Observational Evidences in the Effects of Large‐Scale Reforestation on Precipitation
Bibliographic record
Abstract
Abstract Reforestation is widely recognized for its potentials to enhance downwind precipitation. However, this claim is largely based on climate models or estimates derived from global water budgets. Given the significance of this subject, scientific communities repeatedly call for observational evidences to validate this claim. In this study, we select China's Loess Plateau (LP) as a representative region for large‐scale reforestation, with vegetation cover increasing from 32% in 1999 to 64% in 2019, to statistically test its effects on downwind precipitation. Using satellite‐derived forest data and precipitation observations from 274 ground stations, combined with simulated moisture transport trajectories from FLEXPART‐WRF, we apply nonparametric trend tests and correlation analyses to assess the impact of reforestation on precipitation. The results reveal a significantly positive LAI‐precipitation relation in LP during the growing season, particularly in the northeastern part of the region, highlighting the role of reforestation in increasing regional precipitation. The non‐growing season has little impact on identifying the correlation. We further analyze the potential driving factors and mechanisms behind the observed patterns. No significant effects are detected beyond LP region. This study provides statistical evidences on the influence of reforestation on downwind precipitation. Some uncertainties are also discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".