Climate warming causes intensification of the hydrological cycle resulting in changes to the vernal and autumnal windows in a northern temperate forest
Bibliographic record
Abstract
Climate warming is likely to lead to complex effects on northern forests of the temperate forest biome. We investigated whether rising temperatures altered the timing of snowmelt and snowpack accumulation or extended the forest growing season length in the Turkey Lakes Watershed in central Ontario. Archived satellite imagery was used to track changes in timing of snowpack loss/gain and canopy leaf on/off; the periods between these events were defined as the vernal (spring) and autumnal (fall) windows. We found only a slight extension of the growing season into the autumn period and no increase in the width of the vernal or autumnal windows, indicating that forest growth is not responding significantly to temperature increases during these windows. Archived time series of temperature, precipitation and discharge data for a nested set of catchments ranging in size from headwater (<10 ha) to regional (103 ha) catchments were used to track changes in the magnitude, timing and partitioning of precipitation into evapotranspiration and discharge. We found an intensification of hydrological cycling, with (1) a higher dryness index (PET/P) during the summer growing season and (2) earlier spring snowmelt discharges, and later more concentrated autumn storm discharges during the shoulder seasons. This intensification of the hydrological cycle during the summer growth season and the vernal and autumnal windows may not only limit opportunities for enhanced forest growth, but may be contributing to the recent observations of forest decline within this biome. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.001 | 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".