Peak water in observed glacier-fed streamflow time series
Bibliographic record
Abstract
Glacier peak water describes the initial increasing and subsequent decreasing trend of glacier melt water as a response to global warming. The phenomenon might encourage excessive water use that cannot be sustained in the long-term. Knowing magnitude and time scale of its effect on streamflow trends and changes in partly glacierized catchments is therefore needed. This comparative regional study examined August streamflow records from 1976-2015 in the European Alps, Norway, Western Canada, and Alaska. It aimed to detect whether and when a peak was reached or passed and how strong decreasing post-peak streamflow trends were. A one-peak hypothesis could not be confirmed in many of the records and the variability of individual series' detected peaks and trends is large. Some common patterns in the timing of peaks and general trend directions in the records could be generalized. These suggest: a peak early in the period in Western Canada followed by mostly declining streamflow trends, pre-peak conditions in Alaska resulting in mostly positive streamflow trends, variable peaks in Norway and the Alps from the mid-1990s on with differences for low and highly glacierized catchments. Trends and peaks in climate-variability corrected August streamflow broadly related to phases of regional glacier retreat, but local variability is more complex. Only weak systematic deviations were found related to catchment characteristics. This multi-record and multi-region comparison of streamflow observations suggests that knowledge on a regional phenomenon will need to be complemented with local monitoring and modelling to provide useful information for water resources planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".