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Scientia Silvae Sinicae ›› 2025, Vol. 61 ›› Issue (4): 56-68.doi: 10.11707/j.1001-7488.LYKX20240473

• Special subject: Smart forestry • Previous Articles    

Simulation of Mountain Forest Fire Spread Based on Refined Wind Speed Field

Xu Youcheng1, Wan Xingyong3, Chen Bing4, Zhao Fengjun5, Liu Xiaodong6, Ye Jiangxia2   

  1. 1. School of Soil and Water Conservation，Southwest Forestry University　Kunming 650224;
    2. College of Forestry，Southwest Forestry University　Key Laboratory of Forest Disaster Warning and Control in Yunnan Province　Kunming 650224;
    3. Survey and Planning Research Center of Sichuan Geological Survey Research Institute　Chengdu 610017;
    4. Sichuan Provincial Institute of Forestry and Grassland Inventory and Planning　Chengdu 610081;
    5. Ecology and Nature Conservation Institute，Chinese Academy of Forestry　Key Laboratory of Forest Protection of National Forestry and Grassland Administration　Beijing 100091;
    6. School of Ecology and Nature Conservation, Beijing Forestry University　Beijing 100083
  • Received:2024-08-06 Revised:2024-09-20 Published:2025-04-21

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Abstract: Objective This study aims to explore impact of the fine wind speed field on the accuracy of forest fire spread, through simulating the fine wind speed based on the mountain microenvironment and its interaction mechanism with wind speed , so as to provide reference for the scientific decision-making of firefighting.Method The 2006 Anning“3.29”fire in Yunnan Province was targeted, the key meteorological driving factors affecting the spread of forest fires was analyzed, and GIS geographic simulation of wind velocity field was performed on a 30 m spatial scale. Based on the theory of cellular automata, the simulation of forest fire spread was realized by combining with Wang Zhengfei’s forest fire spread model modified by Mao Xianmin. The simulation accuracy was evaluated by comparing the meteorological simulation results of historical fire archives, and conventional inverse distance weight and Kriging interpolation methods. Result 1) The wind speed field driving factors analyzed with the mechanism model showed that wind speed field was positively correlated with elevation, while negatively correlated with slope, terrain relief, surface roughness, and surface temperature. The average wind speed field field at 30 m scale constructed using multiple linear regression analysis showed that the maximum wind speed field of the average wind speed field field around the fire occurrence area is 3.70 m·s-1, and the minimum is 0.28 m·s-1. 2) Combined with the topography and combustible data around the fire, the fire occurrence process from March 30 to April 3 was simulated. The day-by-day range in the fire history archives was used as a reference, and the accuracy validation results showed that the results based on the refined wind speed field simulation showed high simulation accuracy in different time periods, among which the simulation results on April 1 were optimal with Sørensen coefficient and coincidence accuracy of 0.83 and 93.28%, respectively. The simulation results on March 30 had relatively low accuracy with Sørensen coefficient and coincidence accuracy of 0.65 and 80%, respectively. Compared with the two sets of interpolated wind speed field field simulation results, the overlap accuracy and Sørensen coefficient based on the refined wind speed field simulation results were maximally improved by 6.67%, 11.67%, and 0.11, 0.08, respectively.Conclusion Compared with the conventional inverse distance weight and Kriging interpolation methods, the simulated 30 m-scale wind speed field data performs better in terms of spatial heterogeneity and continuity, and can reflect the spatial pattern of mountain wind speed field in a finer way, thus effectively improving the accuracy of forest fire spread simulation. In this study, the key driving factors affecting the spread of forest fires are refined by spatial modeling using GIS, taking into account the macro-meteorological conditions and micro-surface characteristics, and a more accurate simulation of forest fire spread is achieved.

Key words: forest fire spread, cellular automaton, spatio-temporal refinement, wind speed field

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