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Clast shape analysis and clast transport paths in glacial environments: A critical review of methods and the role of lithology
Earth-Science Reviews 121 (2013) 96–116
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Earth-Science Reviews journal homepage: www.elsevier.com/locate/earscirev
Clast shape analysis and clast transport paths in glacial environments: A critical review of methods and the role of lithology Sven Lukas a,⁎, Douglas I. Benn b, c, Clare M. Boston a, Martin Brook d, Sandro Coray e, David J.A. Evans f, Andreas Graf e, Andreas Kellerer-Pirklbauer g, Martin P. Kirkbride h, Maarten Krabbendam i, Harold Lovell a, c, Martin Machiedo j, Stephanie C. Mills k, Kate Nye f, Benedict T.I. Reinardy l, Fionna H. Ross b, Michael Signer e a
School of Geography, Queen Mary University of London, Mile End Road, London E1 4NS, England, UK School of Geography and Geosciences, University of St Andrews, St Andrews KY16 8YA, Scotland, UK c Department of Geology, The University Centre in Svalbard (UNIS), N-9171 Longyearbyen, Norway d Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand e Institut für Geologie, Universität Bern, Baltzerstr. 1+3, 3012 Bern, Switzerland f Department of Geography, Durham University, South Road, Durham DH1 3LE, England, UK g Institute of Remote Sensing and Photogrammetry, Graz University of Technology, Austria h Department of Geography, University of Dundee, Scotland, UK i British Geological Survey, Murchison House, West Mains Road, Edinburgh EH9 3LA, Scotland, UK j Department of Geography, University of Bergen, Norway k School of Geography, Geology and Environmental Sciences, Centre for Earth & Environmental Science Research, Kingston University London, Penrhyn Road, Kingston upon Thames, KT1 2EE, UK l Instituto Andaluz de Ciencias de la Tierra, Granada, Spain b
a r t i c l e
i n f o
Article history: Received 30 June 2012 Accepted 23 February 2013 Available online 21 March 2013 Keywords: Clast shape Glacial environments Transport path Reconstruction Debris cascade Clast lithology
a b s t r a c t The reconstruction and tracing of transport paths in glaciated (and other) environments have a long tradition in the Earth Sciences. We here present a dataset of clast shape samples from a worldwide selection of glaciated mountain environments in order to assess the reliability of this approach overall and the role of lithology on the performance of clast shape measurements in particular. Our findings demonstrate that the widely-used RA-C40 co-variance approach is applicable to 63% of the 19 catchments investigated, while the alternative RWR-C40 approach is more widely applicable to 75% of these catchments. A systematic assessment of mixing of lithologies at the catchment scale demonstrates that such mixing leads to pronounced overlaps between different control envelopes that had previously been separated, thereby removing the discriminatory power of the method. Mixing of similar lithologies between different catchments shows an even more extreme loss of discriminatory power, which strongly suggests that lithology plays a primary role in determining clast shape, and that catchment-specific processes are superimposed. Systematic analysis of the dataset also shows that nearly all catchments (apart from two) can be grouped into two types. Type I relates to sites in lesser mountain ranges and is characterised by dominantly blocky forms in the subglacial realm, highlighting significant reworking processes. Type II sites are dominantly in high-mountain environments and characterised by a high similarity between subglacial and fluvial control envelopes. This indicates that, although reworking may be pronounced, it is not necessarily effective enough to remove the platy shape that most likely results from extraglacial and supraglacial inputs. Our study highlights the potential of clast shape analysis as a tool that allows generic processes to be identified between catchments, thereby enabling an understanding of debris cascades in glaciated mountain environments. We finish with recommendations for ensuring that future clast shape studies are robust, reproducible and comparable between different sites. © 2013 Elsevier B.V. All rights reserved.
Contents 1. 2.
Introduction . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . 2.1. Field measurement approach . 2.2. Graphic representation of results
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⁎ Corresponding author. Tel.: +44 7882 8417; fax: +44 7882 7032. E-mail address: [email protected] (S. Lukas). 0012-8252/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.earscirev.2013.02.005
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S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
2.2.1. Form . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Roundness and texture . . . . . . . . . . . . . . . . 2.2.3. Co-variance plots . . . . . . . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The discriminatory power of co-variance plots . . . . . . . . . 3.2. The role of lithology on clast shape . . . . . . . . . . . . . . 3.2.1. Lithological variation within individual catchments . . . 3.2.2. Lithological variation between catchments . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. The discriminatory power of co-variance plots . . . . . . . . . 4.2. The role of lithology: different lithologies in the same catchment 4.3. Implications for debris cascades in glaciated environments . . . 5. Recommendations for clast shape sampling . . . . . . . . . . . . . . 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction The shape of sedimentary particles (typically of gravel size), summarised here under the term clast shape, has long attracted the research attention of earth scientists. Systematic studies of clast shape have been carried out for nearly a century (Wentworth, 1922), and data using clast shape analysis may be of significance to several geoscience disciplines, for example Quaternary science, glaciology, petrological and sedimentological provenancing, sediment budgeting and geomorphology. Several discussions have focused on various approaches to quantify clast shape, and these have been reviewed succinctly by Sneed and Folk (1958), Barrett (1980), Illenberger (1991), Benn and Ballantyne (1993, 1994), Blott and Pye (2008) and Demir et al. (in press). In this contribution, we concentrate on particles shaped by erosional, transport and depositional processes in glaciated mountain catchments, where the study of clast shape has a shorter tradition than in, for example, fluvial geomorphology (e.g. Wentworth, 1936; Flint, 1971). Boulton (1978) was the first to formally link the shapes of sedimentary particles to different transport pathways through glaciers, and he also compared this shape signature between glaciers of different thermal regime. Boulton (1978) recognised three transport paths: (a) A supraglacial one, where debris falls onto the glacier surface (for example by rockfall or avalanching) and remains at or near the glacier surface prior to deposition at or near the glacier margin; (b) an englacial route, whereby material that is deposited on the glacier surface in the accumulation area is buried by snow and transferred englacially along flowlines until it melts out in the ablation area; and (c) material that is either transferred from a supraglacial to a subglacial position or that is eroded at the glacier bed. Boulton (1978) conceptualised (a) and (b) as undergoing passive or high-level transport and (c) as active or low-level transport. He showed that dominantly angular and platy clasts occurred in the former two categories and edge-rounded, abraded and blocky clasts in the latter. This theoretical dichotomy has been confirmed and refined by various workers since, who used measurements of clast shape to establish dominant transport pathways that contributed to the sediment budget of moraines (e.g. Matthews and Petch, 1982; Sharp, 1982; Small, 1983; Matthews, 1987; Benn, 1989; Shakesby, 1989; Benn, 1992) or to enlighten the processes operating across entire catchments (e.g. Ballantyne, 1982). All these workers used varying statistical measures to discriminate between different transport paths, which resulted in a great amount of results that were partly incomparable between catchments. It was not until the work of Benn and Ballantyne (1993, 1994) that a uniform approach was advocated, eventually resulting in greater comparability of results between catchments, and also a proliferation of clast shape studies since. The data presented in this article have been collected using the approach of Benn and Ballantyne (1993, 1994) who treat the shape of a clast as the sum of
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three characteristic elements that are superimposed on each other: (a) clast form, defined in terms of ratios between three orthogonal axes, the longest, intermediate and shortest axes (denoted as a, b and c axis, respectively); (b) clast roundness which describes the degree of curvature around the clast edges; and (c) surface texture which includes more delicate features such as facets or striae. Measurement of clast form has been carried out in the field by using a ruler, calliper (Benn, 2004) or shape box (Shakesby, 1979). Clast roundness is usually established using visual comparison charts or descriptive criteria (Benn and Ballantyne, 1994) (Table 1). Surface texture can be subtle and thus more difficult to assess, especially since variation in experience may result in operator bias when recording facets and striae, for example. There is no generally-accepted approach to reporting or statistically analysing surface texture data apart from noting the proportion of facetted or striated clasts as a percentage of each sample in either tabular or textual form (Evans, 1999; Benn, 2004; Evans, 2010). We have excluded surface textural elements from our analysis, because not all lithologies preserve this information equally well (and some not at all); therefore, the usefulness of this element is limited in an assessment of the effects of lithology on clast shape. Benn and Ballantyne (1993, 1994) also advocate the use of ternary diagrams as introduced by Sneed and Folk (1958) for visualisation and statistical interpretation of clast form (Fig. 1a); as a measure of platiness that works well in glaciated catchments, Benn and Ballantyne (1993, 1994) suggest using the ratio of c/a-axis ≤ 0.4 (termed the C40 index), following work by Ballantyne (1982) (Fig. 1a, b, d). Secondly, these authors advocated using the percentage of very angular and angular clasts in a sample (cf. Matthews, 1987) to discriminate between frost-weathered, angular clasts and those that have undergone subglacial edge-rounding. Instead of using sphericity and roundness plots that had been commonly used (e.g. Boulton, 1978), Benn and Ballantyne (1994) demonstrated that plotting both RA (the percentage of angular and very angular clasts in a sample; Fig. 1b, c) and C40-indices in co-variance plots allowed a very effective discrimination between subglacially and supraglacially-
Table 1 Criteria employed in the identification of clast roundness classes. Modified from Benn and Ballantyne (1994). Roundness class Very angular (VA) Angular (A) Sub-angular (SA) Sub-rounded (SR) Rounded (R)
Description
Very acute edges and/or sharp protuberances Acute edges with no evidence of rounding Rounding confined to edges; faces intact Rounding of edges and faces; often facetted Marked rounding of both edges and faces; merging of edges and faces Well-rounded (WR) Distinction between faces and edges not possible
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(a)
Blocky 0.8 0.8 0.6 c /a
b/a
0.6 0.4 0.4
0.2 0 .2
Platy
(b)
0.2
C40 = 64 RA = 96 RWR = 0
0.4 0.6 (a-b)/(a-c)
0.8 Elongated
(c) 100
RA (%)
80
100
60
40
20 50
0 0
20
VA
A
SA
SR
(d)
R
WR
C40 = 26 RA = 0 RWR = 18
40
60
80
100
60
80
100
C40 (%)
0
(e) 50
RWR (%)
40
100
30
20
10 50
0 0 0 VA
A
SA
SR
R
WR
20
40
C40 (%)
Fig. 1. Example of how the different elements of clast shape analysis as devised by Benn and Ballantyne (1993, 1994) are used. (a) Schematic ternary diagram, showing axial scales, endmember clast forms and terminology used here; (b) example ternary diagram and frequency distribution of clast roundness data of a supraglacial sample to illustrate how the C40- and RA-indices are calculated (shown by grey area and black ellipse, respectively); (c) RA-C40-co-variance diagram illustrating how the different indices are used to discriminate between different samples; (d) ternary diagram and frequency distribution of a distal fluvial sample to illustrate how the RWR-index is calculated; (e) RWR-C40-co-variance diagram illustrating how the different indices are used to discriminate between different samples. All data used are from eclogite sampled at Findelengletscher.
transported sediments (Fig. 1e). In particular, this approach was shown to work extremely effectively if samples that had undergone unknown transport processes (e.g. those obtained from moraines) were compared with those that had been obtained from settings where the transport
paths were known (e.g. scree and talus slopes, glacier bed, river bed); the latter category has been termed ‘control samples’ and can be regarded as the backbone of clast shape analysis in glaciated catchments in the last two decades. Studies conducted since the seminal work of
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Benn and Ballantyne (1993, 1994) have confirmed the widespread applicability of this approach to modern and formerly-glaciated mountain environments (e.g. Benn, 1994, 1995; Bennett et al., 1997; Salt and Ballantyne, 1997; Evans, 1999; Glasser et al., 1999; Graham and Midgley, 2000a; Etienne et al., 2003; Benn et al., 2004; Goodsell et al., 2005; Lukas et al., 2005; Benn and Lukas, 2006; Glasser et al., 2006; Lukas, 2007; Kellerer-Pirklbauer et al., 2008; Mills et al., 2009; Evans, 2010; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). In addition to using clast shape analysis as an approach to reconstruct palaeo-transport pathways in glaciated settings, the focus of some studies has been towards understanding the catchment-wide processes, i.e. including the influence of fluvial reworking in the proglacial and subglacial realms along the debris cascade (e.g. Kirkbride, 1989; Evans, 1999, 2000; Benn et al., 2004; Brook and Lukas, 2012; Lukas et al., in press). Despite the aforementioned widespread applicability and proliferation of case studies globally, three particular aspects of clast shape analysis are still poorly understood and merit further investigation. Firstly, although most studies use the RA-C40-co-variance plots proposed by Benn and Ballantyne (1994), it has been shown that the other end of the roundness spectrum, the percentage of rounded and well-rounded clasts (Table 1) and the associated RWR-index (Fig. 1d, e), may be of more use in some settings (Benn, 2004; Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). Yet, the two approaches have never been compared to each other with respect to their effectiveness. Secondly, it is worth noting that not all of the aforementioned studies have restricted their sampling to either a single lithology or have compared only identical lithologies when working on catchments with multiple lithologies. Although there is some data to support the inherent assumption that lithology determines clast shape to a large extent (e.g. Drake, 1970; Barrett, 1980; Ballantyne, 1982; Pérez, 1986; Benn and Ballantyne, 1994; Evans, 1999; Krüger and Kjær, 1999; Benn, 2004; Brook and Lukas, 2012; Lukas et al., 2012), one study has suggested that lithology may not have a large impact on clast shape and that mixing of lithologies may not present a problem (Bennett et al., 1997). Following this suggestion, a number of studies have mixed lithologies within individual samples of 50 clasts (e.g. Graham and Midgley, 2000a; Etienne et al., 2003; Hambrey and Ehrmann, 2004; Goodsell et al., 2005). However, the underlying assumption that lithology plays little, if any, role on the reliability or discriminatory power of clast shape (both at the clast level and as a method) has never been tested systematically, because the study by Bennett et al. (1997) and other studies have mixed up lithologies in individual samples rather than compare control samples in which lithologies have been separated. Thirdly, there is a pronounced lack of knowledge regarding the transferability of clast shape results between catchments. The only recent compilation of sedimentary data to determine palaeo-glacier thermal and dynamic regimes uses clast shape datasets that rely on mixed lithologies (Hambrey and Glasser, 2012). This paper therefore aims to contribute towards closing these gaps through a systematic assessment of a global dataset collected using the same approach (Section 2) by the present authors over the last c. 25 years. In particular, we aim to systematically test whether (a) the proposed RA-C40-co-variance plots are the ideal means of investigating transport processes in every catchment; (b) lithology plays a large or negligible role in determining clast shape; and (c) there are systematic variations between catchments with similar climatic and glaciological boundary conditions. Details of the method of clast shape analysis used here are described in detail below.
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and therefore clast form could be confidently related to known processes. These data then serve as control samples that provide a robust template against which samples of unknown origin/transport history can be compared. In this contribution, we focus entirely on such control samples and purposely exclude any samples from sediments or landforms. We do this, because the latter will always contain a mixture of clasts with their own unique erosional, transportational and depositional histories. Instead, any effects of lithology and other variables (e.g. geographical and glaciological) can be reliably tested on known parameters where other influences are minimal or can be ruled out entirely. At each location, 50 clasts from the same lithology were sampled at random, their three orthogonal axes were measured and roundness was determined visually. Although the influence of clast size on shape has not satisfactorily been tested before, we have restricted our sampling to clasts with maximum a-axes b 25 cm. We have sampled both currently glacierised catchments (n = 15) and those that were last glaciated during the Late Pleistocene (n = 4) to enable a first comparison between the performance of the method in a modern and palaeo-context. 2.2. Graphic representation of results and statistical analysis 2.2.1. Form Triangular diagrams following Sneed and Folk (1958) are particularly useful to display clast shape in an undistorted way that is free from bias (Benn and Ballantyne, 1993; Benn, 2004). On such ternary diagrams (Fig. 1a), the ratios of c:a and b:a axes are used to distinguish three endmembers of a clast continuum: equant or blocky shapes where a ≈ b ≈ c, prolate or elongate shapes where a >> b ≈ c and oblate or platy shapes where a ≈ b > c (Benn, 2004). Ballantyne (1982), Benn (1992) and Benn and Ballantyne (1993, 1994) have shown that the C40-Index, defined as the percentage of clasts with a c:a ratio ≤ 0.4, is a powerful means of distinguishing blocky from elongated clast shapes and works well in glaciated environments (Fig. 1a). All clast form data were plotted in TriPlot (Graham and Midgley, 2000b) that had been modified to include standardised automated calculation of statistical parameters for each sample and includes a tab to input and plot roundness data (see below). 2.2.2. Roundness and texture The estimated roundness classes for each sample (Table 1) are translated into percentages and plotted as frequency distributions. From these classes, the percentages of very angular (VA) and angular (A) classes are added to calculate the RA-index (Fig. 1b, c). In addition, recent research has shown that the round end of the roundness continuum, i.e. the percentage of rounded and well-rounded clasts, summarised in the RWR-index (Fig. 1d, e), may work better in some settings (e.g. Benn, 2004; Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). Therefore, we employ both parameters to assess which performs better in each particular setting. The purpose of using the extreme ends of the roundness continuum (instead of, for example, the two ‘middle’ categories of subangular and subrounded) is to distinguish between the key processes of frost weathering and subglacial edge-rounding on the one hand (RA-index) and subglacial reworking of fluvially-rounded sediment on the other (RWR-index). Therefore, the selection of this index is process-based. Altering the margins to these two categories, such as including the subangular category into the RA-index (e.g. Goodsell et al., 2005), will most likely remove the predictive capacity of clast shape measurements in glaciated environments and is not advisable.
2. Methods 2.1. Field measurement approach Field sampling was first undertaken in positions where the erosional, transportational and depositional processes were recognisable
2.2.3. Co-variance plots In order to distinguish between different erosional, transportational and depositional clast histories, an effective way of plotting these data is necessary. Benn and Ballantyne (1994) suggested that plotting the C40-index versus the RA-index in a co-variance plot is a
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robust procedure. Based upon our collective experience in data manipulation and knowledge of the pertinent literature, we strongly support this approach. We have consistently added a second covariance plot in all our analyses which substitutes the RA- with the RWR-index in order to evaluate which approach is most powerful and, if possible, whether there are reasons for any differences in the output. Following plotting of the individual values, envelopes of likelihood of each control environment may be plotted (e.g. Benn and Lukas, 2006; Lukas, 2007). In order for co-variance analysis to be effective, these envelopes of individual control environments need to be well-clustered, or separated enough to allow samples of unknown transport history to be attributed correctly to the control environments. If control envelopes are overlapping, then an effective differentiation between individual processes is made more difficult, if not impossible. Only few authors have reported this happening for the traditional RA-C40 co-variance plots, and these studies dealt exclusively with high-mountain settings, where glaciofluvial reworking was prominent and resulted in a lack of differentiation between subglacial and glaciofluvial control samples (Brook and Lukas, 2012; Lukas et al., 2012). We will thus systematically assess for all catchments, which of the two approaches is the most appropriate and seek plausible explanations for any difficulties encountered. In order to achieve the highest resolution possible and to avoid the occurrence of such overlaps, we present the data for each catchment separately. 3. Results We here present a dataset from different locations worldwide that comprises 21 lithologies in 15 glaciated catchments from the high Arctic to New Zealand and allows a comparison with four exemplary palaeo-environments (Table 2). The systematic analyses we present below have been conducted on all these samples and are therefore deemed to represent an appropriate reference frame within which general controls on clast shape, such as lithology, geographical context and glaciological parameters (e.g. thermal regime and glacier dynamics) may be examined for similarities and differences. 3.1. The discriminatory power of co-variance plots Co-variance plots for all catchments using both RA-C40 and RWR-C40 are shown in Figs. 2 and 3. An analysis of this dataset shows that the most commonly-employed covariance method, the RA versus C40-index as discussed by Benn and Ballantyne (1994), can distinguish between all control envelopes in two thirds (63%) of all catchments. This takes into account cases where the RA-index works and the RWR-index does not, equating to 21% (n = 5) of all 19 catchments, and where both RA and RWR versus C40-indices are equally valid (42%, n = 10). Although this implies a wide-ranging applicability, it is interesting to note that the RWR versus C40-index is more powerful than the RA versus C40-index, because it allows an effective discrimination in 75% of all cases. This takes into account both the cases where the RWR versus C40-index, as first proposed by Benn (2004) and used by Benn et al. (2004), yields meaningful results and the RA-index does not, and the 42% of all cases where both approaches work equally well. This finding implies that the RA-index is insufficient to allow an effective discrimination between environments with known transport processes in one third of the environments investigated here, whereas the RWR-index covers three quarters of investigated settings. Only in one catchment (Glacier d'Estelette, Fig. 3g–h) is the overlap of subglacial and fluvial control envelopes so strong that neither RA nor RWR indices can distinguish between them (4%). It is noteworthy that the RWR-index generally works better in high-mountain environments (European Alps, Southern Alps, NZ) and that the RA-index fails to discriminate between different control populations in these cases, whereas both RA and RWR-approaches appear to be equally valid for many of the glaciated catchments in the lesser mountain ranges of Svalbard, Iceland
and Norway (Figs. 2, 3). The only exception to this rule is Tasman Glacier, where the RA-index performs better than the RWR (Fig. 3o–p). Furthermore, it is noteworthy that distinct sub-samples of fluvial control envelopes (e.g. at the glacier portal, proximal and distal) display very clear signals of a decrease in angularity with distance in the RA and/or progressive rounding with distance from the glacier portal in the RWR plots (Figs. 2, 3). It is interesting to note that both approaches work well for all four investigated palaeo-environments (Fig. 3q-x). 3.2. The role of lithology on clast shape All control samples from the catchments presented here have deliberately been restricted to a single lithology, meaning that (a) all 50 clasts for each sample and (b) all samples collated in each co-variance plot were of the same lithology. In the tests below, we now systematically mix these different populations to test what influence, if any, mixing has on the discriminatory power of both RA and RWR co-variance plots. To design as thorough a test as possible, we mix (a) similar lithologies from the same geographic locations, to encapsulate any site-specific effects of lithological variability and (b) similar lithologies taken from different catchments. 3.2.1. Lithological variation within individual catchments Three catchments in our global dataset have been sampled for multiple lithologies, either because these catchments contained more than one dominant lithology and it was unclear which would be found in Quaternary sediment exposures (Findelengletscher, Vadret da Grialetsch) or because it was of interest to test if there was a difference between individual lithologies (Fox Glacier). Fig. 4a and b shows the two dominant lithologies plotted together for Vadret da Grialetsch. Here, a systematic variation between gneiss and amphibolite is visible, in that the former shows a higher proportion (by about 20%) of platy clasts (C40) throughout all control categories. Despite this similarity, there are slight internal differences in the way that both lithologies appear to respond to individual processes within the same catchment. For example, the gneisses contain much fewer clasts that are angular or subangular near the portal (Fig. 4a) compared to the amphibolite, which has a much higher RA-index of 26%. This more pronounced angularity of amphibolite is not mirrored by the degree of rounding of both lithologies (Fig. 4b), where both show a similar increase of rounding with distance from the portal. The three lithologies sampled at Findelengletscher show a comparable degree of similarity in that eclogite has a systematically-lower content of platy clasts than mica schist compared to serpentinite; this is most convincingly visible in the RA-C40 co-variance plot (Fig. 4c). However, serpentinite produces a slightly lower proportion of platy clasts compared to mica schist in the fluvial control near the glacier portal, and this slightly dilutes the identified general trend. The changes in clast roundness (RWR) with distance from the glacier portal are of a very different magnitude for the three lithologies. From the ice-margin to the proximal and distal control sites, the RWR-index of serpentinite increases through rounding by 21% and then decreases by 18% (Fig. 4d). For eclogite it first decreases by 4% and then increases by 12%. For mica schist it first decreases by 14% and then increases by 28%. At the same time the RA-index of all samples is consistently zero (Fig. 4c). The difference in C40-index between the highest (serpentinite) and lowest (eclogite) content of platy clasts varies between 20% (supraglacial) and 45% (subglacial). At Fox Glacier (Fig. 4e, f), argillite produces more platy clasts than mica schist compared to greywacke in the supraglacial control envelope, but this relationship is less clear in the fluvial and subglacial control domains, where mica schist contains lower amounts of platy clasts than argillite. Greywacke produces the blockiest clasts throughout. The difference in C40-index between the highest and lowest content of platy clasts varies between 10% (supraglacial; argillite– greywacke) and 20% (distal fluvial; mica schist–greywacke).
Table 2 Study area details and background information. See text and Figs. 2 and 3 for details. ID
Location
Control-samples (n)
Lithology
References geology
References to previous work
Modern glaciers 1 Borebreen, Svalbard 2 Larsbreen, Svalbard
78°25′N, 14°E 78°11′N, 15°27′E
Fluvial (2), supraglacial (2) Fluvial (2), supraglacial (3)
Sandstone Sandstone
Braathen et al. (1999) Dallmann et al. (2001)
3
Scott Turnerbreen, Svalbard
78°06′N, 15°57′E
Fluvial (4), supraglacial (2)
Sandstone
Dallmann et al. (2002)
Machiedo (2008) Etzelmüller et al. (2000) and Lukas et al. (2005) Hodgkins et al. (1999, 2004) and Sletten et al. (2001)
4 5
Flaajökull, Iceland Fåbergstolsbreen, Norway
64°20′N, 15°32′W 61°43′N, 7°8′E
Fluvial (3), sub- (5) supraglacial (4) Fluvial (9), sub- (5) supraglacial (4)
Basalt Gneiss
Sæmundsson (1979) Nordgulen and Andresen (2008)
6 7 8 9 10
Bergsetbreen, Norway Storbreen, Norway Slettmarkbreen, Norway Midtdalsbreen, Norway Pasterze, Austria
61°38′N, 61°32′N, 61°25′N, 60°34′N, 47°05′N,
Fluvial (2), sub- (1) supraglacial (2) Sub- (4) supraglacial (2) Sub- (5) supraglacial (3) Fluvial (4), sub- (2) supraglacial (2) Fluvial (4), sub- (2), supraglacial (2)
Gneiss Gneiss Gneiss Phyllite Prasinite (greenschist)
Nordgulen and Andresen Nordgulen and Andresen Nordgulen and Andresen Nordgulen and Andresen Höck and Pestal (1994)
11 11 12 12 12 13 14 14 14 15
Vadret da Grialetsch, Switzerland Vadret da Grialetsch, Switzerland Findelengletscher, Switzerland Findelengletscher, Switzerland Findelengletscher, Switzerland Glacier d'Estelette, France Fox Glacier, New Zealand Fox Glacier, New Zealand Fox Glacier, New Zealand Tasman Glacier, New Zealand
46°41′N, 9°58′E 46°41′N, 9°58′E 46°1′N, 7°49′E 46°1′N, 7°49′E 46°1′N, 7°49′E 45°46′N, 6°50′E 43°30′S, 170°10′E 43°30′S, 170°10′E 43°30′S, 170°10′E 43°36′S, 170°13′E
Fluvial (4), supraglacial (2) Fluvial (4), supraglacial (2) Fluvial (4), sub- (3), supraglacial (2) Fluvial (4), sub- (4), supraglacial (2) Fluvial (4), sub- (4), supraglacial (2) Fluvial (2), sub- (8), supraglacial (4) Fluvial (14), sub- (6) supraglacial (6 + 4) Fluvial (14), sub- (6) supraglacial (5 + 4) Fluvial (14), sub- (6) supraglacial (6 + 4) Fluvial (4), sub- (4) supraglacial (8)
Gneiss Amphibolite Serpentinite Mica schist Eclogite Gneiss Greywacke Schist Argillite Greywacke
Bearth et al. (1935) Bearth et al. (1935) Bearth (1953) Bearth (1953) Bearth (1953) Rolland et al. (2003) Cox and Barrell (2007) Cox and Barrell (2007) Cox and Barrell (2007) Warren, 1978 (in Hambrey and Ehrmann 2004)
58°15′N, 4°45′W 57°15′N, 6°14′W 57°06′N, 4°20′W 29.35′S, 29.15′E
Fluvial (2), sub- (4), supraglacial (3) Sub- (2) supraglacial (2) Fluvial (3), sub- (1), supraglacial (2) Fluvial (1), supraglacial (1)
Psammite Gabbro Psammite Basalt
Johnstone and Mykura (1989) Emeleus (1991) Stephenson and Gould (1995) Bell and Haskins (1997)
Formerly-glaciated sites 16 NW Scotland 17 Isle of Skye, Scotland (Coire na Creiche) 18 Central Scotland (Monadhliath Mts) 19 Drakensberg, Lesotho, South Africa
7°15′E 8°12′E 8°30′E 7°28′E 12°42′E
(2008) (2008) (2008) (2008)
Benn and Ballantyne (1994) and Ballantyne and Benn (1996) Lukas (2007) Benn (1994) Reinardy et al., in press Kellerer-Pirklbauer (2008) and Kellerer-Pirklbauer et al. (2008) Signer (2008) Signer (2008) Lukas et al. (2012, in press) Lukas et al. (2012, in press) Lukas et al. (2012, in press) Aeschlimann, 1983 (in Wetter, 1987) Brook and Lukas (2012) Brook and Lukas (2012) Brook and Lukas (2012) Kirkbride (1989)
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
Grid coordinates
Lukas (2005) and Benn and Lukas (2006) Benn (1990, 1992) Boston (2012) Mills et al. (2009)
101
102
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Borebreen (sandstone)
(b) Borebreen (sandstone)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
C40 (%) 100
80
80
60
40
100
80
100
80
100
80
100
60
40
20
20
0
0 0
20
40
60
80
100
0
20
40
C40 (%)
60
C40 (%)
(e) Scott Turnerbreen (sandstone)
(f) Scott Turnerbreen (sandstone)
100
50
80
40
RWR (%)
RA (%)
80
(d) Larsbreen (sandstone)
100
RWR (%)
RA (%)
(c) Larsbreen (sandstone)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(h) Flaajökull (basalt)
(g) Flaajökull (basalt) 100
50
80
40
RWR (%)
RA (%)
60
C40 (%)
60
40
20
30
20
10
0
0 0
20
40
60
C40 (%)
80
100
0
20
40
60
C40 (%)
Fig. 2. Co-variance plots for all individual lithologies sampled in all catchments, using both possible variations of RA versus C40-index (left panels) and the corresponding co-variance plots using RWR versus C40-index (right panels).
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(j) Fåbergstolsbreen (gneiss)
(i) Fåbergstolsbreen (gneiss) 100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0 0
20
40
60
80
0
100
0
20
C40 (%)
40
60
80
40
RWR (%)
50
60
40
80
100
80
100
80
100
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(n) Storbreen (gneiss)
(m) Storbreen (gneiss) 100
50
80
40
60
RWR (%)
RA (%)
100
(l) Bergsetbreen (gneiss)
100
20
40
20
30
20
10
0 0
20
40
60
80
0
100
0
20
C40 (%)
40
60
C40 (%)
(o) Slettmarkbreen (gneiss)
(p) Slettmarkbreen (gneiss)
100
50
80
40
RWR (%)
RA (%)
80
C40 (%)
(k) Bergsetbreen (gneiss)
RA (%)
103
60
40
20
30
20
10
0 0
20
40
60
80
100
0
C40 (%)
0
20
40
60
C40 (%) Fig. 2 (continued).
104
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(q) Midtdalsbreen (phyllite)
(r) Midtdalsbreen (phyllite) 50
100 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
30
20
10
20
0
0 0
20
40
60
80
0
100
20
60
80
100
60
80
100
80
100
(t) Pasterze (prasinite)
(s) Pasterze (prasinite)
60
100
50
RWR (%)
80
RA (%)
40
C40 (%)
C40 (%)
60
40
40 30 20
20
10 0
0 0
20
40
60
80
0
100
20
(v) Vadret da Grialetsch (gneiss)
100
50
80
40
RWR (%)
RA (%)
(u) Vadret da Grialetsch (gneiss)
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%)
(w) Vadret da Grialetsch (amphibolite)
(x) Vadret da Grialetsch (amphibolite)
100
50
80
40
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
60
40
30
20
10
20
0
0 0
20
40
C
60 40
80
100
0
20
40
60
C40 (%)
(%) Fig. 2 (continued).
80
100
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Findelengletscher (mica schist)
(b) Findelengletscher (mica schist)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
30
20
10
20
0
0 0
20
40
60
80
100
0
20
100
50
80
40
RWR (%)
RA (%)
60
80
100
80
100
80
100
80
100
(d) Findelengletscher (eclogite)
(c) Findelengletscher (eclogite)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(f) Findelengletscher (serpentinite)
(e) Findelengletscher (serpentinite) 100
50
80
40
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%)
(g) Glacier d'Estelette (gneiss)
(h) Glacier d'Estelette (gneiss)
100
50
80
40
RWR (%)
RA (%)
105
60
40
20
30
20
10
0
0 0
20
40
60
C40 (%)
80
100
0
20
40
60
C40 (%)
Fig. 3. Co-variance plots for all individual lithologies sampled in all catchments, using both possible variations of RA versus C40-index (left panels) and the corresponding co-variance plots using RWR versus C40-index (right panels).
106
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(i) Fox Glacier (argillite)
(j) Fox Glacier (argillite)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
50
80
40
RWR (%)
RA (%)
80
100
80
100
80
100
80
100
(l) Fox Glacier (greywacke)
100
60
40
30
20
10
20
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(n) Fox Glacier (mica schist)
(m) Fox Glacier (mica schist) 100
50
80
40
RWR (%)
RA (%)
60
C40 (%)
(k) Fox Glacier (greywacke)
60
40
20
30
20
10
0
0 0
20
40
60
C
40
80
100
0
20
(%)
40
60
C
40
(%)
(p) Tasman Glacier (greywacke)
(o) Tasman Glacier (greywacke) 100
50
80
40
RWR (%)
RA (%)
40
60
40
20
30
20
10
0
0 0
20
40
60
80
100
C40 (%)
0
20
40
C40 (%) Fig. 3 (continued).
60
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(q) NW Scotland (psammite)
(r) NW Scotland (psammite) 50
100 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
60
80
100
80
100
80
100
80
100
(t) Coire na Creiche (gabbro)
100
50
80
40
RWR (%)
RA (%)
(s) Coire na Creiche (gabbro)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
40
60
C40 (%)
(u) Monadhliath (psammite)
(v) Monadhliath (psammite)
100
50
80
40
60
30
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
40
20
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
40
60
C40 (%)
(w) Drakensberg (basalt)
(x) Drakensberg (basalt)
100
50
80
40
60
30
RWR (%)
RA (%)
107
40
20
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%) Fig. 3 (continued).
