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Use of remote sensing to assess supra-glacial lake depths on the Greenland Ice Sheet. / Laura Cordero-Llana

Swansea University Author: Laura Cordero-Llana

Abstract

The influence that supra-glacial lakes have had in the recent mass loss at the margins of the Greenland ice sheet has been widely studied. Lakes can drain to the lase of a glacier, lubricating the bed, and enhancing acceleration of the glacier and hence ice thinning. Recent studies suggested that me...

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Published: 2012
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42327
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Abstract: The influence that supra-glacial lakes have had in the recent mass loss at the margins of the Greenland ice sheet has been widely studied. Lakes can drain to the lase of a glacier, lubricating the bed, and enhancing acceleration of the glacier and hence ice thinning. Recent studies suggested that melt extent is not directly linked to the dynamic loss but it has been broken to be linked to peak summer speed ups of the ice sheet front. Large volumes of water are necessary to propagate cracks to the glacial bed via hydrofractures. Hydrological models showed that lakes above a critical volume can supply the necessary water for this process, so the ability to measure water depth in lakes remotely is important to study these processes. The aim of this thesis was to test the current models used for water depth calculations based on the optical properties of water. An optimisation model to estimate water depths was developed. Atmospherically-corrected data from ASTER and MODIS were used as an input to the water reflectance model. As a reference dataset, ICESat measurements were used to obtain lake geometries over empty lakes. Differences between modelled and reference depths are used in a minimisation model to obtain parameters for the water-reflectance model, yielding optimised lake depth estimates. The key contribution of this research was the development of a Monte Carlo simulation. This method allows the quantification of uncertainties in water depth and hence water volume, for the first time. This robust analysis provided better understanding of the sensitivity of the model to the input parameters. There is scope to improve current models of depth estimations if more extensive held observations are done.
Keywords: Geomorphology.;Remote sensing.;Geophysics.
College: College of Science