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Publishing Language: Chinese

Study on a predictive model for carbon consumption of surface fuels based on the Byram model

Daotong GENG1,2, Guang YANG1,2( ), Jibin NING1,2, Shangjiong MA1,2
Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, Harbin 150040, China
Northern Forest Fire Management Key Laboratory of State Forestry and Grassland Administration, College of Forestry, Northeast Forestry University, Harbin 150040, China
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Abstract

Objective

The frequency and intensity of forest fires have increased considerably in recent years, driven largely by human activities and climate change. Vast quantities of carbon-containing substances released during forest fires exacerbate climate change through a positive feedback loop. Surface fires, the most common type of forest fire, account for more than half of the total emissions from forest fires. Accurate estimation of carbon emissions from surface fires is therefore essential for understanding their impact on carbon sinks and assessing the role of forest fires in driving climate change.

Methods

This study constructed indoor combustion beds to investigate the relationship between surface fire behavior and carbon consumption during the combustion of surface fuels in a planted Pinus koraiensis forest. Fuel beds were prepared with varying fuel loadings (0.4 kg·m-2, 0.8 kg·m-2, 1.2 kg·m-2, and 1.6 kg·m-2) and fuel moisture contents (5%, 10%, and 15%). Combustion experiments were conducted across 144 plots under varying slope conditions (0°, 10°, 20°, and 30°). The rate of spread, flame length, and fuel consumption during surface fire spread were measured, and fireline intensity was calculated using the Byram model. After the experiments, all combustion residues were collected, and their carbon content was determined using the dry burning method, enabling the calculation of carbon consumption from the fuel. A predictive model for fuel carbon consumption was developed by combining the fuel consumption parameters from the Byram fireline intensity equation with the observed fire behavior data. The model parameters were calibrated using the experimental results.

Results

All 144 combustion experiments produced low-intensity surface fires. Most variable interactions significantly influenced the four forest fire behavior characteristics, except for the interaction between fuel load and moisture content, which mainly affected fuel consumption. The four measured fire behavior characteristics increased with higher fuel loads. As fuel moisture content increased, spread rate, flame length, and fireline intensity decreased, although moisture content did not have a significant effect on fuel consumption. Under low slope conditions (0°~20°), the spread rate and fireline intensity increased gradually with the slope. However, when the slope exceeded 20°, these characteristics increased substantially. Flame length also increased with slope, while fuel consumption decreased. Initially, the model predicted surface fire fuel carbon consumption with limited accuracy, yielding R2 =0.59, MAE=0.22 kg·m-2, and MAPE=73.00%. After refitting the model parameters using data from the combustion tests, predictive accuracy improved considerably, with R2 =0.60, MAE=0.10 kg·m-2, and MAPE=30.99%.

Conclusions

The factors examined in this study generally align with the principles of forest burning, though under specific conditions, fire behavior characteristics may deviate from expected patterns. Refitting the model parameters using laboratory combustion data significantly enhanced the model's applicability and accuracy in predicting carbon consumption from surface fuel burning in Pinus koraiensis plantation forests.

CLC number: TP393.1 Document code: A Article ID: 1000-0054(2025)04-0664-08

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Journal of Tsinghua University (Science and Technology)
Pages 664-671

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Cite this article:
GENG D, YANG G, NING J, et al. Study on a predictive model for carbon consumption of surface fuels based on the Byram model. Journal of Tsinghua University (Science and Technology), 2025, 65(4): 664-671. https://doi.org/10.16511/j.cnki.qhdxxb.2024.27.056

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Received: 24 October 2024
Published: 15 April 2025
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