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S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(b) Vadret da Grialetsch
(a) Vadret da Grialetsch
100
100
Gneiss
Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
Amphibolite 80
RWR (%)
RA (%)
80
60
40
60
40
20
20
0
0 0
20
40
60
80
0
100
20
40
(c) Findelengletscher
100
60
80
100
60
80
100
100
Serpentinite Eclogite Mica schist
80
RWR (%)
80
RA (%)
80
(d) Findelengletscher
100
60
40
60
40
20
20
0
0 0
20
40
60
80
0
100
20
40
C40 (%)
C40 (%)
(e) Fox Glacier
(f) Fox Glacier
100
100
Argillite Greywacke Mica schist
80
RWR (%)
80
RA (%)
60
C40 (%)
C40 (%)
60
40
20
60
40
20
0
0 0
20
40
60
80
100
C40 (%)
0
20
40
C40 (%)
Fig. 4. Co-variance plots for lithologies within individual catchments combined. (a) and (b): Vadret da Grialetsch (gneiss and amphibolite); (c) and (d) Findelengletscher (eclogite, serpentinite, mica schist); (e) and (f): Fox Glacier (argillite, greywacke, mica schist). It is clearly visible that mixing lithologies even within the same catchments results in a loss of discriminatory power in both RA and RWR versus C40-index-plots.
In the cases of Fox Glacier and Findelengletscher, where both fluvial and subglacial control samples could be obtained, the respective control envelopes of these two domains overlap significantly, which contrasts markedly with those plotted for each lithology individually above (Figs. 2, 3).
3.2.2. Lithological variation between catchments In addition to plotting all lithologies from the same catchments (above), we plotted all similar lithologies in one co-variance diagram to see what variation, if any, between similar lithologies in different catchments was present (Fig. 5). It is interesting to note that for all
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(b) All sandstone catchments
(a) All sandstone catchments 100
100
Scott T.B. Larsbreen Borebreen
Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
80
RWR (%)
80
RA (%)
109
60
40
20
60
40
20
0
0 0
20
40
60
80
0
100
20
40
(c) All gneiss catchments
80
100
60
80
100
(d) All gneiss catchments 100
100
Storbreen Bergsetbreen Fåbergstolsb.
80
80
Slettmarkbreen Midtdalsbreen
60
RWR (%)
RA (%)
60
C40 (%)
C40 (%)
V. da Grialetsch G. d‘Estelette
40
20
60
40
20
0
0 0
20
40
60
80
100
0
C40 (%)
20
40
C40 (%)
Fig. 5. Co-variance plots for similar lithologies for different catchments. (a) and (b): sandstones (Borebreen, Larsbreen and Scott Turnerbreen). (c) and (d): gneisses (all Norwegian glaciers, Vadret da Grialetsch, Glacier d'Estelette).
catchments containing sandstone, two of these (Larsbreen and Scott Turnerbreen) have lithologically extremely similar sandstones that belong to the same geological formation. When plotted together (Fig. 5a–b), a less consistent pattern is discernible within all three sandstone-containing catchments compared to the individual plots. Cumulative envelopes for all of these control populations are distinct enough between fluvial and supraglacial in both RA-C40 and RWR-C40 co-variance plots (Fig. 5a, b). The spread of values between different catchments in the same control envelope ranges from 10% (proximal fluvial) to > 30% (supraglacial). For all gneisses plotted together (Fig. 5c–d), the picture that emerges is one of a much wider spread of control envelopes and a widespread loss of discriminatory power, because all three control envelopes overlap in the RWR-C40 co-variance plot, while fluvial and subglacial ones overlap in the RA-C40 plot. This means that only supraglacial clasts can be distinguished from those of other origin in the RA-C40 co-variance plot (Fig. 5c); we regard the close proximity of supraglacial and fluvial control envelopes in the RWRC40 plot as problematic for an effective and unequivocal distinction. A very large spread of values in the C40-indices that ranges from 20% (subglacial) to 45% (fluvial and supraglacial) is also clearly discernible.
4. Discussion 4.1. The discriminatory power of co-variance plots The systematic plotting of both RA and RWR-indices versus C40 demonstrates that clast shape is an extremely powerful method that is applicable to > 95% of all investigated catchments, thereby demonstrating the widespread applicability of the approach proposed by Benn and Ballantyne (1993, 1994) for modern and formerly glaciated catchments. Our analysis also suggests that it is advisable to check both RA and RWR indices for their respective functionality and use the one that distinguishes most clearly between different control sample populations. Comparing the differences between RA and RWR plots could also be beneficial if one aims to elucidate the relative importance of subglacial edge-rounding of supraglacial clasts (RA-index) versus fluvial rounding of edge-rounded clasts, or vice versa (RWR-index). The observed differences in discriminatory power of RA as opposed to RWR-indices in co-variance plots may be best explained through the degree of fluvial reworking in a glaciated catchment. If fluvial reworking of a mixture of supraglacial and subglacial material occurs and is a prominent process in the catchment, then any glacier advance would rework fluvially-remoulded sediment, and hence the fluvial signature
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would be preserved in any subglacially-sampled clasts. Therefore, the envelopes of likelihood for fluvial and subglacial control samples would only be distinguishable through the degree of roundness, but less so on the basis of the amount of platy clasts. In the co-variance diagrams, this would be visible through these two control envelopes being fully overlapping near or on the x-axis (RA-C40) or separated vertically by different degrees of rounding in the two categories (RWR-C40). The latter case has been found in nearly all high-mountain catchments (Figs. 2, 3) and has been reported elsewhere before (e.g. Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). These findings contradict the assertion and seemingly widespread notion that all highmountain catchments are dominated by a high supraglacial load and cautions against an oversimplification of such processes by extrapolating between catchments locally (e.g. Hambrey and Ehrmann, 2004) or globally (e.g. Hambrey and Glasser, 2012) as this can lead to potentially erroneous conclusions. The comparison of RA and RWR-based co-variance plots also shows that those catchments with the highest number of control samples in each category (most notably Fox and Tasman Glacier and Glacier d'Estelette) allow the most detailed analysis and most confident interpretation. Although this may seem obvious, many studies rely on one or two control samples per envelope, and, while this is appropriate as a first approximation, it does not allow analysis at a comparatively high level of detail. For example, fluvial and subglacial control envelopes can be distinguished clearly from one another, but the effect of fluvial (re-) working on clast form only becomes discernible where control samples have been obtained at defined intervals from the portal (e.g. Midtdalsbreen, Findelengletscher, Glacier d'Estelette, Fox Glacier). In all catchments where fluvial control samples were obtained in this detailed, staged manner, clear trends of progressive rounding with distance are visible. Such clear trends, which have been shown in numerous laboratory and field studies by fluvial geomorphologists over the decades (e.g. Knighton, 1998), confirm that co-variance plots are capable of deciphering subtle and complex processes at catchment scale and are also able to act as reliable reference frames for the interpretation of clasts of unknown origin. This rounding trend can take two forms that point to the influences of different processes. Firstly, there may be a clear decrease in angularity; this process can only be observed in the RA-C40 co-variance plots (cf. Fig. 2e: Scott Turnerbreen). The settings where this process is dominant are characterised by a dominantly supraglacial sediment input, so that the initial clast form is angular and platy or elongated. Secondly, roundness may increase with distance from the portal; this process only becomes discernible in the RWR-C40 co-variance plots (e.g. Findelengletscher, Fig. 3b, d, f; Fox Glacier, Fig. 3j, l, n). Based on an analysis of our dataset, this process appears to be linked to settings where (a) there is a dominant subglacial source of the material, and (b) the subglacial and glaciofluvial transport systems are intimately coupled and/or previous proglacial sediments are reworked during glacier advances, such as those stored in overdeepenings developed behind bedrock steps in some alpine settings (e.g. Findelengletscher; Lukas et al., 2012). Thirdly, a mixture of both systems may be present, which would be indicated by the proximity of fluvial control samples to both the supraglacial and subglacial control envelopes (e.g. Fåbergstolsbreen, Fig. 2j). A note of caution must be added at this stage, because the aforementioned relationships do not hold true for all modern catchments presented here. Midtdalsbreen (Fig. 2q–r), for example, shows no such clear relationships in either co-variance plot, which points to a more complex system of reworking and/or an insufficient number of control samples from each site (in general, it emerges that the results are clearest where more than two control samples have been obtained from each control environment). As can be seen from the above analysis, clast shape may also be used to tease out a fairly thorough understanding of an individual glaciated catchment if the sampling strategy has been well designed. The lack of angular clasts in all fluvial control samples at Findelengletscher indicates that all samples obtained from the fluvial
system have been thoroughly rounded and edge-rounded by previous fluvial and subglacial activities before they even reach the glacier portal (Fig. 4c). This has important implications for interpreting the dynamics of this system, because it indicates that clasts that enter the subglacial and glaciofluvial system, even from a supraglacial position, are very effectively reworked over fairly short distances, so that an imprint of a supraglacial origin will not be preserved in any shape or form once a clast enters the subglacial and fluvial systems.
4.2. The role of lithology: different lithologies in the same catchment The analyses of the response of different lithologies within the same catchments, where the same, or at least highly comparable, geographical, climatic and glaciological boundary conditions act on the formation of distinct clast shape, demonstrate that there is a large variation between different lithologies (Fig. 4). When these different lithologies are combined in co-variance analysis (Fig. 4a–b, c–d, e–f), rather than being treated separately (Figs. 2u–x, 3a–f and i–n, respectively), the overlap between the control envelopes of different lithologies is so great that the discriminatory power of the method as a whole is either much-reduced or removed altogether. This effect is most pronounced in catchments where variations in C40 and RA/ RWR-indices are small (e.g. Fox Glacier, Fig. 4e, f), but also remove most of the discriminatory power for catchments where the ranges are wider (e.g. Findelengletscher, Fig. 4c, d). Only for catchments where the difference of C40 between two dominant lithologies is systematic and consistent (near-constant) is the discriminatory power not impacted as much (Fig. 4a, b), although it remains to be seen what the inclusion of a third lithology would do to this relationship. The analysis of broadly similar lithologies shows a very similar, albeit more dramatic, picture to the one described above. A nearcomplete loss of discriminatory power occurs where lithologies from different geographical, climatic and glaciological boundary conditions are mixed (Fig. 5). Although perhaps unsurprising, this suggests that similar lithologies in different regions can respond very differently to processes operating in mountain areas and other cold regions and that an inherently lithological rather than catchment-specific control exists. This is at odds with the study of Bennett et al. (1997), in which all lithologies, except for micaschist, resulted in similar shapes for specific settings. Studies of clast shape in fluvial, slope and glacial processes (e.g. Sneed and Folk, 1958; Selby, 1993; Glasser et al., 1998; Sklar and Dietrich, 2001; Brook and Tippett, 2002; Brook et al., 2004; Krabbendam and Glasser, 2011) suggest that relevant lithological properties which could be the reason for the different lithological response in our study are: i) joint density/spacing — an approximate measure of rock mass strength (Jade and Sitharam, 2003) ii) hardness, which strongly correlates with tensile strength (Augustinus, 1991; Aydin and Basu, 2005) iii) anisotropy, or the pervasiveness of a foliation, such as schistosity or slaty cleavage (e.g. Hall, 1987). Joint spacing would be expected to be particularly important in the first stages of the life of a clast, namely fracturing and removal from the intact valley floor or slopes by plucking, rock slope failure or frost weathering (e.g. Ballantyne, 1982; Matthews, 1987; Benn, 1992; Glasser et al., 1998; Krabbendam and Glasser, 2011; Hall et al., 2012); it would thus be the primary control on the initial form of the clasts. However, the shapes for supraglacial clasts are all rather similar with overlapping envelopes for most lithologies, regardless of lithology. This suggests little variation in initial clast form, and little influence of jointing. It is, however, likely that joint spacing has a strong control on initial clast size.
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The diminution of clasts under study here occurred by abrasion, fracturing and crushing under different boundary conditions (fluvial, subglacial). Both fluvial (Sneed and Folk, 1958) and subglacial (Drake, 1970) studies have shown that, after a certain transport distance, a dynamic equilibrium for clast form can be reached, beyond which clast fracturing and clast abrasion occur at comparable rates. However, this dynamic equilibrium is likely to be different for different lithologies (depending on its properties) and different environments. Despite this wide range of parameters, the following broad responses for lithologies of different physical properties can be postulated (Fig. 6): 1) Hard but isotropic (poorly foliated) rocks would be expected to show a slow initial decrease of angularity, but would ultimately tend to become highly blocky. In the dataset here it can be seen that hard rocks such as basalt (Fig. 2g), eclogite (Fig. 3c) and gabbro (Fig. 3s) all can reach C40 of 20 or less. Other lithologies do not reach similarly
Hard
(a) eclogite
G N E I S S amp hibo lite
Hardness
gabbro quartzite dolo stone
psa
mm
ite
mica schist
serpentin Soft
ite
sand stone
Weak (isotropic)
argillite Anisotropy
Strong
Hard
(b)
Soft
Hardness
e.g. Eclogite, gabbro - slow decrease in angularity - ultimately highly blocky (low C40)
e.g. gneiss, amphibolite - slow decrease in angularity - medium blocky (low-mid C40)
e.g. sandstones - fast rounding - highly blocky (low C40)
Weak (isotropic)
e.g. argillite - fast rounding - platy (high C40)
Anisotropy
Strong
Fig. 6. (a) Schematic plot of the physical properties, anisotropy, and hardness of some rock types, showing overlapping fields for the lithologies included in the present contribution. (b) Schematic summary plot of expected shape changes for lithologies with different physical properties (anisotropy and hardness) based on the data presented in Figs. 2 and 3.
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blocky forms. The contrast is shown convincingly by clasts at Findelengletscher (Fig. 3a–f), where eclogite clasts are significantly blockier (C40 = 18–35) than micaschist and serpentinite (C40 > 45 for all samples) in all but the supraglacial settings. 2) Hard, anisotropic rocks (e.g. some gneiss, ?amphibolite, ?prasinite and harder varieties of micaschist) would show a slow decrease of angularity, but be expected to end up being less blocky. This may explain the difference in final C40-values for the variety of gneiss clasts, as the anisotropy of gneisses can vary greatly. Some gneiss samples end up being very blocky (Fig. 2i, k, m, o), while others are not (Figs. 2q, 3g). 3) Soft and isotropic rocks (such as sandstones) would show potentially a faster response than their hard counterparts. Although one would expect such rocks to end up highly blocky due to fast abrasion, this appears not to be the case, with final C40 around 30–40 or so. This can be explained by continuous clast fracturing concomitant with abrasion in both subglacial and fluvial settings. 4) Soft and anisotropic rocks (argillite and softer varieties of micaschist) would show a fast decrease of angularity, but end up being only moderately blocky. The trends of the micaschist and argillite clasts at Fox Glacier is interesting, in that there is little change in clast form, but a strong rounding in the subglacial to distal fluvial setting. It appears that such rocks cannot become blockier than about C40 = 30. Most samples show a general trend to blockier and more rounded clasts from subglacial to the more distal fluvial settings, as expected. However, some samples do not abide by this general trend, for example psammite in NW Scotland and the Monadhliath (Fig. 3q–r; u, v) and serpentinite at Findelengletscher (Fig. 3e–f). It is not clear whether this is because of a peculiarity of the setting (including the possibility of reworking) or something specific to certain lithologies. One mechanism that could potentially cause such ‘opposite’ behaviour may be clast–clast collisions in subglacial environment. These are high pressure but low velocity, while in the fluvial environment these are low pressure but high velocity. Some lithologies may respond differently to this change than others. However, more research, including on the influence of clast size on clast shape, would be needed to confirm this. It is clear from the above that different lithologies respond very differently to the same boundary conditions, resulting in very different clast shapes within the same catchment (Fig. 6). It follows that the discriminatory power of the method of clast shape measurements is compromised if different lithologies are mixed in the same sample (see also Brook and Lukas (2012) and Lukas et al. (2012, in press)). Finally, what ultimately constrains the response of a clast to its environment is its physical properties, not its lithological name. Some rock types (e.g. eclogite, granite) will have a narrow range of bedrock properties, but many do not. For instance, different sandstones can vary significantly in their hardness, depending on the degree of compaction and induration and the nature of the matrix. Other lithologies, such as gneiss and amphibolite can show great variations in anisotropy. Conversely, some rock types with very different names may well have very similar properties. For instance, it is well possible that Bennett et al. (1997) did not note a significant influence of lithology on clast shape because the range of physical properties of some of the studied lithologies (gneiss, psammite, quartzite, dolostone) may overlap. However, the latter study did not include a dataset of samples from known environments (control samples), but used mixed lithologies within samples of 50 clasts to test the influence of lithology, and therefore that approach needs to be regarded as having been built on a false premise. Our findings suggest that lithology has a strong influence on clast shapes and that any catchment-specific effects, such as the intensity of fluvial reworking, appear to be superimposed on this lithological control, thereby modulating this overall signal further. Without rigorous measuring of physical properties it is difficult to argue that lithology
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Type I
Supraglacial
RA
Supraglacial
Subglacial
Subglacial Fluvial
Fluvial C40
RWR
C40
4.3. Implications for debris cascades in glaciated environments
Fluvial
Fluvial Subglacial
Supraglacial
Lithology Clast shape
Environment
C40
Subglacial
Supraglacial
C40
Maritime, temperate mountain glaciers (corrie, valley and ice cap outlet glaciers)
Maritime, temperate high-mountain glaciers (corrie and valley glaciers)
Angularity/roundness (RA and/or RWR) and platiness (C40-index) are both nearly equally-strong discriminators
Roundness is the dominant discriminator (RA and/or RWR); platiness (C40-index) is a very poor discriminator
Mainly low-anisotropy (‘massive’), e.g. ortho-gneiss, eclogite
Mainly high-anisotropy (‘platy’), e.g. serpentinite, schist, eclogite, paragneiss
Processes
As has been highlighted in the Introduction section, clast shape may be used to enhance our understanding of processes operating at the catchment scale, for example the effects and dominance of reworking processes within the debris cascade. We here test whether any such generic statements can be derived from the present dataset by carefully considering similarities and differences between individual sites, as shown in the co-variance plots (Fig. 2). For this purpose, we plotted the locations of each environmental control envelope (i.e. supraglacial, subglacial, fluvial) in one co-variance plot and compared the resulting plots of all catchments to find similarities. The result is surprising, since all catchments, apart from two exceptions, can be grouped into two distinct generic types (Fig. 7), which will be discussed below. Type I catchments comprise almost exclusively locations in the lesser mountain ranges (i.e. Norway, Svalbard) and are characterised by a clear separation of supraglacial (high C40 and RA, but low RWR), fluvial (medium C40, low RA, medium RWR) and subglacial control envelopes (low C40, low RA and RWR) (Fig. 7a). In terms of processes, it appears most plausible that clasts that enter the system supraglacially (or extraglacially) are first of all shaped by frost action and are highly platy and angular. When such clasts enter the fluvial and subglacial realms, erosion and transport processes would lead to progressive rounding and edge-rounding, respectively, with a concomitant reduction of platy forms; the same would presumably hold true for freshlyeroded material in the subglacial realm (cf. Benn, 1992). However, platy forms appear to be stable in proglacial river beds, where control samples show consistently higher proportions of platy clasts (higher C40) compared to the subglacial controls. In contrast, once supraglacial or fluvial material is entrained and transported subglacially, a clear reduction in c/a ratio is evident, so that blocky forms represent the most stable and perhaps ‘final’ form in such environments, provided sufficient subglacial transport has taken place (cf. MacGregor et al., 2009). Fluvial reworking of such clasts does not appear to create a new, typical ‘fluvial’ one, because a blocky form, once created, is more resistant to further fracture than a platy one (e.g. Ballantyne, 1982; Kirkbride, 1989). The reason behind this is that it is difficult to generate two or more platy forms from a blocky clast, because any pre-existing weaknesses (e.g. foliation, micro-joints etc.) would have most likely been exploited prior to the production of a blocky clast. Likewise, a blocky clast is more protected against uneven force distributions during clast-to-clast collisions. Conversely, the splitting of a platy clast into two blockier ones, by halving it across the a-axis, would be comparatively easy, because the forces acting on either end of such a clast would be greater (e.g. Behrens, 1977; Ballantyne, 1982). Another important control on the emergence of blocky clasts in Type I catchments appears to be lithology, because all are dominated by massive lithologies with a low anisotropy (Deere and Miller, 1966) and thus ideally suited for the eventual production of blocky forms. Joint-spacing is likely to be
(b) Type II
RA
has little influence. Since this is rarely possible in practice, we recommend avoiding the mixing of lithologies. Despite the strong influence of lithology on clast shape, it is not the case that lithology is the sole constraint on clast form and roundness. The plots of sandstone and gneiss clast shape for different catchments show strong overlap of control envelopes and poor to absent discriminatory power (Section 3.2.2, Fig. 5). Although perhaps unsurprising, this suggests that similar lithologies in different regions can respond very differently to glacial and fluvial processes depending on local boundary conditions. This implies that specific lithologies do not have unique responses to the processes they are exposed to. Importantly, this implies that control samples of a specific lithology from one catchment may not be valid if applied to another one, in other words, control samples of a specific lithology must be taken in the same catchment as the target.
