Fixing a snag in carbon emissions estimates from wildfires

Jeffrey E. Stenzel, Kristina J. Bartowitz, Melannie D. Hartman, James A. Lutz, Crystal A. Kolden, Alistair M.S. Smith, Beverly E. Law, Mark E. Swanson, Andrew J. Larson, William J. Parton, Tara W. Hudiburg

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Wildfire is an essential earth-system process, impacting ecosystem processes and the carbon cycle. Forest fires are becoming more frequent and severe, yet gaps exist in the modeling of fire on vegetation and carbon dynamics. Strategies for reducing carbon dioxide (CO2) emissions from wildfires include increasing tree harvest, largely based on the public assumption that fires burn live forests to the ground, despite observations indicating that less than 5% of mature tree biomass is actually consumed. This misconception is also reflected though excessive combustion of live trees in models. Here, we show that regional emissions estimates using widely implemented combustion coefficients are 59%–83% higher than emissions based on field observations. Using unique field datasets from before and after wildfires and an improved ecosystem model, we provide strong evidence that these large overestimates can be reduced by using realistic biomass combustion factors and by accurately quantifying biomass in standing dead trees that decompose over decades to centuries after fire (“snags”). Most model development focuses on area burned; our results reveal that accurately representing combustion is also essential for quantifying fire impacts on ecosystems. Using our improvements, we find that western US forest fires have emitted 851 ± 228 Tg CO2 (~half of alternative estimates) over the last 17 years, which is minor compared to 16,200 Tg CO2 from fossil fuels across the region.

Original languageEnglish
Pages (from-to)3985-3994
Number of pages10
JournalGlobal Change Biology
Volume25
Issue number11
DOIs
StatePublished - Nov 1 2019

Keywords

  • GHG emissions
  • carbon
  • climate change mitigation
  • fire
  • forests
  • modeling

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