the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Sep 2025
Distribution of landfast, drift and glacier ice in Hornsund, Svalbard
Abstract. Co-occurring landfast, drift and glacier ice in fjords respond to climate differently and have diverse impacts on the environment. Here we describe a new method to separate ice types in fjord environments on 2639 binary satellite-derived ice/open water maps at 50 m resolution. We used a set of thresholds to create near-daily maps of landfast, drift and glacier ice of Hornsund, Svalbard over 11.5 years (2012-01-02 to 2023-06-29). The ice was first divided into stationary and moving classes based on ice-pixel persistence through time. The ice was then polygonised and the polygons were ascribed a set of parameters describing their class, time, size and location. Temporal and spatial constraints were imposed on landfast ice. Drift and glacier ice were split based on timing, location and size. Finally, the data were re-rasterized and refined at the pixel level. Over the 11.5 years, the fjord ice was classified as 53 % drift, 35 % as landfast, 8.5 % as glacier, 1.4 % as uncertain ice type while 2.1 % was masked due to radar shadows. There was a great interannual variability in the length of sea ice and landfast ice seasons, and in ice type extent, with no clear long-term trend. Negative correlation existed between the water temperature in the winter months (January–March) and the length of the sea ice and landfast ice season, as well as between the air temperature in winter months and sea ice and landfast ice coverage. Glacier ice coverage depended on air temperature in summer months (July–September) and water temperature autumn months (October–December) where lower temperatures enhanced ice persistence. The method can be adapted to other areas and used in a wide range of analyses including fjord hydrography, nearshore wave transformation or ecological studies.
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Status: final response (author comments only)
- CC1: 'Comment on egusphere-2025-3859', Wang Zihan, 04 Oct 2025
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RC1: 'Comment on egusphere-2025-3859', Anonymous Referee #1, 07 Oct 2025
General comments:
In “Distribution of landfast, drift and glacier ice in Hornsund, Svalbard” Swirad and coauthors undertook the task of splitting the fjord ice into different types based on maps covering years 2012 – 2023 and further relate properties of sea ice (the coverage, length of the sea ice season etc.) to local environmental factors to investigate seasonal and inter-annual trends. The manuscript is well organized and written with clearly defined goals, adequate methods, providing valuable and reasonable results.
The paper represents substantial progress in sea ice investigation by developing an automated method for sea ice classification, thus extending beyond basic ice cover study. Resulting data are valuable and of great importance for broader scientific community and beyond.
However, there are some clarifications and improvements that need to be made before publication. Please find my comments below.
Specific comments:
Line 45: I’m not sure what do you mean by this sentence. I believe what you wanted to convey from Styszyńska and Rozwadowska (2008) is that in situ sea ice is formed when fjord’s water reaches freezing temperature. What about brine release during ice formation?
The last paragraph of Introduction states the goals of the paper. What I lack here is a clear statement about novelty that this study provides. Beyond extending time series from Swirad et al. (2023a), enormous workload and effort has been devoted to classifying/splitting the fjord ice into different types, among other things, by developing an automated method.
Lines 115-116: Was the buffer extended for the DN area as in previous study (Swirad et al. (2024))?
Section 3.2.1 I do not follow the initial division between stationary and moving ice. For example, within 5 days, how exactly is the stationary ice and moving ice defined?
Section 5. I have the most questions about discussion, in particular pair-wise correlation. As it was shown, sea ice formation and evolution is a complex process, dependent on many factors. First of all, it would be useful to give which correlations are statistically significant. This would show exactly which factors, among so many, should be the focus of this part of the discussion. Secondly, is salinity data available? I think, it’s an important factor, when we talk about sea ice. Briefly, the sea water must cool to freezing point. How fast it happens depends on water salinity. The higher initial salinity (at the end of summer season or in autumn) the lower air temperature and sufficiently long winter is required to cool the water and initiate the sea ice formation. This would partly (excluding further modification by e.g. wind) control properties of landfast ice. Deeper, more complex discussion is needed to explain existing relationships.
Technical corrections:
Line 60: change intern-annual to inter-annual
Line 336: change “in the deepest parts” to “the most inner parts”
Line 350: Does this apply to the landfast ice season or to the sea ice season as a whole. From Fig. 10 I can see that the iciest seasons are 2012/2013, 2014/2015, 2019/2020 and 2021/2022? 2012/2013 is even more icy than two seasons mentioned in the text.
Line 355: I would include season 2014/2015 to those with the highest coverage as well. It is also clearly seen in Figure 11.
Lines 387-388: For clarity please add “mean autumn water temperatures”
Line 474: please correct “twice as landfast to” twice as fast”
Line 475: Fjord area gain is shown in Table 7
Line 521: is it winter air or water temperature?
Figure 7. Please consider increasing font size. They are barely legible.
Figure 9. In the caption: there is “a) landfast ice, b) drift ice”, according to the Figure it should be “a) drift ice, b) landfast ice”. Throughout the manuscript you write about landfast ice and in Figure b) there is a Fast ice. Please consider changes in the figure or just add in caption b) landfast ice (Fast ice), also maybe it’s worth to add in line 27 (in Introduction) “Landfast ice (interchangeably called Fast ice)”.
Figure 10. Please consider increasing font size. Figure 8 is a good example of it.
Figure 13: In the caption you refer to panels marked a, b and c but the letters are missing in the Figure. For unification, please change “fast” in the middle panel to “landfast”.
Citation: https://doi.org/10.5194/egusphere-2025-3859-RC1 - RC2: 'Comment on egusphere-2025-3859', Karl Kortum, 16 Oct 2025
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This study provides valuable data and an interesting perspective on fjord ice and its environmental drivers. I do, however, have a few suggestions that might strengthen the manuscript.
First, the classification of pixels into "stationary" and "moving" is central for distinguishing fast ice, but the description is not sufficiently clear. The manuscript states that "The ice pixels were first divided into 'stationary' or 'moving' classes depending on their persistence through time", but does not explain how this was actually implemented in practice. It seems possible that a feature-tracking approach was applied, but this is not stated. Clarification would improve transparency and reproducibility.
Second, the manuscript presents correlations between fjord ice parameters, air temperature, and water temperature, but does not provide p-values. Since the time series are relatively short, especially for water temperature where gaps further reduce the effective sample size, it is difficult to know whether the reported Pearson coefficients are statistically significant or could arise from chance. This is particularly important because the conclusions rely heavily on these correlations to link environmental drivers with ice variability. Including p-values would make the statistical basis of these conclusions much stronger.
Finally, the statement that no interannual trend was observed is currently descriptive. I suggest that the authors quantify the trends in the key time series using a Mann–Kendall test combined with Sen's slope, and report both the trend values and their p-values, regardless of whether they are significant. This would provide a clearer and more transparent basis for the conclusion on long-term changes.