Geochemical tracing and spatial evolution of the sediment bed load of the Romaine River, Québec, Canada

Hardy, F.; Bariteau, L.; Lorrain, Stéphane; Thériault, I.; Gagnon, Graham A.; Messier, Danielle; Rougerie, J.-F.

doi:10.1016/j.catena.2010.01.005

Cited by 26 publications

(20 citation statements)

References 21 publications

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“…One established approach of determining the source of sediment is to trace the elemental geochemistry of sediment back to their lithogenic origins (Olley and Caitcheon, ; Hardy et al , ; Weltje and Brommer, ). This geochemical ‘fingerprinting’ technique is based on the fact that different rock types often produce soils and sediments with unique elemental compositions (Klages and Hsieh, ; Wood, ; Caitcheon et al , ).…”

Section: Introductionmentioning

confidence: 99%

An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations

Laceby

Olley

2014

Hydrological Processes

115

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The identification of sediment sources is fundamental to the management of increasingly scarce water resources. Tracing the origin of sediment with elemental geochemistry is a well‐established approach to determining sediment provenance. Fundamental to the confident apportionment of sediment to their lithogenic sources is the modelling process. Recent approaches have incorporated distributions throughout the modelling process including source contribution terms for two end‐member sources. The shift from modelling source samples to modelling samples drawn from distributions has removed relationships, including potential correlations between elemental concentrations, from the modelling process. Here, we present a novel modelling approach that re‐incorporates correlations between elemental concentrations and models distributions for source contribution terms for multiple source end members. Artificial mixtures, based on catchment sources samples, were created to test the accuracy of this correlated distribution model and also examine modelling approaches used in the literature. The most accurate model incorporates correlations between elements, uses the absolute mixing model difference and does not use any weighting. This model was then applied to identify the sources of sediment in three South East Queensland catchments and demonstrated that Quaternary Alluvium is the most dominant source of sediment in these catchments (μ 44%, σ 12%). This study demonstrates that it is important to understand how different weightings may impact modelling results. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations

Laceby

Olley

2014

Hydrological Processes

115

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“…Hardy et al . () also used stream sediment for discriminating sediment sources in a large catchment but employed sampling through a compositional evolution approach to distinguish changes through the sediment continuum within the catchment. Koiter et al .…”

Section: Introductionmentioning

confidence: 99%

“…Caitcheon, 1998;Blake et al, 2006;Foster et al, 2007), geochemical compositions (e.g. Collins et al, 1998;Jin et al, 2006;Lee et al, 2008;Singh, 2009;Hardy et al, 2010;Zhang et al, 2012;Collins et al, 2013), isotopic ratios (e.g. Douglas et al, 1995;Gingele and De Deckker, 2005), radionuclides (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…found difficulty discriminating between sources in their study because the cultivated source signature resembled a mix of pastures and channel bank sources. This issue is compounded in larger catchments where the task of ascribing sediment sources for intermediate-sized (10-10 000 km 2 ) and large-sized catchments (>10 000 km 2 ) becomes increasingly difficult because of the greater number of sources and sediment residence times (Hardy et al, 2010). This leads to increases in variation and uncertainty encountered in source sample signatures and provides challenges for conventional source sampling techniques, which rely on point source sampling from soils.…”

Section: Introductionmentioning

confidence: 99%

“…They concluded that sampling of source sediment from geologically distinct tributaries at river confluences was the best way to identify spatial patterns in larger catchments. Hardy et al (2010) also used stream sediment for discriminating sediment sources in a large catchment but employed sampling through a compositional evolution approach to distinguish changes through the sediment continuum within the catchment. Koiter et al (2013a) and Lamba et al (2015) also indicate differences in source composition between suspended sediment and fine sediment deposited on the stream bed, suggesting that both be sampled to effectively understand sediment sources.…”

Section: Introductionmentioning

confidence: 99%

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Application of a confluence-based sediment-fingerprinting approach to a dynamic sedimentary catchment, New Zealand

et al. 2015

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Fine sediment is a dynamic component of the fluvial system, contributing to the physical form, chemistry and ecological health of a river. It is important to understand rates and patterns of sediment delivery, transport and deposition. Sediment fingerprinting is a means of directly determining sediment sources via their geochemical properties, but it faces challenges in discriminating sources within larger catchments. In this research, sediment fingerprinting was applied to major river confluences in the Manawatu catchment as a broad‐scale application to characterizing sub‐catchment sediment contributions for a sedimentary catchment dominated by agriculture. Stepwise discriminant function analysis and principal component analysis of bulk geochemical concentrations and geochemical indicators were used to investigate sub‐catchment geochemical signatures. Each confluence displayed a unique array of geochemical variables suited for discrimination. Geochemical variation in upstream sediment samples was likely a result of the varying geological source compositions. The Tiraumea sub‐catchment provided the dominant signature at the major confluence with the Upper Manawatu and Mangatainoka sub‐catchments. Subsequent downstream confluences are dominated by the upstream geochemical signatures from the main stem of Manawatu River. Variability in the downstream geochemical signature is likely due to incomplete mixing caused in part by channel configuration. Results from this exploratory investigation indicate that numerous geochemical elements have the ability to differentiate fine sediment sources using a broad‐scale confluence‐based approach and suggest there is enough geochemical variation throughout a large sedimentary catchment for a full sediment fingerprint model. Combining powerful statistical procedures with other geochemical analyses is critical to understanding the processes or spatial patterns responsible for sediment signature variation within this type of catchment. Copyright © 2015 John Wiley & Sons, Ltd.

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Temporal Variation of Bed Load to Suspended Load Ratio in Kojour River, Iran

Sadeghi

Kheirfam

2015

CLEAN Soil Air Water

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Analysis of the river sediment transport variability under different temporal and hydrological conditions is important in hydraulics, hydrological, and soil and water conservation science and engineering. However, few studies have been conducted to study bed load (BL) and consequently BL to suspended load (SL) relationships and temporal variation of this ratio. The present study was, therefore, conducted to evaluate the temporal variation of the BL–SL ratio in the main river of the Educational and Research Forest Watershed of Tarbiat Modares University (Kojour), Iran. BL, SL, and discharge were simultaneously measured from July 2011 to June 2012, under different hydrological conditions, at a flume located at the main outlet of the watershed. The BL–SL ratios were then analyzed under different conditions, which showed that the discharge, SL, BL, and BL–SL ratio during the study period varied between 60 and 780 L s−1, 2 and 2786 mg L−1, 0.0005 and 13 mg L−1, and 0.01–8.1%, respectively. The mean ratios of the BL–SL ratio in summer, autumn, winter, and spring were 0.16, 0.86, 4.6, and 0.8%, respectively, with standard deviations of 0.067, 0.95, 2.73, and 0.56%, respectively. The mean ratio of BL–SL was about 1%, which is very low compared to figures off‐reported. Flow discharge, precipitation, and sand and gravel mining were also recognized as important factors affecting the temporal variability of BL–SL ratio in the study area.

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Geochemical tracing and spatial evolution of the sediment bed load of the Romaine River, Québec, Canada

Cited by 26 publications

References 21 publications

An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations

An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations

Application of a confluence-based sediment-fingerprinting approach to a dynamic sedimentary catchment, New Zealand

Temporal Variation of Bed Load to Suspended Load Ratio in Kojour River, Iran

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