Seasonal surface melt onset and firn freeze-up across the central Wrangell and St. Elias Mountains

Kindstedt, Ingalise; Johnson, Andrew; Schild, Kristin M.; Copland, Luke; Dow, Christine; Criscitiello, Alison; Winski, Dominic; Kreutz, Karl; Campbell, Seth

doi:10.5194/egusphere-2026-2431

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https://doi.org/10.5194/egusphere-2026-2431

Preprints

01 Jun 2026

| 01 Jun 2026

Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Seasonal surface melt onset and firn freeze-up across the central Wrangell and St. Elias Mountains

Ingalise Kindstedt, Andrew Johnson, Kristin M. Schild, Luke Copland, Christine Dow, Alison Criscitiello, Dominic Winski, Karl Kreutz, and Seth Campbell

Abstract. High-elevation alpine firn is increasingly influenced by surface melt and meltwater retention, yet the spatial extent and timing of these processes remain poorly quantified. Here we present spatially distributed estimates of seasonal surface melt onset and firn freeze-up across the central Wrangell and St. Elias Mountains using time series C-band Synthetic Aperture Radar data from the Sentinel-1 mission, 2015–2024. Melt onset and freeze-up are identified from characteristic changes in backscatter associated with the presence of liquid water in snow and firn. Seasonal melt is detected across nearly all elevations in the range. Melt onset broadly tracks the seasonal rise of the 0 °C isotherm up to ∼3,000 m a.s.l., while freeze-up shows pronounced delays relative to subfreezing air temperatures at mid-elevations, indicating widespread meltwater retention within the firn. Combining freeze-up timing, air temperature, and elevation, we classify firn water-retention regimes and find that dry firn is confined to the highest elevations, covering only 3 % of our area of interest. These results highlight the influence of meltwater on firn evolution in the Wrangell/St. Elias Mountains and demonstrate the utility of SAR for monitoring alpine glacier melt dynamics in data-sparse regions.

Received: 30 Apr 2026 – Discussion started: 01 Jun 2026

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Ingalise Kindstedt, Andrew Johnson, Kristin M. Schild, Luke Copland, Christine Dow, Alison Criscitiello, Dominic Winski, Karl Kreutz, and Seth Campbell

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'Comment on egusphere-2026-2431', Albin Wells, 12 Jun 2026 reply
This study characterizes the melt and refreeze evolution across a portion of the Wrangell and St. Elias mountains. The study offers an interesting method to characterize various zones of dry firn, wet firn, and melt by assessing the variability in the delay of wet snow/firn refreeze (as detected from Sentinel-1 SAR backscatter) compared to the 0˚C isotherm (derived using temperature lapse rates from weather stations across the region). The study is compelling and seamlessly combines remote sensing observations with in-situ observations and insights from models. The paper is also written at a very high quality, was generally easy to follow, and enjoyable to read. However, I have some major reservations about some of the results, particularly the classification of the ablation zone, and limitations associated with the melt detection algorithm.

Main comments
The level of writing of this paper is very high (definitely sufficient for publication). Still, readability would be improved with the removal or vast reduction of acronyms (S1, S1A/B, AOI, VV, VH, SEB, PDD, PR, K/H, ELA), especially for non-technical readers. I would highly recommend writing these out in full. This is ultimately not crucial and up to the discretion of the authors. Note some other acronyms not mentioned that are probably okay to use: SAR, AWS, HOBO, DOY.

The authors claim in the introduction and conclusion to be the first study to assess melt across the region, which is not true. Wells et al. (2026) also used Sentinel-1 SAR backscatter to produce melt onset time series for all glaciers >2 km2 in the region. It may be worth comparing these results in the supplement, which use the same -3 dB threshold from mean winter backscatter but for slightly different data (Wells et al. use cross-polarized SAR, this study uses co-polarized SAR) and slightly different onset elevation determination (Wells et al. use an ‘all melt’ threshold and hypsometric approach per-glacier, this study averages melt onset maps during elevation binning of all pixels in the study domain).

My main concern regards the firn melt zone classification results (as shown in Fig. 7). The accumulation area of the glaciers in this region is roughly 50-55% (Zeller et al., 2025), but this study only maps the ablation area as 12% of the area. In turn, this means that nearly 90% of the glacierized area is some classification of a firn zone, with over 70% being either wet firn, dry to wet firn, or dry firn. The authors need to motivate the physical meaning or interpretation of the transition zones, as the "wet firn to ablation" zone almost certainly contains no firn in reality. For many glaciers in the region, the "wet firn" zone itself is delineated as covering the entire glacier.

This may be the result of processing at the study domain scale, as opposed to per-glacier. As such, melt signals are mixed and aggregated into bins that span different parts of glaciers, especially across the large range of climates (maritime/coastal and continental/interior) and elevations (0 to 5000+ m a.s.l.) within the domain. While this is a major limitation of the study, it may still be okay as long as the authors make it explicitly clear that these results should be interpreted only as a domain-wide estimate and not necessarily indicative of any particular glacier (the authors don’t parse results for specific glaciers, but this could maybe still be explicitly stated). In this case, it also might make sense to remove the map in Fig. 7 and just show the zones in relation to the regional hypsometry to avoid misinterpretation of the results.

My other major concern arises from caution about the melt detection algorithm, particularly over heavy debris cover. Heavy debris can result in that portion of a glacier not exhibiting a clear melt signal. The current algorithm does not detect or identify these pixels as melting, and thus altogether excludes them from the analysis. This is clear from the maps D1-D7, where the terminus of Malaspina glacier, Logan glacier, Chitina glacier, and others (which are heavily debris covered) are never mapped in the melt onset and subsequent maps. The debris covered pixel behavior is also clear in cross-polarized SAR maps on, for example, Chitina glacier (https://alaskasnowlines.streamlit.app/plot_gif?name=Chitina&rgi_id=16307) and Logan glacier (https://alaskasnowlines.streamlit.app/plot_gif?name=Logan&rgi_id=16822), where the backscatter at the debris-covered terminus remains pretty much unchanged throughout time. In the study results, these parts of the glaciers are primarily the regions being mapped as “ablation area” or “wet firn to ablation zone”, even though no melt onset/refreeze interpretations where made over these areas of the glacier to begin with. One way to get around this issue would be to use some sort of “all melt” threshold to account for these lower pixels that are in fact melting but are either debris-covered or generally below the snowline, but this would likely require transitioning to a per-glacier analysis, at least to develop the spatially-distributed melt onset maps.

lastly, in Figure 7, there is an artifact resulting from plotting (hopefully it’s just a cosmetic error!), the DEM, or the mapping algorithm. The glacier areas near the edges of the figure are colored as ablation zones, cutting through other parts of the glaciers in a non-physical way, even in some accumulation areas.

Specific comments (these are mostly suggestions)
L9: The way it’s written, it almost sounds like one needs freeze-up timing, air temperature, and elevation to classify the dry firn zone. The dry firn zone should basically be the elevation above the melt extent. I’m curious if you compare the dry firn zone delineated from your method to the maximum melt extent/melting pixels that you detect (maybe with some threshold to avoid a few individual pixel outliers).

There’s also a very interesting study by Noël et al. (2018) that assesses past and ongoing changes in the firn structure of the Canadian arctic. This probably supports some of the statements you make in the introduction here.

Fig. 1b: somewhere specify that you are only showing elevation of glacierized land. Maybe add a coastline to the map.

I’m not sure how important the overall study domain area and percentage glacier cover is. Instead, it’s more interesting to highlight foremost the amount of glacierized area itself and possibly the number and size of glaciers.

L68: Remove ‘dramatic’

L77: I would similarly suggest writing ‘co-polarization (VV)’ and ‘cross-polarization (VH)’ to make this more readable to a non-technical audience.

L83: possibly write: “...we only use path/frame from descending Sentinel-1 orbits, which are acquired before 10:00 local time, to eliminate biased … acquisitions (all scenes in our study area are acquired before 10:00 or after 18:00).”

L95: Could you explain the rationale for using Nov-Mar scenes for the reference period? I think using Dec-Mar or Jan-Mar might make more sense given the slow refreeze of firn aquifers (especially, for example, on Donjek glacier). This would align with the statement on L227. To be clear, I don’t think this needs to be re-done as I don’t think this would make a difference in the results since the melt signal is so much larger than the refreeze signal, but I am curious about the rationale here. It could be worth showing a histogram of the difference between the mean winter backscatter from Nov-Mar compared to from Jan-Mar for one glacier or one path/frame.

L96: It could be worth mentioning that you may classify areas below the snowline where the surface is ice as ‘dry’ scenes despite the fact that they are melting. This logic is sound in accumulation areas or anywhere above the firn line.

L105: I appreciate the clarity of what the backscatter signal physically indicates.

