Forest recovery and river discharge at the regional scale of Guangdong Province, China
Bibliographic record
Abstract
Information on how large‐scale forest changes affect water resources is important in China as country‐wide reforestation programs are being implemented and concerns have arisen over possible water reduction. In this study, water budget analysis and statistical methods were used to assess the effects of significant forest recovery on river discharge at Guangdong Province based on 50 years of data. We used realized water yield (RWY) as a balance term between the outflows from and inflows to the province to represent the river discharge produced solely in Guangdong Province. The relationship between forest recovery and RWY was inferred after quantitatively examining other contributing variables including precipitation, potential evapotranspiration, development of impervious areas, human water consumption, and reservoir constructions. We applied time series analysis to test the statistical relationship between forest recovery and RWYs at annual, wet season, and dry season intervals. Both approaches showed that large‐scale forest recovery did not cause significant water reduction over the past 50 years. This finding is contrary to the widely held perception of the trade‐off relationship between carbon (reforestation) and water. There were no significant trends in precipitation or in RWY annually and in the wet season, but there was a significant increase of RWY in the dry season over the past 50 years. It is estimated that forest recovery may play a positive role in redistributing water from the wet season to the dry season and, consequently, in increasing water yield in the dry season. The implication of those research findings for future reforestation programs and water resource protection is also discussed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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 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".