A log‐normal spectral analysis of inorganic grain‐size distributions from a Canadian boreal lake core: Towards refining depositional process proxy data from high latitude lakes

Abstract

Better methods for interpreting grain‐size spectra will enhance current understanding of past transport–depositional processes. A high‐resolution inorganic grain‐size dataset has been measured from a freeze core extracted from ‘Alberta Lake E’ a boreal fresh water lake 40 km east of the Athabasca Oil Sands in north‐eastern Alberta, Canada. The grain‐size spectra are remarkably consistent throughout the core, exhibiting a structure comprising six persistent grain‐size distributions below ca 250 μm, plus a rare medium‐sand distribution. Automated deconvolution of the grain‐size spectra produced poor results. Constraining the modes of two of the distributions produced deconvolution solutions that were statistically excellent and consistent with the structure of each spectrum. Statistical analysis of the ‘constrained’ solutions indicates that deconvolution successfully extracted independent grain‐size populations. Conversely, the multimodal spectra generate traditional measures (for example, mean grain size) that are inconsistent combinations of different individual populations and thus are poor proxies of transport–depositional processes. Alberta Lake E is situated in a boreal wetland landscape where sediment delivery is dominated by overland flow transport during spring melt. This context means that the Alberta Lake E grain‐size spectra can be interpreted to reflect: (i) a bedload component transported during short‐duration high discharge events that reflect the intensity of the melt; and (ii) a finer suspended load component representing material whose magnitude is controlled by the volume of the spring melt. Stratigraphically, bedload and suspended load populations demonstrate different short‐wavelength and long‐wavelength cyclicity, suggesting that spring melt is likely to be driven by cyclic external forcing factors. The links between the grain‐size spectra and spring melt have potential for generating proxy records that better capture the external controls over spring melt in boreal systems and the risks associated with these energetic hydrodynamics. This is exemplified by the coarsest Alberta Lake E distributions, which indicate that more intense spring‐melt dynamics occurred in pre‐historical times.

A novel protocol for mapping the spatial distribution of storm derived sediment in lakes

“…Locating sites of high paleo-storm signal preservation potential require coupling a multi-site characterization approach (i.e., intra-lake sampling) with assessment of lake proxies to define resuspension-and runoff-derived sedimentary deposits. Grain size analysis has been demonstrated to be a very useful indicator of lake depositional processes, which can be carried out quickly and is cost effective [16]. The development of end member mixing analysis (EMMA) techniques for the analysis of grain size distributions has been revolutionary, as it now permits researchers to more precisely model the depositional signature [i.e., end members(EM)] of specific catchment and lake hydrological processes [17][18][19][20][21][22].…”

Intra‐clinothem variability in sedimentary texture and process regime recorded down slope profiles

“…The statistical analysis of all ImageJ results was completed using GRADISTAT computer software (Blott & Pye 2001), which enables the rapid analysis of grain-size and sorting statistics (e.g. St-Onge et al, 2004;Gammon et al, 2017).…”

Experimental study on the use of granulometric speciation for the radiometric dating of recent sediments

“…It is worth noting that standard procedures for grain size analysis usually involve small amount of sample and may include gentle ultrasonic and/or the use of some dispersing agents such as metaphosphate (e.g. Gammon et al, 2016). Other methodologies involve pretreatments with HCl (Sun et al, 2018).…”

“…On the other hand, high resolution grain-size analysis (e.g. using laser diffraction techniques) often show multimodal spectra which can be interpreted as the superposition of several log-normal distributions (Gammon et al, 2016). Nevertheless, the Rosin-Rammler approach seems reasonable enough for data on grain size distributions obtained from the sieving method.…”