L110: I’m curious what the Sentinel-1 scene gaps look like (i.e., where you may have >12 days uncertainty in melt or refreeze dates). It would be interesting to plot a horizontal bar for each of the 6 path/frames from 2015-2024, with the bar shaded where scenes are available/used. It might be worth simply ignoring years without sufficient scenes (or, without sufficient scenes around the melt onset or refreeze time) for a given path/frame, instead of including this potentially biased result in the averaging of all years and prescribing a very high uncertainty.

I’m also curious if you could use ascending scenes that pass in the evening to help get better resolution for the refreeze dates. While you mentioned this is biased and cannot be used for melt onset compared to the morning scenes, the slow, sub-surface refreezing process is not as susceptible to diurnal surface melt patterns. This could help give you better temporal resolution and extend the coverage of the middle of your area of interest to the earlier years without S1B data. This would at least be worth doing a quick investigation upon, to see if the refreeze dates from ascending scenes show systematic bias to the ascending scenes or not.

L115: Any reason why the scenes were not downloaded directly in dB scale?

L117: I would probably rephrase this as removing high-angle or very steep terrain.

L144: I would be curious to see what these lapse rates look like

L146: Very cool to see!

L150: I was initially a bit confused reading these definitions. The ‘dry to wet firn’ and ‘wet firn to ablation zone’ are simply transition zones between the other zones, but this list doesn’t offer any insight into how these are classified or any additional physical intuition about these sites. I would perhaps rephrase the way this is brought up (e.g., something like, …we define 3 primary glacier zones and 2 transition zones which account for the complex nature of…), or define them more concretely like the other zones are.

L161: “winter dry snow backscatter” is actually backscatter from ice, debris, or ice lenses in firn that is beneath the snow. I would just say “winter backscatter” since the SAR is not reflecting off of the dry snow itself. Can also probably remove the word “values”.

I’m also curious how you identify pixels that never have a SAR melt signal due to either heavy debris very low on the glacier or the 3% of dry firn zone.

L166: Nice–this adds a lot of useful context! Some mention of stable vs. transitional zones would have been useful before the listed zones because it was a bit confusing as someone unfamiliar with the study/method.

L170: Super cool. I would reorganize this and the following paragraph a bit to explain the vertical gradient calculation before explaining what a high/low vertical gradient means in terms of firn zone stability.

L171: Am I understanding the method correctly: you bin the ∆freeze into 100m elevations, and then do a 1D filtering on the elevation-dependent ∆freeze signal? Is the elevation binning and profiling done per glacier or for the entire study region at once? Does the elevation binning not already smooth the signal enough?

L176: What does “bands <200 m thick” refer to? Is this referring to any detected firn melt zone that doesn’t span at least 200 m elevation range?

Could temperate and cold firn play a role in some of these differences across the region? (not suggesting any changes, just curious about this)

Fig. 5: I like this figure a lot. Why not include 2023 for (b) and (c)?

L248: Outsized

L254: This is very interesting to observe in the data–I’m glad you pointed it out.

L262: I appreciate the discussion here, particularly tying in some of the nuances and work from previous studies.

L283: Zeller et al. (2025) and Bevington and Menounos (2025) produce ELAs for basically all glaciers across the study area you assess. How do those compare with your mapped zones?

L284: Feels strange to spell out and specify ‘ELA’ here after using it so many times already.

L285 and Fig. 5: I’m a bit confused why the “freeze-up” is detected so late at low elevations. Many of the pixels at low elevations return to within 3 dB of the winter mean backscatter very early in the season, after the snowline has retreated above them. What prevents your algorithm from defining these as “dry” pixels after the snowline has retreated?

References
Wells et al. (2026) https://doi.org/10.1038/s41612-026-01321-y

Zeller et al. (2025) https://doi.org/10.1017/jog.2024.65

Noël et al. (2018) https://doi.org/10.1029/2017JF004304

Bevington and Menounos (2025) https://doi.org/10.1088/1748-9326/adc9ca

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Citation: https://doi.org/10.5194/egusphere-2026-2431-RC1

Ingalise Kindstedt, Andrew Johnson, Kristin M. Schild, Luke Copland, Christine Dow, Alison Criscitiello, Dominic Winski, Karl Kreutz, and Seth Campbell

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Short summary

Mountain snow at high elevations is changing as warming causes more melting and water to linger within it. We use satellite radar images from 2015 to 2024 to track when melting starts and when snow refreezes across the Wrangell and St. Elias Mountains on the border of Alaska and Yukon. We find that melting now reaches nearly all elevations, and water often remains trapped well into fall. Only the highest areas stay fully dry, showing widespread impacts of warming on mountain snowpacks.


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