Do the hydrological responses to forest disturbances in large watersheds vary along climatic gradients in the interior of British Columbia, Canada?
Bibliographic record
Abstract
Abstract Hydrological responses to forest disturbances are highly variable among watersheds. Climatic factors including water and energy are major drivers that determine the hydrological responses to forest disturbances. Although there are a number of large watershed studies on identifying the role of climate in the hydrological response to forest disturbances (e.g., logging, insect infestation, and fire), they are mainly concentrated on the precipitation effect. Given that climatic factors including both water and energy interact dynamically with hydrology and forest, and accordingly with forest–water relationships, there is a need for understanding the joint controls of water and energy on hydrological responses to forest changes by use of an integrated climatic index. In this study, 6 large watersheds along climatic gradients (Willow, Cottonwood, Baker, Moffat, Tulameen, and Ashnola) in the interior of British Columbia (BC), Canada, were selected for investigating the effect of climate on hydrological responses to forest disturbances at a large watershed scale by using modified double mass curves and statistical analysis (time series cross‐correlation, linear regression, and Mann–Whitney U test). Key results include the following: (a) in watersheds with a cumulative equivalent clear‐cut area of over 30% (Willow, Baker, Moffat, and Tulameen), mean annual flows were significantly increased by about 21–60 mm due to cumulated forest disturbances and (b) mean annual flow response to forest disturbances varied along climatic gradient. The amount of mean annual flow increase due to forest disturbances in energy‐limited watersheds Willow and Tulameen was as 3 times as that in water‐limited watersheds Baker and Moffat. Similarly, mean annual flow changes due to forest disturbances in wetter years were greater than those in drier years as suggested by results from the study watersheds. These findings highlight the need to develop different strategies for forests management in water‐limited and energy‐limited watersheds to minimize or adapt hydrological changes due to forest disturbances.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".