Fingerprinting sub-basin spatial sediment sources using different multivariate statistical techniques and the Modified MixSIR model

A - Papers appearing in refereed journals

Collins, A. L., Nosrati, K. and Madankan, M. 2018. Fingerprinting sub-basin spatial sediment sources using different multivariate statistical techniques and the Modified MixSIR model. Catena. 164 (May), pp. 32-43.

AuthorsCollins, A. L., Nosrati, K. and Madankan, M.

Information on the relative contributions of sediment from different sources is needed to target sediment control strategies to prevent excess sediment delivery to receptors like dam reservoirs. The overarching scientific objective of this study was therefore to apportion sub-basin spatial source contributions to the supply of fine sediment in an erodible mountainous basin in north-eastern Iran to inform management. The technical objective was to satisfy the scientific objective using a source fingerprinting procedure based on composite signatures selected by different statistical tests. Nine potential geochemical tracers were measured on 21 sediment samples collected to characterise the three sub-basin spatial sediment sources and seven sediment samples collected at
the outlet of the main basin. The statistical analysis employed to select three different composite fingerprints for discriminating the sub-basin sediment sources comprised: (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), and (3) a combination of KW-H and principal components & classification analysis (PCCA). A Bayesian un-mixing model was used to ascribe sub-basin source contributions using the three composite fingerprints. Using KW-H, the respective relative contributions from subbasins 1, 2 and 3 were estimated as 45.6%, 3.8% and 50.6%, compared to 46.8%, 18.8% and 34.4% using KW-H and DFA, and 61%, 2.5% and 36.5% using KW-H and PCCA. Kolmogorov-Smirnov test pairwise comparisons of the distributions of predicted source proportions generated using different composite signatures confirmed statistically significant differences. The root mean square difference between the predicted source proportions based on different composite signatures was ~12%. This study therefore provides more evidence that source tracing studies should deploy a number of composite signatures selected using independent statistical tests to permit appraisal of the consistencies or otherwise in predicted source contributions based on the tracers used. The outputs of this preliminary study will be used to inform the spatial targeting of sediment mitigation.


KeywordsGeochemical tracers
Year of Publication2018
Journal citation164 (May), pp. 32-43
Digital Object Identifier (DOI)
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Research Council of Shahid Beheshti University, Tehran, Iran
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Output statusPublished
Publication dates
Online09 Jan 2018
Publication process dates
Accepted02 Jan 2018
PublisherElsevier Science Bv
Copyright licenseCC BY

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