Study on the Fire Regime in Xiaoxing'an Mountains,China
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摘要:
目的 研究小兴安岭地区的森林火险与火发生规律,调查主要森林类型的可燃物特征,分析潜在火行为,为林火管理提供科学依据。 方法 根据1980—2010年地面气象观测数据,利用R计算火险天气指数,分析火险指数、火发生和过火面积的变化。采用标准地方法调查小兴安岭6种典型林分的可燃物特征,利用可燃物特征分类系统模拟各可燃物类型在不同天气情景下的潜在地表火和树冠火行为特征。 结果 小兴安岭的林火主要发生在春季(4—6月)和秋季(9—10月)2个时段,干旱年份夏季也可能发生森林火灾。森林的地表可燃物积累较多,主要为地表凋落物和木质可燃物(<1000 h),地表火强度随可燃物含水率降低而增大。林下植被以绣线菊(Spiraea salicifolia)-铁线蕨(Adiantum capillus-veneris)为主的林分潜在地表火蔓延速度显著高于林下以暴马丁香(Syringa reticulata subsp. amurensis)-苔草(Carex sp.)为主的林分,但其火强度低于林下以暴马丁香-苔草为主的林分。针叶林和针阔混交林都可能发生树冠火。 结论 林火管理有效减少了火发生。小兴安岭北部林区可燃物载量多,潜在火行为相关指数高,是未来林火管理的重点区域。干旱情景下,6种林分类型均有发生树冠火的可能,可以通过清理可燃物梯和减小针叶树种的密度来降低森林火灾风险。 Abstract:Objective The aim of this study was to describe fire weather and fire regime. We investigated fuel characteristics and analyzed fire potentials in Xiaoxing’an Mountains, which would be valuable for improving the fire management in this region. Method Fire weather index (FWI) was calculated on R platform with the observed climate data from 1980 to 2010 . We presented the temporal and spatial variation of FWI, fire occurrence, and postfire burned areas. Sampling plots (20 m × 20 m) were set up in six primary forest types for fuel investigation during the fire season of 2020. The Fuel Characteristic Classification System (FCCS) was employed to simulate potential surface and crown fires for each forest type under multiple weather scenarios. Result The results showed that fires mainly occurred in spring (from April to June) and autumn (September and October), but appeared somewhat in dry summers. Surface fuels were accumulated significantly, which included wood fuel (<1000 h time lag) and litters. Surface fire intensity increased with the decreasing fuel moisture content. Forests with understory vegetation of Spiraea salicifolia and Adiantum capillus-veneris showed the lower fire intensity and faster potential surface fire spreading than those forests with Syringa reticulata subsp. amurensis and Carex sp. Crown fires occurred in both the coniferous forests and coniferous and broad-leaved mixed. Conclusion Fire management has effectively reduced forest fire potentials in Xiaoxing’an Mountains. The northern subregion was the critical area of forest fire management in the future due to high fuel loadings and fire potentials. All forest types had displayed the probability of crown fire under the drought scenarios. We inferred that the fire risk could be reduced by clearing the fuel ladder and reducing the density of coniferous trees. -
Key words:
- fire regime /
- fuel characteristics /
- fire behavior /
- Xiaoxing'an Mountains
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图 3 1953—1987年和1988—2016年2个时段研究区的火发生频度和场均过火面积分布 (a)1953—1987年火发生频度;(b)1988—2016年火发生频度;(c)1953—1987年场均过火面积;(d)1988—2016年场均过火面积
Figure 3. Distribution of fires and average burned areas per fire in two periods (a) distribution of fire frequency during the period of 1953–1987; (b) distribution of fire frequency during the period of 1988–2016; (c) distribution of average burned areas per fire during the period of 1953–1987; (d) distribution of average burned areas per fire during the period of 1988–2016
