Landscape heterogeneity and marine subsidy generate extensive intrapopulation niche diversity in a large terrestrial vertebrate, J. Anim. Ecol., 78, 126-133, doi: 10.1111/j.1365-2656.2008.01473.x.
12. D'Haen, K., G. Verstraeten, B. Dusar, P. Degryse, J. Haex, and, M. Waelkens, (2012), Unravelling changing sediment sources in a Mediterranean mountain catchment: A Bayesian fingerprinting approach, Hydrol. Processes, 27, 896-910, doi: 10.1002/hyp.9399.
13. D'Haen, K., B. Dusar, G. Verstraeten, P. Degryse, and, H. De Brue, (2013) A sediment fingerprinting approach to understand the geomorphic coupling in an eastern Mediterranean mountainous river catchment, Geomorphology, 197, 64-75, doi: 10.1016/j.geomorph.2013.04.038.
14. Dilks, D. W., R. P. Canale, and, P. G. Meier, (1992), Development of Bayesian Monte Carlo techniques for water quality model uncertainty, Ecol. Modell., 62, 149-162, doi: 10.1016/0304-3800(92)90087-U.
15. Dutton, C., S. C. Anisfeld, and, H. Ernstberger, (2013), A novel sediment fingerprinting method using filtration: Application to the Mara River, East Africa, J. Soils Sediment., 13, 1708-1723, doi: 10.1007/s11368-013-0725-z.
16. Egozcue, J., V. Pawlowsky-Glahn, G. Mateu-Figueras, and, C. Barcelõ-Vidal, (2003), Isometric logratio transformations for compositional data analysis, Math. Geol., 35, 279-300, doi: 10.1023/A:1023818214614.
17. Erhardt, E. B., and, E. J. Bedrick, (2013), A Bayesian framework for stable isotope mixing models, Environ. Ecol. Stat., 20, 377-397, doi: 10.1007/s10651-012-0224-1.
18. Evrard, O., J. Poulenard, J. Némery, S. Ayrault, N. Gratiot, C. Duvert, C. Prat, I. Lefèvre, P. Bonté, and, M. Esteves, (2013), Tracing sediment sources in a tropical highland catchment of central Mexico by using conventional and alterative fingerprinting methods, Hydrol. Processes, 27, 911-922, doi: 10.1002/hyp.9421.
19. Fox, J. F., and, A. N. Papanicolaou, (2007), The use of carbon and nitrogen isotopes to study watershed erosion processes, J. Am. Water Resour. Assoc., 43, 1047-1064, doi: 10.1111/j.1752-1688.2007.00087.x.
20. Fox, J. F., and, A. N. Papanicolaou, (2008), An un-mixing model to study watershed erosion processes, Adv. Water Resour., 31, 96-108, doi: 10.1016/j.advwatres.2007.06.008.
21. Fox, J. F., C. M. Davis, and, D. K. Martin, (2010), Sediment source assessment in a lowland watershed using nitrogen stable isotopes, J. Am. Water Resour. Assoc., 46, 1192-1204, doi: 10.1111/j.1752-1688.2010.00485.x.
22. Franco-Trecu, V., M. Drago, F. G. Riet-Sapriz, A. Parnell, R. Frau, and, P. Inchausti, (2013), Bias in diet determination: Incorporating traditional methods in Bayesian mixing models, PLoS ONE, 8, e80019, doi: 10.1371/journal.pone.0080019.
23. Gruszowski, K. E., I. D. L. Foster, J. A. Lees, and, S. M. Charlesworth, (2003), Sediment sources and transport pathways in a rural catchment, Herefordshire, UK, Hydrol. Processes, 17, 2665 -2681, doi: 10.1002/hyp.1296.
24. Hindell, M. A., C. Lydersen, H. Hop, and, K. M. Kovacs, (2013), Pre-partum diet of adult female bearded seals in years of contrasting ice conditions, PLoS ONE, 7, e38307, doi: 10.1371/journal.pone.0038307.
25. Hiscock, K. M., (1993), The influence of pre-Devensian glacial deposits on the hydrogeochemistry of the chalk aquifer system of north Norfolk, UK, J. Hydrol., 144, 335-369, doi: 10.1016/0022-1694(93)90179-D.
26. Hopkins, J. B., and, J. M. Ferguson, (2012), Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model, PLoS ONE, 7, e28478, doi: 10.1371/journal.pone.0028478.
27. Horowitz A. J., and, K. A. Elrick, (1987), The relation of stream sediment surface area, grain size and composition to trace element chemistry. Appl. Geochem. 2, 437-451.
28. Laceby, J. P., and, J. Olley, (2014), An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations, Hydrol. Processes, doi: 10.1002/hyp.10287.
