A vector-based dynamic coupling approach for optimized wind–fire spread modeling

Wildfire spread is strongly influenced by the dynamic interactions between wind and fire, yet most existing models either oversimplify this relationship with static coupling coefficients or rely on computationally expensive fire–atmosphere systems. This study develops a lightweight vector-based wildfire spread model that introduces a dynamic coupling coefficient (k) to adaptively balance wind and fire influences according to their relative intensities. The model integrates the Rothermel rate-of-spread equation with Huygens’ wavefront principle, capturing wind-fire interplay via adaptive vector coupling without the need for full atmospheric coupling. Validation was conducted using nine wildfire events in Fujian Province, China: six cases were used to compare dynamic versus fixed and uncoupled strategies, while three cases directly benchmarked the model against WRF-Fire. Results show that the dynamic k formulation improves simulation accuracy by up to 9.77% in Kappa and 8.86% in similarity coefficient compared with fixed-weight and uncoupled schemes, while also outperforming WRF-Fire in capturing local perimeter deformation. Moreover, the proposed model reduces runtime from several hours (WRF-Fire) to under one minute per case, achieving real-time applicability while maintaining physical interpretability. These findings highlight the model’s potential as an efficient and robust component in GIS-based decision support systems, offering practical value for wildfire forecasting, risk assessment, and emergency management in data- and time-constrained scenarios.