Empirical modelling of surface fire rate of spread in Pinus koraiensis plantations – a laboratory simulation study

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.