skip to primary navigation skip to content
 

High-resolution distributed modelling of the surface energy balance of valley glaciers

High-resolution distributed modelling of the surface energy balance of valley glaciers

The aim of this project is to develop numerical models for the surface energy balance of valley glaciers, and to explore the sensitivity of the results of such models to factors such as the model spatial resolution. The modelling strategy adopted uses a two-dimensional surface energy balance model to calculate the hourly melt rate over an entire glacier surface throughout the summer melt season. Together with measurements of winter snow depth, this then allows the glacier mass balance to be calculated.

Figure 1

Figure 1. Location map for Midre Lovenbreen. Contours are in metres above sea level, and are based on airborne LiDAR data. The heavy line shows the edge of the glacier.

The surface energy balance model can produce accurate predictions of season-long melt totals over the entire glacier surface, using a high-resolution 20m scale DEM derived from airborne LiDAR data, and hourly meteorological measurements made at an automatic weather station on the glacier itself, supplemented by a synoptic station at the Ny Alesund research base. The model calculates the patterns of solar radiation receipts, including shading of the glacier by the surrounding topography, and tracks the season-long changes in snow depth and surface albedo over the surface of the glacier.

Play this video

Figure 2. Computer-generated 3-d view of Midre Lovenbreen, showing the changing patterns of shading over the glacier surface during late June). The view is looking up-glacier, in a SSW direction. The brightest shades are the glacier itself, when in sun; mid-greys are the ice free topography in sun; dark greys are shaded areas of the glacier surface, and the darkest areas are shaded, ice free terrain. Midnight is when the shadows are pointing 'away' from the camera; the high latitude (~79) means that there is 24 hour daylight at this time of year.

The model results have shown that small-scale topographic features are an important control on the spatial variability of the energy balance of the surface, and hence the summer mass balance. This variability is driven largely by slope, aspect and shading patterns, which affect the receipt of incoming solar radiation, the major positive contributor to glacier surface energy balance.

Figure 2

Figure 3. Calculated season-long summer melt rates, in metres of water, over the entire glacier surface. The inset shows a smaller area in more detail; note the high degree of spatial variability caused largely by small-scale topographic variability affecting the amount of solar radiation absorbed by the glacier surface.

Using the model, and the high quality, high-resolution Airborne LiDAR data obtained in 2003 and 2005, we are also exploring the sensitivity of the model results to the DEM spatial resolution and the interpolation scheme used to generate the DEM. Initial results show that the season-long totals of solar radiation are sensitive to DEM resolution, and particularly to the accurate representation in the DEM of the height of the view-shed around the catchment.

We are also using the model, together with very high spatial resolution remotely sensed multi-spectral imagery, to investigate the effect of fractal surface properties, including snow depth and albedo, on the spatial patterns of melt over the glacier surface.

Publications

  • Arnold, N.S. and Rees, W.G. In Review. Effects of Digital Elevation Model spatial resolution on distributed calculations of solar radiation in a high arctic glacierised catchment. Water Resources Research.
  • Rees, W.G. and Arnold, N.S. 2007. Mass balance and dynamics of a valley glacier measured by high-resolution LiDAR. Polar Record 43(227), 311-319.
  • Arnold, N.S., Rees, W.G., Hodson, A.J. and Kohler, J. 2006. Topographic controls on the energy balance of a high Arctic glacier. Journal of Geophysical Research. 111 F02011, doi 10.1029/2005JF000426 (15 pp).
  • Rees, W.G. and Arnold, N.S. 2006. Scale-dependent roughness of a glacier surface: implications for radar backscatter and aerodynamic roughness modelling. Journal of Glaciology. 52, 177, 214-222.
  • Arnold, N.S., Rees, W.G., Devereaux, B.J. and Amable, G. 2006. Evaluating the potential of high-resolution airborne LiDAR in glaciology. International Journal of Remote Sensing 27 (5-6), 1233-1251.
  • Arnold, N. 2005. Investigating the sensitivity of glacier mass balance/elevation profiles to changing meteorological conditions: model experiments for Haut Glacier d'Arolla, Valais, Switzerland. Arctic, Antarctic and Alpine Research, 37(2), 139-145.
  • Arnold, N. and Rees, W.G. 2003. Self-similarity in glacier surface characteristics. Journal of Glaciology 49(147), 547-554.