表 1 调查林分的基本信息
Table 1. Base information of each forest type
林分类型
Forest type树种组成
Tree species composition郁闭度
Canopy density平均树高
Average height/m胸径
Average DBH/cm密度
Density/
(tree/hm2)海拔
Altitude/m坡向
Aspect坡度
Slope/(°)灌木
Shrub草本
Grass1 5落5桦 0.78 10.8/2.8 16.6/2 1183 347 阳坡 3 暴马
丁香苔草 2 7枫2红1榛 0.85 14.4/2.2 25.1/1.4 1234 481.4 阳坡 3 3 6落4暴 0.8 18.6/1.9 17.4/1.1 921 490.7 阳坡 3 4 6落2椴1红1枫 0.7 10.3/8.6/20.3/5.9 13.7/14.8/29.1/4.1 404 331.1 阳坡 3 绣线菊 铁线蕨 5 5落3白2毛 0.75 7.8/4.7/5.7 11.5/3.3/5.3 890 387.2 阳坡 3 6 7红2落1白 0.75 14.1/17.6/17 21.5/23.2/19.6 344 383.4 阳坡 3 注:落——落叶松(Larix gmelinii);桦——黒桦(Rhamnus maximovicziana);枫——五角枫(Acer pictum subsp. mono);红——红松(Pinus koraiensis);榛——榛(Corylus heterophylla);暴——暴马丁香(Syringa reticulata subsp. amurensis);椴——椴树(Tilia tuan);白——白桦(Betula platyphylla) ;苔草(Carex sp.) ;绣线菊(Spiraea salicifolia) ;铁线蕨(Adiantum capillus-veneris) 。 表 2 可燃物特征分类系统的可燃物参数
Table 2. Fuel parameters in FCCS
可燃物层 Fuel stratum 参数 Parameters 冠层 Canopy 主要、次要乔木(活立木与死木)的郁闭度、平均树高、胸径、密度、可燃物梯高度 Canopy density, average height, diameter at breast height, density, and fuel ladder height of primary and secondary trees (living and dead trees) 灌木 Shrub 主要、次要灌木的盖度、高度、活植株比例、载量及种类Coverage, height, proportion of living plants, loading and species of main and secondary shrubs 草本 Herb 主要、次要草本的盖度、高度、活植株比例、载量及种类Coverage, height, proportion of living plants, loading and species of primary and secondary herbs 倒木 Wood 倒木盖度、各时滞可燃物(1~10000 h)的载量和种类Coverage of fallen wood, loading and type of each time lag (1~10000 h) fuels 地表凋落物与地衣苔藓
Litter-Lichen-Moss地表可燃物、地衣和苔藓的盖度、厚度、种类及载量 Coverage, thickness, species, and loading of surface fuel, lichen, and moss 地下可燃物 Ground fuel 半分解腐殖质和分解腐殖质的盖度、厚度、类型及载量 Coverage, thickness, type, and loading of semi-decomposed humus and decomposed humus 表 3 可燃物含水率情景(%)
Table 3. Fuel moisture scenarios (%)
情景
Scenario含水率情景描述
Scenario description of various fuel moisture content草本
Herb灌木
Shrub树冠
Crown1 h 10 h 100 h D1L1 死可燃物含水率极低、草本植物全部干枯
Very low moisture content of dead fuels, fully cured herb30 60 60 3 4 5 D1L2 死可燃物含水率极低、2/3草本植物干枯
Very low moisture content of dead fuels, 2/3 cured herb60 90 90 3 4 5 D1L3 死可燃物含水率极低、1/3草本植物干枯
Very low moisture content of dead fuels, 1/3 cured herb90 120 120 3 4 5 D1L4 死可燃物含水率极低、草本植物未干枯
Very low moisture content of dead fuels, fully green herb120 150 150 3 4 5 D2L1 死可燃物含水率低、草本植物全部干枯
Low moisture content of dead fuels, fully cured herb30 60 60 6 7 8 D2L2 死可燃物含水率低、2/3草本植物干枯
Low moisture content of dead fuels, 2/3 cured herb60 90 60 6 7 8 D2L3 死可燃物含水率低、1/3草本植物干枯
Low moisture content of dead fuels, 1/3 cured herb90 120 60 6 7 8 D2L4 死可燃物含水率低、草本植物未干枯
Low moisture content of dead fuels, fully green herb120 150 90 6 7 8 D3L1 死可燃物含水率中等、草本植物全部干枯
Moderate moisture content of dead fuels, fully cured herb30 60 90 9 10 11 D3L2 死可燃物含水率中等、2/3草本植物干枯
Moderate moisture content of dead fuels, 2/3 cured herb60 90 90 9 10 11 D3L3 死可燃物含水率中等、1/3草本植物干枯
