Alteration of flow regimes caused by large‐scale forest disturbance: a case study from a large watershed in the interior of British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT Forest disturbance can greatly alter flow regimes and consequently the structures and functions of ecosystems. However, the impacts of forest disturbance on flow regimes have rarely been investigated in large watersheds. In this study, we used a large severely disturbed watershed, the Baker Creek watershed located in the central interior of British Columbia, Canada, to examine how forest disturbance altered the flow regimes and to discuss the possible ecological implications of these alterations. Equivalent clear‐cut area, an indicator combining all types of forest disturbances and accounting for hydrological recovery, was adopted to represent the cumulative forest disturbance levels over time at a watershed scale. Flow duration curves and time series cross‐correlation analysis were used to detect the statistical significance of relationships between flow regimes (magnitude, duration, timing, frequency and variability of high flows and low flows) and forest disturbance (clear‐cut area). The results showed that the magnitude of high flows was significantly increased and the timing of high flows was significantly advanced by forest disturbance. After forest disturbance, the occurrence of high flows with greater return periods became more frequent with increased variations. In addition, forest disturbance significantly increased the magnitude of low flows but with reduced variability. On average, high flows and low flows in the disturbed period were 31·4% and 16·0% greater than those in the reference period, respectively. Possible ecological implications of these hydrological alterations caused by forest disturbance were also discussed. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".