Comparing streamflow analysis and remote sensing observations to assess climate change impact on permafrost degradation
Bibliographic record
Abstract
ABSTRACT Due to polar amplification of climate change, high latitudes are warming up twice as fast as the rest of the world. This warming leads to permafrost thawing, which induces greenhouse gases release, ground subsidence, and modifies surface and subsurface hydrologic regimes. Ground subsidence in turn affects local infrastructure stability. In this context and to better manage future infrastructures and water resources of northern regions, it is crucial to be able to evaluate the thawing rate of permafrost. In many Arctic zones, the frequency of environmental disturbances caused by permafrost thawing increases so rapidly that maintaining an accurate inventory of the state of permafrost at a regional scale represents a great challenge. Moreover, depending on the study area and the permafrost ice content, the thawing rate can vary from millimetres to decimeters per year. Another current challenge is the limited availability of temporal and spatial data on permafrost thawing rates. To address the above challenges, two indirect methods are used: (1) Arctic river streamflow analysis method and (2) Ground settlement analysis method via satellite image observation. Both methods use free-access data that have an exceptionally large temporal and spatial coverage capacity for such a poorly instrumented region. The first method analyses the recession events’ behavior of Arctic streams and relates those behaviors to changes in catchment-scale depth to permafrost that influences storage-discharge dynamics. This work differs from previous hydrological system analysis in northern systems in that it looks at long-term trends (>10 years) in recession intercept to assess permafrost dynamics, while other studies looked at recession characteristics within a season to assess active-layer dynamics. The second method analyses satellite images of the Arctic ground and associates surface elevation change to long-term permafrost degradation due to climate change. Both methods have already been tested through multiple local investigations and gave promising results. The recession flow analysis method has been applied to Yukon river basin, northern Sweden basins and Lena basin in Siberia, while the remote sensing analysis method has been tested on Baffin Island, Herschel Island in Canada, North Slope of Alaska and the Tibetan Plateau. However, no comparative study and no large-scale application have been conducted so far. Extending the analysis to hundreds of Arctic basins and comparing the resulting permafrost-thawing rate values from both methods constitute the innovative aspect of this project. KEY WORDS: climate change, permafrost thawing, storage-discharge dynamics, ground subsidence, satellite images
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".