Will land use land cover change drive atmospheric conditions to become more conducive to wildfires in the United States?
Bibliographic record
Abstract
Abstract The increase in wildfire risk in the United States in recent decades has been linked to rapid growth of the wildland‐urban interface and to changing climate. While there have been numerous studies on wildfires and climate change, few have separately assessed the impact of climate response to land‐use‐land‐cover change (LULCC) on wildfires. In this study, we analyse two 10‐year regional climate simulations driven by the current (2011) and future (2100) land‐use‐land‐cover patterns to assess modifications by the projected LULCC to the frequency and severity of fire‐prone atmospheric conditions described by two fire weather indices, the Canadian Forest Fire Weather Index and the Hot‐Dry‐Windy Index. The simulation corresponding to future land‐use‐land‐cover pattern yields higher surface temperature and vapour pressure deficit and lower precipitation compared to the simulation with the current pattern in areas where urbanized landscapes replace forests and grasslands, such as along the Piedmont and outside the Chicagoland region, while in areas where croplands replace forests, such as the southeast Coastal Plains, the results are reversed. These changes to local and regional atmospheric conditions lead to longer fire seasons and more extreme fire‐weather conditions in much of the eastern United States, specifically in the Southeast and Ohio River Valley where significant urban expansion is projected by the end of the century. Whereas in Southern California where some highly flammable shrublands will be replaced by urban or crop lands, fire‐prone atmospheric conditions are likely to be less frequent and less extreme in the future. However, much of California moves towards a year‐round fire season under the projected LULCC. The results suggest that by altering atmospheric conditions, LULCC may play an important role in determining fire regime, but the effects are highly heterogeneous and regionalized.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".