An examination of geochemical modelling approaches to tracing sediment sources incorporating distribution mixing and elemental correlations
Abstract
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 ...
View more >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 (場4%, s 12%). This study demonstrates that it is important to understand how different weightings may impact modelling results.
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View more >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 (場4%, s 12%). This study demonstrates that it is important to understand how different weightings may impact modelling results.
View less >
Journal Title
Hydrological Processes
Volume
29
Issue
6
Subject
Surface Processes
Physical Geography and Environmental Geoscience
Civil Engineering
Environmental Engineering