The role of catchment characteristics, discharge, and active- layer thaw in seasonal stream chemistry across 10 permafrost catchments
Bibliographic record
Abstract
High-latitude catchments are rapidly warming, leading to altered precipitation regimes, widespread permafrost degradation, and shifts in stream chemistry across major arctic rivers. In headwater catchments, seasonal deepening of flow paths due to active-layer thaw and declining discharge post-snowmelt drive variability in stream chemistry during the open-water period. In North American permafrost regions, activation of deeper mineral layers as the season progresses typically increases major-ion concentrations while decreasing dissolved organic carbon (DOC) concentrations. Despite decades of research on seasonality in stream chemistry within permafrost regions, the relative influence of active-layer thaw and discharge remains unresolved. Additionally, the role of permafrost extent and topography in driving seasonality of these solutes is poorly constrained. To address these knowledge gaps, we measured discharge and sampled major-ion and dissolved organic carbon (DOC) concentrations across 10 permafrost catchments in Yukon Territory, Canada. We analyzed concentration–discharge relationships using generalized additive models to resolve the distinct influence of discharge and seasonal active-layer thaw on stream chemistry and to identify the role of watershed characteristics on the magnitude and seasonality of solute concentrations. After accounting for seasonal variations in discharge, we found that both major ions and DOC were highly seasonal across all catchments, with DOC declining and major-ion concentrations increasing post-freshet. Seasonal variability in major-ion concentrations was primarily driven by active-layer thaw, whereas DOC seasonality was strongly influenced by flushing of soil organic carbon during freshet. While average major-ion concentrations were geologically mediated, greater permafrost extent led to enhanced seasonality in concentrations. Catchments with strong topographical gradients and thinner organic soils had higher specific discharge and lower DOC concentrations but greater relative seasonality. Our results highlight the important role catchment characteristics play in shaping the seasonal variations in and magnitude of solute concentrations in permafrost-underlain watersheds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".