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Geodetic mass balance of Mýrdalsjökull ice cap, 1999−2021: DEM processing and climate analysis

This repository gathers the data I used and produced during my master thesis at the University of Iceland from April to September 2022 with the financial support of the Landsvirkjun. The geodetic mass of Mýrdalsjökull, the fourth largest Icelandic ice cap, was investigated over the period 1999-2021. The untapped SPOT5 archive (2002−2015), the lidar data, the Pléiades imagery (2011−present), aerial photographs from 1999 [1] and the ArcticDEM dataset (2010−2019) [2] were used to create Digital Elevation Models (DEMs) of the ice cap. A pre-processing of the DEMs was first performed: co-registration, filtering and interpolation. Then, applying a Gaussian Process regression (GP) [3], a state-of-the-art method in DEM processing, a spatially and temporally continuous DEM dataset was created, in 15 x 15 m resolution and 1-month interval from 1999 to 2021. Volume and mass changes based on the synthetic GP-generated DEMs were computed and analyzed in 5-year and annual intervals between 1999 and 2019. A local analysis of three glacierized catchments of Mýrdalsjökull (southern catchment, northern catchment and Kötlujökull outlet) was also performed. Errors were estimated using the method from [4]. Tools from the following repositories were used: <ul> <li> demcoreg (<a href="https://doi.org/10.5281/zenodo.5733347">https://doi.org/10.5281/zenodo.5733347</a>): DEM co-registration </li> <li> xdem (<a href="https://doi.org/10.5281/zenodo.4809698">https://doi.org/10.5281/zenodo.4809698</a>): uncertainties computation and DEM manipulation  </li> <li> pyddem (<a href="https://pypi.org/project/pyddem/">https://pypi.org/project/pyddem/</a>): Gaussian Process regression </li> </ul> The complete master thesis can be accessed at :   The repository contains the following data: 1 – DEM_coregistered All DEMs have been coregistered considering the Islandsdem v1.0 as a reference (atlas.lmi.is/dem) The DEM naming works as follow:  Glaciername_DEM_date_sensor_resolution_zmae_projection_otherinformation.tif The files ending with *_filtered.tif (SPOT5, AerialPhotographs) have been filtered using the filtering combination described in 3.1.3. The files ending with *_mosaic.tif (SPOT5, Pléiades) are the result of the mosaicking of several DEMs.   2 – Shapefiles Outlines of Mýrdalsjökull in 1999 [1], 2003, 2010 and 2019 [6] Outlines of the three catchments (South, North and Kötlujökull) in 1999. Reference buffer around Mýrdalsjökull used to crop all DEMs to the same extent.  Equilibrium Line Altitude (ELA) from 2004-10-05.   3 – Gaussian_Process_regression 2 netcdf files: the stack of DEMs (*_DEMstack_*) and the result of the Gaussian Process regression (*_GPregression_*). Both files were obtained thanks to pyddem tools. The Gaussian Process regression was run at a spatial resolution of 15 x 15m and a temporal resolution of 1 month, starting in January 1999 and ending in December 2022.   4 – Mass_balance_results 1 csv file containing mass balance results: <ul> <li> Annual mass balance for the ice cap & the 3 catchments </li> <li> 4-year mass balance for the ice cap & the 3 catchments </li> <li> Results from the comparison with survey dates mass balance (Fig 11(a)) </li> <li> Results from the comparison with [3] (Fig 11(b)) </li> <li> Mass balance overview (Fig 11(c)) </li> </ul>   References [1] Belart, J., Magnússon, E., Berthier, E., Gunnlaugsson, Á. Þ., Pálsson, F., Aðalgeirsdóttir, G., Jóhannesson, T., Thorsteinsson, T., and Björnsson, H. (2020). Mass balance of 14 Icelandic glaciers, 19452017: spatial variations and links with climate. Frontiers in Earth Science, page 163. [2] Porter, C., Morin, P., Howat, I., Noh, M., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., et al. (2018). ArcticDEM. Harvard Dataverse, 1. [3] Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., et al. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856):726731. [4] Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D. (2022). Uncertainty analysis of digital elevation models by spatial inference from stable terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. [5] Hannesdóttir, H., Sigurðsson, O., Þrastarson, R. H., Guðmundsson, S., Belart, J. M., Pálsson, F., Magnússon, E., Víkingsson, S., Kaldal, I., and Jóhannesson, T. (2020). A national glacier inventory and variations in glacier extent in Iceland from the Little Ice Age maximum to 2019. Jökull 2020: 1, 34.   Acknowledgements Pléiades images were acquired at research price thanks to the CNES ISIS programme (http://www.isiscnes.fr). This study uses the lidar mapping of the glaciers in Iceland, funded by the Icelandic Research Fund, the Landsvirkjun research fund, the Icelandic Road Administration, the Reykjavík Energy Environmental and Energy Research Fund, the Klima- og Luftgruppen research fund of the Nordic Council of Ministers, the Vatnajökull National Park, the organization Friends of Vatnajökull, LMÍ, IMO, and the UI research fund.   Dataset Attribution  This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 12
Reuse readiness: 8
Engagement: 4

Topics

Glacier mass balance Remote Sensing Glacier-climate relationship Islandic glaciers