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Data Set ID:

Standing Water Depth on Larsen B Ice Shelf, Version 1

This data set addresses why surface melt water lakes on ice shelves and ice sheets are notably influential in triggering ice-shelf break-up and modulating seasonal ice flow, and are thus principle avenues by which environmental change can be transmitted to the ice sheets of Antarctica and Greenland. A comparative analysis is performed of lake geometry and derived depth (using Landsat image reflectance) in two distinct regions, one a collapsing ice shelf and the other an ablation zone of a land terminating ice sheet, to better characterize the range of surface lake variability.

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  • Surface Water > Water Depth
  • Surface Water > Total Surface Water
Data Format(s):
  • NetCDF
Spatial Coverage:
N: -63, 
S: -67, 
E: -55, 
W: -63
Spatial Resolution:Not SpecifiedSensor(s):ETM+
Temporal Coverage:
  • 21 February 2000 to 22 February 2000
Temporal Resolution1 dayMetadata XML:View Metadata Record
Data Contributor(s):Douglas MacAyeal

Geographic Coverage

Please contact the data provider for the correct Data Citation for this data set.

Literature Citation

As a condition of using these data, we request that you acknowledge the author(s) of this data set by referencing the following peer-reviewed publication.

  • Banwell, A., M. M. Cabellero, N. Arnold, N. Glasser, M. Cathles, and D. R. MacAyeal. 2014. Supraglacial Lakes on the Larsen B Ice Shelf, Antarctica, and at Paakitsoq, Annals of Glaciology. 55. 66.

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Detailed Data Description

Supraglacial meltwater lakes trigger ice-shelf break-up and modulate seasonal ice-sheet flow, and are thus agents by which warming is transmitted to the Antarctic and Greenland ice sheets. To better characterize the range of supraglacial lake variability, a comparative analysis was performed of lake geometry and derived depth using Landsat image reflectance in two distinct regions: one on the pre-collapse (2002) Larsen B Ice Shelf, Antarctica, and the other in the ablation zone of Paakitsoq, a land-terminating region of the Greenland ice sheet. However, this data set only addresses the analysis done on the Larsen B Ice Shelf (Banwell 2014).

On the pre-collapse (2002) Larsen B Ice Shelf, where surface elevations are relatively uniform, surface lakes cover a greater proportion of surface area (5.3 percent vs. 1 percent), but tend to be shallower and more uniform in area, than for lakes in the Paakitsoq Region of Greenland. Other aspects of lake geometry, such as ellipticity of shape, degree of convexity (solidity), and orientation relative to flow direction, are relatively similar between the two regimes. We attribute the notable difference in lake density and depth between ice-shelf and grounded ice to the fact that ice shelves offer a less distinct surface elevation variability with which to divide the region into distinct, large-scale basins of hydrologic drainage. Ice shelves also possess more stimuli to small-scale, localized surface elevation variability due to the various structural features that yield small variations in thickness and which float at different levels by Archimedes' principle. We speculate that lakes on the Larsen B Ice Shelf may be interconnected dynamically via the flexural response to the ice shelf when one or more lakes drain (Banwell 2014).

The major goal of this study was to assess the role of surface melting on ice shelves as a potential driver of ice-shelf instability and collapse. However, there are six sub-goals that were also identified:

  • energy balance modeling of ice-shelf surfaces, with treatment of standing water bodies such as ponds and water-filled crevasses
  • firn water percolation modeling to assess water retention in standing bodies and to assess brine infiltration from the ice front and from crevasses open to seawater from below
  • analysis (through theoretical models and observation) the evolution of surface roughness on ice shelves, as roughness features are likely to be the attractors of surface meltwater bodies
  • mass loading and flexure effects, particularly emphasizing flexure effects leading to fracture, fragmentation and damage that could eventually play into ice-shelf collapse
  • Surface pond and crevasse convection
  • Basal crevasse thermohaline convection (Banwell 2014).

The pursuit of these goals is intended to eventually address the objective of identifying and understanding environmental enabling conditions that trigger ice-shelf instability (Banwell 2014).


