Forest harvesting effects on the magnitude and frequency of peak flows can increase with return period
Bibliographic record
Abstract
Paired watershed studies have limited researchers wishing to disentangle road and harvesting effects on peak flows or to study management schemes other than the existing scenario. The outcomes of many paired watershed studies examining peak flows have also recently been challenged since only an approach that pairs peak flows by frequency can adequately evaluate the effects of harvesting on peak flows. This study takes advantage of a model that has been developed and extensively tested at a site containing a rich set of internal catchment process observations to examine the isolated and combined effects of roads and harvesting on the peak flow regime of a snow‐dominated catchment for return periods of up to 100 years. Contrary to the prevailing perception in forest hydrology, the effects of harvesting are found to increase with return period, which is attributable to the uniqueness of peak flow runoff generation processes in snow‐dominated catchments. Planned harvesting (50% harvest area) is found to have a significant effect (9%–25% over control) on peak flows with recurrence intervals ranging 10–100 years. Peak flow frequency increases after harvesting increase with return period, with the largest events (100 year) becoming 5–6.7 times more frequent, and medium‐sized events (10 year) becoming 1.7–2 times more frequent. Such changes may have substantial ecological, hydrological, and geomorphological consequences within the watershed and farther downstream. Study findings suggest that peak flow regimes are fairly tolerant to the current level of harvesting in this particular watershed but that further harvesting may affect this element significantly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".