Moderate moisture content of dead fuels, 1/3 cured herb90 120 120 9 10 11 D3L4 死可燃物含水率中等、草本植物未干枯
Moderate moisture content of dead fuels, fully green herb120 150 120 9 10 11 D4L1 死可燃物含水率高、草本植物全部干枯
High moisture content of dead fuels, fully cured herb30 60 120 12 13 14 D4L2 死可燃物含水率高、2/3草本植物干枯
High moisture content of dead fuels, 2/3 cured herb60 90 150 12 13 14 D4L3 死可燃物含水率高、1/3草本植物干枯
High moisture content of dead fuels, 1/3 cured herb90 120 150 12 13 14 D4L4 死可燃物含水率高、草本植物未干枯
High moisture content of dead fuels, fully green herb120 150 150 12 13 14 表 4 各林分可燃物特征
Table 4. Fuel characteristics of each forest type
林分
Forest type死树密度
Density of dead trees/
(tree/hm2)可燃物梯
(最低)
Fuel ladder (minimum)/
cm灌木
Shrub草本
Grass地表可燃物
Surface fuel/(t/hm2)平均高度
Average height/cm活植株比例
Percent live/
%载量
Load/
(t/hm2)平均高度
Average height/
cm活植株比例
Percent live/%载量
Loading/
(t/hm2)厚度
Depth/
cm凋落物
Litter1 h 10 h 100 h 1 000 h 1 253 30 55 100 0.4 24 85 0.7 1.8 0.6 0.5 0.5 1.7 0 2 142 9 73 100 0.1 27 100 0.1 1.0 0.8 0.1 0.1 0.2 0 3 0 9 40 100 0.2 15 100 0.5 1.3 0.6 0.1 0.1 0.2 0 4 0 49 61 100 0.2 52 95 0.4 2.0 1.0 0.1 0.1 0.1 0 5 0 9 58 100 0.1 15 50 0.4 1.3 1.3 0.1 0.2 0.3 0 6 0 150 67 100 0.2 15 100 0.2 2.5 0.6 0.1 0.1 0.2 0.3 -
[1] 蔡卫红, 王晓红, 于宏洲, 等, 2013. 基于Rothermel模型的可燃物参数对林火行为影响的计算机仿真[J]. 中南林业科技大学学报, 33(11): 34-41. doi: 10.3969/j.issn.1673-923X.2013.11.007 [2] 杜春英, 于成龙, 刘丹, 2010. 大兴安岭地区雷击火发生环境分析[J]. 中国农业气象, 31(4): 596-599. doi: 10.3969/j.issn.1000-6362.2010.04.020 [3] 郭福涛, 苏漳文, 马祥庆, 等, 2015. 大兴安岭塔河地区雷击火发生驱动因子综合分析[J]. 生态学报, 35(19): 6439-6448. [4] 国家林业局, (2016-12-29)[2020-11-6]. 全国森林防火规划(2016-2025)[EB/OL]. http://www.gov.cn/xinwen/2016-12/29/content_5154054.htm. [5] 侯俊峰, 2017. 伊春林区森林火灾发生规律及风险评估研究 [D]. 北京: 北京林业大学. [6] 胡海清, 李楠, 孙龙, 等, 2011. 伊春地区森林火灾时空分布格局[J]. 东北林业大学学报, 39(10): 67-70. doi: 10.3969/j.issn.1000-5382.2011.10.019 [7] 胡海清, 罗斯生, 罗碧珍, 等, 2017. 森林可燃物含水率及其预测模型研究进展[J]. 世界林业研究, 30(3): 64-69. [8] 李顺, 吴志伟, 梁宇, 等, 2017. 大兴安岭林火发生的时空聚集性特征[J]. 生态学杂志, 36(1): 198-204. [9] 梁慧玲, 郭福涛, 王文辉, 等, 2015. 小兴安岭伊春地区林火发生自然影响因子及其影响力[J]. 东北林业大学学报, 43(12): 29-35. doi: 10.3969/j.issn.1000-5382.2015.12.007 [10] 刘林馨, 2012. 小兴安岭森林生态系统植物多样性及生态服务功能价值研究 [D]. 哈尔滨: 东北林业大学. [11] 刘兴周, 1992. 小兴安岭林火发生和气象因子的关系[J]. 森林防火, (6): 34-38. [12] 刘志华, 杨健, 贺红士, 等, 2011. 黑龙江大兴安岭呼中林区火烧点格局分析及影响因素[J]. 生态学报, 31(6): 1669-1677. [13] 苗庆林, 刘耀香, 田晓瑞, 2014. 林火管理对火动态的影响[J]. 世界林业研究, 27(4): 42-47. [14] 苗庆林, 田晓瑞, 2016. 多气候情景下大兴安岭森林燃烧性评估[J]. 林业科学, 52(10): 109-116. doi: 10.11707/j.1001-7488.20161014 [15] 倪长虹, 邸雪颖, 2009. 黑龙江省大兴安岭雷击火发生规律[J]. 东北林业大学学报, 37(1): 55-57+75. doi: 10.3969/j.issn.1000-5382.2009.01.020 [16] 盛裴轩, 毛节泰, 李建国, 等, 2013. 大气物理学 [M]. 北京: 北京大学出版社. [17] 田晓瑞, 舒立福, 王明玉, 2005. 林火动态变化对我国东北地区森林生态系统的影响[J]. 森林防火, (1): 21-25. doi: 10.3969/j.issn.1002-2511.2005.01.010 [18] 田晓瑞, 舒立福, 赵凤君, 等, 2012. 大兴安岭雷击火发生条件分析[J]. 林业科学, 48(7): 98-103. doi: 10.11707/j.1001-7488.20120716 [19] 田晓瑞, 舒立福, 赵凤君, 等, 2015. 中国主要生态地理区的林火动态特征分析[J]. 林业科学, 51(9): 71-77. [20] 田晓瑞, 王明玉, 殷丽, 等, 2009. 大兴安岭南部春季火行为特征及可燃物消耗[J]. 林业科学, 45(3): 90-95. doi: 10.3321/j.issn:1001-7488.2009.03.016 [21] 田晓瑞, 赵凤君, 舒立福, 等, 2014. 