基于CO2FIX模型的长白落叶松人工林碳汇和木材生产模拟

本研究以帽儿山地区长白落叶松人工林为对象,基于样地调查和文献数据,利用CO₂FIX模型定量模拟轮伐期(30、40、50、60年)、立地指数(12、16、20 m)和初植密度(2500、3333、4444 株·hm^-2)对长白落叶松人工林碳平衡过程的影响,并构建林分尺度下生物量碳库、土壤碳库和林产品碳库之间的碳流通过程。结果表明: CO₂FIX模型对帽儿山地区长白落叶松人工林生物量和蓄积量的生长过程模拟结果具有较高的可靠性,模拟值和实测值平均相对误差分别为6.4%和3.7%。在初植密度3333 株·hm^-2、立地指数16 m、轮伐期40年的基准条件下,长白落叶松人工林总碳储量及各碳库碳储量均随轮伐期呈周期性变化。林分总碳储量和蓄积量均随轮伐期的延长、立地指数的提升和初植密度的增加而增加。当轮伐期分别延长10年和20年时,林分碳储量分别增加12.2%和31.2%,林分蓄积增加36.7%和67.8%;而当轮伐期缩短10年时,林分碳储量和蓄积量则分别降低20.9%和40.4%。与初植密度2500 株·hm^-2相比,初植密度为3333和4444 株·hm^-2时,林分碳储量增长率分别为27.8%和50.9%,蓄积量增长率分别为27.4%和49.1%。当立地指数在12～20 m范围时,每提高4 m,林分碳储量增长36.0%、40.3%,蓄积量增长39.3%、44.2%。在一个轮伐期内,每公顷长白落叶松人工林可固定约271.57 t C;当轮伐期结束时,约有27.47和56.75 t C流转到土壤和木材产品碳库中。因此,当立地条件较好时,采用较高初植密度(4444 株·hm^-2)和较长轮伐期(60年)的管理模式更有利于长白落叶松人工林碳汇和木材效益的最大化。     

内蒙古大青山华北落叶松人工林乔木层碳密度年增量与气象因子的关系

以内蒙古大青山华北落叶松人工林为研究对象，通过树木年轮法和异速生长方程法，计算华北落叶松人工林生物量、碳密度及其年增量的年际变化，并分析碳密度年增量与气温、降水、湿度等气象因子的关系。研究发现：华北落叶松人工林碳密度随着林龄增加的变化曲线可用逻辑斯谛生长方程拟合，在1979—2016年，碳密度由1.05 t/hm~2增加到76.83 t/hm~2。华北落叶松人工林碳密度年增量存在显著的年际差异，总体上呈波动性的“慢-快-慢”趋势，碳密度年增量最高达到3.72 t hm^-2 a^-1,多年平均为2.05 t hm^-2 a^-1。华北落叶松人工林碳密度年增量与上年6月和当年6—8月的降水量显著正相关，与上年11月降水显著负相关；与上年11—12月、当年2月和12月的温度和大气相对湿度分别呈正、负相关；与上年7月、9月及当年8—9月的温度保持显著或极显著正相关。研究表明，温度、湿度和降水主要通过生长季的长短和土壤可利用水分及冬季的雪害冻害影响华北落叶松人工林的碳汇潜力，在未来该地区升温增湿的气候变化趋势下华北...     

大兴安岭林区兴安落叶松人工林植被碳贮量

通过样地调查,研究了大兴安岭林区10、12、15、26和61年生兴安落叶松人工林中乔木、草本和植被总体碳储量,并以空间代替时间的方法,探讨落叶松人工林生长过程中植被碳库贮量变化.结果表明:随林龄的增加,兴安落叶松人工林植被碳库贮量逐渐增加,61 a时达105.69 t.hm-2,碳汇作用显著;15～26 a兴安落叶松人工林的碳汇能力最强.其中,树干碳库贮量占乔木碳库总贮量的54.3%～73.9%,且随林龄增加,其碳库比率和碳密度增加;其余器官碳库比率随林龄增加而减小,碳密度则逐渐增加,直至趋于平衡或末期略有减少.大兴安岭林区兴安落叶松人工林的轮伐期以≥60 a为宜.     

