Water use by terrestrial ecosystems: temporal variability in rainforest and agricultural contributions to evapotranspiration in Mato Grosso, Brazil
Bibliographic record
Abstract
The state of Mato Grosso, Brazil, has experienced rapid land use changes from the expansion of rain-fed agriculture (primarily soybean and pasture). This study presents changes to evapotranspiration contributions from terrestrial ecosystems in Mato Grosso over the 2000–9 period. Instead of focusing on land use change to infer hydrologic change, in this paper we assess hydrologic changes using remote sensing, meteorological and agricultural production data to determine the rainforest, crop and pasture components of total evapotranspiration. Humid tropical rainforest evapotranspiration represented half of the state's total evapotranspiration in 2000 despite occupying only 40% of the total land area. Annual evapotranspiration fluxes from rainforest declined at a rate of 16.2 km3 y−1 (R2 = 0.82, p-value < 0.01) as a result of deforestation between 2000 and 2009, representing a 25% decline in rainforest evapotranspiration since 2000. By 2009, rainforest cover accounted for only 40% of total evapotranspiration. Over the same period, crop evapotranspiration doubled, but this increase was offset by a decline in pasture evapotranspiration. Pasture fluxes were at least five times larger than crop evapotranspiration fluxes in 2000–9, with increases spatially focused at the agricultural frontier. The results highlight the expanding appropriation of soil moisture stocks for use in Mato Grosso's rain-fed agroecosystems.
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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 | 0.002 | 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 | 0.001 |
| 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".