气候变化背景下中国林火响应特征及趋势
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摘要
以中国及典型省份为研究区域,利用森林火灾分省月值数据、实测气象数据和气候变化情景数据,分析研究区域的林火时空格局和气候变化背景下响应特征。使用加拿大火险等级系统(CFFDRS-Canadian Forest Fire Danger Rating System)中的火天气指数(FWI-Fire Weather Index)系统的各组分因子作为基本研究评价因子,在对FWI在中国的适用性和敏感性分析的基础上,研究SRES A2、B2情景下研究区域2010-2100年气象因子、FWI系统各因子变化趋势、火发生趋势以及空间格局。
以大兴安岭为研究区域,在对大兴安岭林火时空特征分析的基础上,分别对实测气象数据和SRES A2、B2情景数据计算FWI系统各组分因子的值,选择关键因子,构建了大兴安岭火发生概率模型,基于火发生概率模型,构建了数量化火险期界定方法,进而对防火期在1972-2006年间的变化进行了分析,在SRES A2、B2情景下,研究了2010-2100年防火期的变化趋势,以及气象因子、FWI系统各组分因子和火发生概率的变化。
最后以湖南省为研究区域,分析了冰雪后,短期内(2008年3月份)卫星热点的空间分布特征,与受害程度的空间关系,分析了森林火灾发生的特点和扑火人员伤亡情况,以及气象因素对火发生的影响。利用遥感数据、地面调查数据及植被分布图,在可燃物分类的基础上。通过地面调查和遥感分析对森林受害程度进行分级,根据冰雪前后同期卫星数据NDVI值的差异可以对林木受害程度进行分级,对不同结构层次可燃物增减情况进行分析。结果表明:
(1)FWI系统的初始值对结果有很大的影响,FWI系统各组分因子的计算结果,受初始值影响的时间长度有很大的不同,受其代表的可燃物物理性状、时滞、尺寸、深度等的综合影响,影响日数最小的是FFMC和ISI,影响日数39天,影响日数最长的是DC,超过366天。
(2)FWI在中国不同省份的适用性和敏感性不同,最适用的省份是云南省、黑龙江省、内蒙古自治区等省份,而湖南省、浙江省、江西省、福建省、湖北省、安徽省、江苏省等省则不适合。FWI最敏感的省份主要位于西南林区,包括云南、贵州和广西,其次是黑龙江、吉林、辽宁、内蒙、四川等省,敏感性高意味着FWI的微小变化就会对火发生产生较大的影响。
(3)1988年以来,中国的森林火灾发生和过火面积处于一个较低的水平,但随着气候变化的影响,2002年以来,森林火灾发生有所增加,中国的森林火灾发生次数有明显的波动周期,主周期是8.29a,次周期为29a。近年来,雷击火、境外火均呈增加的趋势,雷击火发生次数最多的省份为内蒙古自治区和黑龙江省,这两个省份雷击火次数占全国雷击火总数的71.40%。受气候变化影响,雷击火1999-2007年有明显增加的趋势,雷击火次数占全国火灾次数的1.14%,但近年来雷击火次数增加明显,2007年雷击火次数占当年火灾次数的2.55%,次数和比例均增加了1倍多。我国境外火主要发生在广西和云南两省,其它一些与外国接壤的省份也有境外火的发生。森林火灾中人员伤亡数量比较严重的省份主要分布在西南和南方省区。坡度比较大的省份与人员伤亡省份基本一致,人员伤亡严重省份的地形复杂性是决定这些省份人员伤亡严重程度的决定性因素。
(4)2010-2100年,中国林火发生呈增加的趋势,2010s到2090s,SRES A2、B2情景下FWI和火灾次数均是黑龙江省增幅较大,火灾次数增长均超过70%。云南省在两种情景下火灾次数增长具有比较大的差异,在SRES A2情景下火灾次数增长了26.29%,B2情景下增长了60.02%。整体上黑龙江省的FWI增长速率比云南省更大,黑龙江省5-6月份FWI值均有比较大的增长速率,在10月份增速也较快。而云南省FWI增速比较快的月份在SRES B2情景下是1-5月份,在SRES A2情景下是1、2月份,在3月份则出现了下降。
(5)大兴安岭具有2个比较明显的周期,大的周期为17a,小的周期约为6a,火灾轮回期为236a。雷击火和人为火均呈聚集分布,雷击火多分布在北纬49o-54o,东经120o-127o之间。人为火比雷击火的聚集度更高,沿铁路、公路和居民区呈聚集分布,其中在东南部聚集度最高。雷击火和人为火的聚集程度与研究尺度有关。海拔对雷击火和人为火的分布均有一定的影响,雷击火分布的海拔总体上比人为火分布的海拔高。以距离公路、铁路和居民区的距离作为人类活动对人为火影响的一个指标,随着距离的增大,在1606m处火灾次数最多,然后随着距离的增大,火灾次数迅速减少。
