Rainfall partitioning through a mixed cedar swamp and associated C and N fluxes in Southern Ontario, Canada
Bibliographic record
Abstract
Abstract Partitioning of rainfall through a forest canopy into throughfall, stemflow, and canopy interception is a critical process in the water cycle, and the contact of precipitation with vegetated surfaces leads to increased delivery of solutes to the forest floor. This study investigates the rainfall partitioning over a growing season through a temperate, riparian, mixed coniferous‐deciduous cedar swamp, an ecosystem not well studied with respect to this process. Seasonal throughfall, stemflow, and interception were 69.2%, 1.5%, and 29.3% of recorded above‐canopy precipitation, respectively. Event throughfall ranged from a low of 31.5 ± 6.8% for a small 0.8‐mm event to a high of 82.9 ± 2.4% for a large 42.7‐mm event. Rain fluxes of at least 8 mm were needed to generate stemflow from all instrumented trees. Most trees had funnelling ratios <1.0, with an exponential decrease in funnelling ratio with increasing tree size. Despite this, stand‐scale funnelling ratios averaged 2.81 ± 1.73, indicating equivalent depth of water delivered across the swamp floor by stemflow was greater than incident precipitation. Throughfall dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) averaged 26.60 ± 2.96 and 2.02 ± 0.16 mg L −1 , respectively, which were ~11 and three times above‐canopy rain levels. Stemflow DOC averaged 73.33 ± 7.43 mg L −1 , 35 times higher than precipitation, and TDN was 4.45 ± 0.56 mg L −1 , 7.5 times higher than rain. Stemflow DOC concentration was highest from Populus balsamifera and TDN greatest from Thuja occidentalis trees. Although total below‐canopy flux of TDN increased with increasing event size, DOC flux was greatest for events 20–30 mm, suggesting a canopy storage threshold of DOC was readily diluted. In addition to documenting rainfall partitioning in a novel ecosystem, this study demonstrates the excess carbon and nitrogen delivered to riparian swamps, suggesting the assimilative capacity of these zones may be underestimated.
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) | 0.001 | 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".