1961—2010年中国植被区的气候与林火动态变化[J]. 应用生态学报, 25(11): 3279-3286. [22] 于洋, 邹莉, 孙婷婷, 等, 2013. 小兴安岭原始红松林的植被多样性[J]. 草业科学, 30(8): 1175-1181. [23] 张晓芳, 董文站, 程春香, 等, 2009. 小兴安岭伊春林区森林火灾特征及变化规律分析[J]. 黑龙江气象, 26(4): 23-25. doi: 10.3969/j.issn.1002-252X.2009.04.010 [24] 郑琼, 邸雪颖, 金森, 等, 2013. 伊春地区1980—2010年森林火灾时空格局及影响因子[J]. 林业科学, 49(4): 157-163. doi: 10.11707/j.1001-7488.20130424 [25] 宗学政, 田晓瑞, 2021. 可燃物处理对大兴安岭地区主要林型火行为的影响[J]. 林业科学, 57(2): 139-149. doi: 10.11707/j.1001-7488.20210214 [26] Cai L, He H S, Liang Y, et al, 2019. Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China[J]. International Journal of Wildland Fire, 28(3): 205-215. doi: 10.1071/WF18083 [27] Calheiros T, Pereira M G, Nunes J P, 2021. Assessing impacts of future climate change on extreme fire weather and pyro-regions in Iberian Peninsula[J]. Science of The Total Environment, 754: 142233. doi: 10.1016/j.scitotenv.2020.142233 [28] Christopoulou A, Mallinis G, Vassilakis E, et al, 2019. Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: the case of Mount Taygetos (southern Greece)[J]. International Journal of Wildland Fire, 28(7): 521-532. [29] Cronan J B, Wright C S, Petrova M, 2015. Effects of dormant and growing season burning on surface fuels and potential fire behavior in northern Florida longleaf pine (Pinus palustris) flatwoods[J]. Forest Ecology and Management, 354: 318-333. doi: 10.1016/j.foreco.2015.05.018 [30] Curt T, Fréjaville T, 2017. Wildfire policy in Mediterranean France: how far is it efficient and sustainable?[J]. Risk Analysis, 38(3): 472-488. [31] Dasgupta S, Qu J J, Hao X, et al, 2007. Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA[J]. Remote Sensing of Environment, 108(2): 138-150. doi: 10.1016/j.rse.2006.06.023 [32] Eskandari S, Miesel J R, Pourghasemi H R, 2020. The temporal and spatial relationships between climatic parameters and fire occurrence in northeastern Iran[J]. Ecological Indicators, 118: 106720. doi: 10.1016/j.ecolind.2020.106720 [33] Hanes C C, Wang X, Jain P, et al, 2018. Fire-regime changes in Canada over the last half century[J]. Canadian Journal of Forest Research, 49(3): 256-269. [34] Heydari M, Rostamy A, Najafi F, et al, 2017. Effect of fire severity on physical and biochemical soil properties in Zagros oak (Quercus brantii Lindl.) forests in Iran[J]. Journal of Forestry Research, 28(1): 95-104. [35] Hollingsworth L W T, Kurth L L, Parresol B R, et al, 2012. A comparison of geospatially modeled fire behavior and fire management utility of three data sources in the southeastern United States[J]. Forest Ecology and Management, 273: 43-49. doi: 10.1016/j.foreco.2011.05.020 [36] Hurteau M, North M, 2008. Fuel treatment effects on tree-based forest carbon storage and emissions under modeled wildfire scenarios[J]. Frontiers in Ecology and the Environment, 7(8): 409-414. [37] Jyoteeshkumar R P, Sharples J J, Lewis S C, et al, 2021. Modulating influence of drought on the synergy between heatwaves and dead fine fuel moisture content of bushfire fuels in the Southeast Australian region[J]. Weather and Climate Extremes, 31: 100300. doi: 10.1016/j.wace.2020.100300 [38] Keane R E, Gray K, Bacciu V, et al, 2012. Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA[J]. Landscape Ecology, 27(8): 1213-1234. doi: 10.1007/s10980-012-9773-9 [39] Keyser A R, Westerling A L, 2019. Predicting increasing high severity area burned for three forested regions in the western United States using extreme value theory[J]. Forest Ecology and Management, 432: 694-706. doi: 10.1016/j.foreco.2018.09.027 [40] Krawchuk M A, Haire S L, Coop J, et al, 2016. Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America[J]. Ecosphere, 7(12): e01632. [41] Lawson B D, Armitage O B, 2008. Weather guide for the Canadian forest fire danger rating system [M]. Edmonton, Alberta: Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre. [42] Liu Z, 2016. Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China[J]. Scientific Reports, 6: 37572. doi: 10.1038/srep37572 [43] Matthews S, Sullivan A L, Watson P, et al, 2012. Climate change, fuel and fire behaviour in a eucalypt forest[J]. Global Change Biology, 18(10): 3212-3223. doi: 10.1111/j.1365-2486.2012.02768.x [44] McKenzie D M, Raymond C, Kellogg L K B, et al, 2007. Mapping fuels at multiple scales: landscape application of the Fuel Characteristic Classification System[J]. Canadian Journal of Forest Research, 37(12): 2421-2437. doi: 10.1139/X07-056 [45] Miller E A, 2020. A conceptual interpretation of the drought code of the Canadian forest fire weather index system[J]. Fire, 3(2): 23. doi: 10.3390/fire3020023 [46] Mitsopoulos I D, Dimitrakopoulos A P, 2007. Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests[J]. Annals of Forest Science, 64(3): 287-299. doi: 10.1051/forest:2007006 [47] Ottmar R, Sandberg D, Riccardi C, et al, 2007. An overview of the Fuel Characteristic Classification System: quantifying, classifying, and creating fuelbeds for resource planning[J]. Canadian Journal of Forest Research, 37(12): 2383-2393. doi: 10.1139/X07-077 [48] Paritsis J, Landesmann J B, Kitzberger T, et al, 2018. Pine plantations and invasion alter fuel structure and potential fire behavior in a patagonian forest-steppe ecotone[J]. Forests, 9(3): 117. doi: 10.3390/f9030117 [49] Parresol B, Blake J, Thompson A, 2012. Effects of overstory composition and prescribed fire on fuel loading across a heterogeneous managed landscape in the southeastern USA[J]. Forest Ecology and Management, 273: 29-42. doi: 10.1016/j.foreco.2011.08.003 [50] Pausas J G, Fernández-Muñoz S, 2012. Fire regime changes in the Western Mediterranean Basin: from fuel-limited to drought-driven fire regime[J]. Climatic Change, 110(1-2): 215-226. doi: 10.1007/s10584-011-0060-6 [51] Pettinari M L, Chuvieco E, 2016. Generation of a global fuel data set using the Fuel Characteristic Classification System[J]. Biogeosciences, 12(20): 2061-2076. [52] Piqué M, Domènech R, 2018. Effectiveness of mechanical thinning and prescribed burning on fire behavior in Pinus nigra forests in NE Spain[J]. Science