- PAR ID:
- 10398483
- Date Published:
- Journal Name:
- Fire
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2571-6255
- Page Range / eLocation ID:
- 95
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4±5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.more » « less
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Abstract Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, direct attribution of fires to human activities may provide opportunities for near term smoke mitigation by focusing policy, management, and funding efforts on particular ignition sources. We analyze how fires associated with human ignitions (agricultural fires and human-initiated wildfires) impact fire particulate matter under 2.5
µ m (PM2.5) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these agricultural and human-initiated wildfires dominate fire PM2.5in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Smoke from these human levers also makes meaningful contributions to total PM2.5(∼5%–10% in 2003 and 2018). Across CONUS, these two human ignition processes account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM2.5. These findings indicate that a large portion of the smoke exposure and impacts in CONUS are from fires ignited by human activities with large mitigation potential that could be the focus of future management choices and policymaking. -
Abstract Climate shapes geographic and seasonal patterns in global fire activity by mediating vegetation composition, productivity, and desiccation in conjunction with land‐use and anthropogenic factors. Yet, the degree to which climate variability affects interannual variability in burned area across Earth is less understood. Two decades of satellite‐derived burned area records across forested and nonforested areas were used to examine global interannual climate–fire relationships at ecoregion scales. Measures of fuel aridity exhibited strong positive correlations with forested burned area, with weaker relationships in climatologically drier regions. By contrast, cumulative precipitation antecedent to the fire season exhibited positive correlations to nonforested burned area, with stronger relationships in climatologically drier regions. Climate variability explained roughly one‐third of the interannual variability in burned area across global ecoregions. These results highlight the importance of climate variability in enabling fire activity globally, but also identify regions where anthropogenic and other influences may facilitate weaker relationships. Empirical fire modeling efforts can complement process‐based global fire models to elucidate how fire activity is likely to change amidst complex interactions among climatic, vegetation, and human factors.
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