Export of nutrients and suspended solids from major Arctic rivers and their response to permafrost degradation
Bibliographic record
Abstract
The rapid warming of the Arctic has led to permafrost degradation, accelerating the transport of terrestrial materials by rivers. The quantitative assessment of riverine nutrients and total suspended solids (TSS) flux is important to clarify the land–ocean connections in the Arctic. However, much is unknown about the estimates of these components from direct measurements in the Arctic rivers and the response of the components to permafrost degradation. Here, we report the results from the Arctic Great Rivers Observatory (Arctic-GRO) for the six major Arctic rivers (Yenisey, Lena, Ob’, Mackenzie, Yukon, and Kolyma) to investigate the riverine exports of TSS, total dissolved nitrogen (TDN), nitrate (NO3−), bicarbonate (HCO3−), total dissolved phosphorus (TDP), and phosphate (PO43−). The results showed that from 2004 to 2017, the annual TSS, TDN, and NO3− exports to the Arctic Ocean were approximately 106,026 Gg, 692 Gg, and 130 Gg, respectively, and the HCO3−, TDP, and PO43− exports were approximately 79,092 Gg, 32 Gg, and 18 Gg, respectively. There were remarkable variations in component concentrations and fluxes between seasons. More than 80% of the TDN, TDP, PO43−, and TSS exports mainly occurred in spring and summer, and a high HCO3− flux was recorded in summer, while a high NO3− flux in some rivers occurred in winter. The active layer thickness was significantly positively correlated with the annual TDN, NO3−, and HCO3− exports. In addition, the HCO3− flux of the six Arctic rivers increased by 247 Gg per year during 2004–2017. The positive relationship between the active layer thickness and river discharge indicates that permafrost degradation accelerated riverine carbonate, nitrogen, and phosphorus exports. This study demonstrates that riverine exports play an important role both in the Arctic terrestrial and marine ecosystems, and permafrost degradation will likely increase the riverine material exports to the ocean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".