Seasonal variations in water use efficiency during the 2022 extreme drought in the Yangtze River Basin: effects of drought timing and severity
Bibliographic record
Abstract
Water use efficiency (WUE) is critical for understanding the carbon-water coupling of ecosystems, yet its response to extreme droughts remains debated. This study applied the CEVSA-ES model to simulate WUE across the Yangtze River Basin from 2020 to 2022, assessing the effects of extreme drought. The results reveal a significant 12.67% decrease in WUE during summer, while extreme and severe drought areas in autumn experienced an 11% increase in WUE. Seasonal WUE variations were linked to evapotranspiration (ET) and gross primary productivity (GPP): in summer, increased ET led to WUE decline, while reduced ET in areas affected by autumn drought resulted in WUE increase. In contrast, areas with moderate to light drought in autumn saw continued ET increase and WUE decline. These findings suggest that WUE responses to drought depend on drought duration and severity: WUE tends to decline in the early stages of drought and increase in later stages, with the transition timing influenced by drought severity. This study underscores the need to account for the spatiotemporal heterogeneity of drought and its complex regulatory mechanisms on WUE in predicting future climate change effects.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 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".