ASSESSING THE CUMULATIVE IMPACTS OF HYDROPOWER REGULATION ON THE FLOW CHARACTERISTICS OF A LARGE ATLANTIC SALMON RIVER SYSTEM
Bibliographic record
Abstract
ABSTRACT We assessed the influence of hydropower on the flow characteristics of the river Tay, one of the UK's most heavily regulated catchments and important Atlantic salmon fisheries. Hydropower developments in the mid‐20th century preceded flow data collection, resulting in knowledge gaps over how far regulated flows deviate from natural and how ecosystem functioning might have been impacted. We used 29 unregulated catchments in and around the Tay to assess the relationships between hydroclimatic conditions, landscape structure and the overall flow regime, as well as the annual, monthly and daily flow metrics. This allowed the identification of flow characteristics by using an integrated suite of regression approaches (nonlinear, MLR and random forests) to assess likely impacts at 11 regulated sites. The results showed that the impacts of regulation are highly variable in both space and time. Headwater sub‐catchments are most heavily affected, and water imports or exports as part of hydropower schemes can increase or decrease annual runoff by up to 50%, respectively. On a monthly basis, regulation primarily increased summer low flows; winter high flows increased in catchments affected by water imports and reduced where there was a net water export. At larger catchment scales, impacts were relatively small, as unregulated tributaries re‐naturalize the flows and the effects of intra‐basin transfers balance. Non‐stationarity in climate and water use in the catchment dictates that adaptive management of flows may be necessary to protect ecosystems services. Copyright © 2013 John Wiley & Sons, Ltd.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".