RWR
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Fig. 7. Schematic summary of two types of common, recurring co-variance plots found throughout the dataset. See text for a detailed explanation.
a key-control here as could be shown by Ballantyne (1982) for two of the catchments included in the present study. Further evidence for this inference comes from the presence of eclogite, a lithology characterised by low anisotropy, from Findelengletscher, a high-mountain catchment, in this category. Two further lithologies sampled in this catchment (serpentinite and mica schist) are both of higher anisotropy, hence tending towards the production of platy clasts originally, and they both clearly fit into Type II catchments (see below). Therefore, and because other boundary conditions can be excluded, the different response and behaviour of eclogite in the same high-mountain catchment can only be attributed to its lower anisotropy, not to other environmental, catchment-specific parameters. Type II catchments are entirely located in high-mountain regions and show lithological sources of high-anisotropy rocks (e.g. schists, para-gneisses, serpentinite). The pattern that emerges for these catchments is that the extraglacial/supraglacial envelope shows high C40, high RA and low RWR values, not dissimilar to those in Type I catchments. However, both subglacial and fluvial envelopes display a high proportion of platy clasts (evident in medium C40 values for both categories). Subglacial control samples have a higher RA-index and fluvial ones a higher RWR-index, but both envelopes occupy the same range of C40 values in the co-variance plots. Together, this evidence suggests that the dominant input route to the system (at the catchment scale!) is extraglacial and supraglacial, i.e. platy and angular. While
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
the angularity is reduced by subglacial edge-rounding and fluvial rounding, it appears that the rapidity of these high-mountain systems does not enable platy forms to be transformed into comparatively stable blocky ones to the degree that is possibly in Type I catchments. One may speculate that transport distance may be one control here. Likewise, based on previous studies (Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012), fluvial reworking of subglacial sediments and vice versa appear to be more commonplace than in Type I catchments. An absence of a clear change in platiness seems to confirm this interpretation, because the overall clast ‘stability’ does not change. It would be interesting to speculate here as to whether the occurrence of rock types with high anisotropy accentuates this trend. Two exceptions to the aforementioned categories exist. Although similar to Type I catchments, notably with respect to the geographical setting (lesser mountain range) and a low-anisotropical lithology (basalt), Flaajökull, Iceland (Fig. 8a), shows a much wider spread of RWR values in the subglacial control envelope than all other Type I catchments; in part, the subglacial clasts are more rounded than the proglacial fluvial control, pointing at multiple and complex transfers of material between the subglacial and subglacial glaciofluvial realm. This would correspond well to the finding of Evans (2000) who presented convincing stratigraphical evidence of these processes at the base of Skalafellsjökull, Iceland. The second exception is Pasterze, the largest glacier in the Eastern Alps, Austria (Fig. 8b). Although broadly similar
(a) Flaajökull, Iceland
(b) Pasterze, Austria Supraglacial
RA
RA
Supraglacial
Subglacial
Fluvial
Fluvial
Subglacial
C40
C40
Fluvial
Supraglacial
Subglacial Supraglacial C40
Maritime, temperate mountain glacier (ice cap outlet glacier)
Maritime, temperate high-mountain valley glacier
Angularity/roundness (RA and RWR) are good discriminators; platiness (C40-index) is less useful, because of fairly ‘blocky’ input
Angularity/roundness (RA and RWR) are good discriminators; platiness (C40-index) is less useful, because of fairly ‘platy’ input
Basalt (low-anisotropy, ‘massive’)
Prasinite (high-anisotropy, ‘platy’)
Processes
Lithology Clast shape
Environment
C40
RWR
RWR
Fluvial Subglacial
Fig. 8. Schematic summary of two types of exceptions from the generic co-variance plots plotted in Fig. 7. See text for a detailed explanation.
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in the overall arrangement of control envelopes, the spread of C40 values for the supraglacial realm is much broader, incorporating values that are only reached in fluvial and subglacial catchments elsewhere (Figs. 2, 3, 7). In this catchment, the dominant distinguishing criterion between the different control envelopes is roundness; platiness is less useful here. This could be due to a difference in joint-spacing in the local prasinite bedrock outcrops, which may lend themselves to producing fragments towards the blockier end of the spectrum compared to all other high-mountain environments where higher-anisotropy lithologies are present (Figs. 2, 3). However, this reasoning has to remain speculative at this stage, since no detailed data on joint spacing in this catchment are available to test our hypothesis. 5. Recommendations for clast shape sampling From the analyses presented above it has become clear that a dedicated and well-designed sampling programme will give the best results. We recommend starting the sampling programme by focusing on control environments where the erosional, transportational and depositional processes are known or can be well constrained. These include: Talus slopes outside the glacier (“extraglacial”); supraglacial material; clasts from the proglacial (and/or subglacial) fluvial areas; and clasts taken from the subglacial realm, ideally from in-situ basal traction zone till. In modern environments, the level of appreciation of the complexity of all processes contributing to the shaping of clasts is much enhanced if fluvial control samples are taken from locations that are equally spaced along the proglacial stream (e.g. at the glacier portal, 500, 1000, 2000 m downstream). Since this approach is not possible in palaeo-environments, we recommend sampling clasts deposited on gravel bars above the modern floodplain, because clasts transported during modern floods may most closely approximate the energy levels and transport capacity of former proglacial braidplains. Likewise, talus slopes are particularly useful as surrogates for supraglacial material in palaeo-environments, and, in the case of subglacial control, unequivocal exposures of subglacial traction till (sensu Evans et al., 2006) should be used. Ideally, plotting of all control samples and a first co-variance analysis should precede further sampling of clasts with unknown erosional, transportational and depositional histories to maximise time efficiency. This is especially true for catchments where multiple lithologies are present and could be used, because, as the cases of Findelengletscher, Fox Glacier and Vadret da Grialetsch demonstrate, different lithologies respond very differently to erosional, transportational and depositional processes even within the same catchment. In such cases, it is advisable to carry out pilot co-variance tests on each lithology to establish the one most suited for an effective discrimination of the different control environments. We recommend the collection of at least two control samples at each site and for each lithology. For a detailed investigation of catchmentwide processes (e.g. Brook and Lukas, 2012), we recommend at least four samples per control environment. Lithologies should never be mixed at the sample level, meaning that all 50 clasts of a sample and all samples used in one co-variance plot should be of the same lithology. Should clasts be coated by silt that has hardened during drying, for example those obtained from subglacial traction till, then they should ideally be washed, either on site or back in the laboratory. 6. Conclusions We have presented a dataset of clast shape variability in glaciated catchments from different locations worldwide. In order to study the influence of lithology on the performance of the method developed by Benn and Ballantyne (1993, 1994), we restricted this analysis to control samples where the processes leading to the development of
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distinct clast forms were well constrained. Our analysis leads us to draw the following conclusions. (1) The method developed and proposed by Benn and Ballantyne (1993, 1994) is sound and applicable to >95% of the catchments studied. The proposed RA-C40 co-variance plots are applicable to 63% of the 19 catchments we investigated, while the alternative approach that uses the rounded end of the form continuum, the RWR-index, is applicable to 75% of these catchments. This implies a wide-ranging applicability of the method, but we note that the RWR versus C40-index is more powerful than the RA versus C40-index. (2) Sampling of different lithologies in the same catchment has allowed us to determine a systematic and marked influence of lithology on the development of clast shape. Within catchments, mixing of lithologies results in the generation of overlap between different control envelopes that reduces the discriminatory power of the method significantly, in some cases to zero. Where this is the case, contrasting control environments cannot be distinguished from one another, thereby making any reconstruction of unknown transport processes for clasts from landforms and sediments in those catchments impossible or at the very least unreliable. (3) Plotting of similar lithologies from different catchments results in an even stronger loss of discriminatory power of the method, thereby strongly suggesting that lithological variability has a strong influence on clast shape, upon which catchment-specific parameters are superimposed. (4) Comparison of all co-variance plots in our dataset has resulted in two distinct types of process progressions within debris cascades to be reconstructed. Type I catchments comprise almost exclusively locations in the lesser mountain ranges (i.e. Norway, Svalbard) and are characterised by a clear separation of supraglacial (high C40 and RA, but low RWR), fluvial (medium C40, low RA, medium RWR) and subglacial control envelopes (low C40, low RA and RWR). Type II catchments are entirely located in high-mountain regions and the supraglacial envelope shows high C40, high RA and low RWR values, not dissimilar to those in Type I catchments. However, subglacial and fluvial envelopes display a high proportion of platy clasts, occupying the same range of C40 values in the co-variance plots. This evidence suggests that the dominant inputs to the system (at the catchment scale!) are extraglacial and supraglacial and that both fluvial and subglacial reworking do not result in a reduction of platy clasts, but that, instead, edge-rounding and rounding are the key discriminators in high-mountain environments. (5) Our data demonstrate that, with careful sampling design, subtle processes such as progressive rounding of clasts with distance from the glacier portal in the fluvial environment can be teased out. (6) We recommend using the following approach in order to produce reliable and reproducible results that are comparable between catchments for future clast shape studies. Firstly, different lithologies should be analysed separately at all times; mixing at both the sample and co-variance scale introduces an unknown element of error that is likely to make the results unreliable for a robust reconstruction for samples with an unknown transport history. Secondly, both RA and RWR approaches should be used in tandem to establish which one performs better in any given catchment. Ideally, co-variance analysis of control samples would precede any sampling of clasts from landforms and sediments that contain clasts of unknown transport histories to establish which approaches (RA or RWR) and lithologies are the most suitable in any given context. Thirdly, a well thought-out sampling strategy should be designed, with at least two control samples taken at each site. Depending on the purpose of the study, it may be
advisable to sample at defined and regular distances from the glacier portal. (7) In the future, it would be beneficial to investigate the links between topographical, glaciological and lithological controls further and to test our hypothesis that distinct types of debris cascades in glaciated mountain environments may exist.
Acknowledgements This contribution builds on over two decades of work, and several people have been involved in helping gather the data presented here. In addition to numerous undergraduate students, who have been involved in data collection in one way or another, or who have helped shape our thinking through unexpected questions, we are grateful to Derek Cowe, Sue Jenkins and Tina Lukas for their support in the field and back at the desk. The majority of this contribution was written while SL was on research leave from QMUL. BTIR would like to acknowledge the receipt of funding from INTERACT (grant agreement No. 262693) under the European Community's Seventh Framework Programme. The careful and constructive comments of one anonymous reviewer and Richard Shakesby helped to clarify parts of the message of this article and are gratefully acknowledged. References Augustinus, P.C., 1991. Rock resistance to erosion: some further considerations. Earth Surface Processes and Landforms 16, 563–569. Aydin, A., Basu, A., 2005. The Schmidt hammer in rock material characterization. Engineering Geology 81, 1–14. Ballantyne, C.K., 1982. Aggregate clast form characteristics of deposits at the margins of four glaciers in the Jotunheimen Massif, Norway. Norsk Geografisk Tidsskrift 36, 103–113. Ballantyne, C.K., Benn, D.I., 1996. Paraglacial slope adjustment during recent deglaciation and its implications for slope evolution in formerly glaciated environments. In: Anderson, M.G., Brooks, S. (Eds.), Advances in Hillslope Processes. Wiley, Chichester, pp. 1173–1195. Barrett, P.J., 1980. The shape of rock particles, a critical review. Sedimentology 27, 291–303. Bearth, P., 1953. Geologischer Atlas der Schweiz 1:25,000. Blatt 29 (Zermatt) mit Erläuterungen.Schweizerische Geologische Kommission, Bern. Bearth, P., Eugster, H., Leupold, W., Spaenhauer, F., Streckeisen, A., 1935. Scaletta (SA 423). Geologischer Atlas der Schweiz 1:50,000. Behrens, M., 1977. Zur Stereometrie von Geröllen. Mitteilungen aus dem GeologischPaläontologischen Institut der, 47. Universität Hamburg, pp. 1–124. Bell, F.G., Haskins, D.R., 1997. A geotechnical overview of Katse Dam and Transfer Tunnel, Lesotho, with a note on basalt durability. Engineering Geology 46, 175–198. Benn, D.I., 1989. Debris transport by Loch Lomond Readvance glaciers in northern Scotland, basin form and the within-valley asymmetry of lateral moraines. Journal of Quaternary Science 4, 243–254. Benn, D.I., 1990. Scottish Lateglacial moraines: debris supply, genesis and significance. Unpublished PhD thesis, University of St Andrews, 458 pp. Benn, D.I., 1992. The genesis and significance of ‘hummocky moraine’: evidence from the Isle of Skye, Scotland. Quaternary Science Reviews 11, 781–799. Benn, D.I., 1994. Fluted moraine formation and till genesis below a temperate glacier: Slettmarkbreen, Jotunheimen, Norway. Sedimentology 41, 279–292. Benn, D.I., 1995. Fabric signature of subglacial till deformation, Breidamerkurjökull, Iceland. Sedimentology 42, 735–747. Benn, D.I., 2004. Clast morphology. In: Evans, D.J.A., Benn, D.I. (Eds.), A Practical Guide to the Study of Glacial Sediments. Arnold, London, pp. 78–92. Benn, D.I., Ballantyne, C.K., 1993. The description and representation of particle shape. Earth Surface Processes and Landforms 18, 665–672. Benn, D.I., Ballantyne, C.K., 1994. Reconstructing the transport history of glacigenic sediments: a new approach based on the co-variance of clast shape indices. Sedimentary Geology 91, 215–227. Benn, D.I., Lukas, S., 2006. Younger Dryas glacial landsystems in North West Scotland: an assessment of modern analogues and palaeoclimatic implications. Quaternary Science Reviews 25, 2390–2408. Benn, D.I., et al., 2004. The research project — a case study of Quaternary glacial sediments. In: Evans, D.J.A., Benn, D.I. (Eds.), A Practical Guide to the Study of Glacial Sediments. Arnold, London, pp. 209–234. Bennett, M.R., Hambrey, M.J., Huddart, D., 1997. Modification of clast shape in higharctic glacial environments. Journal of Sedimentary Research 67 (3), 550–559. Blott, S.J., Pye, K., 2008. Particle shape: a review and new methods of characterization and classification. Sedimentology 55, 31–63. Boston, C.M., 2012. A Lateglacial plateau icefield in the Monadhliath Mountains, Scotland: reconstruction, dynamics and palaeoclimatic implications. Unpublished PhD Thesis, Queen Mary University of London, 291 pp.
