Robust ecological drought projections for drylands in the 21st century
Bibliographic record
Abstract
Dryland ecosystems may be especially vulnerable to expected 21st century increases in temperature and aridity because they are tightly controlled by moisture availability. However, climate impact assessments in drylands are difficult because ecological dynamics are dictated by drought conditions that are difficult to define and complex to estimate from climate conditions alone. In addition, precipitation projections vary substantially among climate models, enhancing variation in overall trajectories for aridity. Here, we constrain this uncertainty by utilizing an ecosystem water balance model to quantify drought conditions with recognized ecological importance, and by identifying changes in ecological drought conditions that are robust among climate models, defined here as when >90% of models agree in the direction of change. Despite limited evidence for robust changes in precipitation, changes in ecological drought are robust over large portions of drylands in the United States and Canada. Our results suggest strong regional differences in long-term drought trajectories, epitomized by chronic drought increases in southern areas, notably the Upper Gila Mountains and South-Central Semi-arid Prairies, and decreases in the north, particularly portions of the Temperate and West-Central Semi-arid Prairies. However, we also found that exposure to hot-dry stress is increasing faster than mean annual temperature over most of these drylands, and those increases are greatest in northern areas. Robust shifts in seasonal drought are most apparent during the cool season; when soil water availability is projected to increase in northern regions and decrease in southern regions. The implications of these robust drought trajectories for ecosystems will vary geographically, and these results provide useful insights about the impact of climate change on these dryland ecosystems. More broadly, this approach of identifying robust changes in ecological drought may be useful for other assessments of climate impacts in drylands and provide a more rigorous foundation for making long-term strategic resource management decisions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".