Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample mixtures

Abstract

Information on sediment sources in river catchments is required for effective sediment control strategies, to understand sediment, nutrient and pollutant transport, and for developing soil erosion management plans. Sediment fingerprinting procedures are employed to quantify sediment source contributions and have become a widely used tool. As fingerprinting procedures are naturally variable and locally dependant, there are different applications of the procedure. Here, the auto-evaluation of different fingerprinting procedures using virtual sample mixtures is proposed to support the selection of the fingerprinting procedure with the best capacity for source discrimination and apportionment. Surface samples from four land uses from a Central Spanish Pyrenean catchment were used i) as sources to generate the virtual sample mixtures and ii) to characterise the sources for the fingerprinting procedures. The auto-evaluation approach involved comparing fingerprinting procedures based on four optimum composite fingerprints selected by three statistical tests, three source characterisations (mean, median and corrected mean) and two types of objective functions for the mixing model. A total of 24 fingerprinting procedures were assessed by this new approach which were solved by Monte Carlo simulations and compared using the root mean squared error (RMSE) between known and assessed source ascriptions for the virtual sample mixtures. It was found that the source ascriptions with the highest accuracy were achieved using the corrected mean source characterisations for the composite fingerprints selected by the Kruskal Wallis H-test and principal components analysis. Based on the RMSE results, high goodness of fit (GOF) values were not always indicative of accurate source apportionment results, and care should be taken when using GOF to assess mixing model performance. The proposed approach to test different fingerprinting procedures using virtual sample mixtures provides an enhanced basis for selecting procedures that can deliver optimum source discrimination and apportionment.

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

“…While the use of fallout radionuclides in sediment fingerprinting has helped to assess the time passed since the sediment eroded (residence time) (e.g. Palazón et al, 2015;Smith and Blake, 2014;Wilkinson et al, 2015), it remains a challenge to interpret patterns in sediment source contributions in terms of catchment erosion processes.…”

Variability in source sediment contributions by applying different statistic test for a Pyrenean catchment

“…One of the methodological differences within the range of different applications of the sediment fingerprinting procedure in the literature is related to the statistical analysis used to identify the subset of the tracer properties which 'best' discriminate between sources (Palazón et al, 2015b). Although the two step process outlined by Collins et al (1997) which combined Kruskal-Wallis H-test as first step and discriminant function analysis as second step has been extensively used in source fingerprinting studies (e.g., Russell et al, 2001;Minella et al, 2008;Hughes et al, 2009;Schuller et al, 2013;Stone et al, 2014), other procedures have been used to select the most effective optimum composite fingerprint, such as the use of principal component analysis (e.g.…”

It's sedimentary, my dear Watson : implications of watershed processes on the sediment fingerprinting approach

“…Ideally, the fingerprints should provide a clear distinction between the sources of sediment identified. However, recent research by Palazón et al (2015) demonstrated that different fingerprint selection methods yielded different subsets of fingerprint properties that were able to successfully discriminate between different potential sources of sediment and that the method of fingerprint selection ultimately influenced the sediment apportionment results. Furthermore, if the fingerprints are unable to provide good discrimination between potential sources then the sources may need to be grouped together (Section 1.3.1).…”

“…Recent work by Palazón et al (2015) showed that the subset of tracers selected was dependent on the procedure used and that each subset of selected tracers resulted in different apportionment results. Furthermore, a purely statistical approach may not be the most appropriate method, as the conservative behaviour of sediment fingerprints during transport through the watershed or the underlying processes that lead to their ability to discriminate between sources are not considered Sherriff et al 2015).…”

“…Part of the lack of standardization of the approach stems from the fact that the scientific community is still exploring and assessing different approaches and methods to better link potential sediment sources to collected in-stream sediment. Much of the research focus has been on the tracer selection process (e.g., Palazón et al 2015), data manipulation (e.g., and mixing models (e.g., Haddadchi et al 2014), but incorporating process-based information has received less attention. Comprehending the physical and biogeochemical processes that link the sediment sources to in-stream sediment, and incorporating these processes into the sediment fingerprinting approach will improve the robustness and reliability of the sediment fingerprinting approach (Fig.…”