Supraglacial channels (rivers flowing over ice) are an increasingly common feature of glaciers and ice sheets subjected to a warming climate (Kingslake et al., 2016; Bell et al., 2018; Stokes et al., 2019 ). On mountain glaciers, these channels are important discharge routes for meltwater runoff and may also help connect supraglacial to englacial and subglacial systems, with potential implications for glacier flow. On larger ice sheets and ice shelves, supraglacial channels are closely associated with supraglacial lakes, whose development and drainage can impact on ice sheet mass balance (Bell et al., 2018). Perhaps surprisingly, little is known about how these channels evolve over time, both through melt season and over much longer time-scales. Superficially, these channels appear to be similar to river channels, especially those with bedrock beds, in terms of their morphology and flow dynamics, but many questions remain. For example, we do not know whether they have the same hydraulic geometry as river channels; whether their erosion can be modelled using models created for bedrock rivers; or whether flow velocities can be successfully predicted. Understanding these questions will help predict the total discharge that is carried by these systems and will therefore contribute to our understanding of glacier and ice sheet hydrology. Furthermore, an intriguing unknown is whether these supraglacial channels, which can evolve rapidly to changes in glacier shape, might be useful analogues for bedrock river response to much longer-term base-level changes.
The aim of this project is to understand the interactions between flow and channel morphology in supra-glacial channels, and to establish whether their behaviour can be predicted using methods developed for bedrock rivers. This will be established through the following objectives:
– Extensive literature review on supraglacial channels
– Generate an extensive database of channel morphologies from a range of settings using remote sensing techniques
– Undertake a detailed investigation of spatial and temporal changes in channel morphology on a mountain glacier over two field seasons
– Comparing whether the morphology and evolution of supraglacial channels is similar to that of bedrock channel analogues.
Click on an image to expand
sg_channel_1: A potential field location: Glacier du Brenay, near to Arolla, Switzerland. (Google Earth)
sg_channel_2: Laser scanning a glacier; one approach that could be used to measure channel morphology. (Richard Williams)
This project will primarily use a combination of remote sensing analysis and fieldwork.
The remote sensing analysis will use freely available data such as Sentinel 2, Landsat 8, Planet and other imagery (e.g. air photographs) to map the planform morphology of the channels in a range of glaciological settings including mountain glaciers, icefields and ice sheets. In addition, temporal DEMs from the ArcticDEM , Reference Elevation Model of Antarctica and any other available elevation datasets will be used to understand geometry changes in response to glacier surface geometry evolution. The scope and size of the resulting dataset means that this section of the project will draw on expertise within the Durham Data Institute. Analysis may also be feasible using Google Earth Engine . The morphology of the dataset will be analysed using geomorphometric techniques either in a GIS or in Matlab depending on the student’s interests. These data will be compared quantitatively and statistically against bedrock river analogues from various tectonic settings around the globe.
It will also be important to understand the flow dynamics and the smaller-scale morphology within the supraglacial channels including the bed roughness and how it evolves through time, e.g. over several weeks/months of a melt season. Two field seasons will therefore be spent collecting high resolution morphometric and flow data from supra-glacial channels on mountain glaciers in Switzerland, Iceland or a similar location. The fieldwork will include use of drones, differential GPS and/or laser scanning to collect high-resolution topographic data (Hodge et al., 2009; Williams et al., 2014; Williams et al., 2017), and flow gauging to quantify discharge and velocity. Repeat surveys during each field season will show how the morphology of the channel changes through time.
There is also the potential to include experiments in a laboratory flume. The aim of these would be to create a series of channels with a morphology similar to that observed in the field, and to quantify the flow dynamics in them (Hodge and Hoey, 2016). The project could also be developed to observe and spatially map patterns of flow in the field and to use these to assess the predictions of flow models such as Delft3D (Williams et al., 2013 ).
– Literature review: what is known about supra-glacial channels? How do they appear to be similar/different to bedrock channels? Write a review paper.
– Collect remote sensing data. Identify locations and images. Develop and implement method to identify and digitise channels.
– First field season at start of Year 2.
– Analyse field data.
– Analyse remote sensing data. Identify key patterns and trends across different locations and through time. Compare findings to properties of bedrock rivers.
– Second field season at start of Year 3, or flume experiments.
– Analyse new field or flume data.
– Draw together field and remote sensing data to make new predictions of supra-glacial channel dynamics and implications for future changes.
– Write thesis and papers
– Finish writing thesis and papers.
We anticipate that this project will be completed as a series of publications, with support and training in scientific writing. Technical training will include remote sensing data collection and statistical analysis; specific field skills including drone use, surveying skills and hydraulic data collection. IAPETUS2 provides a wide range of training opportunities to its students. With respect to this project, two of the most relevant are the ‘Introduction to modelling in Python’ and ‘Advanced statistics in R’ modules, but we will discuss the student’s needs and interests at the outset of the project. In addition, training will be provided in quantitative analysis of DEMs, as well as Python programming for topographic analysis and running command line tools. The student will gain experience in efficiently analysing large and complex datasets. We anticipate that the student will apply to attend a prestigious international summer school relating to ice and climate (Karthaus).
References & further reading
Bell, R.E., Banwell, A.F., Trusel, L.D. & Kingslake, J. (2018) Antarctic surface hydrology and impacts on ice-sheet mass balance. Nat. Clim Change 8, 1044-1052.
Hodge, R.A. & Hoey, T.B. (2016) A Froude-scaled model of a bedrock-alluvial channel reach: 1. Hydraulics. Journal of Geophysical Research: Earth Surface. 121:1578-1596.
Hodge, R.A., Brasington, J. & Richards, K (2009). In situ characterization of grain-scale fluvial morphology using Terrestrial Laser Scanning. Earth Surface Processes and Landforms. 34:954-968.
Kingslake, J., Ely, J.C., Das, I. & Bell, R.E. (2017). Widespread movement of meltwater onto and across Antarctic ice shelves. Nature 544, 349-352.
Stokes, C.R., Sanderson, J.E., Miles, B.W.J., Jamieson, S.S.R. & Leeson, A.A. (2019). Widespread distribution of supraglacial lakes around the margin of the East Antarctic Ice Sheet. Scientific Reports. 9:13823.
Williams RD, Brasington J, Hicks M, Measures R, Rennie CD, Vericat D. (2013). Hydraulic validation of two-dimensional simulations of braided river flow with spatially continuous aDcp data. Water Resources Research 49: 5183-5205. https://doi.org/10.1002/wrcr.20391.
Williams RD, Brasington J, Vericat D, Hicks DM. (2014). Hyperscale terrain modelling of braided rivers: fusing mobile terrestrial laser scanning and optical bathymetric mapping. Earth Surface Processes and Landforms 39: 167-183. https://doi.org/10.1002/esp.3437.
Williams RD, Tooth S, Gibson M. (2017). The sky is the limit: reconstructing physical geography from an aerial perspective. Journal of Geography in Higher Education 41: 134-146. https://doi.org/10.1080/03098265.2016.1241986.
For further information contact Rebecca Hodge (firstname.lastname@example.org), Richard Williams (Richard.Williams@glasgow.ac.uk), Chris Stokes (email@example.com) or Stewart Jamieson (firstname.lastname@example.org).