Arctic hydrology: a water balance study of Imnavait Creek and Kuparuk River watersheds, Alaska
Bibliographic record
Abstract
Increasing air temperatures are expected to change streamflow patterns in Arctic watersheds. However, the hydro-meteorological datasets necessary to evaluate these changes remain limited in the Arctic. We revised and updated a unique hydrologic dataset from Arctic Alaska with field measurements of continuous streamflow, precipitation, and evapotranspiration to identify changes in the hydrologic cycle. We analyzed water balance components and peak streamflow in Imnavait Creek, Upper Kuparuk River, and Kuparuk River during snowmelt and summer periods. Field measurements show that summer streamflow (mid-June to late-September) has increased in volume and magnitude. While snowmelt runoff still constitutes a sizable portion of annual runoff (28%–58%), the relative proportion of summer runoff is notably increasing. High interannual and spatial variability is observed for both rainfall and summer runoff, whereas comparatively lower interannual variability is observed for snowmelt runoff. This paper compares streamflow changes occurring at three watersheds of varying sizes and identifies reasons for observed changes using snow, rainfall, and evapotranspiration measurements. We also provide hydrologic analysis for design considerations at Arctic Alaska communities, the Dalton Highway, and the oil pipeline; as this infrastructure may be at risk of increased erosional damage due to more frequent and higher magnitude summer flow events.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".