Vegetation change impacts on moisture recycling are closely linked to plant water uptake strategies in the Loess-covered region in China
Why this work is in the frame
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Bibliographic record
Abstract
Understanding how vegetation change affects moisture recycling is crucial for comprehending land–atmosphere coupling. Constrained by moisture and isotope mass balances, we quantified the contributions of evaporation ( f E ) and transpiration moisture ( f T ) to precipitation across different types of vegetation (grassland, shrubland, and forestland), and elucidated the influence of vegetation change on moisture recycling ( f post - f pre ). Furthermore, we assessed the mechanisms behind the changes in moisture recycling from the perspective of plant water uptake. The mean moisture recycling rate ( f ) in the study region during the rainy period was found to be 21 %, contributing 48 mm of local precipitation. Notably, transpiration was the dominant contributor to moisture recycling ( f T / f = 67 %). Following the transition from shallow- to deep-rooted plants, f E decreased while f T increased, with the changes accounting for 17 % and 50 % of mean recycling rate, respectively. Moisture recycling rates were significantly influenced by plant water uptake strategy. The shallow-rooted plants primarily used shallow soil water (0–0.8 m, 63 %), with minimal dependence on lower-deep (2–3 m) and deep (>3 m) soil water, which together accounted for only 13 %. Conversely, the deep-rooted plants relied less on shallow soil water (37 %) and a significantly higher reliance on lower-deep and deep soil water (2–3 m and > 3 m; combined 42 %), particularly during dry spells. Moreover, the increasing contribution of deep soil water at the monthly scale aligned with that of f T . Thus, the transition in vegetation from shallow- to deep-rooted plants increased moisture recycling by using deep soil water for transpiration. This study improves the understanding of hydrological dynamics in the soil–plant–atmosphere continuum (SPAC).
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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 | 0.000 | 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 it