Determining annual cryosphere storage contributions to streamflow using historical hydrometric records
Bibliographic record
Abstract
Abstract Alpine glaciers and perennial snow fields are important hydrologic elements in many mountain environments providing runoff during the late summer and during periods of drought. Because relatively long records of glacier mass–balance data are absent from many glacierized catchments, it remains unclear to what extent shrinking perennial snow and glaciers have affected runoff trends from these watersheds. Here, we employ a hydrograph separation technique that uses a double mass curve in an attempt to isolate changes in runoff due to glacier retreat and disappearance of perennial snow. The method is tested using hydrometric data from 20 glacierized and 16 nonglacierized catchments in the Columbia Basin of Canada. The resulting estimates on cryosphere storage contribution to streamflow were well correlated to other regional estimates on the basis of measurements as well as empirical and mechanistic models. Annual cryosphere runoff changed from +19 to −55% during the period 1975–2012, with an average decline of 26%. For August runoff, these changes ranged from +17 to −66%, with an average decrease of 24%. Reduction of cryosphere contributions to annual and late summer flows is expected to continue in the coming decades as glaciers and the perennial snow patches shrink. Our method to isolate changes in late summer cryospheric storage contributions can be used as a first order estimate on changes in glacier contributions to flow and may help researchers and water managers target watersheds for further analysis.
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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 | 0.002 | 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) | 0.001 | 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".