Improving the representation of fire disturbance in dynamic vegetation models by assimilating satellite data: a case study over the Arctic
Bibliographic record
Abstract
Abstract. Fire provides an impulsive and stochastic pathway for carbon from the terrestrial biosphere to enter the atmosphere. Despite fire emissions being of similar magnitude to net ecosystem exchange in many biomes, even the most complex dynamic vegetation models (DVMs) embedded in general circulation models contain poor representations of fire behaviour and dynamics, such as propagation and distribution of fire sizes. A model-independent methodology is developed which addresses this issue. Its focus is on the Arctic where fire is linked to permafrost dynamics and on occasion can release great amounts of carbon from carbon-rich organic soils. Connected-component labelling is used to identify individual fire events across Canada and Russia from daily, low-resolution burned area satellite products, and the obtained fire size probability distributions are validated against historical data. This allows the creation of a fire database holding information on area burned and temporal evolution of fires in space and time. A method of assimilating the statistical distribution of fire area into a DVM whilst maintaining its fire return interval is then described. The algorithm imposes a regional scale spatially dependent fire regime on a sub-scale spatially independent model; the fire regime is described by large-scale statistical distributions of fire intensity and spatial extent, and the temporal dynamics (fire return intervals) are determined locally. This permits DVMs to estimate many aspects of post-fire dynamics that cannot occur under their current representations of fire, as is illustrated by considering the modelled evolution of land cover, biomass and net ecosystem exchange after a fire.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".