Background: Vegetation, terrain and weather properties vary greatly spatially and temporally, all of which influence fire behavior. Aims: This study aims to enhance the applicability and predictive accuracy of the Rothermel model for mixed fuel spread rates under varying wind conditions in northeast China. Methods: Laboratory fuel beds were constructed of surface fuels from Pinus koraiensis and Quercus mongolica plantations with controlled moisture content, wind speed and packing ratios. A total of 142 controlled flame spread experiments were conducted under varying wind conditions. Empirical rate of spread (ROS) data were then utilized to calibrate key parameters in the Rothermel model. Key results: Observed ROS values ranged from 1.38 to 11.09 m min−1. Direct application of the Rothermel model showed limited accuracy for mixed fuels. Targeted adjustment of the wind coefficient (ϕw) under flat terrain conditions yielded the most significant improvement. Further calibration of the reaction intensity (IR) parameter enhanced model performance substantially. Conclusion: The unmodified Rothermel model inadequately predicts ROS in mixed Pinus koraiensis–Quercus mongolica surface fuels. Parameter calibration using empirical combustion data significantly reduces prediction errors and improves model accuracy. Implications: This research offers a technique for applying the Rothermel model locally in China, thereby assisting in forest fire suppression and management efforts.

The accurate measurement of surface fire carbon emissions is critical for assessing their impact on carbon sinks and role in climate change. This study aims to investigate the relationships between surface fire behaviour characteristics and carbon consumption for Pinus koraiensis plantation forests and construct a carbon consumption prediction model. A total of 288 combustion experiments were conducted on a laboratory burning bed using varying fuel loads, moisture contents, and slope conditions, with measurements taken of surface fire behaviour characteristics and the carbon content of combustion ash. The Byram fireline intensity model was reintegrated to build a predictive model for fuel combustion carbon consumption, and the model parameters were adjusted based on the results of the combustion experiments. The direct use of the Byram fireline intensity model parameters predicted surface fire carbon consumption in Pinus koraiensis plantation forests with significant errors (R² = 0.75; MAE = 0.197 kg m⁻²; MRE = 66.76%). After the parameters were modified using the combustion experiment data, the new model yielded R² = 0.75, MAE = 0.087 kg m⁻², and MRE = 28.28%. This study significantly improved the accuracy of the new model in predicting the carbon consumption of surface fuel combustion in Pinus koraiensis plantation forests.

Background: In contrast to physical and quasi-empirical models, empirical models constructed for local fuel types provide significantly greater accuracy and applicability in predicting rate of spread (ROS). Aims: To present an empirical modelling methodology for specific regions and fuel types. Methods: We constructed experimental fuel beds with four levels of fuel loads, three levels of fuel moisture content (FMC) and eight slope gradients in a laboratory burning bed, resulting in 288 combustion experiments. We used data collected from these experiments to develop an empirical model specifically designed to predict the ROS of surface fires in Pinus koraiensis plantations. Key results: Model evaluation revealed that direct application of Rothermel’s model yielded substantial errors, with a pronounced tendency to overpredict ROS under most conditions. In contrast, the newly developed empirical model, which explicitly incorporates the effects of fuel bed structure, FMC and slope, demonstrated markedly improved accuracy while substantially reducing overprediction bias. Conclusion: Overall, prediction accuracy increased with higher ROS. Using local fuel laboratory combustion data to construct the empirical model resulted in excellent predictive performance. Implications: This study presents a robust methodology for developing predictive models of ROS for surface fires across various fuel types, providing valuable support for forest fire management and suppression efforts in northeastern China.