29. Lewis, M. A., (2011), Borehole drilling and sampling in the Wensum Demonstration Test Catchment, Br. Geol. Surv. Comm. Rep., CR/11/162, 38 pp., National Environment Research Council (NERC), Keyworth, Nottingham, U. K.
30. Lingwall, J. W., W. F. Christensen, and, C. Shane Reese, (2008) Dirichlet based Bayesian multivariate receptor modelling, Environmetrics, 19, 618-629.
31. Lunn, D. J., A. Thomas, N. Best, and, D. Spiegelhalter, (2000) WinBUGS: A Bayesian modelling framework: Concepts, structure, and extensibility, Stat. Comput., 10, 325-337, doi: 10.1023/A:1008929526011.
32. Martínez-Carreras, N., A. Krein, T. Udelhoven, F. Gallart, J. F. Iffly, L. Hoffmann, L. Pfister, and, D. E. Walling, (2010), A rapid spectral-reflectance-based fingerprinting approach for documenting suspended sediment sources during storm runoff events, J. Soil Sediment., 10, 400-413, doi: 10.1007/s11368-009-0162-1.
33. Massoudieh, A., A. Gellis, W. S. Banks, and, M. E. Wieczorek, (2012), Suspended sediment source apportionment in Chesapeake Bay watershed using Bayesian chemical mass balance receptor modelling, Hydrol. Processes, 27, 3363-3374, doi: 10.1002/hyp.9429.
34. Moore, J. W., and, B. X. Semmens, (2008), Incorporating uncertainty and prior information into stable isotope mixing models, Ecol. Lett., 11, 470-480, doi: 10.1111/j.1461-0248.2008.01163.x.
35. Motha, J. A., P. J. Wallbrink, P. B. Hairsine, and, R. B. Grayson, (2003), Determining the sources of suspended sediment in a forested catchment in southeastern Australia, Water Resour. Res., 39 (3), 1056, doi: 10.1029/2001WR000794.
36. Nosrati, K., G. Govers, B. X. Semmens, and, E. J. Ward, (2014), A mixing model to incorporate uncertainty in sediment fingerprinting, Geoderma, 217-218, 173-180, doi: 10.1016/j.geoderma.2013.12.002.
37. Palmer, M. J., and, G. B. Douglas, (2008), A Bayesian statistical model for end member analysis of sediment geochemistry, incorporating spatial dependences, Appl. Stat., 57, 313-327, doi: 10.1111/j.1467-9876.2007.00615.x.
38. Parnell, A. C., R. Inger, S. Bearhop, and, A. L. Jackson, (2010) Source partitioning using stable isotopes: coping with too much variation, PLoS ONE, 5, e9672, doi: 10.1371/journal.pone.0009672.
39. Parnell, A. C., D. L. Phillips, S. Bearhop, B. X. Semmens, E. J. Ward, J. W. Moore, A. L. Jackson, and, R. Inger, (2013) Bayesian stable isotope mixing models, Environmetrics, 24, 387-399, doi: 10.1002/env.2221.
40. Pawlowsky-Glahn, V., and, J. J. Egozcue, (2006) Compositional data and their analysis: An introduction, Geol. Soc. London Spec. Publ., 264, 1-10, doi: 10.1144/GSL.SP.2006.264.01.01.
41. Phillips, D. L., and, J. W. Gregg, (2001) Uncertainty in source partitioning using stable isotopes, Ocecologia, 127, 171-179, doi: 10.1007/s004420000578.
42. Plummer, M., (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling, in Proceeding of the 3rd International Workshop on Distributed Statistical Computing, edited by K. Hornik, F. Leisch, and A. Zeileis, pp. 1-8, Vienna, Austria.
43. Plummer, M., N. Best, K. Cowles, and, K. Vines, (2006), R Package "coda": convergence diagnosis and output analysis for MCMC.
44. 3 semi-arid vegetation transitions, Rapid Commun. Mass Spectrom., 26, 2386-2392, doi: 10.1002/rcm.6257.
45. R Development Core Team (2013), R: A Language and Environment for Statistical Computing, R Found. for Stat. Comput., Vienna, Austria. [Available at http://www.R-project.org.]
46. Rawlins, B. G., (2011), Controls on the phosphorus content of fine stream bed sediments in agricultural headwater catchments at the landscape-scale, Agric. Ecosyst. Environ., 144, 352-363, doi: 10.1016/j.agee.2011.10.002.
47. Rowan, J. S., S. Black, and, S. W. Franks, (2011), Sediment fingerprinting as an environmental forensics tool explaining cyanobacteria blooms in lakes, Appl. Geogr., 32, 832-843, doi: 10.1016/j.apgeog.2011.07.004.
48. Schmelter, M. L., and, D. K. Stevens, (2013), Traditional and Bayesian statistical models in fluvial sediment transport, J. Hydraul. Eng., 139, 336-340, doi: 10.1061/(ASCE)HY.1943-7900.0000672.
