16 Oct 2023
 | 16 Oct 2023
Status: this preprint is currently under review for the journal ESSD.

A High-Resolution Calving Front Data Product for Marine-Terminating Glaciers in Svalbard

Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber

Abstract. The mass loss of glaciers outside the polar ice sheets has been accelerating during the past several decades and has been contributing to global sea-level rise. However, many of the mechanisms of this mass loss process are not well understood, especially the calving dynamics of marine-terminating glaciers, in part due to a lack of high-resolution calving front observations. Svalbard is an ideal site to study the climate sensitivity of glaciers as it is a region that has been undergoing amplified climate variability in both space and time compared to the global mean. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985–2023 ( (Li et al., 2023). This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions. The overall calving front mapping uncertainty across Svalbard is 46 ± 21 m. The newly derived calving front dataset agrees well with recent decadal calving front observations between 2000 and 2020 (Kochtitzky and Copland, 2022) and an annual calving front dataset between 2008 and 2022 (Moholdt et al., 2022). The R2 of the glacier calving front change rates between our product and the latter is 0.98, indicating an excellent match. Using this new calving front dataset, we identified widespread calving front retreats during the past three decades, across most regions in Svalbard except for a handful of glaciers draining the ice caps Vestfonna and Austfonna on Nordaustlandet. In addition, we identified complex patterns of glacier surging events overlaid with seasonal calving cycles. These data and findings provide insights into understanding glacier calving mechanisms and drivers. This new dataset can help improve estimates of glacier frontal ablation as a component of the integrated mass balance of marine-terminating glaciers. 

Tian Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-396', Anonymous Referee #1, 15 Nov 2023
  • RC2: 'Comment on essd-2023-396', Anonymous Referee #2, 16 Nov 2023
  • RC3: 'Comment on essd-2023-396', Anonymous Referee #3, 20 Nov 2023

Tian Li et al.

Data sets

Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023 Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, Jonathan L. Bamber

Model code and software

Charting Outlines by Recurrent Adaptation (COBRA) Konrad Heidler

Tian Li et al.


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Short summary
Our study uses deep learning to create a new high-resolution dataset of calving front locations for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124,919 terminal traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessments.