Spatial patterns of humidity, fuel moisture, and fire danger across a forested landscape
Bibliographic record
Abstract
Spatial variability in fuel moisture driven by changes in microclimate is an important bottom-up factor determining spatial wildfire behaviour, as fuel moisture impacts fire intensity, severity, and spread probability. However, few studies have examined how landscape scale patterns in near-surface microclimates impact fuel moisture patterns. This study quantified patterns of near-surface atmospheric conditions within a heterogeneous forested landscape, and determined how those patterns impact the spatial variability of fuel moisture and fire danger across the landscape. Observations across a forested landscape demonstrated that, in general, spatial variability in near-surface relative humidity and temperature was highest during dry, clear-sky conditions. However, daytime relative humidity was an exception, being relatively homogenous across the landscape and only weakly related to weather conditions. Canopy cover and above-canopy radiation load predicted a significant portion of the spatial patterns in relative humidity and temperature. Changes in canopy cover had the largest impact on near-surface conditions. Open sites saw higher relative humidity, on average, due to nocturnal longwave cooling. A novel fuel moisture model was presented that predicted between 76% and 93% of the variance in observations from independent sites or time periods, which is an improvement on a more complex model currently used operationally. This model was combined with meteorological observations to quantify spatial patterns in fuel moisture and potential fire danger across the landscape. Daytime fuel moisture and potential fire danger exhibited low spatial variability, regardless of weather conditions, and only 1-hour fuel moisture was related to canopy cover or radiation load. Fuel moisture and potential fire danger were more variable at night and that variability increased during cool, moist periods with low wind speeds. Patterns in fuel moisture and potential fire danger were dominated by differences in nocturnal longwave cooling due to changes in canopy cover. Open sites had lower daily mean potential fire danger. When fire danger was extrapolated over a larger study region, daytime conditions remained homogenous. Moreover, radiation load and canopy cover did not have a large enough direct influence on daytime fuel moisture to generate patches within the landscape that remain significantly wetter than the surrounding landscape.
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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".