Roles of forest disturbance and climate variability on streamflow components in snow‐dominated paired watersheds at multiple temporal scales
Bibliographic record
Abstract
Abstract The paired watershed experimental (PWE) approach has long been used as an effective means to assess the impacts of forest change on hydrology in small watersheds (<100 km 2 ). Yet, the effects of climate variability on streamflow are not often assessed in PWE design. In this study, two sets of paired watersheds, (1) Camp and Greata Creeks and (2) 240 and 241 Creeks located in the Southern Interior of British Columbia, Canada, were selected to explore relative roles of forest disturbance and climate variability on streamflow components (i.e., baseflow and surface runoff) at different time scales. Our analyses showed that forest disturbance is positively related to annual streamflow components. However, this relationship is statistically insignificant since forest disturbance can either increase or decrease seasonal streamflow components, which eventually limited the positive effect on streamflow at the annual scale. Interestingly, we found that forest disturbance consistently decreased summer streamflow components in the two PWEs as forest disturbance can augment earlier and quicker snow‐melt processes and hence reduce soil moisture to maintain summer streamflow components. More importantly, this study revealed that climate variability played a more significant role than forest disturbance in both annual and seasonal streamflow components, for instance, climate variability can account for as much as 90% of summer streamflow components variation in Camp, suggesting the role of climate variability on streamflow should be highlighted in the traditional PWE approach to truly advance our understanding of the interactions of forest change, climate variability and water for sustainable water resource management.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".