Lithologic, geomorphic, and climatic controls on sand generation from volcanic rocks in the Sierra Nevada de Santa Marta massif (NE Colombia)

Hatzenbühler, Diana; Caracciolo, Luca; Weltje, Gert Jan; Piraquive, Alejandro; Regelous, Marcel

doi:10.1016/j.sedgeo.2021.106076

Cited by 9 publications

(29 citation statements)

References 74 publications

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“…The mixing model predicts different source rock contributions in different grain sizes, which is a result of a strong granulometric control on sediment composition (Figure 6). The variability of chemistry and mineralogy between different size fractions of one sample is governed by three factors: A size-density sorting effect by which dense minerals are preferably found in fine grain-size fractions (Garzanti et al, 2008;Jonell et al, 2018), the original size of minerals in the source rocks (Hatzenbühler et al, 2022;Weltje et al, 2018), and physical and chemical weathering that break down minerals and enrich fine-grained weathering products in the finer grain-size fractions (Le Pera et al, 2001;von Eynatten et al, 2012von Eynatten et al, , 2016.…”

Section: Discussionmentioning

confidence: 99%

“…However, there are no significant differences in the four river catchments regarding mean temperature, precipitation, or land use (Table 1) that would explain differences in CIX values or different plagioclase weathering behavior. In addition to weathering intensity, such indicators heavily depend on the source rock composition (Garzanti & Resentini, 2016; Nesbitt & Young, 1984), grain size (Hatano et al., 2019), and original texture and mineral sizes in the source rocks (Hatzenbühler et al., 2022; Heins, 1993; Weltje et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Our recommendation for sediment fingerprinting and mixing modeling of source rock contributions with geochemical data is to use several narrow grain sizes to reduce the effects of temporal and granulometric variability. The selection of specific grain‐size fraction(s) for mixing modeling requires knowledge of source rock composition and texture, as some (e.g., volcanic) source rocks may not supply significant amounts of sand‐sized detritus to the bedload (e.g., Hatzenbühler et al., 2022). In our case, however, coarse grain sizes (<1 Φ) gave more realistic source rock contributions, approximately equal to their areal distribution in the catchments, than fine grain sizes (>3 Φ).…”

Section: Discussionmentioning

confidence: 99%

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

Stutenbecker,

Scheuvens,

Hinderer

et al. 2023

JGR Earth Surface

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The sampling of fluvial sediment is subject to many sources of uncertainty, for example, time and location, and the number of samples collected. It is nevertheless commonly assumed that a sample taken at one time and location provides a somewhat averaged compositional signal. Any spatial or temporal variability of this signal is often neglected. This study investigates how the composition of bed load sand changes over an observation period of 1 year in four river basins with differing bedrock geology in southwestern Germany. Up to 12 bulk sediment samples were taken at the same locations using the same approach and analyzed for their granulometry and geochemistry. The results indicate that (a) different grain sizes yield different compositions due to source rock composition and hydraulic sorting effects, (b) bulk sediment composition changes temporally due to changing grain‐size distribution, and (c) compared to the bulk sample, the composition of narrow grain sizes is temporally more stable but nevertheless has an average variability of 15%. Because heavy mineral‐bound elements such as Zr have the highest variability, we relate a major component of compositional variability to temporally varying heavy mineral concentrations in response to hydrodynamic processes. Mixing modeling demonstrates that the fluvial sand faithfully reflects its catchment geology and that the sediment sources do not change substantially during the observation period, even during a flooding event. We conclude (a) that the causes for compositional variability may be disentangled using chemical and granulometric time series data and (b) that narrow grain sizes yield representative source rock contributions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Stutenbecker,

Scheuvens,

Hinderer

et al. 2023

JGR Earth Surface

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“…As expected, samples SEV, GUC, COR, and FRI cluster closely together on the MDS map, opposite to samples MAR, TAP, AGI, and RAN. This grouping makes geological sense, as SEV, GUC, COR, and FRI were collected from river catchments that drain metamorphic lithologies (migmatite, gneiss, and metadiorite), whereas samples MAR, TAP, AGI, and RAN were collected from catchments that drain igneous lithologies (Hatzenbühler et al., 2022).…”

Section: Methods 1: Treatment By Rowmentioning

confidence: 99%

“…The geological details of the data set are not relevant to the present discussion and are only briefly mentioned in this paper. The reader is referred to a separate paper for further details about the field setting (Hatzenbühler et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis

et al. 2023

Self Cite

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is a multivariate ordination technique that has gained considerable popularity in recent years as a method to interpret large data sets in sedimentary provenance analysis (Vermeesch, 2013). Given a table of pairwise "dissimilarities" between samples, MDS produces a lower (typically two-) dimensional "map" in which similar samples plot close together and dissimilar samples plot far apart. MDS can be applied to a wide variety of different provenance proxies by choosing an appropriate dissimilarity measure. Vermeesch (2019) distinguishes between three different types of provenance data, each of which is associated with a specific database format (Figure 1):1. Distributional data such as detrital zircon U-Pb ages can be stored in lists of decimal numbers, where each list represents a sample and typically contains a different number of values (i.e., single grain ages). Two samples can be compared using the Kolmogorov-Smirnov distance or related nonparametric statistics (Vermeesch, 2018a). The resulting dissimilarity matrix fulfills the metric requirements and is therefore suitable for both classical and nonmetric MDS (Vermeesch, 2013).

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Petrographic composition and heavy minerals in modern river sand: A global database

Liang,

Hu,

Garzanti

et al. 2023

Geoscience Data Journal

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In the case of modern river sediments, source rocks, tectonic setting, and climatic and geomorphological conditions are fully known, diagenetic effects are by definition none, and physical and chemical processes during erosion, transport, and deposition can be investigated directly in any required detail. Their study thus provides the fundamental information needed to understand the impact of various controls on sediment generation and sediment routing systems in diverse geological settings. With the growing interest in source‐to‐sink sedimentary systems for economic purposes, researchers have focused more and more on modern environments, producing an ever‐increasing amount of compositional data on river sediments. To better integrate and utilize these large datasets, we systematically compiled a global database of 4,208 fluvial sediment samples compiled from 100 published papers, book chapters, and dissertations and included 3,747 petrographic and 1,943 heavy‐mineral analyses. The analysed samples are mostly located in Asia, Africa, Europe, and South America, with fewer examples from North America and Oceania. Each sample is complemented by metadata describing references, river name, geographic location, sampling date, analytical method, and grain size, so that samples can be grouped and filtered to meet different needs including comparison with other datasets.

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Lithologic, geomorphic, and climatic controls on sand generation from volcanic rocks in the Sierra Nevada de Santa Marta massif (NE Colombia)

Cited by 9 publications

References 74 publications

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

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

Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis

Petrographic composition and heavy minerals in modern river sand: A global database

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