The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
利用融合火点排放源、人为源和生物源的WRF-Chem(Weather Research and Forecasting Model coupled with Chemistry)模式,模拟2015年9月30日08:00(北京时间)起的72 h发生在淮河流域的一次农作物秸秆大面积露天焚烧过程,研究了农作物秸...利用融合火点排放源、人为源和生物源的WRF-Chem(Weather Research and Forecasting Model coupled with Chemistry)模式,模拟2015年9月30日08:00(北京时间)起的72 h发生在淮河流域的一次农作物秸秆大面积露天焚烧过程,研究了农作物秸秆焚烧释放的气态污染物和颗粒物对区域城市空气质量的影响。通过有无火点两组试验分析了此次秸秆焚烧对流域内河南、山东、江苏和安徽四省83座城市CO、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)、PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)和O3浓度的定量影响,结果表明:(1)融合NCAR-FINN(Fire Inventory from NCAR)火点排放资料的WRF-Chem模式较好地再现了此次秸秆焚烧及火点烟羽扩散过程。同时结合EDGAR-HTAP(Emission Database for Global Atmospheric Research on Hemispheric Transport of Air Pollution)人为源和MEGAN(Model of Emission of Gases and Aerosols from Nature)生物源的WRF-FIRE(考虑火点排放试验)对流域内城市大气污染物的模拟效果较为理想,尤其对秸秆焚烧释放的污染物CO、PM10和PM2.5和产生的二次污染物O3浓度的模拟。(2)秸秆焚烧所释放的污染物造成流域内城市一次污染物CO、PM10和PM2.5浓度的增加,火点中心和下风向城市增幅最为明显,最大小时浓度增幅达到3倍标准差。气态污染物CO和相比PM10粒径更小的PM2.5可随风扩散至更远的地区,对城市浓度影响更大。(3)此外,秸秆焚烧也使得火点中心城市和下风向城市二次污染物O3浓度增加,但小时浓度增幅极值区分布在火点下风向烟羽末端太阳光照充足的地区,最大小时浓度增幅接近3倍标准差。秸秆焚烧对区域城市空气质量的影响存在明显的空间分布差异且对城市各大气污染成分的影响也不相同。展开更多
风场是影响林火蔓延行为最关键的因素之一。许多林火发生在地形复杂多变的山区,会产生复杂的局部风场。为探究动态风场对林火蔓延预测的影响,以“3·30”西昌泸山森林火灾为研究对象,利用WRF(weather research and forecasting)模...风场是影响林火蔓延行为最关键的因素之一。许多林火发生在地形复杂多变的山区,会产生复杂的局部风场。为探究动态风场对林火蔓延预测的影响,以“3·30”西昌泸山森林火灾为研究对象,利用WRF(weather research and forecasting)模式模拟动态风场,并与林火蔓延模型耦合。分别将模拟风场和均一风场分别输入林火蔓延模型,基于林火蔓延过程中实际火场范围数据,分析动态风场对林火蔓延模型预测结果的影响。结果表明,耦合WRF模式和林火蔓延模型考虑了分辨率更高、且更为准确的局部风场,能够实现对森林火灾蔓延的动态模拟。预测得到的火场范围和均一风场相比,与实际范围具有更高的相似度。展开更多
利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事...利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事件及其天气形势、边界层气象特征、输送路径、PM2.5及其化学成分浓度变化等特征,以及秸秆燃烧对华北和北京地区细颗粒物浓度和地面短波辐射的影响。与观测资料的对比结果显示,模式可以很好地模拟北京地区地面气象要素和PM2.5质量浓度,考虑秸秆燃烧排放源可以明显改进北京PM2.5浓度模拟的准确性,但在重度污染情况下,模式总体上低估气溶胶光学厚度和高估地面短波辐射。10月7~11日北京地区重霾事件主要是不利气象条件下人为污染物累积和区域输送造成,也受到华北地区南部秸秆燃烧的影响。河南北部、河北南部和山东西部大面积秸秆燃烧释放的气态污染物和颗粒物在南风的作用下输送至北京,秸秆燃烧对北京地区地面PM2.5、有机碳(OC)、硝酸盐、铵盐、硫酸盐和黑碳(BC)的平均贡献率分别为24.6%、36.8%、23.2%、22.6%、7.1%和19.8%,秸秆燃烧产生的气溶胶可以导致北京地面平均短波辐射最大减小超过20 W m^-2,约占总气溶胶导致地表短波辐射变化的24%。展开更多
