Measurement and modelling of canopy water fluxes in representative forest stands and a matorral community of a small Sierra Madre Oriental watershed, Northeastern Mexico
Bibliographic record
Abstract
The quantitative importance of canopy water fluxes was evaluated within the four principal vegetation communities of a small watershed in the central Sierra Madre Oriental, northeastern Mexico. The partitioning of cumulative gross precipitation into throughfall during wet season periods in red oak (84.1 ± 2.3%) and cedar-white oak-ash (84.3 ± 3.4%) forests, as well as a matorral subinerme brush community (83.3 ± 3.3%) were not significantly different (α = 0.05). In addition, cumulative throughfall partitioning by a pine-oak forest canopy during wet + dry season periods was not significantly different (83.6 ± 1.8%), suggesting that the rainfall regime of the area may be more important than the vegetation cover present in determining the proportion of cumulative season-long gross precipitation partitioned into throughfall. Although stemflow was assumed to be a negligible component of the canopy water balance of the forest stands in this watershed, this flux was not inconsequential in the matorral subinerme community, accounting for 8.5 ± 1.9% of the cumulative gross precipitation input derived from 25 wet season events. Given the relatively large cumulative stemflow flux observed in the matorral subinerme community, cumulative canopy interception loss was found to be significantly smaller from this community than from the sub-temperate forest stands. The revised Gash analytical canopy water flux model (Valente et al., 1997) using within-stand derived model parameters was found to simulate cumulative canopy interception loss to within 2.9% of the observed flux in the pine-oak stand, and simulated cumulative throughfall to within 2.0% of the observed flux in the cedar-white oak-ash stand. However, poor model performance was found using predetermined parameter values, suggesting that the transferability of this model may be limited. The model was also found to perform poorly at the gross precipitation event scale and at the point spatial scale. Relationships developed between the mean during-event evaporation rate and red oak stand characteristics suggest the need for a multi-layer analytical model. The spatial nature of throughfall, especially in the red oak community, throughfall gauge requirements, and the importance of during-event evaporation losses from these Madrean plant communities are also evaluated.
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 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 | 0.000 | 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 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".