Forest cover change and water yield in large forested watersheds: A global synthetic assessment
Bibliographic record
Abstract
Abstract The effects of forest cover change on water yield have long been studied across the globe. Several reviews have summarized the impacts of forest change and water yield from the small and paired watershed experiments, but no any synthetic assessment has been conducted on the basis of studies of large watersheds (>1,000 km 2 ). We conducted a synthetic analysis on the basis of the studies from 162 large studied watersheds across the globe to explore how forest cover change affects annual water yield. Our first‐ever assessment confirms that deforestation increases annual water yield and reforestation decreases it, which is consistent with results from paired watershed experiments. More importantly, we found that forest cover and climate variability play a coequal role in annual water yield variations. The effects of forest cover change and climate variability on annual water yield variations can be additive or offsetting. Thus, their interactions can critically determine the magnitudes and directions of water yield changes. We also found that the hydrological sensitivities to forest cover change in smaller and dryer watersheds are higher than those in larger and wetter ones. The implications of these findings for sustainable water and watershed management are discussed in the context of future land cover and climate changes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".