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Clast shape analysis and clast transport paths in glacial environments: A critical review of methods and the role of lithology Sven Lukas a,⁎, Douglas I. Benn b, c, Clare M. Boston a, Martin Brook d, Sandro Coray e, David J.A. Evans f, Andreas Graf e, Andreas Kellerer-Pirklbauer g, Martin P. Kirkbride h, Maarten Krabbendam i, Harold Lovell a, c, Martin Machiedo j, Stephanie C. Mills k, Kate Nye f, Benedict T.I. Reinardy l, Fionna H. Ross b, Michael Signer e a
School of Geography, Queen Mary University of London, Mile End Road, London E1 4NS, England, UK School of Geography and Geosciences, University of St Andrews, St Andrews KY16 8YA, Scotland, UK c Department of Geology, The University Centre in Svalbard (UNIS), N-9171 Longyearbyen, Norway d Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand e Institut für Geologie, Universität Bern, Baltzerstr. 1+3, 3012 Bern, Switzerland f Department of Geography, Durham University, South Road, Durham DH1 3LE, England, UK g Institute of Remote Sensing and Photogrammetry, Graz University of Technology, Austria h Department of Geography, University of Dundee, Scotland, UK i British Geological Survey, Murchison House, West Mains Road, Edinburgh EH9 3LA, Scotland, UK j Department of Geography, University of Bergen, Norway k School of Geography, Geology and Environmental Sciences, Centre for Earth & Environmental Science Research, Kingston University London, Penrhyn Road, Kingston upon Thames, KT1 2EE, UK l Instituto Andaluz de Ciencias de la Tierra, Granada, Spain b
a r t i c l e
i n f o
Article history: Received 30 June 2012 Accepted 23 February 2013 Available online 21 March 2013 Keywords: Clast shape Glacial environments Transport path Reconstruction Debris cascade Clast lithology
a b s t r a c t The reconstruction and tracing of transport paths in glaciated (and other) environments have a long tradition in the Earth Sciences. We here present a dataset of clast shape samples from a worldwide selection of glaciated mountain environments in order to assess the reliability of this approach overall and the role of lithology on the performance of clast shape measurements in particular. Our findings demonstrate that the widely-used RA-C40 co-variance approach is applicable to 63% of the 19 catchments investigated, while the alternative RWR-C40 approach is more widely applicable to 75% of these catchments. A systematic assessment of mixing of lithologies at the catchment scale demonstrates that such mixing leads to pronounced overlaps between different control envelopes that had previously been separated, thereby removing the discriminatory power of the method. Mixing of similar lithologies between different catchments shows an even more extreme loss of discriminatory power, which strongly suggests that lithology plays a primary role in determining clast shape, and that catchment-specific processes are superimposed. Systematic analysis of the dataset also shows that nearly all catchments (apart from two) can be grouped into two types. Type I relates to sites in lesser mountain ranges and is characterised by dominantly blocky forms in the subglacial realm, highlighting significant reworking processes. Type II sites are dominantly in high-mountain environments and characterised by a high similarity between subglacial and fluvial control envelopes. This indicates that, although reworking may be pronounced, it is not necessarily effective enough to remove the platy shape that most likely results from extraglacial and supraglacial inputs. Our study highlights the potential of clast shape analysis as a tool that allows generic processes to be identified between catchments, thereby enabling an understanding of debris cascades in glaciated mountain environments. We finish with recommendations for ensuring that future clast shape studies are robust, reproducible and comparable between different sites. © 2013 Elsevier B.V. All rights reserved.
Contents 1. 2.
Introduction . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . 2.1. Field measurement approach . 2.2. Graphic representation of results
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⁎ Corresponding author. Tel.: +44 7882 8417; fax: +44 7882 7032. E-mail address: [email protected] (S. Lukas). 0012-8252/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.earscirev.2013.02.005
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2.2.1. Form . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Roundness and texture . . . . . . . . . . . . . . . . 2.2.3. Co-variance plots . . . . . . . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The discriminatory power of co-variance plots . . . . . . . . . 3.2. The role of lithology on clast shape . . . . . . . . . . . . . . 3.2.1. Lithological variation within individual catchments . . . 3.2.2. Lithological variation between catchments . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. The discriminatory power of co-variance plots . . . . . . . . . 4.2. The role of lithology: different lithologies in the same catchment 4.3. Implications for debris cascades in glaciated environments . . . 5. Recommendations for clast shape sampling . . . . . . . . . . . . . . 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction The shape of sedimentary particles (typically of gravel size), summarised here under the term clast shape, has long attracted the research attention of earth scientists. Systematic studies of clast shape have been carried out for nearly a century (Wentworth, 1922), and data using clast shape analysis may be of significance to several geoscience disciplines, for example Quaternary science, glaciology, petrological and sedimentological provenancing, sediment budgeting and geomorphology. Several discussions have focused on various approaches to quantify clast shape, and these have been reviewed succinctly by Sneed and Folk (1958), Barrett (1980), Illenberger (1991), Benn and Ballantyne (1993, 1994), Blott and Pye (2008) and Demir et al. (in press). In this contribution, we concentrate on particles shaped by erosional, transport and depositional processes in glaciated mountain catchments, where the study of clast shape has a shorter tradition than in, for example, fluvial geomorphology (e.g. Wentworth, 1936; Flint, 1971). Boulton (1978) was the first to formally link the shapes of sedimentary particles to different transport pathways through glaciers, and he also compared this shape signature between glaciers of different thermal regime. Boulton (1978) recognised three transport paths: (a) A supraglacial one, where debris falls onto the glacier surface (for example by rockfall or avalanching) and remains at or near the glacier surface prior to deposition at or near the glacier margin; (b) an englacial route, whereby material that is deposited on the glacier surface in the accumulation area is buried by snow and transferred englacially along flowlines until it melts out in the ablation area; and (c) material that is either transferred from a supraglacial to a subglacial position or that is eroded at the glacier bed. Boulton (1978) conceptualised (a) and (b) as undergoing passive or high-level transport and (c) as active or low-level transport. He showed that dominantly angular and platy clasts occurred in the former two categories and edge-rounded, abraded and blocky clasts in the latter. This theoretical dichotomy has been confirmed and refined by various workers since, who used measurements of clast shape to establish dominant transport pathways that contributed to the sediment budget of moraines (e.g. Matthews and Petch, 1982; Sharp, 1982; Small, 1983; Matthews, 1987; Benn, 1989; Shakesby, 1989; Benn, 1992) or to enlighten the processes operating across entire catchments (e.g. Ballantyne, 1982). All these workers used varying statistical measures to discriminate between different transport paths, which resulted in a great amount of results that were partly incomparable between catchments. It was not until the work of Benn and Ballantyne (1993, 1994) that a uniform approach was advocated, eventually resulting in greater comparability of results between catchments, and also a proliferation of clast shape studies since. The data presented in this article have been collected using the approach of Benn and Ballantyne (1993, 1994) who treat the shape of a clast as the sum of
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three characteristic elements that are superimposed on each other: (a) clast form, defined in terms of ratios between three orthogonal axes, the longest, intermediate and shortest axes (denoted as a, b and c axis, respectively); (b) clast roundness which describes the degree of curvature around the clast edges; and (c) surface texture which includes more delicate features such as facets or striae. Measurement of clast form has been carried out in the field by using a ruler, calliper (Benn, 2004) or shape box (Shakesby, 1979). Clast roundness is usually established using visual comparison charts or descriptive criteria (Benn and Ballantyne, 1994) (Table 1). Surface texture can be subtle and thus more difficult to assess, especially since variation in experience may result in operator bias when recording facets and striae, for example. There is no generally-accepted approach to reporting or statistically analysing surface texture data apart from noting the proportion of facetted or striated clasts as a percentage of each sample in either tabular or textual form (Evans, 1999; Benn, 2004; Evans, 2010). We have excluded surface textural elements from our analysis, because not all lithologies preserve this information equally well (and some not at all); therefore, the usefulness of this element is limited in an assessment of the effects of lithology on clast shape. Benn and Ballantyne (1993, 1994) also advocate the use of ternary diagrams as introduced by Sneed and Folk (1958) for visualisation and statistical interpretation of clast form (Fig. 1a); as a measure of platiness that works well in glaciated catchments, Benn and Ballantyne (1993, 1994) suggest using the ratio of c/a-axis ≤ 0.4 (termed the C40 index), following work by Ballantyne (1982) (Fig. 1a, b, d). Secondly, these authors advocated using the percentage of very angular and angular clasts in a sample (cf. Matthews, 1987) to discriminate between frost-weathered, angular clasts and those that have undergone subglacial edge-rounding. Instead of using sphericity and roundness plots that had been commonly used (e.g. Boulton, 1978), Benn and Ballantyne (1994) demonstrated that plotting both RA (the percentage of angular and very angular clasts in a sample; Fig. 1b, c) and C40-indices in co-variance plots allowed a very effective discrimination between subglacially and supraglacially-
Table 1 Criteria employed in the identification of clast roundness classes. Modified from Benn and Ballantyne (1994). Roundness class Very angular (VA) Angular (A) Sub-angular (SA) Sub-rounded (SR) Rounded (R)
Description
Very acute edges and/or sharp protuberances Acute edges with no evidence of rounding Rounding confined to edges; faces intact Rounding of edges and faces; often facetted Marked rounding of both edges and faces; merging of edges and faces Well-rounded (WR) Distinction between faces and edges not possible
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(a)
Blocky 0.8 0.8 0.6 c /a
b/a
0.6 0.4 0.4
0.2 0 .2
Platy
(b)
0.2
C40 = 64 RA = 96 RWR = 0
0.4 0.6 (a-b)/(a-c)
0.8 Elongated
(c) 100
RA (%)
80
100
60
40
20 50
0 0
20
VA
A
SA
SR
(d)
R
WR
C40 = 26 RA = 0 RWR = 18
40
60
80
100
60
80
100
C40 (%)
0
(e) 50
RWR (%)
40
100
30
20
10 50
0 0 0 VA
A
SA
SR
R
WR
20
40
C40 (%)
Fig. 1. Example of how the different elements of clast shape analysis as devised by Benn and Ballantyne (1993, 1994) are used. (a) Schematic ternary diagram, showing axial scales, endmember clast forms and terminology used here; (b) example ternary diagram and frequency distribution of clast roundness data of a supraglacial sample to illustrate how the C40- and RA-indices are calculated (shown by grey area and black ellipse, respectively); (c) RA-C40-co-variance diagram illustrating how the different indices are used to discriminate between different samples; (d) ternary diagram and frequency distribution of a distal fluvial sample to illustrate how the RWR-index is calculated; (e) RWR-C40-co-variance diagram illustrating how the different indices are used to discriminate between different samples. All data used are from eclogite sampled at Findelengletscher.
transported sediments (Fig. 1e). In particular, this approach was shown to work extremely effectively if samples that had undergone unknown transport processes (e.g. those obtained from moraines) were compared with those that had been obtained from settings where the transport
paths were known (e.g. scree and talus slopes, glacier bed, river bed); the latter category has been termed ‘control samples’ and can be regarded as the backbone of clast shape analysis in glaciated catchments in the last two decades. Studies conducted since the seminal work of
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
Benn and Ballantyne (1993, 1994) have confirmed the widespread applicability of this approach to modern and formerly-glaciated mountain environments (e.g. Benn, 1994, 1995; Bennett et al., 1997; Salt and Ballantyne, 1997; Evans, 1999; Glasser et al., 1999; Graham and Midgley, 2000a; Etienne et al., 2003; Benn et al., 2004; Goodsell et al., 2005; Lukas et al., 2005; Benn and Lukas, 2006; Glasser et al., 2006; Lukas, 2007; Kellerer-Pirklbauer et al., 2008; Mills et al., 2009; Evans, 2010; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). In addition to using clast shape analysis as an approach to reconstruct palaeo-transport pathways in glaciated settings, the focus of some studies has been towards understanding the catchment-wide processes, i.e. including the influence of fluvial reworking in the proglacial and subglacial realms along the debris cascade (e.g. Kirkbride, 1989; Evans, 1999, 2000; Benn et al., 2004; Brook and Lukas, 2012; Lukas et al., in press). Despite the aforementioned widespread applicability and proliferation of case studies globally, three particular aspects of clast shape analysis are still poorly understood and merit further investigation. Firstly, although most studies use the RA-C40-co-variance plots proposed by Benn and Ballantyne (1994), it has been shown that the other end of the roundness spectrum, the percentage of rounded and well-rounded clasts (Table 1) and the associated RWR-index (Fig. 1d, e), may be of more use in some settings (Benn, 2004; Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). Yet, the two approaches have never been compared to each other with respect to their effectiveness. Secondly, it is worth noting that not all of the aforementioned studies have restricted their sampling to either a single lithology or have compared only identical lithologies when working on catchments with multiple lithologies. Although there is some data to support the inherent assumption that lithology determines clast shape to a large extent (e.g. Drake, 1970; Barrett, 1980; Ballantyne, 1982; Pérez, 1986; Benn and Ballantyne, 1994; Evans, 1999; Krüger and Kjær, 1999; Benn, 2004; Brook and Lukas, 2012; Lukas et al., 2012), one study has suggested that lithology may not have a large impact on clast shape and that mixing of lithologies may not present a problem (Bennett et al., 1997). Following this suggestion, a number of studies have mixed lithologies within individual samples of 50 clasts (e.g. Graham and Midgley, 2000a; Etienne et al., 2003; Hambrey and Ehrmann, 2004; Goodsell et al., 2005). However, the underlying assumption that lithology plays little, if any, role on the reliability or discriminatory power of clast shape (both at the clast level and as a method) has never been tested systematically, because the study by Bennett et al. (1997) and other studies have mixed up lithologies in individual samples rather than compare control samples in which lithologies have been separated. Thirdly, there is a pronounced lack of knowledge regarding the transferability of clast shape results between catchments. The only recent compilation of sedimentary data to determine palaeo-glacier thermal and dynamic regimes uses clast shape datasets that rely on mixed lithologies (Hambrey and Glasser, 2012). This paper therefore aims to contribute towards closing these gaps through a systematic assessment of a global dataset collected using the same approach (Section 2) by the present authors over the last c. 25 years. In particular, we aim to systematically test whether (a) the proposed RA-C40-co-variance plots are the ideal means of investigating transport processes in every catchment; (b) lithology plays a large or negligible role in determining clast shape; and (c) there are systematic variations between catchments with similar climatic and glaciological boundary conditions. Details of the method of clast shape analysis used here are described in detail below.
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and therefore clast form could be confidently related to known processes. These data then serve as control samples that provide a robust template against which samples of unknown origin/transport history can be compared. In this contribution, we focus entirely on such control samples and purposely exclude any samples from sediments or landforms. We do this, because the latter will always contain a mixture of clasts with their own unique erosional, transportational and depositional histories. Instead, any effects of lithology and other variables (e.g. geographical and glaciological) can be reliably tested on known parameters where other influences are minimal or can be ruled out entirely. At each location, 50 clasts from the same lithology were sampled at random, their three orthogonal axes were measured and roundness was determined visually. Although the influence of clast size on shape has not satisfactorily been tested before, we have restricted our sampling to clasts with maximum a-axes b 25 cm. We have sampled both currently glacierised catchments (n = 15) and those that were last glaciated during the Late Pleistocene (n = 4) to enable a first comparison between the performance of the method in a modern and palaeo-context. 2.2. Graphic representation of results and statistical analysis 2.2.1. Form Triangular diagrams following Sneed and Folk (1958) are particularly useful to display clast shape in an undistorted way that is free from bias (Benn and Ballantyne, 1993; Benn, 2004). On such ternary diagrams (Fig. 1a), the ratios of c:a and b:a axes are used to distinguish three endmembers of a clast continuum: equant or blocky shapes where a ≈ b ≈ c, prolate or elongate shapes where a >> b ≈ c and oblate or platy shapes where a ≈ b > c (Benn, 2004). Ballantyne (1982), Benn (1992) and Benn and Ballantyne (1993, 1994) have shown that the C40-Index, defined as the percentage of clasts with a c:a ratio ≤ 0.4, is a powerful means of distinguishing blocky from elongated clast shapes and works well in glaciated environments (Fig. 1a). All clast form data were plotted in TriPlot (Graham and Midgley, 2000b) that had been modified to include standardised automated calculation of statistical parameters for each sample and includes a tab to input and plot roundness data (see below). 2.2.2. Roundness and texture The estimated roundness classes for each sample (Table 1) are translated into percentages and plotted as frequency distributions. From these classes, the percentages of very angular (VA) and angular (A) classes are added to calculate the RA-index (Fig. 1b, c). In addition, recent research has shown that the round end of the roundness continuum, i.e. the percentage of rounded and well-rounded clasts, summarised in the RWR-index (Fig. 1d, e), may work better in some settings (e.g. Benn, 2004; Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). Therefore, we employ both parameters to assess which performs better in each particular setting. The purpose of using the extreme ends of the roundness continuum (instead of, for example, the two ‘middle’ categories of subangular and subrounded) is to distinguish between the key processes of frost weathering and subglacial edge-rounding on the one hand (RA-index) and subglacial reworking of fluvially-rounded sediment on the other (RWR-index). Therefore, the selection of this index is process-based. Altering the margins to these two categories, such as including the subangular category into the RA-index (e.g. Goodsell et al., 2005), will most likely remove the predictive capacity of clast shape measurements in glaciated environments and is not advisable.