Network Common Data Form (netCDF) (.nc)

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File and Directory Structure

Data are available on the FTP site in the directory. Within this directory, there are three netCDF files. Within these files, there are sub files. Refer to Table 1 for a listing of the sub files within the files.
area centerxy lakedepth
centerlatlon pixelsize
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File Size
File Name Size 108 KB 44 KB 120990 KB
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118 MB

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Spatial Coverage

North Latitued: 63.0 S
South Latitued: 67.0 S
East Longitude: 55.0 W
West Longitude: 63.0 W

Spatial Resolution

30 meters

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Temporal Coverage

Data were collected from 21 February 2000 to 22 February 2000.

Temporal Resolution

One day.

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Parameter or Variable

Water Depth
Total Surface Water

Sample Data Record

Figure 1 is sample data from the data file. The image is the sub file: lakedepth.

lake depth file
Figure 1. Sample Data Image of the lakedepth File
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Software and Tools

In order to view the data, you will need to download the free software Panoply netCDF, HDF and GRIB Data Viewer from NASA.

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Data Acquisition and Processing

Data Acquisition Methods

Two Landsat 7 ETM+ images were analysed for this study. For the Larsen B Ice Shelf, the image dated 21 February 2000 was used (Scene ID: L71216106_10620000221) as this is the most cloud-free image available within two years of the break-up event. This image also forms the basis of a prior study of lake patterning and morphology on the Larsen B Ice Shelf (Glasser and Scambos 2008), and thus serves as a fiduciary representation of the state of the Larsen B Ice Shelf two years prior to its collapse.

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Derivation Techniques and Algorithms

Using the same 21 February 2000 image, Glasser and Scambos (2008) produced a detailed structural glaciological analysis of overall changes in surface structures on the Larsen B Ice Shelf prior to its collapse in late February 2002. Although this study, which used manual digitization, identified general patterns of lake positions, areas and shapes of supraglacial lakes, it did not perform a quantitative analysis of these properties and, importantly, did not analyse lake depth. Thus, in our study, we also statistically analyse the shape files of lakes used in the Glasser and Scambos (2008) study, in order to compare, and thus validate, the results of our study using an automated algorithm

Processing Steps

Lake Boundaries and Area

Image pixels were classified into lake-covered or bare ice/snow using Landsat image reflectance data, following Box and Ski (2007). Each Landsat band was first converted from digital numbers to radiance and then from radiance to reflectance using the equations of Chander (2009). Then, to make this classification, the blue/red ratio of reflectance (involving Landsat bands 1 and 3; 450-515 and 630-690 nm, respectively) was evaluated from the Landsat image. As this ratio increases toward the lake centres, where water is deepest, and decreases towards the edges, where water is most shallow, it was necessary to carefully identify the value of this ratio corresponding to bare ice at lake edges. Based on experimental results, and on known areas of lakes on the Greenland Ice Sheet, Box and Ski (2007) suggest that the threshold value of blue/red ratio of reflectance should be in the range 1.05-1.25 at the edges of lakes. Further to the study by Box and Ski (2007), we found that the algorithm needs to be adapted to avoid problems associated with floating lake ice on lake surfaces. Unless these areas were masked, negative lake depths were found, due to the high reflectance of the ice compared to the open water.

Once the pixels representing flooded areas had been established, the bw boundaries function in MATLAB was used to identify lake boundaries. Subsequently, the region-props function in MATLAB was used to identify the number of pixels that is the surface areas of lakes within the closed edges as a means of determining lake area.

Lake Depth

Supraglacial lake depths were estimated using a method developed by Sneed and Hamilton (2007), originally applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (VNIR1, 520-600 nm) imagery, but also applicable to Landsat 7 ETM+ imagery (Band 2; 525-605 nm) (Sneed and Hamilton 2011). The approach for extracting water depth and lake-bottom albedo is based on the Beer-Lambert law (Ingle and Crouch 1988), which describes the attenuation of radiation through a water column:



Ι(Ζ,λ) The water-leaving spectral intensity at some depth
Ι(Ο,λ) The spectral intensity at zero depth
Κλ The spectral attenuation
z z is depth written in terms of reflectance, and inverted to logarithmic form (Philpot 1989), z is determined by:

equation 2


Ad The bottom or substrate albedo (reflectance)
R The reflectance for optically deep water
Rw The reflectance of some pixel of interest
g Is given by:

equation 3


Kd The diffuse attenuation coefficient for down- welling light
a The beam absorption coefficient
Du An upwelling light distribution function or the reciprocal of the upwelling average cosine (Mobley 1994).

