Forest transpiration in Brazilian drylands: measurement and validation of a hydrological model
Bibliographic record
Abstract
Transpiration, particularly in dryland forests, plays a major role in the water cycle. The one-million km2 Caatinga Biome is a data-scarce region in the Brazilian Semiarid, where water availability is highly dependent on rainfall. This work aims to measure the natural Caatinga vegetation transpiration through sap flow monitoring (between Feb 2016 and Dec 2017) and to validate both a hydrological and a remote sensing-based model. Besides, we correlate transpiration with climatological variables, such as air temperature, relative humidity and net radiation during rainy and transition seasons. The hydrological model used was the Distributed Catchment Scale Model (DiCaSM), and the remote sensing-based model employed was the Penman-Monteith-Leuning (PML-V2). Measured transpiration in situ was on overall average 0.58 mm.day-1 for rainy and transition seasons. There is evidence that sap flow does not provide a good representation of transpiration in dry seasons: sap flow is high, whereas, according to the Literature, evapotranspiration (and, thus, transpiration) is negligible (< 0.01 mm.day-1) due to a very low soil water content. Transpiration estimated by DiCaSM presented a well-defined seasonal variability, with values close to zero during the driest months, in agreement to measured transpiration (Tukey p-value < 0.05). The remote sensing-based transpiration model also showed a well-defined seasonal pattern, but overestimated transpiration in almost the entire measurement period. During the rainy and the transition seasons positive correlations were observed, as expected, between i) transpiration and air temperature, ii) transpiration and air humidity and iii) transpiration and net radiation. Low air humidity and the release of water vapor through transpiration help to increase local air humidity. The spatial distribution of transpiration rates was also assessed. The study area did not depict a great spatial dynamic, and this fact may be explained by a low spatial resolution and the uniformity of the natural forest.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".