Environmental Research Letters (Jan 2022)

Wildfire burn severity and emissions inventory: an example implementation over California

  • Qingqing Xu,
  • Anthony LeRoy Westerling,
  • Andrew Notohamiprodjo,
  • Christine Wiedinmyer,
  • Joshua J Picotte,
  • Sean A Parks,
  • Matthew D Hurteau,
  • Miriam E Marlier,
  • Crystal A Kolden,
  • Jonathan A Sam,
  • W Jonathan Baldwin,
  • Christiana Ade

DOI
https://doi.org/10.1088/1748-9326/ac80d0
Journal volume & issue
Vol. 17, no. 8
p. 085008

Abstract

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Wildfire severity is a key indicator of both direct ecosystem impacts and indirect emissions impacts that affect air quality, climate, and public health far beyond the spatial footprint of the flames. Comprehensive, accurate inventories of severity and emissions are essential for assessing these impacts and setting appropriate fire management and health care preparedness strategies, as is the ability to project emissions for future wildfires. The frequency of large wildfires and the magnitude of their impacts have increased in recent decades, fueling concerns about decreased air quality. To improve the availability of accurate fire severity and emissions estimates, we developed the wildfire burn severity and emissions inventory (WBSE). WBSE is a retrospective spatial burn severity and emissions inventory at 30 m resolution for event-based assessment and 500 m resolution for daily emissions calculation. We applied the WBSE framework to calculate burn severity and emissions for historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California, U.S., a substantially more extended period than existing inventories. We assigned the day of burning and daily emissions for each fire during 2002–2020. The framework described here can also be applied to estimate severity for smaller wildfires and can also be used to estimate emissions for fires simulated in California for future climate and land-use scenarios. The WBSE framework implemented in R and Google Earth Engine can provide quick estimates once a desired fire perimeter is available. The framework developed here could also easily be applied to other regions with user-modified vegetation, fuel data, and emission factors.

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