主要管理措施对人工林土壤碳的影响

人工林碳汇在全球碳循环及温室气体减排中发挥着重要作用。人工林是处于人为调控下的生态系统类型,经营管理措施是影响人工林土壤碳平衡的重要因素。通过科学合理的生态系统管理,增强人工林的土壤碳汇,对减缓气候变化具有十分重要的意义。本文综述了主要经营管理措施(造林树种、轮伐期、采伐、灌溉和施肥)对人工林土壤碳储量与碳通量影响的研究进展,结果表明:人工林经营管理措施可通过改变林地的温度、水分、养分和土壤结构,来影响土壤有机碳储量和土壤呼吸等碳循环过程。但目前人工林管理对土壤碳影响的研究还很不足,一些营林措施还未展开相关研究。未来应针对人工林管理措施对土壤碳的影响做更全面的定量研究。     

山西太岳山不同林龄华北落叶松林土壤微生物特性!

本文以山西太岳山3个林龄(18、35和51年生)华北落叶松林为对象,研究其土壤微生物生物量、土壤真菌群落结构多样性特征,并利用通径分析,探讨土壤和凋落物养分含量对土壤微生物的影响。结果表明:随着华北落叶松林年龄的增加,土壤微生物生物量碳逐渐增加,微生物生物量碳占其与可溶性有机碳之和的比例也逐渐增加;土壤微生物生物量碳/氮比在51年生华北落叶松林中最大(13),约为其他两个林龄华北落叶松林的1.6倍;土壤微生物碳熵在35年生华北落叶松林中最低(1.5%),在18年生华北落叶松林中最高(2.8%)。土壤微生物生物量氮、真菌Shannon指数、土壤和凋落物碳/氮比在不同林龄华北落叶松林中的变化趋势均为35年生18年生51年生。通径分析结果表明,真菌群落结构多样性对土壤微生物生物量碳有较大的直接作用,凋落物自身化学组成对土壤微生物生物量氮有显著影响,土壤碳/氮比和微生物生物量碳/氮比是调控真菌群落结构多样性的直接因素。总的来说,35年生华北落叶松林的土壤有机碳活性最小,土壤碳库稳定性较好,养分状况优于另外两个林龄华北落叶松林。     

不同林龄长白落叶松人工林碳储量

基于7—41 a长白落叶松人工林样地生物量调查,探讨了不同发育阶段长白落叶松人工林碳储量的时空变化规律。结果表明:随林龄的增大,长白落叶松人工林林木和各器官生物量增加,树干所占比例增加,生物量转换因子(BEF)、根茎比(R)等参数分布正常。林下植被层、倒落木质物层生物量随林龄增大呈增加趋势。群落总碳储量的空间分布序列是:乔木层倒落木质物层林下植被层。未成林期、幼龄林、中龄林、近熟林和成熟林群落的碳储分别为6.585、66.934、90.019、125.103、162.683t.hm-2,乔木层碳储量分别为3.254、58.521、78.086、108.02、138.096 t.hm-2,倒落木质物层和林下植被层碳储量平均值分别为10.859、1.988 t.hm-2。乔木层、倒落木质物层和林下植被层碳储量占总量的平均比率分别为85.99%、2.17%和11.85%。在不同发育阶段群落和乔木层碳储量的年生产力呈先降后升的变化趋势,中龄林的碳储量累积速率高于幼龄林及成熟林,碳素年固定量分别为0.940、3.889、3.615、3.628、3.968 t.hm-2,乔木层年生产力分别为0.465、3.39、3.137、3.133、3.368 t.hm-2。林下植被层年生产力呈"U"形变化,平均值为0.079 t.hm-2。倒落木质物层的年生产力呈线性增长,平均值为0.423 t.hm-2。研究认为长白落叶松人工林群落碳储量随林龄增加的变化规律明显,碳汇潜力巨大。     

大兴安岭林区兴安落叶松人工林土壤有机碳贮量

通过样地调查,研究了大兴安岭林区10、15、26和61年生兴安落叶松人工林0～ 40cm土壤有机碳(SOC)贮量,以及原始兴安落叶松林皆伐后营造人工林过程中SOC碳源/汇的变化.结果表明:随林龄的增加,兴安落叶松人工林SOC贮量呈现先减少后增加的趋势,转折点在林龄15 ～26 a.与原始落叶松林相比,兴安落叶松人工林土壤碳库初期(10 ～26 a)表现为碳源,之后逐渐转变为碳汇,林龄61 a时SOC贮量达158.91· hm-2.兴安落叶松人工林土壤碳库的垂直分布表现为初期下层SOC贮量高于上层,26 a后上层高于下层,说明人为干扰对该地区森林土壤碳库垂直分布产生了强烈的影响.大兴安岭林区兴安落叶松人工林的主伐年龄以＞60 a为宜.     

