Greening depletes soil moisture while enhancing atmospheric humidity in global drylands
Bibliographic record
Abstract
Abstract Investigating the bidirectional causal relationships between climate and vegetation is essential for understanding ecosystem dynamics in drylands under global warming. Previous studies have largely focused on the influence of climate on vegetation, with insufficient consideration given to feedback effects and time lags. Here, we use leaf area index (LAI), enhanced vegetation index (EVI), soil moisture (SM), and vapor pressure deficit (VPD) data for 1982‒2023 in global drylands to investigate bidirectional time-lagged causal effects between climate and vegetation. We quantify these causal effects and explore the soil-vegetation-atmospheric moisture coupling strength along climate and tree cover gradients. Our results demonstrate an overall positive effect of SM and a negative effect of VPD on vegetation greening (i.e. LAI and EVI) in global drylands, while the causal effect of VPD (23.7%‒31.6%) is more widespread than that of SM (12.6%‒12.7%). We also find dryland greening depletes SM and replenishes atmospheric moisture, albeit the latter to a lesser extent. The causal effect magnitudes of soil and atmospheric moisture on vegetation decreases with increasing tree cover, while the causal effect of SM on greening shows significantly steeper decline with increasing tree cover (p < 0.001). These findings contribute to a more comprehensive understanding of dryland vegetation dynamics under a changing climate.
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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) | 0.001 | 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".