Stomatal response to decreased relative humidity constrains the acceleration of terrestrial evapotranspiration
Bibliographic record
Abstract
Abstract Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979–2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".