Changes in hydrological regime regulate POC export across permafrost-dominated Arctic River basins
Bibliographic record
Abstract
Warming-driven acceleration of hydrological processes is altering the carbon cycle in permafrost-dominated Arctic regions, yet the underlying drivers remain unclear. This study analyzes ArcticGRO data (2003–2021) from six major Arctic rivers (Ob, Yenisei, Lena, Kolyma, Yukon, and Mackenzie) to investigate trends and spatial–temporal variations in riverine particulate organic carbon (POC). The annual POC flux from these six rivers, estimated using the Load Estimator (LOADEST), averaged 2.78 Tg. Only the Lena River showed a notable annual decrease in POC flux (−3.9%/yr, p < 0.001) and concentration (−12%/yr, p < 0.001), while the Yukon River exhibited increasing streamflow (+0.98%/yr, p < 0.001) and POC flux (+3.2%/yr, p < 0.001). POC flux variations were primarily governed by streamflow and POC concentration, with higher concentrations in spring floods period and lower during winter. Spatial differences were linked to drainage density ( Dd ) and forest coverage ( Fc ). The Yukon River basin, with a higher Dd of 0.2 km/km 2 and lower Fc approximately 24%, exhibits the highest POC concentrations (2.3 mg/L). In contrast, the Yenisei River basin has the lowest POC concentration (∼0.4 mg/L), along with a relatively low drainage density ( Dd = 0.18 km/km 2 ) and a high forest cover ( Fc = 67%). Permafrost conditions constrained riverine POC export, with isotopic evidence indicating a shift from a carbon sink to a source, as POC carbon age increased by ∼ 200 to 1700 years (4%–68%) annually, peaking in winter (700–2500 years) after 2012. Rivers with lower permafrost coverage (e.g., Ob, Yenisei), exhibit higher winter POC fluxes contributions (10%–20%), while others contributed < 5%, suggesting the role of permafrost degradation in winter carbon export. This study emphasizes the need to assess climate-driven hydrological shifts and permafrost thaw in shaping Arctic land-to-ocean carbon fluxes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".