Greening mediates climate and CO2 induced water use efficiency effects on freshwater yield
Bibliographic record
Abstract
Forests are crucial for the production of high-quality freshwater resources. Complex interactions between climate change and forest processes can result in uncertainty in the availability of freshwater to downstream communities and the environment. Previous studies reported consistent increasing trends in global river discharge during the last century, which has been explained by either climate factors (usually called “hydrological intensification”) or suppressed transpiration due to CO2-induced stomatal closure. In this study, we study long-term changes in hydrological partitioning of precipitation between evapotranspiration and runoff generation (as mm per year) along a gradient of forested watershed along the eastern temperate forest biome. The precipitation is increasing at faster rates than runoff at most of these study catchments, which suggests long-term increases in evapotranspiration. These divergent trends in precipitation versus runoff rates are significantly correlated to long-term trends in NDVI and growing season length at the watershed scale, while climate variables cannot provided significant explanation. These findings suggest that the combined effect of increased temperatures and CO2 fertilization have led to increased leaf area and lengthened growing season, which may counteract the effect of the CO2-induced stomatal closure across the eastern US. This study emphasizes the importance of understanding vegetation responses to climate change to predict future flow regimes in forested watersheds.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".