(6)大兴安岭防火期日数在整体上增加的同时,防火戒严期日数的增加趋势更大。防火期与防火戒严期首日间隔的天数呈明显缩短的趋势。1987年以后火灾次数明显降低,从2000年开始火灾次数开始有比较大的增加,但这种增加在各个月份是不均衡的,主要由6月份和8月份雷击火次数大量增加引起来的,而尤以8月份增加速率最快。雷击火次数占总火灾次数比例加大。
(7)2010-2100年,在SRES A2情景下,大兴安岭所有火发生概率2090s比2010s增加了95.66%,人为火发生概率增加了71.07%,雷击火发生概率增加了292.98%,重大森林火灾发生概率增加了118.05%。在SRES B2情景下,2090s比2010s所有火发生概率增加了41.42%,人为火发生概率增加了33.11%,雷击火发生概率增加了84.18%,重大森林火灾发生概率增加了34.56%。在两种情景下,均是雷击火增加比例最大,在气候变化影响下,雷击火的发生将会日趋严重。2010-2100年人为火发生最危险的区域主要位于大兴安岭的东南部,雷击火发生最危险的区域主要分布于大兴安岭的北部。
(8)2008年1月中旬到2月上旬发生在中国南方的冰雪灾害对森林植被产生了重要的影响,南方冰雪灾害对森林造成严重的影响,处于受害区确认为森林火灾的卫星热点占总数的61.00%。2008年3月份火灾次数和过火面积异常增高,共发生火灾3097起,过火面积23227.68ha,火灾次数超过1999-2007年3月份火灾次数的总和,是1999-2007年3月份火灾次数总和的120.65%,3月份平均火灾次数的10.86倍。过火面积是1999-2007年3月份总和的88.40%,3月份平均过火面积的4.69倍。人员伤亡人数40人,是1999-2007年3月份人员伤亡总和的72.73%,平均伤亡人数的6.56倍。冰雪灾害后,2008年3月火灾次数、过火面积和人员伤亡人数的异常增高已经超出了气温和降水对火发生正常影响的范围。I级受害面积400.07万ha,占总森林面积41.68%,II级受害面积403.95万ha,占总森林面积41.93%,III级受害面积100.90万ha,占总森林面积10.42%,IV级受害面积57.76万ha,占总森林面积5.96%。林木受害后主要表现为地表可燃物载量急剧增加,对于不同可燃物、不同受害程度增加的量有很大不同,地表可燃物载量最大增长倍数为32.81倍,最高地表可燃物载量可达142.82t/ha。
Two spatial scale study areas are selected in this paper, the large scale is China and typical province, the small scale is Daxinganling Mountains. Using fire statistics, meteorological data and climate change scenarios data of different time scales to study spatial and temporal patterns of forest fires, response of forest fires to climate change. In this paper, we studied the suitability and sensitivity of FWI in China, studied the trend of meteorological factors, FWI system components, fire occurrence and FWI spatial pattern in 2010-2100 under SRES A2, B2 climate change scenarios. Build fire occurrence probability models of Daxinganling Mountains, and given a quantitative method to determine fire season.
Freaky snow and ice storms have plagued southern China since mid-January to mid-February 2008, this long time snow and ice damaged forest ecosystem severely, and also greatly impacted forest fire occurrence and firefighters’safety. We studied forest fire hotpot distribution from satellite image, forest fire occurrence, firefighters injured and dead number, the impact of weather on fire occurrence, and assessed the impact of snow and ice damage on fire occurrence and firefighting safety in Hunan Province. We used MODIS 1B dataset, ground truth data and plant distribution data to classify the fuel into Broadleaf Forest, Masson Pine Forest, Chinese Fir Forest, Mixed forest, Other Coniferous Forest, and Bamboo Forest. We classified the damage level of forest based on ground truth and remote sensing analysis, and analyzed the increased or decreased amount of different fuel. We classified the damage level into 4 classes based on the difference of NDVI change before and after the snow damage.
(1)The starting value of FWI system has great impact on the results of FWI system components, the impact days of starting value are influenced by physical properties, time lag days, and size of different components which represented. The minimum days that starting value influenced is FFMC and the ISI, the impact days are 39, the longest is DC, more than 366 days.
(2)The suitability and sensitivity of FWI in different provinces in China is different, the most applicable provinces are Yunnan province, Heilongjiang, Inner Mongolia Autonomous Region, for Hunan, Zhejiang, Jiangxi, Fujian, Hubei, Anhui, Jiangsu Provinces, FWI are not suitable. Most of the provinces which are sensitive to FWI are the Southwest Forestry provinces, including Yunnan, Guizhou and Guangxi, followed by Heilongjiang, Jilin, Liaoning, Inner Mongolia, Sichuan and other provinces, the high sensitivity means that small changes of FWI will have a great impact on fire occurrence.
(3)Forest fire number of China has obvious fluctuation, the main cycle is 8.29a, and the second cycle is 29a. In recent years, the lightning fires, foreign fires showed an increasing trend, distribution of fire casualties in different provinces has the same pattern with slope of China, the complexity of the terrain is the main factor to cause different casualties level in different provinces.
(4)2010-2100, forest fires in China has the tendency to increase, forest fire number will increase more than 70% from 2010s to 2090s under A2 and B2 scenarios in Heilongjiang province. In Yunnan province, the increase percentage is great different in A2 and B2 scenarios. Forest fire number will increase 26.29% under A2 scenario, and forest fire number will increase 60.02% under B2 scenario. The overall growth rate FWI in Heilongjiang is greater than in Yunnan province. The FWI has the fastest growth rate in May and June. In Yunan province, the fastest growth rate of FWI is from January to May under B2 scenario and the fastest growth rate of FWI are January and February under B2 scenario.
(5)There are two obvious cycles in Daxinganling Mountains, the main cycle is 17a and the sub-cycle is 6a. The fire cycle of Daxinganling is 236a. The spatial distribution of lightning fires and human caused fires is aggregation distribution, and human caused fires have more intense aggregation degree. Most of lightning fires distribute between 49o-54onorth latitude and 120o-127o east longitude. Human caused fires distribute along railway and roads, the southeast part of Daxinganling Mountains has the most intense aggregation degree. The aggregation degree of human caused fires and lightning fires is correlated with study scale, and the aggregation degree will change along the change of study scale. Altitude is correlated with the distribution of human caused fires and lightning fires, the lightning fires distribute in higher altitude than human caused fires as a whole. The near distance to roads, railways and residential places is one of the indicators to measure the ability of human activities. With the increasing of distance, the human caused fire number reached the maximum value at 1606m, and then fire number decreased fast with the distance becomes longer.
(6)In Daxinganling Mountains, fire season days increased, and fire restricted season days has more significant tendency to increase. The difference of the first day of fire season and fire restricted season decreased obviously. Fire number after 1987 decreased dramatically, and fire number increased from 2000, and the increased mount is not equal in different month. The increased total fire number in each year after 2000 is due to the increasd fire number in June and August; especially the fire number has the fastest increasing rate in August. The percentage of lightning fire of total fire number increased greatly.
