High variation in the surface extent of freshwater ponds creates dynamic Arctic tundra landscapes in the lowlands of Eastern Siberia
Bibliographic record
Abstract
Abstract Greenhouse gas emissions from Arctic tundra ponds and permafrost thaw provide important positive feedbacks to global warming. However, a high landscape heterogeneity and small size of ponds make it challenging to assess trends in surface water extent and associated carbon and energy fluxes, especially in the understudied Eastern Siberian tundra. Here, we show that surface water extent in these landscapes can be highly dynamic, shaped by small-scale pond processes. Using a time-series of aerial imagery at 12 cm resolution spanning eight years (2014–2021), we classified surface water at three sites in Kytalyk National Park and traced all 465 ponds (i.e. patches of surface water) larger than 1 m2. The total surface water extent at the sites varied between 102%-124% relative to the time-series mean, without significant trends in contrast to previous reports. Individual pond area fluctuated by 52% on average, and two thirds of ponds were present for less than six years. One-quarter of ponds showed evidence for thermokarst or vegetation colonisation as drivers of change, based on our high-resolution surface elevation models. These findings highlight that tundra ponds in Siberia can be highly dynamic in nature and stresses the need for improved change detection of very small surface water bodies in remote sensing analyses to better model carbon and energy fluxes in the tundra biome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".