the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-Source Synthesis, Harmonization, and Inventory of Critical Infrastructure and Human-Impacted Areas in Permafrost Regions of Alaska (SIRIUS)
Abstract. The Arctic region has undergone warming at a rate more than three times higher than the global average. This warming has led to the degradation of near-surface permafrost, resulting in a loss of ground stability. This instability not only poses a primary threat to Arctic infrastructure and human-impacted areas, but can also lead to secondary ecological hazards from infrastructure failure associated with hazardous materials. This development underscores the need for a comprehensive inventory of critical infrastructure and human-impacted areas, that is linked to environmental data to assess their susceptibility to permafrost degradation as well as the ecological consequences that may arise from infrastructure failure. In this study, we provide such an inventory for Alaska, a vast state covering approximately 1.5 million km2, with a population of over 733,000 people and a history of industrial development on permafrost. Our SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI), (ii) OpenStreetMap , (iii) the pan-Arctic catchments summary database (ARCADE), (iv) the permafrost extent, probability and mean annual ground temperatures, and (v) the contaminated sites database and reports to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The integration steps involved harmonizing spatial references, extents, and geometries, the usage of text mining techniques to generate additional geospatial data on contaminated sites – including contaminants, cleanup duration, and affected medium – from textual reports, and the incorporation of a uniform usage type classification scheme for infrastructure. The combination of SACHI and OSM enhanced the detail of the usage type classification for infrastructure from 5 to 13 categories, which allows for the identification of elements critical to Arctic communities beyond industrial sites. Further, the new inventory unites the high level of spatial accuracy from OSM with high level of completeness from SACHI. The SIRIUS dataset is presented as a GeoPackage, enabling spatial analysis and queries of its components, either in dependence or combination with one another.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2023-393', Bretwood Higman, 31 Jan 2024
Overall this seems like a useful step forward in characterizing the distribution of hazardous materials sites in Alaska, and the meshing of OSM data with other data sets is clever. Some minor revisions, especially to make the figures more clear, would benefit the paper (see comments in the attached pdf.)
- AC1: 'Reply on RC1', Soraya Kaiser, 18 Apr 2024
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RC2: 'Comment on essd-2023-393', Anonymous Referee #2, 14 Feb 2024
Review Kaiser et al., 2023
The combination of available infrastructure data sets to increase the semantic content is a good idea that has the potential to improve future analysis regarding contaminated sites and related hazards. Especially the increased number of usage categories is promising for many applications. The usage examples are well done and will be helpful for future users of the data set.
Below I outlined several points that would improve the manuscript in my opinion.
- Figure 2 may be valuable for users of the data but could go into the appendix. Instead, a figure like a flowchart or decision tree on how the input data sets were combined may be beneficial for the reader for a better understanding of the process.
- Regarding OpenStreetMap: As I understood from the text, the authors use OSM as an alternative source of linear infrastructure instead of the satellite derived Bartsch et al dataset. Previous publications including Bartsch et al 2021, which the authors heavily cite throughout the text, point out that OSM has some major drawbacks, including inconsistencies, and lacking some more recent infrastructure developments. In line 147-148 the authors note that they use OSM for the linear infrastructure, not SACHII, for buildings they describe a decision tree of combining the data sets (from line 207). Is there a reason a similar approach was not used for linear infrastructures, especially given the concerns previous publications have raised about OSM. Maybe just some additional discussion could be included on this.
- Figure 6: interbal – internal?
- Figure 10 could go into the appendix.
- Line 470: Is this issue not shown in Figure 8? Maybe include a reference here to this figure, just for improved understanding for the reader.
- Maybe I missed it in the text, but your input data sets have different extents? So would the accuracy of your output not differ between areas where both/all inputs were available and those that rely solely on OSM data?
Citation: https://doi.org/10.5194/essd-2023-393-RC2 - AC2: 'Reply on RC2', Soraya Kaiser, 18 Apr 2024
Data sets
SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka Soraya Kaiser et al. https://doi.org/10.5281/zenodo.8311243
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