(7)In Daxinganling Mountains, the total fire occurrence probability increased 95.66%, human caused fire occurrence probability increased 71.07%, lightning fire occurrence probability increased 292.98%, and big fire occurrence probability increased 118.05% from 2010s to 2090s under the A2 scenario. The total fire occurrence probability increased 41.42%, human caused fire occurrence probability increased 33.11%, lightning fire occurrence probability increased 84.18%, and big fire occurrence probability increased 34.56% from 2010s to 2090s under the B2 scenario. The lightning fire has the maximum increased probability both in these two climate change scenarios, the lightning fires will become more severe in the future.
(8)61.00% hotspots distribute in the area damaged by snow and ice. Fire number and burned area is extremely high in March 2008, fire number is 3097, burned area is 23227.68ha, fire number is over the total of 1999 to 2007 in March, accounts for 120.65% of the total fire number of 1999-2007 in March, is 10.86 times of the average fire number of 1999-2007 in March. Burned area accounts for 88.40% of the total burned area of 1999-2007 in March, and are 4.69 times of the average burned area of 1999-2007 in March. The number of injured and dead fighters is 40, accounts for 72.73% of the total number of 1999-2007 in March, and is 6.56 times of the average number of injured and dead of 1999-2007 in March. The abnormal increase of fire number, burned area and the number of injured and dead go beyond the limit of weather change impact on forest fire occurrence. The number of injured and dead has significant correlation with fire number and burned area. Air temperature and rainfall does not have significant impact on fire number and burned area. The extreme increase of fire number, burned area and the number of injured and dead are impacted by weather, human activities under the background of great change of fuel accumulation, flammability, and fuel structure. The area of class I is 400.07×104ha, account for 41.68% of total area, the area of class II is 403.95×104ha, account for 41.93% of total area, class III is 100.90×104ha, account for 10.42% of total area, and class IV is 57.76×104ha, account for 5.96% of total area. After the damage the surface fuel loading increased dramatically, the increased amount is different for different fuel and different damage level. The maximal increased times of surface fuel is 32.81, and the maximal fuel loading is 142.82t/ha.
以大兴安岭为研究区域,在对大兴安岭林火时空特征分析的基础上,分别对实测气象数据和SRES A2、B2情景数据计算FWI系统各组分因子的值,选择关键因子,构建了大兴安岭火发生概率模型,基于火发生概率模型,构建了数量化火险期界定方法,进而对防火期在1972-2006年间的变化进行了分析,在SRES A2、B2情景下,研究了2010-2100年防火期的变化趋势,以及气象因子、FWI系统各组分因子和火发生概率的变化。
