How to evaluate sediment fingerprinting source apportionments

Abstract

Purpose Evaluating sediment fingerprinting source apportionments with artificial mixtures is crucial for supporting decision-making and advancing modeling approaches. However, artificial mixtures are rarely incorporated into fingerprinting research and guidelines for model testing are currently lacking. Here, we demonstrate how to test source apportionments using laboratory and virtual mixtures by comparing the results from Bayesian and bootstrapped modeling approaches. Materials and methods Laboratory and virtual mixtures (n = 79) with known source proportions were created with soil samples from two catchments in Fukushima Prefecture, Japan. Soil samples were sieved at 63 µm and analyzed for colorimetric and geochemical parameters. The MixSIAR Bayesian framework and a bootstrapped mixing model (BMM) were used to estimate source contributions to the artificial mixtures. In addition, we proposed and demonstrated the use of multiple evaluation metrics to report on model uncertainty, residual errors, performance, and contingency criteria. Results and discussion Overall, there were negligible differences between source apportionments for the laboratory and virtual mixtures, for both models. The comparison between MixSIAR and BMM illustrated a trade-off between accuracy and precision in the model results. The more certain MixSIAR solutions encompassed a lesser proportion of known source values, whereas the BMM apportionments were markedly less precise. Although model performance declined for mixtures with a single source contributing greater than 0.75 of the material, both models represented the general trends in the mixtures and identified their major sources. Conclusions Virtual mixtures are as robust as laboratory mixtures for assessing fingerprinting mixing models if analytical errors are negligible. We therefore recommend to always include virtual mixtures as part of the model testing process. Additionally, we highlight the value of using evaluation metrics that consider the accuracy and precision of model results, and the importance of reporting uncertainty when modeling source apportionments.

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

“…This would allow some degree of independent validation of PSDs as a fingerprint. The independent validation of sediment source fingerprinting estimates has been rarely undertaken (e.g., Batista et al, 2022; Gaspar et al, 2019). To validate estimated source proportions using PSDs as a fingerprint herein, we used sediment budget estimates generated using conventional water sampling; this has, to date, been used in few sediment fingerprinting studies (e.g., Collins et al, 1998; Dabrin et al, 2021; Tiecher et al, 2022), mainly due to the extra costs associated with the installation of equipment and sampling (Collins et al, 2020, 2017; Collins & Walling, 2004).…”

Determination of sediment sources following a major wildfire and evaluation of the use of color properties and polycyclic aromatic hydrocarbons (PAHs) as tracers

“…The evaluation of MixSIAR outputs can be undertaken using artificial mixtures, which include those created in the laboratory (e.g., Haddadchi et al 2014 ; Gaspar et al 2019 ) and those created virtually (e.g., Batista et al 2022 ). In this study, virtual mixtures were used due to the ease with which the analysis is undertaken (i.e., mathematically) and due to the high cost of analysis that makes laboratory mixtures prohibitive.…”

“…In this study, virtual mixtures were used due to the ease with which the analysis is undertaken (i.e., mathematically) and due to the high cost of analysis that makes laboratory mixtures prohibitive. Additionally, Batista et al ( 2022 ) found the use of virtual mixtures to be nearly as useful as laboratory mixtures, though in the case of PAHs, an untested tracer, this may not be the case. A full evaluation of the use of virtual mixtures was not the main objective of this paper (see Batista et al 2022 ), but to evaluate both model output and subsequently, source discrimination, 15 virtual mixtures for both tributaries and the mainstem sources ( n = 60) were analyzed for both color and PAHs.…”

“…Additionally, Batista et al ( 2022 ) found the use of virtual mixtures to be nearly as useful as laboratory mixtures, though in the case of PAHs, an untested tracer, this may not be the case. A full evaluation of the use of virtual mixtures was not the main objective of this paper (see Batista et al 2022 ), but to evaluate both model output and subsequently, source discrimination, 15 virtual mixtures for both tributaries and the mainstem sources ( n = 60) were analyzed for both color and PAHs. Mixtures were created by multiplying the proportion of each source in the mixture by the mean source tracer value.…”

The Role of Paddy Fields in the Sediment of a Small Agricultural Catchment in the Three Gorges Reservoir Region by the Sediment Fingerprinting Method

“…Physical and biogeochemical properties of sediment were regarded as the reflection of its soil sources in the fingerprinting approach. Accordingly, it is feasible to use these differences in properties to identify the sources of the target sediment [8,[19][20][21].…”