Understanding the Non‐Linear Response of Summer Evapotranspiration to Clouds in a Temperate Forest Under the Impact of Vegetation Water Content
Bibliographic record
Abstract
Abstract Understanding the cloud impact on forest evapotranspiration (ET) is crucial for studying the interaction of vegetation‐cloud‐atmosphere. Combining long‐term (2003–2010) satellite passive microwave observations and in‐situ measurements, a non‐linear response of canopy‐scale ET to cloud increase was found at a temperate forest in Northeast China. As cloud increased, an initial enhancement (4%–10%) in ET occurred under less cloudy sky, while a significant reduction (>20%) in ET occurred under more cloudy sky. The phenomenon existed under both high and low vegetation water content (VWC) indicated by satellite microwave emissivity difference vegetation index (EDVI). Analysis showed that this was the combined effect from the enhancement (5%–30%) in evaporative fraction (EF) and the reduction (5%–50%) in net radiation under cloud increase. Decoupling analysis based on coefficients (ρ) of path analysis model showed that enhanced EF (ρ > 0.61) rather than radiation (ρ < 0.47) dominated the ET enhancement under less cloudy sky, while the control of reduced radiation became stronger (ρ > 0.63) and could not be compensated by increased EF (ρ < 0.48) under more cloudy sky. EF enhancement under clouds was strongly correlated with the decline in canopy resistance (Rs) which was dominated by vapor pressure deficit (VPD). Higher VWC increased ET via reducing Rs and enlarging EF. This positive effect of VWC was more noticeable under less cloudy sky. Associated mechanisms could be related to the dynamic controls of plant physiology and environmental conditions induced by VWC and clouds. This study highlighted the dynamic effect of clouds and VWC on forest ET and improved our knowledge of vegetation‐cloud interactions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".