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文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
文摘利用融合火点排放源、人为源和生物源的WRF-Chem(Weather Research and Forecasting Model coupled with Chemistry)模式,模拟2015年9月30日08:00(北京时间)起的72 h发生在淮河流域的一次农作物秸秆大面积露天焚烧过程,研究了农作物秸秆焚烧释放的气态污染物和颗粒物对区域城市空气质量的影响。通过有无火点两组试验分析了此次秸秆焚烧对流域内河南、山东、江苏和安徽四省83座城市CO、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)、PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)和O3浓度的定量影响,结果表明:(1)融合NCAR-FINN(Fire Inventory from NCAR)火点排放资料的WRF-Chem模式较好地再现了此次秸秆焚烧及火点烟羽扩散过程。同时结合EDGAR-HTAP(Emission Database for Global Atmospheric Research on Hemispheric Transport of Air Pollution)人为源和MEGAN(Model of Emission of Gases and Aerosols from Nature)生物源的WRF-FIRE(考虑火点排放试验)对流域内城市大气污染物的模拟效果较为理想,尤其对秸秆焚烧释放的污染物CO、PM10和PM2.5和产生的二次污染物O3浓度的模拟。(2)秸秆焚烧所释放的污染物造成流域内城市一次污染物CO、PM10和PM2.5浓度的增加,火点中心和下风向城市增幅最为明显,最大小时浓度增幅达到3倍标准差。气态污染物CO和相比PM10粒径更小的PM2.5可随风扩散至更远的地区,对城市浓度影响更大。(3)此外,秸秆焚烧也使得火点中心城市和下风向城市二次污染物O3浓度增加,但小时浓度增幅极值区分布在火点下风向烟羽末端太阳光照充足的地区,最大小时浓度增幅接近3倍标准差。秸秆焚烧对区域城市空气质量的影响存在明显的空间分布差异且对城市各大气污染成分的影响也不相同。
文摘风场是影响林火蔓延行为最关键的因素之一。许多林火发生在地形复杂多变的山区,会产生复杂的局部风场。为探究动态风场对林火蔓延预测的影响,以“3·30”西昌泸山森林火灾为研究对象,利用WRF(weather research and forecasting)模式模拟动态风场,并与林火蔓延模型耦合。分别将模拟风场和均一风场分别输入林火蔓延模型,基于林火蔓延过程中实际火场范围数据,分析动态风场对林火蔓延模型预测结果的影响。结果表明,耦合WRF模式和林火蔓延模型考虑了分辨率更高、且更为准确的局部风场,能够实现对森林火灾蔓延的动态模拟。预测得到的火场范围和均一风场相比,与实际范围具有更高的相似度。
文摘利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事件及其天气形势、边界层气象特征、输送路径、PM2.5及其化学成分浓度变化等特征,以及秸秆燃烧对华北和北京地区细颗粒物浓度和地面短波辐射的影响。与观测资料的对比结果显示,模式可以很好地模拟北京地区地面气象要素和PM2.5质量浓度,考虑秸秆燃烧排放源可以明显改进北京PM2.5浓度模拟的准确性,但在重度污染情况下,模式总体上低估气溶胶光学厚度和高估地面短波辐射。10月7~11日北京地区重霾事件主要是不利气象条件下人为污染物累积和区域输送造成,也受到华北地区南部秸秆燃烧的影响。河南北部、河北南部和山东西部大面积秸秆燃烧释放的气态污染物和颗粒物在南风的作用下输送至北京,秸秆燃烧对北京地区地面PM2.5、有机碳(OC)、硝酸盐、铵盐、硫酸盐和黑碳(BC)的平均贡献率分别为24.6%、36.8%、23.2%、22.6%、7.1%和19.8%,秸秆燃烧产生的气溶胶可以导致北京地面平均短波辐射最大减小超过20 W m^-2,约占总气溶胶导致地表短波辐射变化的24%。