最后以湖南省为研究区域,分析了冰雪后,短期内(2008年3月份)卫星热点的空间分布特征,与受害程度的空间关系,分析了森林火灾发生的特点和扑火人员伤亡情况,以及气象因素对火发生的影响。利用遥感数据、地面调查数据及植被分布图,在可燃物分类的基础上。通过地面调查和遥感分析对森林受害程度进行分级,根据冰雪前后同期卫星数据NDVI值的差异可以对林木受害程度进行分级,对不同结构层次可燃物增减情况进行分析。结果表明:
(1)FWI系统的初始值对结果有很大的影响,FWI系统各组分因子的计算结果,受初始值影响的时间长度有很大的不同,受其代表的可燃物物理性状、时滞、尺寸、深度等的综合影响,影响日数最小的是FFMC和ISI,影响日数39天,影响日数最长的是DC,超过366天。
(2)FWI在中国不同省份的适用性和敏感性不同,最适用的省份是云南省、黑龙江省、内蒙古自治区等省份,而湖南省、浙江省、江西省、福建省、湖北省、安徽省、江苏省等省则不适合。FWI最敏感的省份主要位于西南林区,包括云南、贵州和广西,其次是黑龙江、吉林、辽宁、内蒙、四川等省,敏感性高意味着FWI的微小变化就会对火发生产生较大的影响。
(3)1988年以来,中国的森林火灾发生和过火面积处于一个较低的水平,但随着气候变化的影响,2002年以来,森林火灾发生有所增加,中国的森林火灾发生次数有明显的波动周期,主周期是8.29a,次周期为29a。近年来,雷击火、境外火均呈增加的趋势,雷击火发生次数最多的省份为内蒙古自治区和黑龙江省,这两个省份雷击火次数占全国雷击火总数的71.40%。受气候变化影响,雷击火1999-2007年有明显增加的趋势,雷击火次数占全国火灾次数的1.14%,但近年来雷击火次数增加明显,2007年雷击火次数占当年火灾次数的2.55%,次数和比例均增加了1倍多。我国境外火主要发生在广西和云南两省,其它一些与外国接壤的省份也有境外火的发生。森林火灾中人员伤亡数量比较严重的省份主要分布在西南和南方省区。坡度比较大的省份与人员伤亡省份基本一致,人员伤亡严重省份的地形复杂性是决定这些省份人员伤亡严重程度的决定性因素。
(4)2010-2100年,中国林火发生呈增加的趋势,2010s到2090s,SRES A2、B2情景下FWI和火灾次数均是黑龙江省增幅较大,火灾次数增长均超过70%。云南省在两种情景下火灾次数增长具有比较大的差异,在SRES A2情景下火灾次数增长了26.29%,B2情景下增长了60.02%。整体上黑龙江省的FWI增长速率比云南省更大,黑龙江省5-6月份FWI值均有比较大的增长速率,在10月份增速也较快。而云南省FWI增速比较快的月份在SRES B2情景下是1-5月份,在SRES A2情景下是1、2月份,在3月份则出现了下降。
(5)大兴安岭具有2个比较明显的周期,大的周期为17a,小的周期约为6a,火灾轮回期为236a。雷击火和人为火均呈聚集分布,雷击火多分布在北纬49o-54o,东经120o-127o之间。人为火比雷击火的聚集度更高,沿铁路、公路和居民区呈聚集分布,其中在东南部聚集度最高。雷击火和人为火的聚集程度与研究尺度有关。海拔对雷击火和人为火的分布均有一定的影响,雷击火分布的海拔总体上比人为火分布的海拔高。以距离公路、铁路和居民区的距离作为人类活动对人为火影响的一个指标,随着距离的增大,在1606m处火灾次数最多,然后随着距离的增大,火灾次数迅速减少。
(6)大兴安岭防火期日数在整体上增加的同时,防火戒严期日数的增加趋势更大。防火期与防火戒严期首日间隔的天数呈明显缩短的趋势。1987年以后火灾次数明显降低,从2000年开始火灾次数开始有比较大的增加,但这种增加在各个月份是不均衡的,主要由6月份和8月份雷击火次数大量增加引起来的,而尤以8月份增加速率最快。雷击火次数占总火灾次数比例加大。
(7)2010-2100年,在SRES A2情景下,大兴安岭所有火发生概率2090s比2010s增加了95.66%,人为火发生概率增加了71.07%,雷击火发生概率增加了292.98%,重大森林火灾发生概率增加了118.05%。在SRES B2情景下,2090s比2010s所有火发生概率增加了41.42%,人为火发生概率增加了33.11%,雷击火发生概率增加了84.18%,重大森林火灾发生概率增加了34.56%。在两种情景下,均是雷击火增加比例最大,在气候变化影响下,雷击火的发生将会日趋严重。2010-2100年人为火发生最危险的区域主要位于大兴安岭的东南部,雷击火发生最危险的区域主要分布于大兴安岭的北部。
(8)2008年1月中旬到2月上旬发生在中国南方的冰雪灾害对森林植被产生了重要的影响,南方冰雪灾害对森林造成严重的影响,处于受害区确认为森林火灾的卫星热点占总数的61.00%。2008年3月份火灾次数和过火面积异常增高,共发生火灾3097起,过火面积23227.68ha,火灾次数超过1999-2007年3月份火灾次数的总和,是1999-2007年3月份火灾次数总和的120.65%,3月份平均火灾次数的10.86倍。过火面积是1999-2007年3月份总和的88.40%,3月份平均过火面积的4.69倍。人员伤亡人数40人,是1999-2007年3月份人员伤亡总和的72.73%,平均伤亡人数的6.56倍。冰雪灾害后,2008年3月火灾次数、过火面积和人员伤亡人数的异常增高已经超出了气温和降水对火发生正常影响的范围。I级受害面积400.07万ha,占总森林面积41.68%,II级受害面积403.95万ha,占总森林面积41.93%,III级受害面积100.90万ha,占总森林面积10.42%,IV级受害面积57.76万ha,占总森林面积5.96%。林木受害后主要表现为地表可燃物载量急剧增加,对于不同可燃物、不同受害程度增加的量有很大不同,地表可燃物载量最大增长倍数为32.81倍,最高地表可燃物载量可达142.82t/ha。
