Modelling the effects of ice transport and sediment sources on the form of detrital thermochronological age probability distributions from glacial settings

Abstract

Abstract. The impact of glaciers on the Quaternary evolution of mountainous landscapes remains controversial. Although in situ or bedrock low-temperature thermochronology offers insights on past rock exhumation and landscape erosion, the method also suffers from potential biases due to the difficulty of sampling bedrock buried under glaciers. Detrital thermochronology attempts to overcome this issue by sampling sediments at e.g. the catchment outlet, a component of which may originate from beneath the ice. However, detrital age distributions not only reflect the catchment exhumation, but also spatially variable patterns and rates of surface erosion and sediment transport. In this study, we use a new version of a glacial landscape evolution model, iSOSIA, to address the effect of erosion and sediment transport by ice on the form of synthetic detrital age distributions. Sediments are tracked as Lagrangian particles formed by bedrock erosion, and their transport is restricted to ice or hillslope processes, neglecting subglacial hydrology, until they are deposited. We base our model on the Tiedemann Glacier (British Columbia, Canada), which has simple morphological characteristics, such as a linear form and no connectivity to large tributary glaciers. Synthetic detrital age distributions are generated by specifying an erosion history, then sampling sediment particles at the frontal moraine of the modelled glacier. Results show that sediment sources, reflecting different processes such as glacier and hillslope erosion, can have distinct bedrock age distribution signatures, and estimating such distributions should help to identify predominant sources in the sampling site. However, discrepancies between the detrital and bedrock age distributions occur due to (i) the selective storage of a large proportion of sediments in small tributary glaciers and in lateral moraines, (ii) the large range of particle transport times due to varying transport lengths and strong variability of glacier ice velocity, (iii) the heterogeneous pattern of erosion, and (iv) the advective nature of glacier sediment transport along ice streamlines. This last factor leads to a poor lateral mixing of particle detrital signatures inside the frontal moraine, and then local sampling of the frontal moraine is likely to reflect local sources upstream. Therefore, sampling randomly across the moraine is preferred for a more representative view of the catchment age distribution. Finally, systematic comparisons between synthetic (U-Th)/He and fission track detrital ages, with different bedrock age-elevation profiles and different relative age uncertainties, show that the nature of the age-elevation relationship and age uncertainties largely control the ability to track sediment sources in the detrital record. However, depending on the erosion pattern spatially, qualitative first-order information may still be extracted from a thermochronological system with high uncertainties (>30 %). Overall, our results demonstrate that detrital age distributions in glaciated catchments are strongly impacted not only by erosion and exhumation but also by sediment transport processes and their spatial variability. However, when combined with bedrock age distributions, detrital thermochronology offers a novel means to constrain the transport pattern and time of sediment particles.

Modelling alpine glacier geometry and subglacial erosion patterns in response to contrasting climatic forcing

“…In SA simulations, lower precipitation rates or low temperatures resulted in a significant impact on glacier geometry, triggering considerable reductions in both glacier length and thickness. Our multi‐tributary synthetic landscape (as proposed in Bernard et al, 2020) emphasized ice‐flux increase for higher precipitations with tributary joining (Figure 5a), resulting in large glacier geometry differences (both length and thickness) between scenarios with similar ELAs (Figures 3a and b). In ST simulations, a similar effect for glacier thickness is observed, although of lower magnitude (Figure 3c, associated with a decrease in ELA as shown in Figure 2b).…”

“…Given the large variability in the input temperature/precipitation scenarios (Figure 2c), the connectivity between the trunk glacier and some tributaries varies between the different climatic scenarios (Figure 3a), with some tributaries joining when increasing (decreasing) annual precipitation for SA (respectively ST) simulations (see the next section for details and consequences on ice geometry). Such variability in tributary connectivity creates a more complex but realistic ice simulation, as also suggested by Bernard et al (2020).…”

Glacial Catchment Erosion From Detrital Zircon (U‐Th)/He Thermochronology: Patagonian Andes

“…The ∼16 kg moraine sample is a mixture of material from the interior of the moraine and was collected at four different locations. Those four locations were equally distributed along the lakeward flank of the moraine, a sampling strategy that was also recently suggested based on data of modeled transport paths of detritus within a glacier (Bernard et al, 2020). We therefore assume that the sample is representative for the entire catchment.…”

“…Despite the advances of previous studies for understanding catchment geomorphic processes, few studies to date have applied tracer thermochronology to glaciated settings (Clinger et al, 2020;Ehlers et al, 2015;Glotzbach et al, 2013;Lang et al, 2018;Stock et al, 2006;Tranel et al, 2011), and our knowledge of where modern or ancient glaciers derived sediment from is incomplete. Tracer thermochronology observations are needed to evaluate glacial erosion and transport laws used in process-based glacial surface process models (e.g., Bernard et al, 2020;Braun et al, 1999;Headley & Ehlers, 2015;Herman & Braun, 2008;Herman et al, 2011;MacGregor et al, 2009;Tomkin & Braun, 2002;Yanites & Ehlers, 2016).…”

“…10 2 -10 3 years) to long (10 4 -10 6 years) timescales (e.g., Bernard et al, 2020;Egholm et al, 2012;Herman et al, 2011Koppes et al, 2015;Riihimaki et al, 2005;Yanites & Ehlers, 2012. Furthermore, numerical models were used to study glacier properties, such as basal sliding velocity or concentration of intra-and subglacial debris, with regard to erosion mechanisms and their rates.…”