the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Comprehensive Database of Thawing Permafrost Locations Across Alaska
Abstract. The Arctic is warming nearly four times faster than the global average, leading to widespread permafrost thaw degradation with profound implications for ecosystems, infrastructure, and global climate feedbacks. While gradual permafrost thaw occurs over decades, abrupt thaw events – such as thermokarst formation or retrogressive thaw slumps – can rapidly alter ecosystems and severely damage infrastructure. Although abrupt thaw is increasingly widespread, comprehensive datasets that map its spatial distribution at regional scales for land managers and local governments are still lacking. To address this gap, we created the Alaska Permafrost Thaw Database, an open-access, collaborative database which compiles 19,540 permafrost thaw and thermokarst locations across Alaska from 44 sources, integrating field observations, remote sensing products, and the published literature. This database spans observations from 1950 through present and incorporates datasets of varying spatial resolution, ranging from field-based point measurements to remotely sensed products (1–125 m), providing statewide coverage across Alaska. The dataset includes abrupt thaw features and sites experiencing gradual top-down thaw that can help to support comparative analysis and predictive modeling. We used this database to explore relationships between thaw type (abrupt vs. non-abrupt) and topographic metrics (i.e., slope, relative elevation, and potential incoming solar radiation), analyze the distribution of various thaw features across Alaska’s major ecoregions, and compare the database to current spatial datasets of ground ice and Yedoma. Our analysis shows abrupt thaw features are more prevalent in lowlands and depressions while gradual top-down and lateral thaw features are more commonly associated with areas receiving higher potential incoming solar radiation such as south facing slopes and open clearings. We also found substantial mismatches between ice-driven thaw processes and existing ground ice and Yedoma maps, likely reflecting the coarse resolution of current mapping products relative to the fine-scale nature of field measurements and highlighting the limitations of current datasets for local-scale prediction. The database provides direct, empirical evidence of actively thawing and stable permafrost locations and can be used to inform and validate ground ice mapping. By comparing the database with physiographic characteristics and remotely sensed measurements, the database can guide future field campaigns in areas with little to no observations. As permafrost thaw transforms Arctic landscapes, high-resolution, accessible spatial data – such as our thaw database – will be critical for informing climate mitigation and adaptation strategies. The Alaska Permafrost Thaw Database is openly available at Zenodo (https://doi.org/10.5281/zenodo.16996415), which provides a link to the GitHub repository and access to all versions; this paper describes version 2.0.0.
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- RC1: 'Comment on essd-2025-557', Anonymous Referee #1, 25 Jan 2026 reply
Data sets
The Alaska Permafrost Thaw Database (Version 2.0.0) Hailey Webb, Ethan Pierce, Benjamin W. Abbott, William B. Bowden, Yaping Chen, Yating Chen, Thomas A. Douglas, Joel F. Eklof, Eugénie S. Euskirchen, Miriam C. Jones, Moritz Langer, Isla H. Myers-Smith, Irina Overeem Jens Strauss, Katey Walter Anthony, Kang Wang, Matthew A. Whitley, Merritt R. Turetsky https://doi.org/10.5281/zenodo.16996415
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- 1
The authors present a comprehensive compilation of evidence for abrupt and non-abrupt permafrost thaw in Alaska, integrating 19,540 locations from 44 diverse sources spanning seven decades. In addition, the authors use the database to evaluate existing permafrost maps and to explore the spatial patterns of the two types of thaw using auxiliary data. Given the significance of abrupt permafrost thaw for both local and global effects, combined with the current lack of regional-scale datasets for land managers, the database represents a unique and valuable contribution that will be useful to a broad audience.
Prior to publication, the following issues must be addressed:
Figure quality and consistency needs to be increased (see specific comments below)
Some of the discussion material focused on abrupt thaw impacts should be moved to the introduction or removed
The available inventory of permafrost thaw features is limited not only by where abrupt thaw is taking place, but also possibly by where investigations are being made. You touch on this point in L367-L372. Can you also comment on how the violation of the assumption of 'random sampling' might affect the results of your statistical tests, and which regions, if any, you think might be overrepresented
Specific manuscript comments
L66-68 In the beginning of paragraph 2, the contrast to gradual thaw should also be stated in terms of impacts. The comparison of cm/yr (rate of change) vs (duration and impact) for abrupt thaw could be made more commensurate
L86: "events": but items listed in parentheses are landforms/features. Is the 'event' the initiation of these features?
L116: how were lines converted to points?
L124: consider rephrasing for clarity: "we have not manually verified each individual feature, but rather the features in the database reflect the accuracy of their source datasets" or something
L125: "lacking validation data of" -> "lack of validation data for"
L127: what are the ongoing opportunities for community feedback?
L127: Rather than asserting that it is an effective means, you could state that by providing accuracy, the reliability and limitations of the dataset are provided transparently. Separately, you can comment on community feedback (being specific) and opportunities for continued improvement.
L146: This section could be reworded for clarity: "We chose not to ... case for all areas"
Figure 1: This figure needs to be cleaned up:
- the resolution of this image should be increased.
- There is a problem with the "intermontane boreal" legend item in 1b
- The titles are redundant - information is in caption and legend.
- the grid lines in the viscinity of the legend are not oriented correctly
- while not essential, an outline of the Alaska state border (for consistency with other figures) and/or a map of neighbouring territories would improve the figure
Figure 2: The resolution of this image should be increased, or converted to a vector format.
Sec. 3.3: There appears to be significant spatial structure in Figure 4, suggesting that the mapping 'errors' may represent a systematic bias rather than random mapping resolution mismatch. Can you comment on where these discrepencies tend to occur and how that information could be used to better interpret the maps.
Figure 3: histogram titles are redundant. Information is in axes and caption. Change axis label from 'elevation' to 'relative elevation'
Figure 5 & caption: Rather than call the left hand colour blue/green, please use a colour with a less ambiguous name.
- clarify in figure caption that agreement is based on your aggregated high-mid class
- figure title is unnecessary
Figure 6 caption: typo "independent"
Figure 6: Text titles are redundant (e.g. "Non-ice-dependent abrupt thaw proecesses"
Figure 6: for greater visual clarity, consider reducing the marker point size in this and other maps.
L347: "three military training lands in the U.S. Army Fort Wainwright " is this correct?
L353-364: this could be tightened up. L353-358 in particular is more suitable for the introduction.
Comments on dataset and repository:
1. versioning of dataset: consider including v2.0.0 or v2 in paper title, it is easy to miss in the abstract.
2. I would strongly recommend using tagged commits in git (you could also put version number in a file) instead of separate directories for versioning. Similarly, using a generic name for your files (e.g. Alaska_Permafrost_Thaw_Database.csv) will make it easier to maintain workflows as more contributions are added and the version number changes. Your scripts could then read the version file to embed the version into the geojson, if necessary.
3. In the two csv files, be consistent with column naming (e.g. DataSourceType vs DtSrcTy) and with style (e.g. all in lowercase_with_underscores / snake_case or all with FirstLetterCapitalized / PascalCase). It will make it easier for people to use the dataset.
4. Add a unique identifier for each feature: FeatureName may not be unique as more data are added.
5. There appear to be duplicated columns between the two csv files (Thaw_Database and Topographic_Variables). Why not just merge these into a single file?
6. The topographic variables file does not seem to be mentioned in the paper. If multiple files are included in the dataset, please ensure the directory tree is well described.