Two spatial scale study areas are selected in this paper, the large scale is China and typical province, the small scale is Daxinganling Mountains. Using fire statistics, meteorological data and climate change scenarios data of different time scales to study spatial and temporal patterns of forest fires, response of forest fires to climate change. In this paper, we studied the suitability and sensitivity of FWI in China, studied the trend of meteorological factors, FWI system components, fire occurrence and FWI spatial pattern in 2010-2100 under SRES A2, B2 climate change scenarios. Build fire occurrence probability models of Daxinganling Mountains, and given a quantitative method to determine fire season.
Freaky snow and ice storms have plagued southern China since mid-January to mid-February 2008, this long time snow and ice damaged forest ecosystem severely, and also greatly impacted forest fire occurrence and firefighters’safety. We studied forest fire hotpot distribution from satellite image, forest fire occurrence, firefighters injured and dead number, the impact of weather on fire occurrence, and assessed the impact of snow and ice damage on fire occurrence and firefighting safety in Hunan Province. We used MODIS 1B dataset, ground truth data and plant distribution data to classify the fuel into Broadleaf Forest, Masson Pine Forest, Chinese Fir Forest, Mixed forest, Other Coniferous Forest, and Bamboo Forest. We classified the damage level of forest based on ground truth and remote sensing analysis, and analyzed the increased or decreased amount of different fuel. We classified the damage level into 4 classes based on the difference of NDVI change before and after the snow damage.
(1)The starting value of FWI system has great impact on the results of FWI system components, the impact days of starting value are influenced by physical properties, time lag days, and size of different components which represented. The minimum days that starting value influenced is FFMC and the ISI, the impact days are 39, the longest is DC, more than 366 days.
(2)The suitability and sensitivity of FWI in different provinces in China is different, the most applicable provinces are Yunnan province, Heilongjiang, Inner Mongolia Autonomous Region, for Hunan, Zhejiang, Jiangxi, Fujian, Hubei, Anhui, Jiangsu Provinces, FWI are not suitable. Most of the provinces which are sensitive to FWI are the Southwest Forestry provinces, including Yunnan, Guizhou and Guangxi, followed by Heilongjiang, Jilin, Liaoning, Inner Mongolia, Sichuan and other provinces, the high sensitivity means that small changes of FWI will have a great impact on fire occurrence.
(3)Forest fire number of China has obvious fluctuation, the main cycle is 8.29a, and the second cycle is 29a. In recent years, the lightning fires, foreign fires showed an increasing trend, distribution of fire casualties in different provinces has the same pattern with slope of China, the complexity of the terrain is the main factor to cause different casualties level in different provinces.
