Using hydrological modelling to improve the Fire Weather Index system over tropical peatlands of peninsular Malaysia, Sumatra and Borneo
Bibliographic record
Abstract
Background Tropical peatland fires contribute to global carbon emissions and air pollution. Aims Enhance the globally used Canadian Fire Weather Index (FWI) system specifically over drained and undrained tropical peatlands in southeast Asia. Methodology We included simulated tropical peatland hydrology in the FWI, creating a new peatland-specific version of the FWI (FWIpeat). FWIpeat, the original FWI (FWIref) and the drought code (DC) were evaluated against satellite-based active fire occurrence from 2002 to 2018. Key results The DC shows superior performance in explaining fire occurrence over undrained tropical peatlands. Over drained peatlands, DC and FWIpeat show similar results, both outperforming FWIref. A comparison with an earlier study over boreal peatlands indicates much smaller improvements from FWIpeat for tropical peatlands, possibly due to a lower accuracy of the hydrological input data. Conclusions Our results highlight the importance of including information on deeper soil layers, i.e. the DC or groundwater table, when assessing fire danger. Implications Although this study offers a promising approach for operational fire management over tropical peatlands, we emphasise the need for further research to refine the hydrological input data and explore additional constraints from Earth observation data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".