Drivers of fire in the boreal forests: Data constrained design of a prognostic model of burned area for use in dynamic global vegetation models
Bibliographic record
Abstract
Boreal regions are an important component of the global carbon cycle because they host large stocks of aboveground and belowground carbon. Since boreal forest evolution is closely related to fire regimes, shifts in climate are likely to induce changes in ecosystems, potentially leading to a large release of carbon and other trace gases to the atmosphere. Prediction of the effect of this potential climate feedback on the Earth system is therefore important and requires the modeling of fire as a climate driven process in dynamic global vegetation models (DGVMs). Here, we develop a new data‐based prognostic model, for use in DGVMs, to estimate monthly burned area from four climate (precipitation, temperature, soil water content and relative humidity) and one human‐related (road density) predictors for boreal forest. The burned area model is a function of current climatic conditions and is thus responsive to climate change. Model parameters are estimated using a Markov Chain Monte Carlo method applied to on ground observations from the Canadian Large Fire Database. The model is validated against independent observations from three boreal regions: Canada, Alaska and Siberia. Provided realistic climate predictors, the model is able to reproduce the seasonality, intensity and interannual variability of burned area, as well as the location of fire events. In particular, the model simulates well the timing of burning events, with two thirds of the events predicted for the correct month and almost all the rest being predicted 1 month before or after the observed event. The predicted annual burned area is in the range of various current estimates. The estimated annual relative error (standard deviation) is twelve percent in a grid cell, which makes the model suitable to study quantitatively the evolution of burned area with climate.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".