(4)2010-2100, forest fires in China has the tendency to increase, forest fire number will increase more than 70% from 2010s to 2090s under A2 and B2 scenarios in Heilongjiang province. In Yunnan province, the increase percentage is great different in A2 and B2 scenarios. Forest fire number will increase 26.29% under A2 scenario, and forest fire number will increase 60.02% under B2 scenario. The overall growth rate FWI in Heilongjiang is greater than in Yunnan province. The FWI has the fastest growth rate in May and June. In Yunan province, the fastest growth rate of FWI is from January to May under B2 scenario and the fastest growth rate of FWI are January and February under B2 scenario.
(5)There are two obvious cycles in Daxinganling Mountains, the main cycle is 17a and the sub-cycle is 6a. The fire cycle of Daxinganling is 236a. The spatial distribution of lightning fires and human caused fires is aggregation distribution, and human caused fires have more intense aggregation degree. Most of lightning fires distribute between 49o-54onorth latitude and 120o-127o east longitude. Human caused fires distribute along railway and roads, the southeast part of Daxinganling Mountains has the most intense aggregation degree. The aggregation degree of human caused fires and lightning fires is correlated with study scale, and the aggregation degree will change along the change of study scale. Altitude is correlated with the distribution of human caused fires and lightning fires, the lightning fires distribute in higher altitude than human caused fires as a whole. The near distance to roads, railways and residential places is one of the indicators to measure the ability of human activities. With the increasing of distance, the human caused fire number reached the maximum value at 1606m, and then fire number decreased fast with the distance becomes longer.
(6)In Daxinganling Mountains, fire season days increased, and fire restricted season days has more significant tendency to increase. The difference of the first day of fire season and fire restricted season decreased obviously. Fire number after 1987 decreased dramatically, and fire number increased from 2000, and the increased mount is not equal in different month. The increased total fire number in each year after 2000 is due to the increasd fire number in June and August; especially the fire number has the fastest increasing rate in August. The percentage of lightning fire of total fire number increased greatly.
(7)In Daxinganling Mountains, the total fire occurrence probability increased 95.66%, human caused fire occurrence probability increased 71.07%, lightning fire occurrence probability increased 292.98%, and big fire occurrence probability increased 118.05% from 2010s to 2090s under the A2 scenario. The total fire occurrence probability increased 41.42%, human caused fire occurrence probability increased 33.11%, lightning fire occurrence probability increased 84.18%, and big fire occurrence probability increased 34.56% from 2010s to 2090s under the B2 scenario. The lightning fire has the maximum increased probability both in these two climate change scenarios, the lightning fires will become more severe in the future.
(8)61.00% hotspots distribute in the area damaged by snow and ice. Fire number and burned area is extremely high in March 2008, fire number is 3097, burned area is 23227.68ha, fire number is over the total of 1999 to 2007 in March, accounts for 120.65% of the total fire number of 1999-2007 in March, is 10.86 times of the average fire number of 1999-2007 in March. Burned area accounts for 88.40% of the total burned area of 1999-2007 in March, and are 4.69 times of the average burned area of 1999-2007 in March. The number of injured and dead fighters is 40, accounts for 72.73% of the total number of 1999-2007 in March, and is 6.56 times of the average number of injured and dead of 1999-2007 in March. The abnormal increase of fire number, burned area and the number of injured and dead go beyond the limit of weather change impact on forest fire occurrence. The number of injured and dead has significant correlation with fire number and burned area. Air temperature and rainfall does not have significant impact on fire number and burned area. The extreme increase of fire number, burned area and the number of injured and dead are impacted by weather, human activities under the background of great change of fuel accumulation, flammability, and fuel structure. The area of class I is 400.07×104ha, account for 41.68% of total area, the area of class II is 403.95×104ha, account for 41.93% of total area, class III is 100.90×104ha, account for 10.42% of total area, and class IV is 57.76×104ha, account for 5.96% of total area. After the damage the surface fuel loading increased dramatically, the increased amount is different for different fuel and different damage level. The maximal increased times of surface fuel is 32.81, and the maximal fuel loading is 142.82t/ha.
引文
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