2. Methods 2.1. Field measurement approach Field sampling was first undertaken in positions where the erosional, transportational and depositional processes were recognisable
2.2.3. Co-variance plots In order to distinguish between different erosional, transportational and depositional clast histories, an effective way of plotting these data is necessary. Benn and Ballantyne (1994) suggested that plotting the C40-index versus the RA-index in a co-variance plot is a
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robust procedure. Based upon our collective experience in data manipulation and knowledge of the pertinent literature, we strongly support this approach. We have consistently added a second covariance plot in all our analyses which substitutes the RA- with the RWR-index in order to evaluate which approach is most powerful and, if possible, whether there are reasons for any differences in the output. Following plotting of the individual values, envelopes of likelihood of each control environment may be plotted (e.g. Benn and Lukas, 2006; Lukas, 2007). In order for co-variance analysis to be effective, these envelopes of individual control environments need to be well-clustered, or separated enough to allow samples of unknown transport history to be attributed correctly to the control environments. If control envelopes are overlapping, then an effective differentiation between individual processes is made more difficult, if not impossible. Only few authors have reported this happening for the traditional RA-C40 co-variance plots, and these studies dealt exclusively with high-mountain settings, where glaciofluvial reworking was prominent and resulted in a lack of differentiation between subglacial and glaciofluvial control samples (Brook and Lukas, 2012; Lukas et al., 2012). We will thus systematically assess for all catchments, which of the two approaches is the most appropriate and seek plausible explanations for any difficulties encountered. In order to achieve the highest resolution possible and to avoid the occurrence of such overlaps, we present the data for each catchment separately. 3. Results We here present a dataset from different locations worldwide that comprises 21 lithologies in 15 glaciated catchments from the high Arctic to New Zealand and allows a comparison with four exemplary palaeo-environments (Table 2). The systematic analyses we present below have been conducted on all these samples and are therefore deemed to represent an appropriate reference frame within which general controls on clast shape, such as lithology, geographical context and glaciological parameters (e.g. thermal regime and glacier dynamics) may be examined for similarities and differences. 3.1. The discriminatory power of co-variance plots Co-variance plots for all catchments using both RA-C40 and RWR-C40 are shown in Figs. 2 and 3. An analysis of this dataset shows that the most commonly-employed covariance method, the RA versus C40-index as discussed by Benn and Ballantyne (1994), can distinguish between all control envelopes in two thirds (63%) of all catchments. This takes into account cases where the RA-index works and the RWR-index does not, equating to 21% (n = 5) of all 19 catchments, and where both RA and RWR versus C40-indices are equally valid (42%, n = 10). Although this implies a wide-ranging applicability, it is interesting to note that the RWR versus C40-index is more powerful than the RA versus C40-index, because it allows an effective discrimination in 75% of all cases. This takes into account both the cases where the RWR versus C40-index, as first proposed by Benn (2004) and used by Benn et al. (2004), yields meaningful results and the RA-index does not, and the 42% of all cases where both approaches work equally well. This finding implies that the RA-index is insufficient to allow an effective discrimination between environments with known transport processes in one third of the environments investigated here, whereas the RWR-index covers three quarters of investigated settings. Only in one catchment (Glacier d'Estelette, Fig. 3g–h) is the overlap of subglacial and fluvial control envelopes so strong that neither RA nor RWR indices can distinguish between them (4%). It is noteworthy that the RWR-index generally works better in high-mountain environments (European Alps, Southern Alps, NZ) and that the RA-index fails to discriminate between different control populations in these cases, whereas both RA and RWR-approaches appear to be equally valid for many of the glaciated catchments in the lesser mountain ranges of Svalbard, Iceland
and Norway (Figs. 2, 3). The only exception to this rule is Tasman Glacier, where the RA-index performs better than the RWR (Fig. 3o–p). Furthermore, it is noteworthy that distinct sub-samples of fluvial control envelopes (e.g. at the glacier portal, proximal and distal) display very clear signals of a decrease in angularity with distance in the RA and/or progressive rounding with distance from the glacier portal in the RWR plots (Figs. 2, 3). It is interesting to note that both approaches work well for all four investigated palaeo-environments (Fig. 3q-x). 3.2. The role of lithology on clast shape All control samples from the catchments presented here have deliberately been restricted to a single lithology, meaning that (a) all 50 clasts for each sample and (b) all samples collated in each co-variance plot were of the same lithology. In the tests below, we now systematically mix these different populations to test what influence, if any, mixing has on the discriminatory power of both RA and RWR co-variance plots. To design as thorough a test as possible, we mix (a) similar lithologies from the same geographic locations, to encapsulate any site-specific effects of lithological variability and (b) similar lithologies taken from different catchments. 3.2.1. Lithological variation within individual catchments Three catchments in our global dataset have been sampled for multiple lithologies, either because these catchments contained more than one dominant lithology and it was unclear which would be found in Quaternary sediment exposures (Findelengletscher, Vadret da Grialetsch) or because it was of interest to test if there was a difference between individual lithologies (Fox Glacier). Fig. 4a and b shows the two dominant lithologies plotted together for Vadret da Grialetsch. Here, a systematic variation between gneiss and amphibolite is visible, in that the former shows a higher proportion (by about 20%) of platy clasts (C40) throughout all control categories. Despite this similarity, there are slight internal differences in the way that both lithologies appear to respond to individual processes within the same catchment. For example, the gneisses contain much fewer clasts that are angular or subangular near the portal (Fig. 4a) compared to the amphibolite, which has a much higher RA-index of 26%. This more pronounced angularity of amphibolite is not mirrored by the degree of rounding of both lithologies (Fig. 4b), where both show a similar increase of rounding with distance from the portal. The three lithologies sampled at Findelengletscher show a comparable degree of similarity in that eclogite has a systematically-lower content of platy clasts than mica schist compared to serpentinite; this is most convincingly visible in the RA-C40 co-variance plot (Fig. 4c). However, serpentinite produces a slightly lower proportion of platy clasts compared to mica schist in the fluvial control near the glacier portal, and this slightly dilutes the identified general trend. The changes in clast roundness (RWR) with distance from the glacier portal are of a very different magnitude for the three lithologies. From the ice-margin to the proximal and distal control sites, the RWR-index of serpentinite increases through rounding by 21% and then decreases by 18% (Fig. 4d). For eclogite it first decreases by 4% and then increases by 12%. For mica schist it first decreases by 14% and then increases by 28%. At the same time the RA-index of all samples is consistently zero (Fig. 4c). The difference in C40-index between the highest (serpentinite) and lowest (eclogite) content of platy clasts varies between 20% (supraglacial) and 45% (subglacial). At Fox Glacier (Fig. 4e, f), argillite produces more platy clasts than mica schist compared to greywacke in the supraglacial control envelope, but this relationship is less clear in the fluvial and subglacial control domains, where mica schist contains lower amounts of platy clasts than argillite. Greywacke produces the blockiest clasts throughout. The difference in C40-index between the highest and lowest content of platy clasts varies between 10% (supraglacial; argillite– greywacke) and 20% (distal fluvial; mica schist–greywacke).
Table 2 Study area details and background information. See text and Figs. 2 and 3 for details. ID
Location
Control-samples (n)
Lithology
References geology
References to previous work
Modern glaciers 1 Borebreen, Svalbard 2 Larsbreen, Svalbard
78°25′N, 14°E 78°11′N, 15°27′E
Fluvial (2), supraglacial (2) Fluvial (2), supraglacial (3)
Sandstone Sandstone
Braathen et al. (1999) Dallmann et al. (2001)
3
Scott Turnerbreen, Svalbard
78°06′N, 15°57′E
Fluvial (4), supraglacial (2)
Sandstone
Dallmann et al. (2002)
Machiedo (2008) Etzelmüller et al. (2000) and Lukas et al. (2005) Hodgkins et al. (1999, 2004) and Sletten et al. (2001)
4 5
Flaajökull, Iceland Fåbergstolsbreen, Norway
64°20′N, 15°32′W 61°43′N, 7°8′E
Fluvial (3), sub- (5) supraglacial (4) Fluvial (9), sub- (5) supraglacial (4)
Basalt Gneiss
Sæmundsson (1979) Nordgulen and Andresen (2008)
6 7 8 9 10
Bergsetbreen, Norway Storbreen, Norway Slettmarkbreen, Norway Midtdalsbreen, Norway Pasterze, Austria
61°38′N, 61°32′N, 61°25′N, 60°34′N, 47°05′N,
Fluvial (2), sub- (1) supraglacial (2) Sub- (4) supraglacial (2) Sub- (5) supraglacial (3) Fluvial (4), sub- (2) supraglacial (2) Fluvial (4), sub- (2), supraglacial (2)
Gneiss Gneiss Gneiss Phyllite Prasinite (greenschist)
Nordgulen and Andresen Nordgulen and Andresen Nordgulen and Andresen Nordgulen and Andresen Höck and Pestal (1994)
11 11 12 12 12 13 14 14 14 15
Vadret da Grialetsch, Switzerland Vadret da Grialetsch, Switzerland Findelengletscher, Switzerland Findelengletscher, Switzerland Findelengletscher, Switzerland Glacier d'Estelette, France Fox Glacier, New Zealand Fox Glacier, New Zealand Fox Glacier, New Zealand Tasman Glacier, New Zealand
46°41′N, 9°58′E 46°41′N, 9°58′E 46°1′N, 7°49′E 46°1′N, 7°49′E 46°1′N, 7°49′E 45°46′N, 6°50′E 43°30′S, 170°10′E 43°30′S, 170°10′E 43°30′S, 170°10′E 43°36′S, 170°13′E
Fluvial (4), supraglacial (2) Fluvial (4), supraglacial (2) Fluvial (4), sub- (3), supraglacial (2) Fluvial (4), sub- (4), supraglacial (2) Fluvial (4), sub- (4), supraglacial (2) Fluvial (2), sub- (8), supraglacial (4) Fluvial (14), sub- (6) supraglacial (6 + 4) Fluvial (14), sub- (6) supraglacial (5 + 4) Fluvial (14), sub- (6) supraglacial (6 + 4) Fluvial (4), sub- (4) supraglacial (8)
Gneiss Amphibolite Serpentinite Mica schist Eclogite Gneiss Greywacke Schist Argillite Greywacke
Bearth et al. (1935) Bearth et al. (1935) Bearth (1953) Bearth (1953) Bearth (1953) Rolland et al. (2003) Cox and Barrell (2007) Cox and Barrell (2007) Cox and Barrell (2007) Warren, 1978 (in Hambrey and Ehrmann 2004)
58°15′N, 4°45′W 57°15′N, 6°14′W 57°06′N, 4°20′W 29.35′S, 29.15′E
Fluvial (2), sub- (4), supraglacial (3) Sub- (2) supraglacial (2) Fluvial (3), sub- (1), supraglacial (2) Fluvial (1), supraglacial (1)
Psammite Gabbro Psammite Basalt
Johnstone and Mykura (1989) Emeleus (1991) Stephenson and Gould (1995) Bell and Haskins (1997)
Formerly-glaciated sites 16 NW Scotland 17 Isle of Skye, Scotland (Coire na Creiche) 18 Central Scotland (Monadhliath Mts) 19 Drakensberg, Lesotho, South Africa
7°15′E 8°12′E 8°30′E 7°28′E 12°42′E
(2008) (2008) (2008) (2008)
Benn and Ballantyne (1994) and Ballantyne and Benn (1996) Lukas (2007) Benn (1994) Reinardy et al., in press Kellerer-Pirklbauer (2008) and Kellerer-Pirklbauer et al. (2008) Signer (2008) Signer (2008) Lukas et al. (2012, in press) Lukas et al. (2012, in press) Lukas et al. (2012, in press) Aeschlimann, 1983 (in Wetter, 1987) Brook and Lukas (2012) Brook and Lukas (2012) Brook and Lukas (2012) Kirkbride (1989)
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
Grid coordinates
Lukas (2005) and Benn and Lukas (2006) Benn (1990, 1992) Boston (2012) Mills et al. (2009)
101
102
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Borebreen (sandstone)
(b) Borebreen (sandstone)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
C40 (%) 100
80
80
60
40
100
80
100
80
100
80
100
60
40
20
20
0
0 0
20
40
60
80
100
0
20
40
C40 (%)
60
C40 (%)
(e) Scott Turnerbreen (sandstone)
(f) Scott Turnerbreen (sandstone)
100
50
80
40
RWR (%)
RA (%)
80
(d) Larsbreen (sandstone)
100
RWR (%)
RA (%)
(c) Larsbreen (sandstone)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(h) Flaajökull (basalt)
(g) Flaajökull (basalt) 100
50
80
40
RWR (%)
RA (%)
60
C40 (%)
60
40
20
30
20
10
0
0 0
20
40
60
C40 (%)
80
100
0
20
40
60
C40 (%)
Fig. 2. Co-variance plots for all individual lithologies sampled in all catchments, using both possible variations of RA versus C40-index (left panels) and the corresponding co-variance plots using RWR versus C40-index (right panels).
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(j) Fåbergstolsbreen (gneiss)
(i) Fåbergstolsbreen (gneiss) 100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0 0
20
40
60
80
0
100
0
20
C40 (%)
40
60
80
40
RWR (%)
50
60
40
80
100
80
100
80
100
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(n) Storbreen (gneiss)
(m) Storbreen (gneiss) 100
50
80
40
60
RWR (%)
RA (%)
100
(l) Bergsetbreen (gneiss)
100
20
40
20
30
20
10
0 0
20
40
60
80
0
100
0
20
C40 (%)
40
60
C40 (%)
(o) Slettmarkbreen (gneiss)
(p) Slettmarkbreen (gneiss)
100
50
80
40
RWR (%)
RA (%)
80
C40 (%)
(k) Bergsetbreen (gneiss)
RA (%)
103
60
40
20
30
20
10
0 0
20
40
60
80
100
0
C40 (%)
0
20
40
60
C40 (%) Fig. 2 (continued).
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S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(q) Midtdalsbreen (phyllite)
(r) Midtdalsbreen (phyllite) 50
100 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
30
20
10
20
0
0 0
20
40
60
80
0
100
20
60
80
100
60
80
100
80
100
(t) Pasterze (prasinite)
(s) Pasterze (prasinite)
60
100
50
RWR (%)
80
RA (%)
40
C40 (%)
C40 (%)
60
40
40 30 20
20
10 0
0 0
20
40
60
80
0
100
20
(v) Vadret da Grialetsch (gneiss)
100
50
80
40
RWR (%)
RA (%)
(u) Vadret da Grialetsch (gneiss)
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%)
(w) Vadret da Grialetsch (amphibolite)
(x) Vadret da Grialetsch (amphibolite)
100
50
80
40
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
60
40
30
20
10
20
0
0 0
20
40
C
60 40
80
100
0
20
40
60
C40 (%)
(%) Fig. 2 (continued).
80
100
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Findelengletscher (mica schist)
(b) Findelengletscher (mica schist)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
30
20
10
20
0
0 0
20
40
60
80
100
0
20
100
50
80
40
RWR (%)
RA (%)
60
80
100
80
100
80
100
80
100
(d) Findelengletscher (eclogite)
(c) Findelengletscher (eclogite)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(f) Findelengletscher (serpentinite)
(e) Findelengletscher (serpentinite) 100
50
80
40
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%)
(g) Glacier d'Estelette (gneiss)
(h) Glacier d'Estelette (gneiss)
100
50
80
40
RWR (%)
RA (%)
105
60
40
20
30
20
10
0
0 0
20
40
60
C40 (%)
80
100
0
20
40
60
C40 (%)
Fig. 3. Co-variance plots for all individual lithologies sampled in all catchments, using both possible variations of RA versus C40-index (left panels) and the corresponding co-variance plots using RWR versus C40-index (right panels).
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S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(i) Fox Glacier (argillite)
(j) Fox Glacier (argillite)
100
50 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
50
80
40
RWR (%)
RA (%)
80
100
80
100
80
100
80
100
(l) Fox Glacier (greywacke)
100
60
40
30
20
10
20
0
0 0
20
40
60
80
0
100
20
40
60
C40 (%)
C40 (%)
(n) Fox Glacier (mica schist)
(m) Fox Glacier (mica schist) 100
50
80
40
RWR (%)
RA (%)
60
C40 (%)
(k) Fox Glacier (greywacke)
60
40
20
30
20
10
0
0 0
20
40
60
C
40
80
100
0
20
(%)
40
60
C
40
(%)
(p) Tasman Glacier (greywacke)
(o) Tasman Glacier (greywacke) 100
50
80
40
RWR (%)
RA (%)
40
60
40
20
30
20
10
0
0 0
20
40
60
80
100
C40 (%)
0
20
40
C40 (%) Fig. 3 (continued).
60
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(q) NW Scotland (psammite)
(r) NW Scotland (psammite) 50
100 Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
40
RWR (%)
RA (%)
80
60
40
20
30
20
10
0
0 0
20
40
60
80
100
0
20
60
80
100
80
100
80
100
80
100
(t) Coire na Creiche (gabbro)
100
50
80
40
RWR (%)
RA (%)
(s) Coire na Creiche (gabbro)
60
40
20
30
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
40
60
C40 (%)
(u) Monadhliath (psammite)
(v) Monadhliath (psammite)
100
50
80
40
60
30
RWR (%)
RA (%)
40
C40 (%)
C40 (%)
40
20
20
10
0
0 0
20
40
60
80
0
100
20
C40 (%)
40
60
C40 (%)
(w) Drakensberg (basalt)
(x) Drakensberg (basalt)
100
50
80
40
60
30
RWR (%)
RA (%)
107
40
20
20
10
0
0 0
20
40
60
80
100
0
20
40
60
C40 (%)
C40 (%) Fig. 3 (continued).
