Search

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 µ m 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Spectrocolourimetric measurements provide a relatively inexpensive, quick and non-destructive alternative to the analysis of geochemical and organic matter properties. When used in the analysis of sediments and their potential sources, these colour parameters may provide important information on the dominant processes (i.e. erosion) occurring in the Critical Zone. Here, they are used to investigate whether eroded sediment is derived from forest (i.e. natural), cultivated (i.e. anthropogenic) or subsoil sources in order to assess their potential to monitor the effect of decontamination in regions impacted by fallout from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident. Materials and methods: Fifteen spectrocolourimetric properties (L*, a*, b*, C*, h, x, y, z, L, a, b, u*, v*, u', v') were measured in potential source (n = 37) and sediment (n = 400) samples collected during 13 campaigns from 2011 to 2017 after major flood events in two catchments (total surface area of 450 km2) draining the main FDNPP radioactive pollution plume. Potential sources included topsoil from forest and cultivated sources along with subsoil material originating from landslides, channel banks and the decontamination of cultivated areas. The optimum set of parameters used in the mixed linear model to calculate the sediment source contributions was obtained through the use of a range test, the Kruskal–Wallis H test and a linear discriminant analysis. Results and discussion: Nine selected colour parameters correctly classified 100% of the source samples (i.e. forest, subsoil and cultivated sources). The results illustrate that cultivated landscapes were the main source of sediment to these river systems (mean 56%, SD 34%) followed by subsoil (mean 26%, SD 16%) and forest sources (mean 21%, SD 24%). However, these contributions varied strongly over time, with a peak of subsoil contributions (mean 57%, SD 17%) in Fall 2015, coinciding with the occurrence of a typhoon after the remediation works. These results were consistent with monitoring studies conducted in the same area that showed the major impact of typhoon Etau in September 2015 on sediment and radiocaesium fluxes. Conclusions: These original results demonstrate that spectrocolourimetric measurements may contribute to the routine monitoring of the effectiveness of remediation works in this post-accidental context. Owing to the inexpensive, rapid and non-destructive analyses, spectrocolourimetric-based tracing methods have significant potential to provide information on the dominant erosion processes occurring in the Critical Zone. [ABSTRACT FROM AUTHOR]

• Geochemical tracers were not conservative in a catchment with highly weathered soils. • Magnetic and UV tracers had low discriminating potential between land uses. • The low discrimination between sources results in great uncertainties in the results. • Quantifying sediment contributions from more than three sources remains challenging. • Despite limited discrimination, results of all tracers combinations remained consistent. An important step in the sediment source fingerprinting approach is the selection of the appropriate tracing parameters to maximize source discrimination. The use of multiple tracing properties may reduce uncertainties and increase discrimination between sources. Accordingly, this study investigates the discrimination and quantifies the contribution of sediment sources delivering sediment to a river draining a homogeneous subtropical agricultural catchment based on the combination of ultraviolet–visible spectra derived parameters (UV), magnetic (M), and geochemical tracers (GEO). The investigated catchment (Conceição River − 804 km2), located in Southern Brazil, has predominantly deep and strongly weathered Ferralsols. The main land-uses found in the area are cropland (89%), pasture (5%) and forest (5%). A total of 187 samples were collected to characterise the five main sediment sources, including cropland, pastures, unpaved roads, gullies and stream banks. A total of 53 tracers, including 21 geochemical tracers, two magnetic properties and 30 parameters derived from UV spectra, were analysed. Tracers were selected following a three step procedure, including: (i) an interquartile range test, (ii) a Kruskal–Wallis H test, and (iii) a linear discriminant function analysis (LDA). The LDA was performed using six different sets of variables: (i) GEO only; (ii) UV only; (iii) M + UV (MUV); (iv) GEO + UV (GUV); (v) GEO + M (GM) and (vi) GEO + M + UV (GMUV). The selected tracers were introduced into a mass balance mixing model to estimate the source contributions to in-stream sediment by minimizing the sum of square residuals. Most geochemical tracers were considered not conservative by using the interquartile range test in this catchment with highly weathered soils. The GM approach resulted in the highest percentage of samples correctly classified (SCC), with 74%, followed by the approaches with GMUV and GUV, with 73%. Alternative tracers, UV individually or combined with M tracers, correctly classified only 59 and 60% of the samples, respectively. Moreover, they did not provide significant additional discrimination power even when combined with the GEO tracers. The apportionment model resulted in similar source contribution results for all approaches, with the absence of significant difference when comparing the mean source contributions obtained for the entire set of sediment samples (Cropland: 17–23%; Pastures: 24–34%; Unpaved Roads: 3–12%; Stream Banks: 26–31%; Gullies: 14–19%). Due to the strong homogeneity of soil types found in the Conceição catchment, these differences in source contributions remained very low and the results of the mixing model were impacted by the high number of potential sources and the relatively limited quality of the sediment source discrimination. According to the model results, the low discrimination between the potential sediment sources illustrates the difficulties for discriminating land-used based sediment sources, with more than three potential sources, in homogeneous catchments with highly weathered soils (e.g. Ferralsols, Nitisols) under tropical conditions. [ABSTRACT FROM AUTHOR]

