Water Storage and Release in Permafrost Catchments: Insights From Hydrometrics, End‐Member Mixing, and Water Age Characterization
Bibliographic record
Abstract
Abstract Seasonality strongly influences hydrological and chemical transport in permafrost‐underlain mountain catchments. In spring, snowmelt delivers large volumes of water, but frozen ground limits infiltration, causing shallow flow pathways to quickly route water to streams. As thaw progresses, storage capacity increases, flow paths deepen, and previously frozen water becomes mobile. Changing storage capacity and activation of deeper flow paths can alter the degree of storage turnover and transit time distributions of outgoing fluxes. Here we characterize the storage and release of water in two headwater catchments underlain by continuous permafrost located in Tombstone Territorial Park in Yukon, Canada. Our objectives were to: (a) evaluate the rate, timing, and magnitude of all hydrological fluxes, (b) utilize Bayesian mixing analysis to partition runoff into rain and snow contributions, and (c) apply the StorAge Selection (SAS) framework to characterize water age dynamics in both catchments. Results show ∼400 mm of precipitation entered the basins, ∼45% as snow, which melted over 4 weeks. Evapotranspiration (ET) was roughly equal to discharge, increasing throughout the summer. Mixing results suggest nearly all (>90%) of runoff during freshet was snow water in both catchments, indicating limited mixing with old water. In contrast, most of the rain left the basins as ET. The water balance and SAS framework highlight significant contributions from melting ground ice post‐freshet. Additionally, high flows resulted in a more uniform SAS function, indicating greater mixing of storage. ET was comprised of mainly young water, likely due to the high field capacity of organic soils and the shallow rooting of tundra vegetation.
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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 | 0.001 | 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) | 0.000 | 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".