Evidence for strong bottom-up controls on fire severity during extreme events
Bibliographic record
Abstract
Abstract Background Record fire years in recent decades have challenged post-fire forest recovery in the western United States and beyond. To improve management responses, it is critical that we understand the conditions under which management can mitigate severe wildfire impacts, and when it cannot. Here, we evaluated the influence of top-down and bottom-up fire severity forcings on 17 wildfires occurring during two consecutive record-setting years in the eastern Cascade Mountains of Washington State. Despite much of the area having been burned after an extended period of fire exclusion, nearly one-third of the forested area burned at low severity. Results Using random forest modeling and Shapley local importance measures, we found that weather and fuels were both dominant drivers of fire severity, and past fuel treatments were successful at reducing severity—even during extreme fire progression days. First-entry fires were more typically driven by top-down climate and weather variables, while for reburns (i.e., overlapping fire footprints within the period of record), severity was largely mitigated by reduced fuels and a positive influence of topography (e.g., burning downslope). Likewise, reburns overall exhibited lower fire severity than first entry fires, suggesting strong negative feedbacks associated with past fire footprints. The normalized difference moisture index (NDMI)—an indicator of live fuel loading and moisture levels—was a leading predictor of fire severity for both first-entry fires and reburns. NDMI values < 0 (i.e., low biomass) were associated with reduced fire severity, while values > 0.25 (i.e., high biomass) were associated with increased severity. Forest management was effective across a variety of conditions, especially under low to moderate wind speeds (< 17 m·s −1 ), and where canopy base heights were ≥ 1.3 m. Conclusions Our findings support previous work demonstrating strong top-down weather and climate controls on fire severity along with bottom-up spatial controls of fuels and topography on patterns of fire severity. Local importance measures refined our understanding of the conditions under which bottom-up factors successfully mitigated fire severity. Our results indicate a clear role for fuels and fire management—including wildland fire use—to restore characteristic composition and structure to the landscape and to moderate fire severity.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".