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S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(b) Vadret da Grialetsch
(a) Vadret da Grialetsch
100
100
Gneiss
Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
Amphibolite 80
RWR (%)
RA (%)
80
60
40
60
40
20
20
0
0 0
20
40
60
80
0
100
20
40
(c) Findelengletscher
100
60
80
100
60
80
100
100
Serpentinite Eclogite Mica schist
80
RWR (%)
80
RA (%)
80
(d) Findelengletscher
100
60
40
60
40
20
20
0
0 0
20
40
60
80
0
100
20
40
C40 (%)
C40 (%)
(e) Fox Glacier
(f) Fox Glacier
100
100
Argillite Greywacke Mica schist
80
RWR (%)
80
RA (%)
60
C40 (%)
C40 (%)
60
40
20
60
40
20
0
0 0
20
40
60
80
100
C40 (%)
0
20
40
C40 (%)
Fig. 4. Co-variance plots for lithologies within individual catchments combined. (a) and (b): Vadret da Grialetsch (gneiss and amphibolite); (c) and (d) Findelengletscher (eclogite, serpentinite, mica schist); (e) and (f): Fox Glacier (argillite, greywacke, mica schist). It is clearly visible that mixing lithologies even within the same catchments results in a loss of discriminatory power in both RA and RWR versus C40-index-plots.
In the cases of Fox Glacier and Findelengletscher, where both fluvial and subglacial control samples could be obtained, the respective control envelopes of these two domains overlap significantly, which contrasts markedly with those plotted for each lithology individually above (Figs. 2, 3).
3.2.2. Lithological variation between catchments In addition to plotting all lithologies from the same catchments (above), we plotted all similar lithologies in one co-variance diagram to see what variation, if any, between similar lithologies in different catchments was present (Fig. 5). It is interesting to note that for all
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(b) All sandstone catchments
(a) All sandstone catchments 100
100
Scott T.B. Larsbreen Borebreen
Fluvial glacier portal Fluvial proximal Fluvial distal Subglacial Supraglacial/extraglacial
80
RWR (%)
80
RA (%)
109
60
40
20
60
40
20
0
0 0
20
40
60
80
0
100
20
40
(c) All gneiss catchments
80
100
60
80
100
(d) All gneiss catchments 100
100
Storbreen Bergsetbreen Fåbergstolsb.
80
80
Slettmarkbreen Midtdalsbreen
60
RWR (%)
RA (%)
60
C40 (%)
C40 (%)
V. da Grialetsch G. d‘Estelette
40
20
60
40
20
0
0 0
20
40
60
80
100
0
C40 (%)
20
40
C40 (%)
Fig. 5. Co-variance plots for similar lithologies for different catchments. (a) and (b): sandstones (Borebreen, Larsbreen and Scott Turnerbreen). (c) and (d): gneisses (all Norwegian glaciers, Vadret da Grialetsch, Glacier d'Estelette).
catchments containing sandstone, two of these (Larsbreen and Scott Turnerbreen) have lithologically extremely similar sandstones that belong to the same geological formation. When plotted together (Fig. 5a–b), a less consistent pattern is discernible within all three sandstone-containing catchments compared to the individual plots. Cumulative envelopes for all of these control populations are distinct enough between fluvial and supraglacial in both RA-C40 and RWR-C40 co-variance plots (Fig. 5a, b). The spread of values between different catchments in the same control envelope ranges from 10% (proximal fluvial) to > 30% (supraglacial). For all gneisses plotted together (Fig. 5c–d), the picture that emerges is one of a much wider spread of control envelopes and a widespread loss of discriminatory power, because all three control envelopes overlap in the RWR-C40 co-variance plot, while fluvial and subglacial ones overlap in the RA-C40 plot. This means that only supraglacial clasts can be distinguished from those of other origin in the RA-C40 co-variance plot (Fig. 5c); we regard the close proximity of supraglacial and fluvial control envelopes in the RWRC40 plot as problematic for an effective and unequivocal distinction. A very large spread of values in the C40-indices that ranges from 20% (subglacial) to 45% (fluvial and supraglacial) is also clearly discernible.
4. Discussion 4.1. The discriminatory power of co-variance plots The systematic plotting of both RA and RWR-indices versus C40 demonstrates that clast shape is an extremely powerful method that is applicable to > 95% of all investigated catchments, thereby demonstrating the widespread applicability of the approach proposed by Benn and Ballantyne (1993, 1994) for modern and formerly glaciated catchments. Our analysis also suggests that it is advisable to check both RA and RWR indices for their respective functionality and use the one that distinguishes most clearly between different control sample populations. Comparing the differences between RA and RWR plots could also be beneficial if one aims to elucidate the relative importance of subglacial edge-rounding of supraglacial clasts (RA-index) versus fluvial rounding of edge-rounded clasts, or vice versa (RWR-index). The observed differences in discriminatory power of RA as opposed to RWR-indices in co-variance plots may be best explained through the degree of fluvial reworking in a glaciated catchment. If fluvial reworking of a mixture of supraglacial and subglacial material occurs and is a prominent process in the catchment, then any glacier advance would rework fluvially-remoulded sediment, and hence the fluvial signature
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would be preserved in any subglacially-sampled clasts. Therefore, the envelopes of likelihood for fluvial and subglacial control samples would only be distinguishable through the degree of roundness, but less so on the basis of the amount of platy clasts. In the co-variance diagrams, this would be visible through these two control envelopes being fully overlapping near or on the x-axis (RA-C40) or separated vertically by different degrees of rounding in the two categories (RWR-C40). The latter case has been found in nearly all high-mountain catchments (Figs. 2, 3) and has been reported elsewhere before (e.g. Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012). These findings contradict the assertion and seemingly widespread notion that all highmountain catchments are dominated by a high supraglacial load and cautions against an oversimplification of such processes by extrapolating between catchments locally (e.g. Hambrey and Ehrmann, 2004) or globally (e.g. Hambrey and Glasser, 2012) as this can lead to potentially erroneous conclusions. The comparison of RA and RWR-based co-variance plots also shows that those catchments with the highest number of control samples in each category (most notably Fox and Tasman Glacier and Glacier d'Estelette) allow the most detailed analysis and most confident interpretation. Although this may seem obvious, many studies rely on one or two control samples per envelope, and, while this is appropriate as a first approximation, it does not allow analysis at a comparatively high level of detail. For example, fluvial and subglacial control envelopes can be distinguished clearly from one another, but the effect of fluvial (re-) working on clast form only becomes discernible where control samples have been obtained at defined intervals from the portal (e.g. Midtdalsbreen, Findelengletscher, Glacier d'Estelette, Fox Glacier). In all catchments where fluvial control samples were obtained in this detailed, staged manner, clear trends of progressive rounding with distance are visible. Such clear trends, which have been shown in numerous laboratory and field studies by fluvial geomorphologists over the decades (e.g. Knighton, 1998), confirm that co-variance plots are capable of deciphering subtle and complex processes at catchment scale and are also able to act as reliable reference frames for the interpretation of clasts of unknown origin. This rounding trend can take two forms that point to the influences of different processes. Firstly, there may be a clear decrease in angularity; this process can only be observed in the RA-C40 co-variance plots (cf. Fig. 2e: Scott Turnerbreen). The settings where this process is dominant are characterised by a dominantly supraglacial sediment input, so that the initial clast form is angular and platy or elongated. Secondly, roundness may increase with distance from the portal; this process only becomes discernible in the RWR-C40 co-variance plots (e.g. Findelengletscher, Fig. 3b, d, f; Fox Glacier, Fig. 3j, l, n). Based on an analysis of our dataset, this process appears to be linked to settings where (a) there is a dominant subglacial source of the material, and (b) the subglacial and glaciofluvial transport systems are intimately coupled and/or previous proglacial sediments are reworked during glacier advances, such as those stored in overdeepenings developed behind bedrock steps in some alpine settings (e.g. Findelengletscher; Lukas et al., 2012). Thirdly, a mixture of both systems may be present, which would be indicated by the proximity of fluvial control samples to both the supraglacial and subglacial control envelopes (e.g. Fåbergstolsbreen, Fig. 2j). A note of caution must be added at this stage, because the aforementioned relationships do not hold true for all modern catchments presented here. Midtdalsbreen (Fig. 2q–r), for example, shows no such clear relationships in either co-variance plot, which points to a more complex system of reworking and/or an insufficient number of control samples from each site (in general, it emerges that the results are clearest where more than two control samples have been obtained from each control environment). As can be seen from the above analysis, clast shape may also be used to tease out a fairly thorough understanding of an individual glaciated catchment if the sampling strategy has been well designed. The lack of angular clasts in all fluvial control samples at Findelengletscher indicates that all samples obtained from the fluvial
system have been thoroughly rounded and edge-rounded by previous fluvial and subglacial activities before they even reach the glacier portal (Fig. 4c). This has important implications for interpreting the dynamics of this system, because it indicates that clasts that enter the subglacial and glaciofluvial system, even from a supraglacial position, are very effectively reworked over fairly short distances, so that an imprint of a supraglacial origin will not be preserved in any shape or form once a clast enters the subglacial and fluvial systems.
4.2. The role of lithology: different lithologies in the same catchment The analyses of the response of different lithologies within the same catchments, where the same, or at least highly comparable, geographical, climatic and glaciological boundary conditions act on the formation of distinct clast shape, demonstrate that there is a large variation between different lithologies (Fig. 4). When these different lithologies are combined in co-variance analysis (Fig. 4a–b, c–d, e–f), rather than being treated separately (Figs. 2u–x, 3a–f and i–n, respectively), the overlap between the control envelopes of different lithologies is so great that the discriminatory power of the method as a whole is either much-reduced or removed altogether. This effect is most pronounced in catchments where variations in C40 and RA/ RWR-indices are small (e.g. Fox Glacier, Fig. 4e, f), but also remove most of the discriminatory power for catchments where the ranges are wider (e.g. Findelengletscher, Fig. 4c, d). Only for catchments where the difference of C40 between two dominant lithologies is systematic and consistent (near-constant) is the discriminatory power not impacted as much (Fig. 4a, b), although it remains to be seen what the inclusion of a third lithology would do to this relationship. The analysis of broadly similar lithologies shows a very similar, albeit more dramatic, picture to the one described above. A nearcomplete loss of discriminatory power occurs where lithologies from different geographical, climatic and glaciological boundary conditions are mixed (Fig. 5). Although perhaps unsurprising, this suggests that similar lithologies in different regions can respond very differently to processes operating in mountain areas and other cold regions and that an inherently lithological rather than catchment-specific control exists. This is at odds with the study of Bennett et al. (1997), in which all lithologies, except for micaschist, resulted in similar shapes for specific settings. Studies of clast shape in fluvial, slope and glacial processes (e.g. Sneed and Folk, 1958; Selby, 1993; Glasser et al., 1998; Sklar and Dietrich, 2001; Brook and Tippett, 2002; Brook et al., 2004; Krabbendam and Glasser, 2011) suggest that relevant lithological properties which could be the reason for the different lithological response in our study are: i) joint density/spacing — an approximate measure of rock mass strength (Jade and Sitharam, 2003) ii) hardness, which strongly correlates with tensile strength (Augustinus, 1991; Aydin and Basu, 2005) iii) anisotropy, or the pervasiveness of a foliation, such as schistosity or slaty cleavage (e.g. Hall, 1987). Joint spacing would be expected to be particularly important in the first stages of the life of a clast, namely fracturing and removal from the intact valley floor or slopes by plucking, rock slope failure or frost weathering (e.g. Ballantyne, 1982; Matthews, 1987; Benn, 1992; Glasser et al., 1998; Krabbendam and Glasser, 2011; Hall et al., 2012); it would thus be the primary control on the initial form of the clasts. However, the shapes for supraglacial clasts are all rather similar with overlapping envelopes for most lithologies, regardless of lithology. This suggests little variation in initial clast form, and little influence of jointing. It is, however, likely that joint spacing has a strong control on initial clast size.
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
The diminution of clasts under study here occurred by abrasion, fracturing and crushing under different boundary conditions (fluvial, subglacial). Both fluvial (Sneed and Folk, 1958) and subglacial (Drake, 1970) studies have shown that, after a certain transport distance, a dynamic equilibrium for clast form can be reached, beyond which clast fracturing and clast abrasion occur at comparable rates. However, this dynamic equilibrium is likely to be different for different lithologies (depending on its properties) and different environments. Despite this wide range of parameters, the following broad responses for lithologies of different physical properties can be postulated (Fig. 6): 1) Hard but isotropic (poorly foliated) rocks would be expected to show a slow initial decrease of angularity, but would ultimately tend to become highly blocky. In the dataset here it can be seen that hard rocks such as basalt (Fig. 2g), eclogite (Fig. 3c) and gabbro (Fig. 3s) all can reach C40 of 20 or less. Other lithologies do not reach similarly
Hard
(a) eclogite
G N E I S S amp hibo lite
Hardness
gabbro quartzite dolo stone
psa
mm
ite
mica schist
serpentin Soft
ite
sand stone
Weak (isotropic)
argillite Anisotropy
Strong
Hard
(b)
Soft
Hardness
e.g. Eclogite, gabbro - slow decrease in angularity - ultimately highly blocky (low C40)
e.g. gneiss, amphibolite - slow decrease in angularity - medium blocky (low-mid C40)
e.g. sandstones - fast rounding - highly blocky (low C40)
Weak (isotropic)
e.g. argillite - fast rounding - platy (high C40)
Anisotropy
Strong
Fig. 6. (a) Schematic plot of the physical properties, anisotropy, and hardness of some rock types, showing overlapping fields for the lithologies included in the present contribution. (b) Schematic summary plot of expected shape changes for lithologies with different physical properties (anisotropy and hardness) based on the data presented in Figs. 2 and 3.
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blocky forms. The contrast is shown convincingly by clasts at Findelengletscher (Fig. 3a–f), where eclogite clasts are significantly blockier (C40 = 18–35) than micaschist and serpentinite (C40 > 45 for all samples) in all but the supraglacial settings. 2) Hard, anisotropic rocks (e.g. some gneiss, ?amphibolite, ?prasinite and harder varieties of micaschist) would show a slow decrease of angularity, but be expected to end up being less blocky. This may explain the difference in final C40-values for the variety of gneiss clasts, as the anisotropy of gneisses can vary greatly. Some gneiss samples end up being very blocky (Fig. 2i, k, m, o), while others are not (Figs. 2q, 3g). 3) Soft and isotropic rocks (such as sandstones) would show potentially a faster response than their hard counterparts. Although one would expect such rocks to end up highly blocky due to fast abrasion, this appears not to be the case, with final C40 around 30–40 or so. This can be explained by continuous clast fracturing concomitant with abrasion in both subglacial and fluvial settings. 4) Soft and anisotropic rocks (argillite and softer varieties of micaschist) would show a fast decrease of angularity, but end up being only moderately blocky. The trends of the micaschist and argillite clasts at Fox Glacier is interesting, in that there is little change in clast form, but a strong rounding in the subglacial to distal fluvial setting. It appears that such rocks cannot become blockier than about C40 = 30. Most samples show a general trend to blockier and more rounded clasts from subglacial to the more distal fluvial settings, as expected. However, some samples do not abide by this general trend, for example psammite in NW Scotland and the Monadhliath (Fig. 3q–r; u, v) and serpentinite at Findelengletscher (Fig. 3e–f). It is not clear whether this is because of a peculiarity of the setting (including the possibility of reworking) or something specific to certain lithologies. One mechanism that could potentially cause such ‘opposite’ behaviour may be clast–clast collisions in subglacial environment. These are high pressure but low velocity, while in the fluvial environment these are low pressure but high velocity. Some lithologies may respond differently to this change than others. However, more research, including on the influence of clast size on clast shape, would be needed to confirm this. It is clear from the above that different lithologies respond very differently to the same boundary conditions, resulting in very different clast shapes within the same catchment (Fig. 6). It follows that the discriminatory power of the method of clast shape measurements is compromised if different lithologies are mixed in the same sample (see also Brook and Lukas (2012) and Lukas et al. (2012, in press)). Finally, what ultimately constrains the response of a clast to its environment is its physical properties, not its lithological name. Some rock types (e.g. eclogite, granite) will have a narrow range of bedrock properties, but many do not. For instance, different sandstones can vary significantly in their hardness, depending on the degree of compaction and induration and the nature of the matrix. Other lithologies, such as gneiss and amphibolite can show great variations in anisotropy. Conversely, some rock types with very different names may well have very similar properties. For instance, it is well possible that Bennett et al. (1997) did not note a significant influence of lithology on clast shape because the range of physical properties of some of the studied lithologies (gneiss, psammite, quartzite, dolostone) may overlap. However, the latter study did not include a dataset of samples from known environments (control samples), but used mixed lithologies within samples of 50 clasts to test the influence of lithology, and therefore that approach needs to be regarded as having been built on a false premise. Our findings suggest that lithology has a strong influence on clast shapes and that any catchment-specific effects, such as the intensity of fluvial reworking, appear to be superimposed on this lithological control, thereby modulating this overall signal further. Without rigorous measuring of physical properties it is difficult to argue that lithology
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
(a) Type I
Supraglacial
RA
Supraglacial
Subglacial
Subglacial Fluvial
Fluvial C40
RWR
C40
4.3. Implications for debris cascades in glaciated environments
Fluvial
Fluvial Subglacial
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Lithology Clast shape
Environment
C40
Subglacial
Supraglacial
C40
Maritime, temperate mountain glaciers (corrie, valley and ice cap outlet glaciers)
Maritime, temperate high-mountain glaciers (corrie and valley glaciers)
Angularity/roundness (RA and/or RWR) and platiness (C40-index) are both nearly equally-strong discriminators
Roundness is the dominant discriminator (RA and/or RWR); platiness (C40-index) is a very poor discriminator
Mainly low-anisotropy (‘massive’), e.g. ortho-gneiss, eclogite
Mainly high-anisotropy (‘platy’), e.g. serpentinite, schist, eclogite, paragneiss
Processes
As has been highlighted in the Introduction section, clast shape may be used to enhance our understanding of processes operating at the catchment scale, for example the effects and dominance of reworking processes within the debris cascade. We here test whether any such generic statements can be derived from the present dataset by carefully considering similarities and differences between individual sites, as shown in the co-variance plots (Fig. 2). For this purpose, we plotted the locations of each environmental control envelope (i.e. supraglacial, subglacial, fluvial) in one co-variance plot and compared the resulting plots of all catchments to find similarities. The result is surprising, since all catchments, apart from two exceptions, can be grouped into two distinct generic types (Fig. 7), which will be discussed below. Type I catchments comprise almost exclusively locations in the lesser mountain ranges (i.e. Norway, Svalbard) and are characterised by a clear separation of supraglacial (high C40 and RA, but low RWR), fluvial (medium C40, low RA, medium RWR) and subglacial control envelopes (low C40, low RA and RWR) (Fig. 7a). In terms of processes, it appears most plausible that clasts that enter the system supraglacially (or extraglacially) are first of all shaped by frost action and are highly platy and angular. When such clasts enter the fluvial and subglacial realms, erosion and transport processes would lead to progressive rounding and edge-rounding, respectively, with a concomitant reduction of platy forms; the same would presumably hold true for freshlyeroded material in the subglacial realm (cf. Benn, 1992). However, platy forms appear to be stable in proglacial river beds, where control samples show consistently higher proportions of platy clasts (higher C40) compared to the subglacial controls. In contrast, once supraglacial or fluvial material is entrained and transported subglacially, a clear reduction in c/a ratio is evident, so that blocky forms represent the most stable and perhaps ‘final’ form in such environments, provided sufficient subglacial transport has taken place (cf. MacGregor et al., 2009). Fluvial reworking of such clasts does not appear to create a new, typical ‘fluvial’ one, because a blocky form, once created, is more resistant to further fracture than a platy one (e.g. Ballantyne, 1982; Kirkbride, 1989). The reason behind this is that it is difficult to generate two or more platy forms from a blocky clast, because any pre-existing weaknesses (e.g. foliation, micro-joints etc.) would have most likely been exploited prior to the production of a blocky clast. Likewise, a blocky clast is more protected against uneven force distributions during clast-to-clast collisions. Conversely, the splitting of a platy clast into two blockier ones, by halving it across the a-axis, would be comparatively easy, because the forces acting on either end of such a clast would be greater (e.g. Behrens, 1977; Ballantyne, 1982). Another important control on the emergence of blocky clasts in Type I catchments appears to be lithology, because all are dominated by massive lithologies with a low anisotropy (Deere and Miller, 1966) and thus ideally suited for the eventual production of blocky forms. Joint-spacing is likely to be
(b) Type II
RA
has little influence. Since this is rarely possible in practice, we recommend avoiding the mixing of lithologies. Despite the strong influence of lithology on clast shape, it is not the case that lithology is the sole constraint on clast form and roundness. The plots of sandstone and gneiss clast shape for different catchments show strong overlap of control envelopes and poor to absent discriminatory power (Section 3.2.2, Fig. 5). Although perhaps unsurprising, this suggests that similar lithologies in different regions can respond very differently to glacial and fluvial processes depending on local boundary conditions. This implies that specific lithologies do not have unique responses to the processes they are exposed to. Importantly, this implies that control samples of a specific lithology from one catchment may not be valid if applied to another one, in other words, control samples of a specific lithology must be taken in the same catchment as the target.
