A restitution method to reconstruct the 2001–13 surface evolution of Hurd Glacier, Livingston Island, Antarctica, using surface mass balance data

Abstract The surface restitution method we present reconstructs the evolution of a glacier surface between two time-separated surface topographies using seasonal surface mass balance (SMB) data. A conservative and systematic error analysis is included, based on the availability of surface elevation...

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Bibliographic Details
Published in:Journal of Glaciology
Main Authors: Mensah, Darlington, Lapazaran, Javier J., Otero, Jaime, Recio-Blitz, Cayetana
Other Authors: Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 2021
Subjects:
Online Access:http://dx.doi.org/10.1017/jog.2021.104
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021001040
Description
Summary:Abstract The surface restitution method we present reconstructs the evolution of a glacier surface between two time-separated surface topographies using seasonal surface mass balance (SMB) data. A conservative and systematic error analysis is included, based on the availability of surface elevation measurements within the period. The method is applied from 2001 to 2013 at Hurd Glacier (a 4 km 2 glacier), where we have sufficient SMB and elevation data. We estimate surface elevation changes in two steps: (1) elevation change due to SMB and (2) elevation change due to glacier dynamics. Four different models of the method are compared depending on whether or not accumulation is memorised at each time step and whether they employ balance profiles or SMB maps. Models are validated by comparing a set of surface measurements retrieved in 2007 with the corresponding restituted elevations. Although surface elevation change between 2001 and 2007 was larger than 10 m, more than 80% of the points restituted by the four models showed errors below ±1 m compared to only 33% when predicted by a linear interpolator. As error estimates between models differ by 0.10 m, we recommend the simplest model, which does not memorise accumulation and interpolates SMB by elevation profiles.