Spectrocolorimetric properties provide a quick, low-cost and non-destructive alternative to the measurement of geochemical and organic matter parameters. Their potential to discriminate between forest, cultivated and subsoil sources was tested in soils and sediment collected in coastal catchments draining the main radioactive pollution plume of Fukushima Prefecture, Japan.Fifteen spectrocolorimetric properties were determined in potential source (n=37) and sediment (n=400) samples collected during 13 campaigns from 2011–2017 in two catchments (covering a total surface area of 450 km²) draining the main radioactive pollution plume. Potential sources included topsoil from forest and cropland sources along with subsoil material originating from landslides, channel banks and the decontamination of cultivated areas. The optimum set of parameters used in the mixed linear model to calculate the sediment source contributions was obtained through the use of a range test, the Kruskal-Wallis H-test, and a linear discriminant analysis. Nine colour parameters correctly classified 100% of the source samples (forest, subsoil and cultivated sources). The results illustrate that cultivated landscapes were the main source of sediment to these river systems (mean 56%, SD 34%) followed by subsoil (mean 26%, SD 16%) and forest sources (mean 21%, SD 24%). However, these contributions strongly varied over time, with a peak of subsoil contributions (mean 57%, SD 17%) in Fall 2015, coinciding with the occurrence of a typhoon after the remediation works. These original results demonstrate that spectrocolorimetric measurements provide a powerful tool for the monitoring of the effectiveness of remediation works in this post-accidental context. [ABSTRACT FROM AUTHOR]

An important step in the sediment source fingerprinting approach is the selection of the appropriate tracing parameters to maximise source discrimination. In order to reduce uncertainties and increase discrimination between sources it may be necessary to use multiple tracing parameters. Accordingly, this study investigates the discrimination and apportionment of sediment sources in a rural agricultural catchment obtained by combining colour, magnetic, and geochemical fingerprinting approaches. The Conceição River catchment (804 km²) has predominantly deep and highly weathered Ferralsols with land-use consisting of croplands (73%), pastures (18%), forests (8%) and other uses (1%). A total of 189 samples were taken from the main sediment sources, including: Croplands (CR, n=78), pastures (P, n=24), unpaved roads (UR, n=38), gullies (G, n=15) and stream bank (SB, n=34). Sediment samples were taken from the surface bed (n=10) of the river and with time integrated samplers (n=4). Twenty-two geochemical tracers, 6 magnetic properties and 24 colour parameters were analyzed. Tracers were selected following a three step procedure, including: (i) a conservative range test (95% confident interval), (ii) a Kruskal–Wallis H test, and (iii) discriminant function analysis (DFA). The DFA was performed using four different sets of variables: (i) geochemical variables only (G); (ii) geochemical+magnetic+colour (GMC); (iii) geochemical+colour (GC); (iv) geochemical+magnetic (GM). The selected tracers were introduced into a modified version of the classical Solver-based mixing model that in order to determine the relative contribution of different sources to in-stream sediment through simultaneously minimizing mixing model difference. The G and GC DFAs both resulted 69% of samples correct classified (SCC) as no colour parameters were selected by the DFA. The GMC and GM approaches improved the discrimination, both resulting in 76% of SCC. For the G and GC approaches, the average source contribution for the 12 sediment samples were P 47%, SB 29%, CR 19%, UR 5% and G 0%. For the GMC and GM approaches, the contribution of each source was P 41%, SB 37%, CR 14%, UR 8% and G 0%. These results are counterintuitive to field observations where cropland is anticipated to contribute more sediment than pastures. Future research should use artificial mixtures to validate these results. Both magnetic and colour parameters hold potential to improve discrimination between sources, particularly magnetic parameters in catchments with high weathered soils. [ABSTRACT FROM AUTHOR]