兴安落叶松林碳平衡及管理活动影响研究 （英文）

 在利用大兴安岭地区根河落叶松(Larix gmelini)林生态系统定位研究站的实际观测资料验证CENTURY模型的基础上,探讨了林业经营管理方式对兴安落叶松林碳循环的影响,指出：1）目前兴安落叶松林是一个碳汇,每年净吸收碳2.65 t·hm-2。2）砍伐将使兴安落叶松林生物量和生产力下降,土壤碳含量则有所增加。干扰强度越大则其植物总生物量、生产力和土壤碳含量变化幅度越大,伐后恢复时间也越长。3）连年去除枯枝落叶处理使兴安落叶松林土壤碳含量下降,土壤越来越贫瘠。植物总生物量在前30年迅速增加,之后则趋于稳     

长白落叶松人工林乔木层生物量分布特征及其固碳能力研究

基于8~56 a长白落叶松人工林样地生物量调查数据,建立了长白落叶松林各器官生物量模型,探讨了不同林龄长白落叶松人工林干材、树皮、树枝、树叶、树根的生物量分布与变化规律及单木与林分乔木层的固碳能力。结果表明:随着林龄的增大,长白落叶松人工林林木及各器官生物量均呈现不同程度的增加趋势,单株木生物量由8 a时的0.174 kg增加至56 a时的328.196 kg,林分乔木层生物量由8 a时的0.519 t·hm-2增加至56 a时的251.39 t·hm-2,其中树干所占比例最大,且增幅最大。长白落叶松人工林单木平均碳储量为74.822 kg,56 a林分乔木层碳密度为130.455 t·hm-2,平均碳密度达63.113 t·hm-2,各器官碳储量变化规律明显。长白落叶松人工林幼龄林、中龄林、近熟林、成熟林林分乔木层的年平均固碳量分别为0.087、1.193、1.703、2.124 t·hm-2,固碳量年平均增长率排序为中龄林幼龄林成熟林近熟林。研究认为,长白落叶松人工林单株木及林分各器官生物量随林龄增加具有明显的变化规律,成熟林分固碳水平最高,中龄林分后期固碳潜力最大。     

江西省森林碳蓄积过程及碳源/汇的时空格局

森林碳蓄积是研究森林与大气碳交换以及估算森林吸收或排放含碳气体的关键参数,不同年龄森林的碳源/汇功能差异则体现出森林生态系统碳蓄积过程的时间特征。以森林资源清查的样方数据作为数据源,通过刻画主要树种的林分蓄积生长曲线、林龄与净初级生产力(NPP)之间的关系,驱动区域碳收支模型(InTEC)模拟江西省1950—2008年的森林碳蓄积过程,了解山江湖工程实施以来的森林碳源/汇状况。结果表明,20世纪80年代以前,江西省森林年平均NPP波动于450—813 gCm-2a-1之间,年净增生物量碳26.55—36.23 TgC/a,年净增木质林产品碳0.01—0.3 TgC/a;80年代初,NPP和年净增生物量碳分别降至307.39 gC m-2a-1和17.31 TgC/a,而年净增木质林产品碳却高达0.6 TgC/a,说明森林被大量砍伐进入林产品碳库;1985年山江湖工程实施后,大面积造林使得年净增碳蓄积呈现急剧上升趋势,生物量和木质林产品碳蓄积分别上升至目前的42.37 TgC/a和0.79 TgC/a,而平均NPP值增加缓慢、碳汇功能降低,说明林分质量有待提高;90年代后碳汇功能开始稳步增强,说明造林面积的迅速增加是引起江西省森林碳增汇的主要驱动因素,但未来森林增汇潜力应源于森林生长和有效的经营管理。     