of the Total Environment, 618: 1539-1546. doi: 10.1016/j.scitotenv.2017.09.316 [53] Riccardi C, Prichard S, Sandberg D, et al, 2007. Quantifying physical characteristics of wildland fuels using the Fuel Characteristic Classification System[J]. Canadian Journal of Forest Research, 37(12): 2413-2420. doi: 10.1139/X07-175 [54] Rodrigues M, Jiménez-Ruano A, de la Riva J, 2020. Fire regime dynamics in mainland Spain. Part 1: drivers of change[J]. Science of The Total Environment, 721: 135841. doi: 10.1016/j.scitotenv.2019.135841 [55] Sandberg D V, Riccardi C L, Schaaf M D, 2007. Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification System[J]. Canadian Journal of Forest Research, 37(12): 2456-2463. doi: 10.1139/X07-093 [56] Schoennagel T, Veblen T T, Negron J F, et al, 2012. Effects of mountain pine beetle on fuels and expected fire behavior in lodgepole pine forests, Colorado, USA[J]. Plos One, 7(1): e30002. doi: 10.1371/journal.pone.0030002 [57] Sherriff R L, Veblen T T, 2007. A spatially-explicit reconstruction of historical fire occurrence in the ponderosa pine zone of the Colorado front range[J]. Ecosystems, 10(2): 311-323. doi: 10.1007/s10021-007-9022-2 [58] Stocks B J, Lynham T J, Lawson B D, et al, 1989. Canadian forest fire danger rating system: an overview[J]. The Forestry Chronicle, 65(4): 258-265. doi: 10.5558/tfc65258-4 [59] Stocks B J, Mason J A, Todd J B, et al, 2002. Large forest fires in Canada, 1959–1997[J]. Journal of Geophysical Research: Atmospheres, 107: 8149. [60] Su Z, Hu H, Wang G, et al, 2018. Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China[J]. Geomatics, Natural Hazards and Risk, 9(1): 1207-1229. doi: 10.1080/19475705.2018.1505667 [61] Tian X R, Cui W, Shu L, 2020. Evaluating fire management effectiveness with a burn probability model in Daxing’anling, China[J]. Canadian Journal of Forest Research, 50: 670-679. doi: 10.1139/cjfr-2019-0413 [62] Tian X R, McRae D J, Jin J, et al, 2011. Wildfires and the Canadian Forest Fire Weather Index system for the Daxing’anling region of China[J]. International Journal of Wildland Fire, 20(4): 963-973. [63] Torres F T P, Romeiro J M N, Santos A C d A, et al, 2018. Fire danger index efficiency as a function of fuel moisture and fire behavior[J]. Science of the Total Environment, 631-632: 1304-1310. doi: 10.1016/j.scitotenv.2018.03.121 [64] Wang X L, Wotton B M, Cantin A S, et al, 2017. Cffdrs: an R package for the Canadian forest fire danger rating system[J]. Ecological Processes, 6(1): 5. doi: 10.1186/s13717-017-0070-z [65] Yang S, Ge M, Li X, et al, 2020. The spatial distribution of the normal reference values of the activated partial thromboplastin time based on ArcGIS and GeoDA[J]. International Journal of Biometeorology, 64: 779-790. doi: 10.1007/s00484-020-01868-2 [66] Zhao F, Liu Y, Shu L, 2020. Change in the fire season pattern from bimodal to unimodal under climate change: The case of Daxing'anling in northeast China[J]. Agricultural and Forest Meteorology, 291: 108075. doi: 10.1016/j.agrformet.2020.108075