To determine Ad we took the mean reflectance value of the ring of pixels around the lake that are barely covered with water that is those adjacent to the water-covered pixels, as detected by the blue/red ratio of reflectance. Although Sneed and Hamilton (2007) used the same Ad for their entire region of interest, as our region is larger we chose to calculate a unique Ad for each lake. For the Larsen B Ice Shelf, values for Ad ranged from 0.30 to 0.79 (with a mean value of 0.68), and for Paakitsoq, values for Ad ranged from 0.17 to 0.76 (with a mean value of 0.66).

To determine R, the reflectance from optically deep water where the influence of bottom reflectance is nil, we used the value of reflectance from water that is deeper than 40 m in the image. It was necessary to take care when selecting pixels that were far from shorelines, to ensure that R estimates were not biased by water that was too shallow, turbid water or pixels containing floating ice. This approach assumes that the substrate (bottom) of the lake is homogeneous, the impact of suspended or dissolved organic or inorganic matter in the water column is negligible on absorption, there is no inelastic scattering, for example, Raman scattering or fluorescence, and that the lake surface is not significantly roughened due to wind (Sneed and Hamilton 2007). Once the depth of each flooded pixel had been calculated, the regionprops function in MATLAB was again used to determine the MaxIntensity, that is the maximum lake depth, and the MeanIntensity, that is the mean lake depth, for each of the identified lakes.

Lake Shape, Orientation and Eccentricity

Once lake edges, and thus areas, had been delineated following Box and Ski (2007), and depths had been established following Sneed and Hamilton (2007), the MATLAB regionprops function was used to obtain other lake properties. As illustrated in Figure 2, this function works by best-fitting ellipses to the identified lakes. Lake properties which this function is able to diagnose include:

  • eccen- tricity that is the ratio from 0 to 1 of the distance between the foci of the ellipse and its long axis length, where 0 indicates that the ellipse is a circle and 1 indicates a line segment
  • orientation from 0° to 90° from the average ice-flow direction on the ice shelf/sheet in either a clockwise or anticlockwise direction
  • solidity from 0 to 1, denoting the proportion of the pixels in the convex hull of the lake that are also bound within the lake itself; lakes with sinuous boundaries tend to have low solidity, circular lakes have a solidity of 1.

Figure 2 shows a schematic of optimal fit of an ellipse (with f indicating the foci) to the outline of a previously identified lake. The ellipse and original lake are equal in area. The angle between the long axis of the ellipse and the flow direction (either clockwise or anticlockwise) determines the ellipse orientation.

figure 2
Figure 2. Supraglacial Lakes on the Larsen B Ice Shelf and at Paakitsoq (Banwell 2014)
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Larsen B Lake Patterns and Characteristics

The most appropriate value for the blue/red band threshold for the Larsen B Ice Shelf to appropriately discriminate bare ice/snow from water is found to be 1.2, which results in the identification of 3227 lakes. Refer to Figure 3. This threshold value is chosen because it produces a pattern of lakes on the ice-shelf surface that is most similar to the pattern of lakes identified visually on the Landsat image and documented by Glasser and Scambos (2008). If a slightly lower threshold value of 1.1 is chosen, only 272 separate flooded areas are identified, as the majority of the entire surface of the Larsen B Ice Shelf is erroneously classified as lake-covered. If a slightly higher threshold value of 1.3 is chosen, only 1419 lakes are identified, and the small lakes, in particular, are no longer identified (Banwell 2014).

In Figure 3, although some lake depths are >4 m, for visualization purposes 4 m is plotted as the maximum depth here. Three areas, a, b and c, are enlarged to show varying lake characteristics and patterns across the ice-shelf surface. Marginal areas that can be grounded ice, bare land surface, or ocean surface, are shaded grey.

