Combining suspended sediment monitoring and fingerprinting to determine the spatial origin of fine sediment in a mountainous river catchment

Abstract

International audienceAn excess of fine sediment (grain size <2 mm) supply to rivers leads to reservoir siltation, water contamination and operational problems for hydroelectric power plants in many catchments of the world, such as in the French Alps. These problems are exacerbated in mountainous environments characterized by large sediment exports during very short periods. This study combined river flow records, sediment geochemistry and associated radionuclide concentrations as input properties to a Monte Carlo mixing model to quantify the contribution of different geologic sources to river sediment. Overall, between 2007 and 2009, erosion rates reached 249 ± 75 t km−2 yr−1 at the outlet of the Bléone catchment, but this mean value masked important spatial variations of erosion intensity within the catchment (85-5000 t km−2 yr−1). Quantifying the contribution of different potential sources to river sediment required the application of sediment fingerprinting using a Monte Carlo mixing model. This model allowed the specific contributions of different geological sub-types (i.e. black marls, marly limestones, conglomerates and Quaternary deposits) to be determined. Even though they generate locally very high erosion rates, black marls supplied only a minor fraction (5-20%) of the fine sediment collected on the riverbed in the vicinity of the 907 km2 catchment outlet. The bulk of sediment was provided by Quaternary deposits (21-66%), conglomerates (3-44%) and limestones (9-27%). Even though bioengineering works conducted currently to stabilize gullies in black marl terrains are undoubtedly useful to limit sediment supply to the Bléone river, erosion generated by other substrate sources dominated between 2007 and 2009 in this catchment

Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample mixtures

“…Most procedures involve an optimisation procedure based on minimisation of an objective function reflecting the difference between the measured property values associated with the sample mixture and those predicted by the mixing model for a given set of relative source contributions. In other studies, the objective function is based on the sum of squares of the relative errors and several variants of this objective function have been proposed (e.g., Motha et al, 2003Motha et al, , 2004Hughes et al, 2009;Evrard et al, 2011;Navratil et al, 2012). In this study the predicted relative apportionments from each mass balance equation were solved minimising two objective functions and a goodness of fit (GOF) was obtained for each option.…”

Evaluating sampling efficiency when estimating sediment source contributions to suspended sediment in rivers by fingerprinting

“…Collins et al, 2010a;Evrard et al, 2011;Owens et al, 2000;Franz et al, 2013). The method has been used in a wide range of objectives, scales, types and number of sources, and tracer variables (Ben Slimane et al, 2013;Tiecher et al, 2015;Zebracki et al, 2015).…”

Relative Contribution of Rill/Interrill and Gully/Channel Erosion to Small Reservoir Siltation in Mediterranean Environments

“…A detailed description of this procedure is provided by Evrard et al . (). To characterise the properties of both groups of sources, we assumed that their concentrations ( c i , j ) could be represented by a normal distribution (Equation .…”