Reexamining forest disturbance thresholds for managing cumulative hydrological impacts
Bibliographic record
Abstract
Abstract Forest disturbance thresholds, defined as those at or above which significant hydrological impacts are caused, are important guides to support forest and watershed management decisions for protecting hydrological functions and minimizing negative environmental impacts. Our literature review suggests that despite their significance, the research on this topic is surprisingly limited (<20 publications), where the paired watershed experiments (PWEs) primarily designed for detecting hydrological responses to forest cover change at the small watersheds were used to derive the thresholds. However, the widely used thresholds (e.g., 20%) based on the PWEs were identified from visual interpretation rather than determined from hydrological response curves, suffering from methodological shortcomings, and thus, may lack reliability. To advance this topic, we provided a robust technique (the modified double mass curve, MDMC) for quantitatively determining forest disturbance thresholds on annual mean flow as it allows the development of a hydrological response curve between cumulative hydrological effects and forest disturbance over time at the watershed scale. We applied this robust technique in eight large watersheds in British Columbia, Canada, and found that the forest disturbance thresholds ranged from 12 to 25%. We highly recommend that the widely used forest disturbance thresholds must be reexamined, and more studies are needed with rigorous methods and in consideration of other hydrological variables in forested watersheds.
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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 | 0.000 | 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 | 0.001 |
| 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 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".