Figure 3. Depths of Lakes on the Larsen B Ice Shelf Using Reflectance of the 21 February 2000 Landsat Image (Banwell 2014)

As also recognized by Glasser and Scambos (2008), we identify a variety of different domains on the ice-shelf surface, each displaying different lake characteristics. We have highlighted lakes within three areas of these domains in Figure 3. In area a of Figure 3, we see fairly linearly shaped lakes, with their long axis diverging from the mean ice-flow direction from west to east. This is indicative of ice-flow divergence where fast-flowing glaciers enter the ice shelf from the west. Although lake depths generally vary from 1 to 4 m here, the deepest identified lake on the ice shelf also falls within this region; calculated to be 6.8 m at its deepest point. In area b of Figure 3, longitudinal features, which are aligned roughly parallel with ice flow, dominate. These features are up to 30 km in length and 2-3 m in depth. Thus, compared to area a, ice flow in this region is likely to be convergent rather than divergent along the flow direction. In area c of Figure 3, we see lakes that are fairly circular, that is their eccentricity is close to one and have a larger mean area than the majority of lakes on the ice shelf. We suggest that these characteristics are due to slower ice flow in this region. For additional information on structural features of the Larsen B Ice Shelf related to flow, as well as the area of the Larsen B Ice Shelf that disintegrated, refer to Glasser and Scambos (2008). Through an increased lifespan, lakes would be able to undergo more enlargement by bottom ablation than other lakes on the ice shelf. The majority of the lakes in this area are also covered with floating ice, seen as white areas in Figure 3c. Although the outer rings of open, lake-ice-free water of the lakes are calculated to be from 0.5 to 1.5 m in depth, we cannot calculate the depth of the central, probably deepest regions, because of the ice cover (Banwell 2014).

Over the entire ice shelf, we calculate the mean lake area to be 0.10 km2 (with standard deviation, δ = 0.29km2 ) and the total surface area covered by lakes to be 315 km2 . This is 5.3 percent of the total area of ice shelf analysed, with a mean lake density of 0.55 km-2. We calculate that lakes covered ~10 percent of the 3200 km2 of ice shelf which disintegrated in a 35 day period beginning on 31 January 2002 (Scambos et. al. 2004). As we discuss below, this larger percentage of lake cover constitutes one of the most important differences between lakes on the Larsen B Ice Shelf and lakes on the Greenland Ice Sheet (Banwell 2014).

The mean lake depth on the Larsen B Ice Shelf is calculated to be 0.82 m (δ = 0.56 m), and the mean maximum lake depth is 1.6 m (δ = 0.99 m). Refer to Figure 4. The mean eccentricity is 0.84 (δ = 0.13 m), the mean solidity is 0.80 (δ = 0.14) and the average mean orientation of the long axis of ellipses (best- fitted to the lakes) is 46° away from the flow direction (δ = 28 8°). The latter assumes that the average flow direction is from west to east (Vieli et. al. 2006, their fig. 5).

Using the mean lake depth (0.82 m), the total number of lakes on the Larsen B Ice Shelf (3227), the average lake area (0.1 km2) and the assumption that the ice shelf has a uniform thickness of 200 m (Sandhäger et. al. 2005), we calculate that there are 5.2 x 108 MJkm2 of potential energy stored on the surface as free water (equivalent to 8.7 x 104MJkm2). This calculation is useful, as it gives an indication of the amount of energy available for the drainage of lakes by hydro-fracture, the process that was probably the main driver behind the disintegration of the ice shelf (Scambos et. al. 2003) and (Scambos et. al. 2009).

In Figure 4, six plots are showing (a) maximum depth, (b) mean depth, (c) mean area, (d) eccentricity, (e) solidity, and (f) orientation from the mean flow direction of lakes on both the Larsen B Ice Shelf (N= 3227) and at Paakitsoq (N= 239) in order to clearly capture the scale and differences between the two lake systems.

On each box, the red mark is the median and the edges of the box are the 25th and 75th percentiles (q1 and q3, respectively).

The length of the whiskers (dotted lines) is equal to q3 + 1.5(q3-q1).

Figure 4. Plots Showing Maximum Depth, Mean Depth, Mean Area, Eccentricity, Solidity, and Orientation from the Mean Flow Direction of Lakes on both the Larsen B Ice Shelf and Paakitsoq (Banwell 2014)
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References and Related Publications

Contacts and Acknowledgments

Douglas MacAyeal 
University of Chicago
Department of Geophysical Sciences
5734 S. Ellis Ave.
Chicago, IL 60637


This research was supported by NSF OPP Grant Number 0944248.

Document Information


April 2014

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