Vegetation cover—another dominant factor in determining global water resources in forested regions
Bibliographic record
Abstract
Forested catchments provide critically important water resources. Due to dramatic global forest change over the past decades, the importance of including forest or vegetation change in the assessment of water resources under climate change has been highly recognized by Intergovernmental Panel on Climate Change (IPCC); however, this importance has not yet been examined quantitatively across the globe. Here, we used four remote sensing-based indices to represent changes in vegetation cover in forest-dominated regions, and then applied them to widely used models: the Fuh model and the Choudhury-Yang model to assess relative contributions of vegetation and climate change to annual runoff variations from 2000 to 2011 in forested landscape (forest coverage >30%) across the globe. Our simulations show that the global average variation in annual runoff due to change in vegetation cover is 30.7% ± 22.5% with the rest attributed to climate change. Large annual runoff variation in response to vegetation change is found in tropical and boreal forests due to greater forest losses. Our simulations also demonstrate both offsetting and additive effects of vegetation cover and climate in determining water resource change. We conclude that vegetation cover change must be included in any global models for assessing global water resource change under climate change in forest-dominant areas.
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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 | 0.000 | 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 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".