Characterizing runoff response to rainfall in permafrost catchments and its implications for hydrological and biogeochemical fluxes in a warming climate
Bibliographic record
Abstract
Abstract. Understanding how Arctic catchments respond to rainfall is critical for anticipating hydrological and biogeochemical effects of a warming climate. We use ensemble rainfall-runoff analysis (ERRA) to identify how runoff response to rainfall varies with meteorological, subsurface, and geomorphic conditions across three permafrost catchments: Upper Kuparuk (Alaska) and the Goose and Ptarmigan catchments (Cape Bounty, Canadian High Arctic). ERRA enables us to quantify event-scale runoff responses to rainfall using high-resolution, multi-year hydrometeorological datasets, and test how variations in rainfall intensity, thaw depth, antecedent wetness, and active layer detachments (ALDs) affect runoff behavior. Our results show that peak runoff response increases by more than five-fold in response to increases in antecedent streamflow (a proxy for antecedent moisture), and is also higher in summers with higher average precipitation. By contrast, warmer winters and springs, likely linked to deeper thaw and increased subsurface storage capacity, are associated with reduced runoff sensitivity to rainfall. Furthermore, a paired watershed comparison shows that streamflow and riverine fluxes of dissolved solids, suspended sediment, and particulate organic carbon are more readily mobilized by rainfall inputs when ALDs are present. Considered together, these findings highlight the difficulty in generalizing climate-driven runoff trends in permafrost regions subject to competing and interacting controls, such as precipitation intensity, storage capacity and permafrost stability. Our findings offer a more nuanced alternative to broad classifications of Arctic landscapes as “drying” or “wetting” under climate change.
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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 | 0.000 | 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".