Impact of climate change on the fire weather in Indian forests
Bibliographic record
Abstract
Forest fires strongly depend on local weather conditions. Weather conditions conducive for occurrence and growth of fires is known as “fire weather”. This work investigates how climate change can affect the future fire weather in Indian forests using the Canadian Forest Fire Danger Rating System – Fire Weather Index (CFFDRS-FWI), a well-known fire danger assessment system. To drive this model, we used a high-resolution dynamically downscaled climate projection DSCESM for a baseline (2006-2015) and an end-century (2091-2100) period to compute the metric ‘Fire Weather Index (FWI)’. We divided the forest areas of the country into 5 zones based on climate and forest types viz., Himalayan (HIM), Northeast (NE), Central India (CEN), Deccan (DEC) and Western Ghats (WG) zones. Then, we developed thresholds for five fire weather danger classes using the baseline FWI in conjunction with observed fire count from MODIS active fire data. The baseline and future FWI, fire weather danger, Seasonal Severity Ratings (SSR) and characteristics of the fire weather season were compared to estimate the effect of climate change on forest fire danger.Results show that there is considerable heterogeneity in the baseline as well as future fire weather danger across, and even within, the different forest zones of India. Climate change is likely to have a strong effect on fire weather. Days exceeding the Very High FWI threshold are likely to increase by about 30-40 days by the end-century despite a modest increase of about 5% in annual FWI values. SSR analysis suggests a maximum increase in fire disturbances during the pre-monsoon months of March-April-May. About 55% of forest area over India will experience increased fire danger in this season. The least effect will be in the post monsoon season in September-October-November. The fire season is also expected to lengthen up to 59 days depending upon the forest type. The forests which are most likely to be affected by fire disturbances by end-century are moist deciduous and evergreen forests in the northern WG, mixed dry deciduous forests in central and southern CEN, Pine and Sal forests in the HIM and the scrub forests in the DEC zone. Climate change is unlikely to affect the fire weather danger in the NE.This study is one of the first attempts to quantify the effects of climate change on forest fire hazard in India. It has significant policy implications and can be valuable for fire management authorities for designing appropriate fire suppression and mitigating measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".