Using particle size distributions to fingerprint suspended sediment sources—Evaluation at laboratory and catchment scales

Abstract

Applications of sediment source fingerprinting studies are growing globally despite the high costs and workloads associated with the analyses of conventional fingerprint properties on target sediment samples collected using traditional methods. To this end, there is a need to test new fingerprint properties that can overcome these challenges. Sediment particle size could potentially contribute here since it is relatively easy to measure but, until now, has rarely been deployed as a fingerprint itself. Instead, particle size has been used to ensure that source and target sediment samples are more directly comparable on the basis of the fingerprints used. Accordingly, this work examined whether particle size distributions (PSDs) could be used as a reliable fingerprint for apportioning sediment sources, in combination with a grain size un‐mixing model. Application of PSDs as a fingerprint was tested at two scales: (i) in a laboratory setting where soil samples with known PSDs were used to generate artificial mixtures to evaluate un‐mixing model results, and (ii) a catchment setting comparing PSDs in a confluence‐based approach to test if downstream target sediment PSDs could be un‐mixed into the contributions of sediment coming from an upstream and a tributary sampling site. Laboratory results showed that the known proportions of the two, three and four soil samples in the artificial mixtures were predicted accurately using the AnalySize grain size un‐mixing model, giving average absolute errors of 9%, 8% and 6%, respectively. Catchment results showed variable performances when comparing un‐mixing results with sediment budget estimations, with the best results obtained at higher discharge values during storm runoff events. Overall, our results suggest the potential of using PSDs for estimating contributions of sediment sources delivering SS with distinct PSDs when sources are located at short distance to the downstream sampling site.

Understanding sediment in river systems—Building on a 50‐year contribution

“…Firstly, Miller et al (2022) have used environmental magnetism to differentiate successfully sediment sources by lithology in areas underlain by igneous, metamorphic, and sedimentary rocks in the near‐natural landscape of southern Kruger National Park (Mpumalanga Province), South Africa. Secondly, Lake et al (2022), in an investigation based on both laboratory and field study, suggest that particle‐size distributions, whilst often crucial to ensuring the comparability of source and target sediment samples in fingerprinting studies, may of themselves offer a cheaper and more easily applied fingerprint method than many of the properties more traditionally applied for this purpose. Thirdly, a study by Navas et al (2022) to fingerprint sediment sources in proglacial tropical highlands (Cordillera Blanca) of Perú applied a novel procedure for tracer selection which involved a conservativeness index and a ranking based on consensus.…”

Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru

“…Tracers used in sediment fingerprinting studies include color properties [36][37][38], major and trace elemental composition [39,40], rare earth elements [33,[41][42][43], radionuclide characteristics [38,44,45], and organic matter [46,47]. Furthermore, authors around the world, e.g., [48,49] have used this technique to determine sediment sources; in South America, studies have been carried out in Argentina [50], Brazil [51], and Venezuela [52]. In Peru, however, sediment fingerprinting has been applied only on studies involving marine environments [53], but nothing has been carried out in inland regions of the country.…”