锦屏县森林火险气象等级预报研究

Research on the Meteorological Grade Forecast of Forest Fire Risk in Jinping County

摘要: 锦屏县作为全国重要杉木生产基地和集体林权综合改革试验区，森林资源丰富但火灾威胁严峻。本研究以锦屏县为对象，针对其复杂地形与气候特征，构建本地化森林火险气象等级预报模型，旨在提升火灾防控能力。通过收集近五年气象、森林资源及火灾历史数据，采用主成分分析和多元回归方法，建立森林火险指数模型，并划分五级火险等级。模型验证表明，2024年3月11日森林火灾案例中，预报结果与实际火情吻合度较高。研究进一步提出多部门协同的预警发布与响应机制，通过气象部门官网、短信平台等多渠道发布信息，并依据火险等级实施差异化防火措施。案例分析发现，模型应用显著降低了极端干旱期的火灾风险。然而，模型在极端天气与复杂地形的适应性、数据质量与部门协作方面仍需优化。建议融合人工智能技术提升预测精度，加强偏远地区气象监测，推动数据共享平台建设。本研究为南方集体林区火险预报提供了本地化实践参考，对生态保护与可持续发展具有重要意义。

Abstract: Jinping County is an important Chinese fir production base and a comprehensive reform pilot area for collective forest rights in China. It is rich in forest resources but faces severe fire threats. This study takes Jinping County as the research object. In view of its complex terrain and climate characteristics, a local forest fire danger weather rating prediction model was constructed to improve the fire prevention and control ability. By collecting meteorological, forest resource and fire history data in the past five years, using principal component analysis and multiple regression methods, a forest fire danger index model was established, and five levels of fire danger ratings were classified. The model verification shows that in the forest fire case on March 11, 2024, the prediction result was highly consistent with the actual fire situation. The study further proposed a multi-department collaborative early warning release and response mechanism, which releases information through multiple channels such as the official website of the meteorological department and the SMS platform, and implements differential fire prevention measures according to the fire danger rating. The case analysis shows that the application of the model significantly reduced the fire risk during the extreme drought period. However, the model still needs to be optimized in terms of adaptability to extreme weather and complex terrain, data quality and departmental cooperation. It is recommended to integrate artificial intelligence technology to improve prediction accuracy, strengthen meteorological monitoring in remote areas, and promote the construction of a data sharing platform. This study provides a local practical reference for forest fire danger prediction in southern collective forest areas, and is of great significance for ecological protection and sustainable development.