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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ENVIRONMENTAL INFLUENCES ON LARGE DAILY WILDFIRE GROWTH IN CALIFORNIA

Authors: Holt Hanley; Dr. Patrick Brown;

ENVIRONMENTAL INFLUENCES ON LARGE DAILY WILDFIRE GROWTH IN CALIFORNIA

Abstract

Wildfires have become a major environmental, social, and economic problem in California. The consequences can be especially detrimental when they exhibit behavior like very large daily growth (an individual fire burning >10,000 acres over a 24-hour period). Environmental conditions influencing the risk of large daily growth include weather variables such as temperature, wind, relative humidity, and precipitation; fuel variables such as type, loading, availability, and moisture content; as well as topographic variables such as slope, aspect, elevation, and shape. However, there remains great uncertainty in the importance of these variables relative to each other and the existence of any threshold values in these variables. Our study applied random forest modeling using multivariate and high spatiotemporal data for 16,013 wildfire days in California from 2003 to 2020 to determine feature importance for the task of predicting whether a fire would burn >10,000 acres over a 24-hour period. Shapely Additive Explanations indicate that 100-hour dead fuel moisture, maximum daily air temperature, and soil moisture provide the highest predictive power for large daily growth. Additionally, our study identifies thresholds where the probability of large daily growth significantly increases. These thresholds include a 100-hour dead fuel moisture value of <10%, a maximum air temperature of >75 F, and a 0-10 cm soil moisture of <12%. Finally, we establish the number of days per year that these thresholds are being crossed has increased substantially over the last four decades.

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Keywords

large daily wildfire growth, fire behavior triangle, climate change, machine learning, random forest

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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