Both Forest Harvesting and Hydropower Dams Yielded Negative Impact on Low Flow Regimes in the Zagunao River Watershed, Southwest China
Bibliographic record
Abstract
Forest harvesting and hydropower dams can significantly affect flow regimes (magnitude, timing, duration, frequency, and variability), resulting in changes in degraded aquatic ecosystems and unstable water supply. Despite numerous studies on the effects of forest harvesting on mean flows, the impact of forest harvesting on flow regimes has been less investigated. A great difficulty lies in separating the hydrological effect of forest harvesting from that of climate variability and other watershed disturbances such hydropower dams. In this study, the Upper Zagunao River watershed (2242 km2) was selected as an example to provide a quantitative assessment of the effects of forest harvesting and hydropower dams on low flow regimes. The key findings include: (1) Forest harvesting led to a significant reduction in the magnitude and return period of low flows, and a significant increment in the variability and duration of low flows; (2) the recovery of low flow regimes occurred 40 years after forest harvesting as forest recovery processed; and (3) hydropower dams caused significant impact on all components of low flow regimes, e.g., a reduction in the magnitude, return period, and timing of low flows, and an increment in the variability and duration of low flows. Our findings highlight the negative impact of both forest harvesting and hydropower dams on low flow regimes in the Upper Zagunao River watershed. A watershed management strategy for offsetting the negative effect of hydropower dams on low flow regimes by restoring hydrological functions of subalpine forests is highly recommended in subalpine watersheds of the Upper Yangtze River.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".