Double funnelling in a mature coastal British Columbia forest: spatial patterns of stemflow after infiltration
Bibliographic record
Abstract
Abstract Many studies have focused on the amount of stemflow in different forests and for different rainfall events, but few studies have focused on how stemflow intensity varies during events or the infiltration of stemflow into the soil. Stemflow may lead to higher water delivery rates at the base of the tree compared with throughfall over the same area and fast and deeper infiltration of this water along roots and other preferential flow pathways. In this study, stemflow amounts and intensities were measured and blue dye experiments were conducted in a mature coniferous forest in coastal British Columbia to examine double funnelling of stemflow. Stemflow accounted for only 1% of precipitation and increased linearly with event total precipitation. Funnelling ratios ranged from less than 1 to almost 20; smaller trees had larger funnelling ratios. Stemflow intensity generally was highest for periods with high‐intensity rainfall later in the event. The maximum stemflow intensities were higher than the maximum precipitation intensities. Dye tracer experiments showed that stemflow infiltrated primarily along roots and was found more frequently at depth than near the soil surface. Lateral flow of stemflow was observed above a dense clay layer for both the throughfall and stemflow experiments. Stemflow appeared to infiltrate deeper (122 cm) than throughfall (85 cm), but this difference was in part a result of site‐specific differences in maximum soil depth. However, the observed high stemflow intensities combined with preferential flow of stemflow may lead to enhanced subsurface stormflow. This suggests that even though stemflow is only a very minor component of the water balance, it may still significantly affect soil moisture, recharge, and runoff generation. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".