A quantitative assessment on the response of flow regimes to cumulative forest disturbances in large snow‐dominated watersheds in the interior of British Columbia, Canada
Bibliographic record
Abstract
Abstract Frequent forest disturbances can significantly affect flow regimes (magnitude, timing, frequency, duration, and variability) and associated aquatic ecological functions. However, the lack of a comprehensive understanding of the impacts of forest disturbances on flow regimes impedes the development of adaptive strategies for sustainable water resource management and ecosystem protection. In this study, an innovative methodology that combines the time series cross‐correlation analysis and paired‐year approach was developed and applied in two snow‐dominated large watersheds (the Baker Creek and Willow River watersheds in the central interior of British Columbia (BC)) to provide a quantitative assessment on how forest disturbances affect the components of flow regimes at a large watershed scale. The results showed that the magnitude, variability, and return period of high flows in the Baker Creek watershed were increased on average by 154.3%, 324.2%, and 11 years, respectively, and the timing of high flows was advanced by about 9 days during the disturbed periods. In contrast, forest disturbances caused only a 36.2% increment in the magnitude and a 4‐day increase in the duration of high flows in the Willow River watershed. The responses of low flow regimes were less sensitive than those of high flow regimes. Only the magnitude and variability of low flows in the Baker Creek watershed and the duration of low flows in the Willow River watershed were significantly altered by forest disturbances. These findings can have implications for water management and ecosystem protection in the context of growing anthropogenic disturbances. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 teacher head, 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".