49. ex and other sediment source fingerprints to document suspended sediment sources in small forested catchments in south-central Chile, J. Environ. Radioact., 124, 147-159, doi: 10.1016/j.jenvrad.2013.05.002.
50. Semmens, B. X., E. J. Ward, J. W. Moore, and, C. T. Darimont, (2009), Quantifying inter- and intra-population niche variability using hierarchical Bayesian stable isotope mixing models, PLoS ONE, 4, e6187, doi: 10.1371/journal.pone.0006187.
51. Slimane, A. B., D. Raclot, O. Evrard, M. Sanaa, I. Lefèvre, M. Ahmadi, M. Tounsi, C. Rumpel, A. B. Mammou, and, Y. Le Bissonnais, (2013), Fingerprinting sediment sources in the outlet reservoir of a hilly cultivated catchment in Tunisia, J. Soil Sediment., 13, 801-815, doi: 10.1007/s11368-012-0642-6.
52. Small, I. F., J. S. Rowan, and, S. W. Franks, (2002), Quantitative sediment fingerprinting using a Bayesian uncertainty estimation framework, in The Structure Function and Management Implications of Fluvial Sedimentary Systems, vol. 276, edited by, F. J. Dyer, M.C. Thomas, and, J. M. Olley, pp. 443-450, IAHS Publ., Oxfordshire, U. K.
53. Smith, A.E., P. B. Ryan, and, J. S. Evans, (1992), The effect of negating correlations when propagating uncertainty and estimating the population distribution of risk, Risk Anal., 12, 467-474.
54. Smith, H. G., and, W. H. Blake, (2014), Sediment fingerprinting in agricultural catchments: A critical re-examination of source discrimination and data corrections, Geomorphology, 204, 177-191, doi: 10.1016/j.geomorph.2013.08.00.
55. Sun, D., and, J. O. Berger, (2006), Objective priors for the multivariate normal model, Ann. Statist., 36, 963-982, doi:10:1214/07-AOS501.
56. Tarroux, A., J. Bêty, G. Gauthier, and, B. Dominique, (2012), The marine side of a terrestrial carnivore: Intra-population variation in use of allochthonous resources by artic foxes, PLoS ONE, 7, e42427, doi: 10.1371/journal.pone.0042427.
57. Thompson, J., R. Cassidy, D. G. Doody, and, R. Flynn, (2013), Predicting critical source areas of sediment in headwater catchments, Agric. Ecosyst. Environ., 179, 41-52, doi: 10.1016/j.agee.2013.07.010.
58. Van den Meersche, K., K. Soetaert, and, J. J. Middelbury, (2008), A Bayesian compositional estimator for microbial taxonomy based on biomarkers, Limnol. Oceanogr. Methods, 6, 190-199.
59. Van den Meersche, K., K. Soetaert, and, D. Van Oevelen, (2009), xsample(): An R function for sampling linear inverse problems, J. Stat. Softw., Code Snippets, 30, 1-15.
60. Walling, D. E., (2013) The evolution of sediment source fingerprinting investigations in fluvial systems, J. Soil. Sediment., 13, 1658-1675, doi: 10.1007/s11368-013-0767-2.
61. Walling, D. E., A. L. Collins, G. K. McMellin, (2003), A reconnaissance survey of the source of interstitial fine sediment recovered from salmonid spawning gravels in England and Wales, Hydrobiologia, 497, 91-108, doi: 10.1023/A:1025413721647.
62. Ward, E. J., B. X. Semmens, and, D. E. Schindler, (2010), Including source uncertainty and prior information in the analysis of stable isotope mixing models, Environ. Sci. Technol., 44, 4645-4650, doi: 10.1021/es100053v.
63. Ward, E. J., B. X. Semmens, D. L. Phillips, J. W. Moore, and, N. Bouwes, (2011) A quantitative approach to combine sources in stable isotope mixing models, Ecosphere, 2, 1-11, doi: 10.1890/ES10-00190.1.
64. Wilkinson, S. N., G. J. Hancock, R. Bartley, A. A. Hawdon, and, R. J. Keen, (2013), Using sediment tracing to assess processes and spatial patterns of erosion in grazed rangelands, Burdekin River basin, Australia, Agric. Ecosyst. Environ., 180, 90-102, doi: 10.1016/j.agee.2012.02.002.
65. Yao, Y., E. Zhang, N. Kuhn, R. Jones, P. Langdon, J. Shen, and, P. Greenwood, (2013) Sediment provenance in the Shudu Lake basin, northwest Yunnan Province, China, as revealed by composite fingerprinting, DIE ERDE, 144, 17-29, doi: 10.12854/erde-144-2.