Responses of evapotranspiration to droughts across global forests: a systematic assessment
Bibliographic record
Abstract
Forest evapotranspiration (ET) is one of the most important factors influencing the terrestrial hydrological cycle and is prone to being influenced by increasing drought events. This highlights the need to understand the interaction between global forest ET and drought. Consequently, we drew 710 sets of ecosystem-scale ET observations from 69 forest sites around the world and then systematically assessed the ET anomalies during droughts across the dominant forest and climate types. Overall, the response of forest ET to drought is non-monotonic. Under severe and extreme droughts with long durations, ET was reduced in most of forests in the world. We attributed the decreased forest ET to both restricted water supply and restricted water consumption by forests; however, lower magnitude and short-term droughts generally increased forest ET, and in some cases, ET even exceeded precipitation during droughts. We attributed this to the increased surface evaporation and the utilization of deep water by deep root systems. Specifically, we find that the positive anomaly of ET under droughts implied the drought paradox, which acts to accelerate the terrestrial hydrological cycle and further amplify the drought. This study as a global synthetic analysis of case studies on the site scale may have great potential for understanding the importance of the drought-modulated forest water cycle and the possibility of increasing drought stress with the effects of the drought paradox.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".