Purpose This study tests the feasibility of using a submersible spectrophotometer as a novel method to trace and apportion suspended sediment sources in situ and at high temporal frequency. Methods Laboratory experiments were designed to identify how absorbance at different wavelengths can be used to un-mix artificial mixtures of soil samples (i.e. sediment sources). The experiment consists of a tank containing 40 L of water, to which the soil samples and soil mixtures of known proportions were added in suspension. Absorbance measurements made using the submersible spectrophotometer were used to elucidate: (i) the effects of concentrations on absorbance, (ii) the relationship between absorbance and particle size and (iii) the linear additivity of absorbance as a prerequisite for un-mixing. Results The observed relationships between soil sample concentrations and absorbance in the ultraviolet visible (UV–VIS) wavelength range (200–730 nm) indicated that differences in absorbance patterns are caused by soil-specific properties and particle size. Absorbance was found to be linearly additive and could be used to predict the known soil sample proportions in mixtures using the MixSIAR Bayesian tracer mixing model. Model results indicate that dominant contributions to mixtures containing two and three soil samples could be predicted well, whilst accuracy for four-soil sample mixtures was lower (with respective mean absolute errors of 15.4%, 12.9% and 17.0%). Conclusion The results demonstrate the potential for using in situ submersible spectrophotometer sensors to trace suspended sediment sources at high temporal frequency.
Purpose Identifying best practices for sediment fingerprinting or tracing is important to allow the quantification of sediment contributions from catchment sources. Although sediment fingerprinting has been applied with reasonable success, the deployment of this method remains associated with many issues and limitations. Methods Seminars and debates were organised during a 4-day Thematic School in October 2021 to come up with concrete suggestions to improve the design and implementation of tracing methods. Results First, we suggest a better use of geomorphological information to improve study design. Researchers are invited to scrutinise all the knowledge available on the catchment of interest, and to obtain multiple lines of evidence regarding sediment source contributions. Second, we think that scientific knowledge could be improved with local knowledge and we propose a scale of participation describing different levels of involvement of locals in research. Third, we recommend the use of state-of-the-art sediment tracing protocols to conduct sampling, deal with particle size, and examine data before modelling and accounting for the hydro-meteorological context under investigation. Fourth, we promote best practices in modelling, including the importance of running multiple models, selecting appropriate tracers, and reporting on model errors and uncertainty. Fifth, we suggest best practices to share tracing data and samples, which will increase the visibility of the fingerprinting technique in geoscience. Sixth, we suggest that a better formulation of hypotheses could improve our knowledge about erosion and sediment transport processes in a more unified way. Conclusion With the suggested improvements, sediment fingerprinting, which is interdisciplinary in nature, could play a major role to meet the current and future challenges associated with global change. Supplementary information The online version contains supplementary material available at 10.1007/s11368-022-03203-1.
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.
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