Influencing factors and prediction models of surface fire spread rate of typical coniferous forest in Karst ecosystem of Guizhou province

Objective The Karst ecosystem is relatively vulnerable to forest fire due to interlaced agriculture and forestry and the fire source, which poses serious impact on the restoration of Karst ecosystem. Thus, investigating the spreading rate of forest fire under no-wind and zero-slope conditions is of great significance for the restoration of vegetation and forest fire prevention in this region.

Method Taking the surface fuels under three typical coniferous forests in the Karst ecosystem as case study, we quantified the spreading rate of forest fire with coniferous beds in different moisture content, height and load under no-wind and zero-slope conditions through 188 ignition experiments.

Result (1) Under the same conditions, there was no significant difference in forest fire spreading rate between Pinus yunnanensis and Pinus armandii, but both rates were significantly higher than that of Pinus massoniana (P < 0.001). (2) The moisture content of the fuelbed could significantly retard the spreading rate of the all fuelbed, while the height of the fuelbed has a significant promoting effect, and the loading has the promoting effect on the spreading rate of the needle bed of Pinus yunnanensis and Pinus armandii. (3) Fitting with additive model, the mean absolute error of spreading rate of fuelbed of Pinus massoniana, Pinus yunnanensis and Pinus armandii was 0.013, 0.029 and 0.020 m/min respectively, and the mean absolute error of the multiplier model of the forest fire spreading rate of fuelbed of Pinus massoniana, Pinus yunnanensis and Pinus armandii was 0.014, 0.023, 0.018 m/min, respectively. Besides, both prediction models could explain over 75% of the dynamic of forest fire spreading rate. (4) The mean absolute error and mean relative error of the extrapolation error of additive model are 0.108 m/min and 42.50%, respectively. The extrapolation effect of the multiplicative model is better than that of the additive model, with the mean absolute error and relative error of 0.086 m/min and 28.20%m respectively.

Conclusion This study reveled the influence of fulebed characteristics on the forest fire spreading rate in Karst ecosystem. The spreading rate of the three coniferous beds is high under the experiment condition, and it is possible to spread and cause disasters. Based on prediction accuracy and extrapolation effect, it is suggested to select multiple model to simulate forest fire spreading rate. These findings provide valuable understandings and insights for forest fire prevention in Karst ecosystem.