Biomass burning fuel consumption rates: a field measurement database
Bibliographic record
Abstract
Abstract. Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burned. These fuel consumption (FC) rates depend on the biomass available to burn and the fraction of the biomass that is actually combusted, and can be combined with estimates of area burned to assess emissions. While burned area can be detected from space and estimates are becoming more reliable due to improved algorithms and sensors, FC rates are either modeled or taken selectively from the literature. We compiled the peer-reviewed literature on FC rates for various biomes and fuel categories to better understand FC rates and variability, and to provide a~database that can be used to constrain biogeochemical models with fire modules. We compiled in total 76 studies covering 10 biomes including savanna (15 studies, average FC of 4.6 t DM (dry matter) ha−1), tropical forest (n = 19, FC = 126), temperate forest (n = 11, FC = 93), boreal forest (n = 16, FC = 39), pasture (n = 6, FC = 28), crop residue (n = 4, FC = 6.5), chaparral (n = 2, FC = 32), tropical peatland (n = 4, FC = 314), boreal peatland (n = 2, FC = 42), and tundra (n = 1, FC = 40). Within biomes the regional variability in the number of measurements was sometimes large, with e.g. only 3 measurement locations in boreal Russia and 35 sites in North America. Substantial regional differences were found within the defined biomes: for example FC rates of temperate pine forests in the USA were 38% higher than Australian forests dominated by eucalypt trees. Besides showing the differences between biomes, FC estimates were also grouped into different fuel classes. Our results highlight the large variability in FC rates, not only between biomes but also within biomes and fuel classes. This implies that care should be taken with using averaged values, and our comparison with FC rates from GFED3 indicates that also modeling studies have difficulty in representing the dynamics governing FC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".