Lateral moraine elevation differences along a debris covered moraine
<p>Debris-covered glaciers in the Himalaya play an important role in the high-altitude water cycle.<br> The thickness of the debris layer is a key control of the melt rate of those glaciers, yet little is known about<br> the relative importance of the three potential sources of debris supply: the rockwalls, the glacier bed and the<br> lateral moraines. In this study, we hypothesize that mass movement from the lateral moraines is a significant<br> debris supply to debris-covered glaciers, in particular when the glacier is disconnected from the rockwall due to<br> downwasting. To test this hypothesis, eight high-resolution and highly accurate digital elevation models from the<br> lateral moraines of the debris-covered Lirung Glacier in Nepal are used. These are created using structure from<br> motion (SfM), based on images captured using an unmanned aerial vehicle between May 2013 and April 2018.<br> The analysis shows that mass transport results in an elevation change on the lateral moraines with an average rate<br> of -0.31 +/- 0.26 m/year during this period, partly related to sub-moraine ice melt. There is a higher elevation<br> change rate observed in the monsoon (-0.39 +/- 0.74 m/year) than in the dry season (-0.23 +/- 0.68 m/year).<br> The lower debris aprons of the lateral moraines decrease in elevation at a faster rate during both seasons, probably<br> due to the melt of ice below. The surface lowering rates of the upper gullied moraine, with no ice core below,<br> translate into an annual increase in debris thickness of 0.08 m/year along a narrow margin of the glacier surface,<br> with an observed absolute thickness of approximately 1 m, reducing melt rates of underlying glacier ice. Further<br> research should focus on how large this negative feedback is in controlling melt and how debris is redistributed<br> on the glacier surface. This dataset contains the elevation differences on the moraine as presented in the van Woerkom et al. (2019).</p>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 8