Vanishing weekly cycles in American and Canadian hydropeaking rivers
Bibliographic record
Abstract
Sub-daily and weekly streamflow cycles termed hydropeaking are common features in regulated rivers across the globe. Weekly periodicity in flows arises from fluctuating hydropower demand and production tied to socioeconomic activity, typically with higher consumption during weekdays followed by reductions on weekends. In this presentation, we will introduce a novel weekly hydropeaking index to quantify the intensity and prevalence of weekly hydropeaking cycles at 368 sites across the United States of America (USA) and Canada over 1920-2019. Our results reveal a robust weekly hydropeaking signal exists at 1.3% of available sites starting in 1920 with a fraction that peaks at 16.7% of sites in 1963. Highly hydropeaking signals then diminish to only 3.3% of available sites in 2019, marking a 21st century declining pattern in hydropeaking intensity across parts of North America. Application of the Mann-Kendall Test reveals 95 locally significant declines in weekly hydropeaking intensity between 1980-2019. Our results can be attributed to diminishing differences between streamflow on weekends versus weekdays in regulated rivers across Canada and the USA. We will conclude the presentation with a discussion on how these findings may be tied to shifts in socioeconomic activity, alternative modes of electricity production, and legislative and policy changes impacting water management in regulated systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".