#arcticdem — Public Fediverse posts

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
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https://doi.org/10.1029/2026EF008578 <-- shared paper
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https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
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https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
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https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
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H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
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https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
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https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
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https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
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#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites