Climate Change Increases the Severity and Duration of Soil Water Stress in the Temperate Forest of Eastern North America
Bibliographic record
Abstract
Under climate change, drought conditions are projected to intensify and soil water stress is identified as one of the primary drivers of the decline of forests. While there is strong evidence of such megadisturbance in semi-arid regions, large uncertainties remain in North American temperate forests and fine-scale assessments of future soil water stress are needed to guide adaptation decisions. The objectives of this study were to (i) assess the impact of climate change on the severity and duration of soil water stress in a temperate forest of eastern North America and (ii) identify environmental factors driving the spatial variability of soil water stress levels. We modeled current and future soil moisture at a 1 km resolution with the Canadian Land Surface Scheme (CLASS). Despite a slight increase in precipitation during the growing season, the severity (95 th percentile of absolute soil water potential) and duration (number of days where absolute soil water potential is greater than or equal to 9,000 hPa) of soil water stress were projected to increase on average by 1,680 hPa and 6.7 days in 80 years under RCP8.5, which correspond to a 33 and 158% increase compared to current levels. The largest increase in severity was projected to occur in areas currently experiencing short periods of soil water stress, while the largest increase in duration is rather likely to occur in areas already experiencing prolonged periods of soil water stress. Soil depth and, to a lesser extent, soil texture, were identified as the main controls of the spatial variability of projected changes in the severity and duration of soil water stress. Overall, these results highlight the need to disentangle impacts associated with an increase in the severity vs. in the duration of soil water stress to guide the management of temperate forests under climate change.
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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".