Snowpack disrupts relationship between young water fraction and isotope amplitude ratio; approximately one fifth of mountain streamflow less than one year old
Bibliographic record
Abstract
Abstract Previous “fraction of young water” (Fyw) estimates based on relative annual isotopic amplitudes in precipitation (Ap) and streamflow (As) produced low Fyw values in mountain catchments, which is contrary to extensive research that reports rapid water transmission in mountains. This study investigated this discrepancy by testing the effect of snow accumulation on the model that underpins the Fyw method. A Monte‐Carlo analysis of simulations for 20,000 randomly‐generated catchment model configurations used 10 years of precipitation inputs for the Upper Elbow River catchment in the Rocky Mountains (Alberta, Canada) to model discharge with and without snowpack storage of winter precipitation. Neither direct nor modified precipitation input produced a 1:1 relationship between As/Ap and Fyw, undermining the applicability of the original Fyw method in mountain watersheds with large seasonal snow accumulation. With snowpack‐modified input a given As/Ap ratio corresponds to a range of Fyw values, which can still provide semi‐quantitative information. In the small (435 km2) Elbow River catchment a Fyw range of 7–23% supports previous findings of rapid transmission in mountain catchments. Further analysis showed that the improved discharge prediction (Nash–Sutcliffe efficiency > 0.9) correlates with higher Fyw values and demonstrated that the interannual shifts in δ18O can be used to estimate of new water (<1 year) fraction in winter streamflow, and the estimate of 20% for the Elbow River further supports rapid transmission in mountain catchments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".