Carbon cycle of larch plantation based on CO2FIX model

Aims
Plantations play important roles in modifying regional carbon budget and maintaining regional carbon balance. In this study, we assessed larch plantation (Larix gmelinii var. principis-rupprechtii) carbon dynamics in Weichang County from a perspective of the forest biomass-soil-wood-products chain. Our objectives were to elucidate the carbon sink capacity of larch plantation and the influences of biomass, soil and wood product pools on carbon balance.
Methods
CO2FIX model was used to evaluate the carbon storage and flow of larch plantation over a time span of 120 years. Input data for model were derived from practical investigations and published papers. We validated the simulated results and found that this model was suitable in the region and the simulated results were reliable.
Important findings
(1) Soil was the largest carbon pool for larch plantation and the wood product pool had the smallest carbon storage. Meanwhile, carbon storage in wood products gradually increased with time. (2) In a rotation of 50 years from secondary poplar-birch forest to larch plantation, 250 t C·hm^-2 was sequestrated by the larch plantation. 70% of the carbon was transferred into soil in the form of litter and logging slash and the other 30% was transferred into wood products. (3) Larch plantation was a carbon sink during most of its growing period and turned to temporary carbon source when it was harvested. Larch plantation could sequestrate about 0.3 t C·hm^-2·a^-1 in the long term. Our results indicated the importance of wood product carbon pool in carbon dynamics of plantation, which facilitated our understanding in the carbon dynamics and capacity of plantation.     

浑善达克沙地榆树疏林和小叶杨人工林碳密度特征及其与林龄的关系

榆树(Ulmus pumila)疏林是浑善达克沙地的地带性隐域植被, 小叶杨(Populus simonii)是该区域主要的防风固沙造林树种。该文通过测定两种森林生态系统乔木层(叶、枝、干、根)、草本层(地上植被和地下根系)和土壤层(0-100 cm)的碳含量, 比较了两种森林生态系统的碳密度及其分配特征, 并运用空间代替时间的方法, 阐明了乔木层、土壤层和总碳密度随林龄增加的变化特征, 估算了两种森林生态系统的固碳速率。结果表明, 榆树疏林乔木层和土壤层平均碳含量都低于小叶杨人工林, 榆树疏林生态系统总碳密度是小叶杨人工林的1/2。两种森林生态系统的总碳密度中, 乔木层碳密度和土壤层碳密度总占比98%以上; 土壤层与植被层碳密度的比值随林龄的增加而降低, 过熟林时该比值分别为1.66 (榆树疏林)和1.87 (小叶杨人工林); 榆树疏林和小叶杨人工林的乔木层、土壤层和生态系统的总碳密度随林龄的增加而增加, 其中乔木层碳密度及榆树疏林总碳密度与林龄均呈现出显著的线性正相关关系。小叶杨人工林乔木层的固碳速率约为榆树疏林的5倍, 榆树疏林生态系统和小叶杨人工林生态系统的总固碳速率分别为0.81 Mg C·hm^-2·a^-1和5.35 Mg C·hm^-2·a^-1。这一研究结果有利于估算沙地森林生态系统的碳储量, 为区域生态环境恢复和增加碳汇的政策制定提供依据。     

Carbon density characteristics of sparse Ulmus pumila forest and Populus simonii plantation in Onqin Daga Sandy Land and their relationships with stand age

Aims
Sparse Ulmus pumila forest is an intrazonal vegetation in Onqin Daga Sandy Land, while Populus simonii has been widely planted for windbreak and sand dune stabilization in the same region. Our objective was to compare the differences in carbon (C) density of these two forests and their relationships with stand age.
Methods
We measured the C content of tree organs (leaf, twig, stem, and root), herb layers (above ground vegetation and below ground root) and soil layers (up to 100 cm) in sparse Ulmus pumila forests and Populus simonii plantations of different stand ages, and then computed C density and their proportions in total ecosystem carbon density. In addition, we illustrated the variation in carbon density-stand age relationship for tree layer, soil layer and whole ecosystem. We finally estimated the C sequestration rates for these two forests by the space-for-time substitution approach.
Important findings
The average C contents of tree layer and soil layer for sparse Ulmus pumila forests were lower than those for Populus simonii plantations. The total C density of sparse Ulmus pumila forests was half of that of Populus simonii plantations. The carbon density of soil and tree layers accounted for more than 98% of ecosystem C density in the two forests. Irrespective of forest type, the C density ratios of soil to vegetation decreased with stand age. This ratio was 1.66 for sparse Ulmus pumila forests and 1.87 for Populus simonii plantations when they were over-matured. The C density of tree layer, soil layer, and total ecosystem in both forests increased along forest development. There were significantly positive correlations between tree layer’s C density and stand age in both forests and between the total ecosystem C density of sparse Ulmus pumila forests and stand age. The C sequestration rate of tree layer was 5-fold higher in Populus simonii plantation than in sparse Ulmus pumila forest. The ecosystem-level C sequestration rate was 0.81 Mg C·hm^-2·a^-1 for sparse Ulmus pumila forest and 5.35 Mg C·hm^-2·a^-1 for Populus simonii plantation. These findings have implications for C stock estimation of sandy land forest ecosystems and policy-making of ecological restoration and C sink enhancement in the studied area.     

