Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes
Bibliographic record
Abstract
Land-use and land-cover change (LULCC) can dramatically affect the magnitude, seasonality and main drivers of evaporation (E) and transpiration (T), together as evapotranspiration (ET), with effects on overall ecosystem function, as well as both the hydrological cycle and climate system at multiple scales. Our understanding of tropical ecosystem responses to LULCC and global change processes is still limited, mainly due to a lack of ground-based observations that cover a variety of ecosystems, land-uses and land-covers. In this study, we used a network of nine eddy covariance flux towers installed in natural (forest, savanna, wetland) and managed systems (rainfed and irrigated cropland, pastureland) to explore how LULCC affects ET and its components in the Amazon, Cerrado and Pantanal biomes. At each site, tower-based ET measurements were partitioned into T and E to investigate how these fluxes varied between different land-uses and seasons. We found that ET, T and E decreased significantly during the dry season, except in Amazon forest ecosystems where T rates were maintained throughout the year. In contrast to Amazon forests, Cerrado and Pantanal ecosystems showed stronger stomatal control during the dry season. Cropland and pasture sites had lower ET and T compared to native vegetation in all biomes, but E was greater in Pantanal pasture when compared to Pantanal forest. The T fraction of ET was correlated with LAI and EVI, but relationships were weaker in Amazon forests. Our results highlight the importance of understanding the effects of LULCC on water fluxes in tropical ecosystems, and the implications for climate change mitigation policies and land management.
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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 | 0.001 |
| 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 | 0.000 | 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".