Naturalizing the freezeup regimes of regulated rivers and exploring implications to spring ice‐jam flooding
Bibliographic record
Abstract
Abstract Naturalization of the flow hydrograph and ice regime is a key step in assessment of ecological and socioeconomic impacts of regulation across large portions of Europe, Asia and North America, where many rivers are dammed for hydropower generation. Building on previous naturalization of early‐freshet flows that influence the nature of breakup and jamming events, novel methodology is developed to estimate natural freezeup flows and thence determine associated water levels, also known to influence subsequent breakup events. Using reservoir inflows, the new methodology is applied to the lower portion of the regulated Peace River, Canada, which forms the northern boundary of the Peace‐Athabasca Delta (PAD), a Ramsar wetland of international importance that partially depends on spring ice‐jam flooding for recharge of its high‐elevation, or “perched” basins. The PAD provides habitat for numerous aquatic, terrestrial and avian species and is vital to the maintenance of indigenous culture and lifeways. Naturalized freezeup levels in the lower Peace River are shown to be nearly always lower than corresponding regulated values, with the difference averaging ~1.6 m. Consistent with known physics of river ice breakup processes, the present results suggest that ice‐jam flood frequency would likely have been greater under natural conditions. Though potentially adverse from the ecological standpoint, reduction of spring ice‐jam flooding can benefit riverside communities. Implications of the present results to and comparison with, other Canadian and international rivers are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".