Accuracy verification of optical fingerprinting methods in sediment tracing study

Chen, Dan; Dai, Wei; Li, Mengjie; Wang, Bing; Zeng, Yi; Ni, Lingshan; Fang, Nianqiao; Shi, Zhihua

doi:10.1002/hyp.14870

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…However, the visible colour parameters are quite sensitive to spatial and temporal variations (García-Comendador et al, 2023). The acquisition of other spectral regions such as Vis-NIR, NIR and MIR appears to be more robust (Chen et al, 2023), especially as these regions are a powerful and reliable way to obtain extensive range of properties and information on the sample with the advantages of being rapid, costeffective and non-destructive measurement (Soriano-Disla et al, 2014). Of note, in order to ensure the exchange of spectra within the community, the adoption of a common protocol and/or the provision of calibration spectra using inter-calibration samples should be discussed, as spectra tend to be instrument-dependent (Pimstein et al, 2011).…”

Section: Conservativity Assessmentmentioning

confidence: 99%

Sensitivity of source sediment fingerprinting modelling to tracer selection methods

Chalaux-Clergue,

Bizeul,

Batista

et al. 2023

Preprint

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Abstract. In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last two decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the so-called three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, contribution modelling tendency and performance on a single dataset. As for the tree-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracing properties analysed in soil (through the consideration of three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated in order to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was used to predict source contributions on virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their more or less restrictive identification of conservative properties, which were found to be associated with different sediment source contribution tendencies. The less restrictive selections of tracers were associated with a dominant and constant contribution of forests to sediment, whereas the most restrictive selections were associated with dominant and constant contributions of cropland to sediment. In contrast, intermediately restrictive selection of tracers led to more balanced contributions of both cropland and forest to sediment production. Virtual mixtures allowed to compute several evaluation metrics, which supported a better understanding of each tracer selection modelling accuracy. However, strong divergences were observed between the predicted contributions of virtual mixtures and the predicted sediment source contributions. These divergences may likely be attributed to the occurrence of a non-(fully) conservative behaviour of potential tracing properties during erosion, transport and deposition processes, which could not be reproduced when generated the virtual mixtures. Among the compared tracer selection methods, the three-step method using the mean ± SD and hinge range test criteria provided the most reliable tracer selection methods. In the future, it would be fundamental to generate more reliable metrics to assess conservativeness, to support more reliable modelling and more realistic virtual mixture generation to correctly evaluate modelling accuracy. These improvements may contribute to trustworthy sediment fingerprinting techniques for supporting efficient soil conservation and watershed management.

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Section: Conservativity Assessmentmentioning

confidence: 99%

Sensitivity of source sediment fingerprinting modelling to tracer selection methods

Chalaux-Clergue,

Bizeul,

Batista

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Using Artificial Mixtures to Test the Impacts of Tracer Combination and Model Selection on Sediment Source Fingerprinting Accuracy in a Burned Area

Liang,

Shi,

Wen

et al. 2024

Preprint

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Sensitivity of source sediment fingerprinting to tracer selection methods

Chalaux-Clergue,

Bizeul,

Batista

et al. 2024

SOIL

View full text Add to dashboard Cite

Abstract. In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last 2 decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, modelled contributions and performance on a single dataset. As for the three-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracer properties analysed in soil (three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was then used to predict source contributions on both virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their sensitivity to the conservative behaviour of properties, which was found to be associated with different predicted source contribution tendencies along the sediment core. The methods selecting the largest number of tracers were associated with a dominant and constant contribution of forests to sediment. In contrast, the methods selecting the lowest number of tracers were associated with a dominant and constant contribution of cropland to sediment. Furthermore, the intermediate selection of tracers led to more balanced contributions of both cropland and forest to sediments. The prediction of the virtual mixtures allowed us to compute several evaluation metrics, which are generally used to support the evaluation of model accuracy for each tracer selection method. However, strong differences or the absence of correspondence were observed between the range of predicted contributions obtained for virtual mixtures and those values obtained for actual sediments. These divergences highlight the fact that evaluation metrics obtained for virtual mixtures may not be directly transferable to models run for actual samples and must be interpreted with caution to avoid over-interpretation or misinterpretation. These divergences may likely be attributed to the occurrence of a not (fully) conservative behaviour of potential tracer properties during erosion, transport and deposition processes, which could not be fully reproduced when generating the virtual mixtures with currently available methods. Future research should develop novel metrics to quantify the conservative behaviour of tracer properties during erosion and transport processes. Furthermore, new methods should be designed to generate virtual mixtures closer to reality and to better evaluate model accuracy. These improvements would contribute to the development of more reliable sediment fingerprinting techniques, which are needed to better support the implementation of effective soil and water conservation measures at the catchment scale.

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Accuracy verification of optical fingerprinting methods in sediment tracing study

Cited by 3 publications

References 60 publications

Sensitivity of source sediment fingerprinting modelling to tracer selection methods

Sensitivity of source sediment fingerprinting modelling to tracer selection methods

Using Artificial Mixtures to Test the Impacts of Tracer Combination and Model Selection on Sediment Source Fingerprinting Accuracy in a Burned Area

Sensitivity of source sediment fingerprinting to tracer selection methods

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