RWR
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Fig. 7. Schematic summary of two types of common, recurring co-variance plots found throughout the dataset. See text for a detailed explanation.
a key-control here as could be shown by Ballantyne (1982) for two of the catchments included in the present study. Further evidence for this inference comes from the presence of eclogite, a lithology characterised by low anisotropy, from Findelengletscher, a high-mountain catchment, in this category. Two further lithologies sampled in this catchment (serpentinite and mica schist) are both of higher anisotropy, hence tending towards the production of platy clasts originally, and they both clearly fit into Type II catchments (see below). Therefore, and because other boundary conditions can be excluded, the different response and behaviour of eclogite in the same high-mountain catchment can only be attributed to its lower anisotropy, not to other environmental, catchment-specific parameters. Type II catchments are entirely located in high-mountain regions and show lithological sources of high-anisotropy rocks (e.g. schists, para-gneisses, serpentinite). The pattern that emerges for these catchments is that the extraglacial/supraglacial envelope shows high C40, high RA and low RWR values, not dissimilar to those in Type I catchments. However, both subglacial and fluvial envelopes display a high proportion of platy clasts (evident in medium C40 values for both categories). Subglacial control samples have a higher RA-index and fluvial ones a higher RWR-index, but both envelopes occupy the same range of C40 values in the co-variance plots. Together, this evidence suggests that the dominant input route to the system (at the catchment scale!) is extraglacial and supraglacial, i.e. platy and angular. While
S. Lukas et al. / Earth-Science Reviews 121 (2013) 96–116
the angularity is reduced by subglacial edge-rounding and fluvial rounding, it appears that the rapidity of these high-mountain systems does not enable platy forms to be transformed into comparatively stable blocky ones to the degree that is possibly in Type I catchments. One may speculate that transport distance may be one control here. Likewise, based on previous studies (Benn et al., 2004; Evans et al., 2010; Brook and Lukas, 2012; Lukas et al., 2012), fluvial reworking of subglacial sediments and vice versa appear to be more commonplace than in Type I catchments. An absence of a clear change in platiness seems to confirm this interpretation, because the overall clast ‘stability’ does not change. It would be interesting to speculate here as to whether the occurrence of rock types with high anisotropy accentuates this trend. Two exceptions to the aforementioned categories exist. Although similar to Type I catchments, notably with respect to the geographical setting (lesser mountain range) and a low-anisotropical lithology (basalt), Flaajökull, Iceland (Fig. 8a), shows a much wider spread of RWR values in the subglacial control envelope than all other Type I catchments; in part, the subglacial clasts are more rounded than the proglacial fluvial control, pointing at multiple and complex transfers of material between the subglacial and subglacial glaciofluvial realm. This would correspond well to the finding of Evans (2000) who presented convincing stratigraphical evidence of these processes at the base of Skalafellsjökull, Iceland. The second exception is Pasterze, the largest glacier in the Eastern Alps, Austria (Fig. 8b). Although broadly similar
(a) Flaajökull, Iceland
(b) Pasterze, Austria Supraglacial
RA
RA
Supraglacial
Subglacial
Fluvial
Fluvial
Subglacial
C40
C40
Fluvial
Supraglacial
Subglacial Supraglacial C40
Maritime, temperate mountain glacier (ice cap outlet glacier)
Maritime, temperate high-mountain valley glacier
Angularity/roundness (RA and RWR) are good discriminators; platiness (C40-index) is less useful, because of fairly ‘blocky’ input
Angularity/roundness (RA and RWR) are good discriminators; platiness (C40-index) is less useful, because of fairly ‘platy’ input
Basalt (low-anisotropy, ‘massive’)
Prasinite (high-anisotropy, ‘platy’)
Processes
Lithology Clast shape
Environment
C40
RWR
RWR
Fluvial Subglacial
Fig. 8. Schematic summary of two types of exceptions from the generic co-variance plots plotted in Fig. 7. See text for a detailed explanation.
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in the overall arrangement of control envelopes, the spread of C40 values for the supraglacial realm is much broader, incorporating values that are only reached in fluvial and subglacial catchments elsewhere (Figs. 2, 3, 7). In this catchment, the dominant distinguishing criterion between the different control envelopes is roundness; platiness is less useful here. This could be due to a difference in joint-spacing in the local prasinite bedrock outcrops, which may lend themselves to producing fragments towards the blockier end of the spectrum compared to all other high-mountain environments where higher-anisotropy lithologies are present (Figs. 2, 3). However, this reasoning has to remain speculative at this stage, since no detailed data on joint spacing in this catchment are available to test our hypothesis. 5. Recommendations for clast shape sampling From the analyses presented above it has become clear that a dedicated and well-designed sampling programme will give the best results. We recommend starting the sampling programme by focusing on control environments where the erosional, transportational and depositional processes are known or can be well constrained. These include: Talus slopes outside the glacier (“extraglacial”); supraglacial material; clasts from the proglacial (and/or subglacial) fluvial areas; and clasts taken from the subglacial realm, ideally from in-situ basal traction zone till. In modern environments, the level of appreciation of the complexity of all processes contributing to the shaping of clasts is much enhanced if fluvial control samples are taken from locations that are equally spaced along the proglacial stream (e.g. at the glacier portal, 500, 1000, 2000 m downstream). Since this approach is not possible in palaeo-environments, we recommend sampling clasts deposited on gravel bars above the modern floodplain, because clasts transported during modern floods may most closely approximate the energy levels and transport capacity of former proglacial braidplains. Likewise, talus slopes are particularly useful as surrogates for supraglacial material in palaeo-environments, and, in the case of subglacial control, unequivocal exposures of subglacial traction till (sensu Evans et al., 2006) should be used. Ideally, plotting of all control samples and a first co-variance analysis should precede further sampling of clasts with unknown erosional, transportational and depositional histories to maximise time efficiency. This is especially true for catchments where multiple lithologies are present and could be used, because, as the cases of Findelengletscher, Fox Glacier and Vadret da Grialetsch demonstrate, different lithologies respond very differently to erosional, transportational and depositional processes even within the same catchment. In such cases, it is advisable to carry out pilot co-variance tests on each lithology to establish the one most suited for an effective discrimination of the different control environments. We recommend the collection of at least two control samples at each site and for each lithology. For a detailed investigation of catchmentwide processes (e.g. Brook and Lukas, 2012), we recommend at least four samples per control environment. Lithologies should never be mixed at the sample level, meaning that all 50 clasts of a sample and all samples used in one co-variance plot should be of the same lithology. Should clasts be coated by silt that has hardened during drying, for example those obtained from subglacial traction till, then they should ideally be washed, either on site or back in the laboratory. 6. Conclusions We have presented a dataset of clast shape variability in glaciated catchments from different locations worldwide. In order to study the influence of lithology on the performance of the method developed by Benn and Ballantyne (1993, 1994), we restricted this analysis to control samples where the processes leading to the development of
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distinct clast forms were well constrained. Our analysis leads us to draw the following conclusions. (1) The method developed and proposed by Benn and Ballantyne (1993, 1994) is sound and applicable to >95% of the catchments studied. The proposed RA-C40 co-variance plots are applicable to 63% of the 19 catchments we investigated, while the alternative approach that uses the rounded end of the form continuum, the RWR-index, is applicable to 75% of these catchments. This implies a wide-ranging applicability of the method, but we note that the RWR versus C40-index is more powerful than the RA versus C40-index. (2) Sampling of different lithologies in the same catchment has allowed us to determine a systematic and marked influence of lithology on the development of clast shape. Within catchments, mixing of lithologies results in the generation of overlap between different control envelopes that reduces the discriminatory power of the method significantly, in some cases to zero. Where this is the case, contrasting control environments cannot be distinguished from one another, thereby making any reconstruction of unknown transport processes for clasts from landforms and sediments in those catchments impossible or at the very least unreliable. (3) Plotting of similar lithologies from different catchments results in an even stronger loss of discriminatory power of the method, thereby strongly suggesting that lithological variability has a strong influence on clast shape, upon which catchment-specific parameters are superimposed. (4) Comparison of all co-variance plots in our dataset has resulted in two distinct types of process progressions within debris cascades to be reconstructed. Type I catchments comprise almost exclusively locations in the lesser mountain ranges (i.e. Norway, Svalbard) and are characterised by a clear separation of supraglacial (high C40 and RA, but low RWR), fluvial (medium C40, low RA, medium RWR) and subglacial control envelopes (low C40, low RA and RWR). Type II catchments are entirely located in high-mountain regions and the supraglacial envelope shows high C40, high RA and low RWR values, not dissimilar to those in Type I catchments. However, subglacial and fluvial envelopes display a high proportion of platy clasts, occupying the same range of C40 values in the co-variance plots. This evidence suggests that the dominant inputs to the system (at the catchment scale!) are extraglacial and supraglacial and that both fluvial and subglacial reworking do not result in a reduction of platy clasts, but that, instead, edge-rounding and rounding are the key discriminators in high-mountain environments. (5) Our data demonstrate that, with careful sampling design, subtle processes such as progressive rounding of clasts with distance from the glacier portal in the fluvial environment can be teased out. (6) We recommend using the following approach in order to produce reliable and reproducible results that are comparable between catchments for future clast shape studies. Firstly, different lithologies should be analysed separately at all times; mixing at both the sample and co-variance scale introduces an unknown element of error that is likely to make the results unreliable for a robust reconstruction for samples with an unknown transport history. Secondly, both RA and RWR approaches should be used in tandem to establish which one performs better in any given catchment. Ideally, co-variance analysis of control samples would precede any sampling of clasts from landforms and sediments that contain clasts of unknown transport histories to establish which approaches (RA or RWR) and lithologies are the most suitable in any given context. Thirdly, a well thought-out sampling strategy should be designed, with at least two control samples taken at each site. Depending on the purpose of the study, it may be
advisable to sample at defined and regular distances from the glacier portal. (7) In the future, it would be beneficial to investigate the links between topographical, glaciological and lithological controls further and to test our hypothesis that distinct types of debris cascades in glaciated mountain environments may exist.
Acknowledgements This contribution builds on over two decades of work, and several people have been involved in helping gather the data presented here. In addition to numerous undergraduate students, who have been involved in data collection in one way or another, or who have helped shape our thinking through unexpected questions, we are grateful to Derek Cowe, Sue Jenkins and Tina Lukas for their support in the field and back at the desk. The majority of this contribution was written while SL was on research leave from QMUL. BTIR would like to acknowledge the receipt of funding from INTERACT (grant agreement No. 262693) under the European Community's Seventh Framework Programme. The careful and constructive comments of one anonymous reviewer and Richard Shakesby helped to clarify parts of the message of this article and are gratefully acknowledged. References Augustinus, P.C., 1991. Rock resistance to erosion: some further considerations. Earth Surface Processes and Landforms 16, 563–569. Aydin, A., Basu, A., 2005. The Schmidt hammer in rock material characterization. Engineering Geology 81, 1–14. Ballantyne, C.K., 1982. Aggregate clast form characteristics of deposits at the margins of four glaciers in the Jotunheimen Massif, Norway. Norsk Geografisk Tidsskrift 36, 103–113. Ballantyne, C.K., Benn, D.I., 1996. Paraglacial slope adjustment during recent deglaciation and its implications for slope evolution in formerly glaciated environments. In: Anderson, M.G., Brooks, S. (Eds.), Advances in Hillslope Processes. Wiley, Chichester, pp. 1173–1195. Barrett, P.J., 1980. The shape of rock particles, a critical review. Sedimentology 27, 291–303. Bearth, P., 1953. Geologischer Atlas der Schweiz 1:25,000. Blatt 29 (Zermatt) mit Erläuterungen.Schweizerische Geologische Kommission, Bern. Bearth, P., Eugster, H., Leupold, W., Spaenhauer, F., Streckeisen, A., 1935. Scaletta (SA 423). Geologischer Atlas der Schweiz 1:50,000. Behrens, M., 1977. Zur Stereometrie von Geröllen. Mitteilungen aus dem GeologischPaläontologischen Institut der, 47. Universität Hamburg, pp. 1–124. Bell, F.G., Haskins, D.R., 1997. A geotechnical overview of Katse Dam and Transfer Tunnel, Lesotho, with a note on basalt durability. Engineering Geology 46, 175–198. Benn, D.I., 1989. Debris transport by Loch Lomond Readvance glaciers in northern Scotland, basin form and the within-valley asymmetry of lateral moraines. Journal of Quaternary Science 4, 243–254. Benn, D.I., 1990. Scottish Lateglacial moraines: debris supply, genesis and significance. Unpublished PhD thesis, University of St Andrews, 458 pp. Benn, D.I., 1992. The genesis and significance of ‘hummocky moraine’: evidence from the Isle of Skye, Scotland. Quaternary Science Reviews 11, 781–799. Benn, D.I., 1994. Fluted moraine formation and till genesis below a temperate glacier: Slettmarkbreen, Jotunheimen, Norway. Sedimentology 41, 279–292. Benn, D.I., 1995. Fabric signature of subglacial till deformation, Breidamerkurjökull, Iceland. Sedimentology 42, 735–747. Benn, D.I., 2004. Clast morphology. In: Evans, D.J.A., Benn, D.I. (Eds.), A Practical Guide to the Study of Glacial Sediments. Arnold, London, pp. 78–92. Benn, D.I., Ballantyne, C.K., 1993. The description and representation of particle shape. Earth Surface Processes and Landforms 18, 665–672. Benn, D.I., Ballantyne, C.K., 1994. Reconstructing the transport history of glacigenic sediments: a new approach based on the co-variance of clast shape indices. Sedimentary Geology 91, 215–227. Benn, D.I., Lukas, S., 2006. Younger Dryas glacial landsystems in North West Scotland: an assessment of modern analogues and palaeoclimatic implications. Quaternary Science Reviews 25, 2390–2408. Benn, D.I., et al., 2004. The research project — a case study of Quaternary glacial sediments. In: Evans, D.J.A., Benn, D.I. (Eds.), A Practical Guide to the Study of Glacial Sediments. Arnold, London, pp. 209–234. Bennett, M.R., Hambrey, M.J., Huddart, D., 1997. Modification of clast shape in higharctic glacial environments. Journal of Sedimentary Research 67 (3), 550–559. Blott, S.J., Pye, K., 2008. Particle shape: a review and new methods of characterization and classification. Sedimentology 55, 31–63. Boston, C.M., 2012. A Lateglacial plateau icefield in the Monadhliath Mountains, Scotland: reconstruction, dynamics and palaeoclimatic implications. Unpublished PhD Thesis, Queen Mary University of London, 291 pp.
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