Compositional dynamics of suspended sediment in the Rhine River: sources and controls

Abstract

Purpose Information on the geochemical composition of suspended sediments in rivers is crucial to identify sediment source type or area. In large river basins, however, the relation between sediment composition and its controlling factors is often obscured. This study aims to assess and improve the conceptual understanding on the factors and mechanisms that control the composition of suspended sediments in the River Rhine, one of the large European rivers, and to identify the dominant source types of elements. Materials and methods We performed log-linear regression analysis and principal component analysis (PCA) on biweekly monitoring data of suspended sediment composition, supplemented with daily measurements of suspended sediment concentrations (SSC) and discharge at the Lobith monitoring station near the German-Dutch border for the period 2011-2016. Results and discussion The statistical analyses show a consistent grouping of elements that display contrasting temporal variation or different responses to increased discharge. The contrasting behaviour also becomes manifest in the results from the PCA. A first component that explains about the half of the total variance in the entire dataset reflects the variation in clay content in the suspended sediment. A second component reflects anthropogenic pollution and explains about a quarter of the total variance. A third component probably reflects variation in sediment provenance. Conclusions The majority of the temporal variation in suspended sediment composition can be attributed to variations in grain size (clay content), organic matter content, and anthropogenic pollution. Only a minority of the variation can be attributed to variations in the contributions from different upstream source areas. This variation represented by the third and higher components from the PCA can potentially be used for sediment provenance analysis.

Temporal Variability of Fluvial Sand Composition: An Annual Time Series From Four Rivers in SW Germany

“…The importance of time‐series analysis was also demonstrated by Rousseau et al. (2019) and van der Perk and Espinoza Vilches (2020), who used a similar approach for suspended sediment. Where such detailed sampling over several months is not possible due to practical (e.g., temporal or financial) reasons, an average temporal geochemical variability of 15%–20% should be taken into consideration.…”

“…The exceptional Gersprenz February 2020 sample demonstrates that even during extreme events, the provenance of suspended and bedload material does not change substantially in this river. Time series from the Rhine river (van der Perk & Espnoza Vilches, 2020) and the Amazon, Orinoco, and Maroni rivers (Rousseau et al., 2019), in contrast, have identified source changes based on suspended sediment chemistry. This might be related to their large catchment sizes and consequently higher spatial dynamics, and/or to the generally faster transport time (and therefore signal transfer time) of suspended compared to bedload sediment (Tofelde et al., 2021).…”

“…Consequently, reported sediment mineralogy and chemistry are limited to a few samples or even only one measurement in many rivers (Müller et al., 2021). Complete time series are very rare and limited to suspended sediment (Rousseau et al., 2019; van der Perk & Espinoza Vilches, 2020), which tends to be dominated by silt and clay (Stone & Saunderson, 1992; Walling & Moorehead, 1987; Walling & Woodward, 2000; Xu, 2002). However, many geological methods such as provenance and cosmogenic nuclide analysis target the sand‐sized sediment transported as bed load (e.g., Garzanti et al., 2009; Ingersoll et al., 1984; van Dongen et al., 2019), for which the short‐term compositional variability remains poorly documented.…”

Data analysis of heavy metal content in riverwater: multivariate statistical analysis and inequality expressions

“…The amount of heavy metals has indicated large variations depending on the locations. This variation may be due to geological and geographical features in different locations [46]. Skewness values are used to analyze the asymmetrical or symmetrical distribution of elements.…”

“…The series is right-skewed (positive) when the skewness > 0. However, the series is left-skewed (negative) when the skewness < 0, which means it is not symmetrical [46]. The results of the descriptive statistics of 13 elements for a total of 38 surface water samples are given in Table 3.…”