Quantifying how sources of uncertainty in combustible biomass propagate to prediction of wildland fire emissions
Maureen C. Kennedy
A D , Susan J. Prichard B , Donald McKenzie B and Nancy H. F. French C
+ Author Affiliations
- Author Affiliations
A School of Interdisciplinary Arts and Sciences, Division of Sciences and Mathematics, University of Washington, 1900 Commerce Street, Tacoma, WA 98402, USA.
B School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, WA 98195-2100, USA.
C Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
D Corresponding author. Email: mkenn@uw.edu
International Journal of Wildland Fire 29(9) 793-806 https://doi.org/10.1071/WF19160
Submitted: 4 October 2019 Accepted: 14 May 2020 Published: 16 June 2020
Abstract
Smoke emissions from wildland fires contribute to concentrations of atmospheric particulate matter and greenhouse gases, influencing public health and climate. Prediction of emissions is critical for smoke management to mitigate the effects on visibility and air quality. Models that predict emissions require estimates of the amount of combustible biomass. When measurements are unavailable, fuel maps may be used to define the inputs for models. Mapped products are based on averages that poorly represent the inherent variability of wildland fuels, but that variability is an important source of uncertainty in predicting emissions. We evaluated the sensitivity of emissions estimates to wildland fuel biomass variability using two models commonly used to predict emissions: Consume and the First Order Fire Effects Model (FOFEM). Flaming emissions were consistently most sensitive to litter loading (Sobol index 0.426–0.742). Smouldering emissions were most often sensitive to duff loading (Sobol 0.655–0.704) under the extreme environmental scenario. Under the moderate environmental scenario, FOFEM-predicted smouldering emissions were similarly sensitive to sound and rotten coarse woody debris (CWD) and duff fuel components (Sobol 0.193–0.376). High variability in loading propagated to wide prediction intervals for emissions. Direct measurements of litter, duff and coarse wood should be prioritised to reduce overall uncertainty.
Additional keywords: prediction interval, sensitivity analysis, uncertainty analysis.
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