Observations of canopy storage capacity and wet canopy evaporation in a humid boreal forest
Bibliographic record
Abstract
Abstract Evaporation of intercepted rain by a canopy is an important component of evapotranspiration, particularly in the humid boreal forest, which is subject to frequent precipitation and where conifers have a large surface water storage capacity. Unfortunately, our knowledge of interception processes for this type of environment is limited by the many challenges associated with experimental monitoring of the canopy water balance. The objective of this study is to observe and estimate canopy storage capacity and wet canopy evaporation at the sub‐daily and seasonal time scales in a humid boreal forest. This study relies on field‐based estimates of rainfall interception and evapotranspiration partitioning at the Montmorency Forest, Québec, Canada (mean annual precipitation: 1600 mm, mean annual evapotranspiration: 550 mm), in two balsam fir‐white birch forest stands. Evapotranspiration was monitored using eddy covariance sensors and sap flow systems, whereas rainfall interception was measured using 12 sets of throughfall and six stemflow collectors randomly placed inside six 400‐m 2 plots. Changes in the amount of water stored on the canopy were also directly monitored using the stem compression method. The amount of water intercepted by the forest canopy was 11 ± 5% of the total rainfall during the snow‐free (5 July–18 October) measurement periods of 2017 and 2018. The maximum canopy storage estimated from rainfall interception measurements was on average 1.6 ± 0.7 mm, though a higher value was found using the stem compression method (2.2 ± 1.6 mm). Taking the average of the two forest stands studied, evaporation of intercepted water represented 21 ± 8% of evapotranspiration, while the contribution of transpiration and understory evapotranspiration was 36 ± 9% and 18 ± 8%. The observations of each of the evapotranspiration terms underestimated the total evapotranspiration observed, so that 26 ± 12% of it was not attributed. These results highlight the importance to account for the evaporation of rain intercepted by humid boreal forests in hydrological models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".