Carbon storage,spatial distribution and the influence factors in Tianshan forests

Aims
Accurate estimation of carbon density and storage is among the key challenges in evaluating ecosystem carbon sink potentials for reducing atmospheric CO₂ concentration. It is also important for developing future conservation strategies and sustainable practices. Our objectives were to estimate the ecosystem carbon density and storage of Picea schrenkiana forests in Tianshan region of Xinjiang, and to analyze the spatial distribution and influencing factors.
Methods
Based on field measurements, the forest resource inventories, and laboratory analyses, we studied the carbon storage, its spatial distribution, and the potential influencing factors in Picea schrenkiana forest of Tianshan. Field surveys of 70 sites, with 800 m² (28.3 m × 28.3 m) for plot size, was conducted in 2011 for quantifying arbor biomass (leaf, branch, trunk and root), grass and litterfall biomass, soil bulk density, and other laboratory analyses of vegetation carbon content, soil organic carbon content, etc.
Important findings
The carbon content of the leaf, branch, trunk and root of Picea schrenkiana is varied from 46.56% to 52.22%. The vegetation carbon content of arbor and the herbatious/litterfall layer was 49% and 42%, respectively. The forest biomass of Picea schrenkiana was 187.98 Mg·hm^-2, with 98.93% found in the arbor layer. The biomass in all layers was in the order of trunk (109.81 Mg·hm^-2) > root (39.79 Mg·hm^-2) > branch (23.62 Mg·hm^-2) > leaf (12.76 Mg·hm^-2). From the age-group point of view, the highest and the lowest biomass was found at the mature forest (228.74 Mg·hm^-2) and young forest (146.77 Mg·hm^-2), respectively. The carbon density and storage were 544.57 Mg·hm^-2 and 290.84 Tg C, with vegetation portion of 92.57 Mg·hm^-2 and 53.14 Tg C, and soil portion of 452.00 Mg·hm^-2 and 237.70 Tg C, respectively. The spatial distribution of carbon density and storage appeared higher in the western areas than those in the eastern regions. In the western Tianshan Mountains (e.g., Ili district), carbon density was the highest, whereas the central Tianshan Mountains (e.g., Manas County, Fukang City, Qitai County) also had high carbon density. In the eastern Tianshan Mountains (e.g., Hami City), it was low. This distribution seemed consistent with the changes in environmental conditions. The primary causes of carbon density difference might be a combined effects of multiple environmental factors such as terrain, precipitation, temperature, and soil.     

吉林省森林植被固碳现状与速率

通过对吉林省森林植被的普遍调查、典型调查以及植被样品含碳率测定, 结合吉林省2009年和2014年森林清查数据, 估算了区域森林植被的碳储量、碳密度及固碳速率。研究结果表明: 林下植被的生物量在不同林分和同类林分中存在较大的差异, 整体不足乔木层生物量的3%, 灌木植物的生物量略高于草本植物和幼树。不同林分类型的乔木含碳率介于45.80%-52.97%之间, 整体表现为针叶林高于阔叶林; 灌木和草本植物分别为39.79%-47.25%和40%左右。吉林省森林植被碳转换系数以0.47或0.48更为准确, 若以0.50或0.45作为植被的碳转换系数计算碳储量, 会造成±5.26%的偏差。吉林省森林植被不仅维持着较高的碳库水平, 而且极具碳汇能力; 2009年和2014年碳储量分别为471.29 Tg C和505.76 Tg C, 累计碳增量34.47 Tg C, 平均每年碳增量6.89 Tg C·a^-1; 碳密度由64.58 t·hm^-2增至66.68 t·hm^-2, 平均增加2.10 t·hm^-2, 固碳速率0.92 t·hm^-2·a^-1。森林植被碳储量的增长主体是蒙古栎(Quercus mongolica)林和阔叶混交林, 合计碳增量占总体的90.34%。受植被发育引起的生物量增长、林分龄组晋级以及森林经营所引起的面积变化影响, 各龄组植被碳增量为幼龄林>过熟林>近熟林>中龄林, 成熟林表现为负增长; 固碳速率为过熟林>幼龄林>近熟林>中龄林>成熟林。森林植被碳储量和碳密度的市/区分布整体表现为自东向西明显的降低变化; 碳增量以东北和中东部地区较高, 西部地区较低; 固碳速率整体以南部的通化地区和白山地区相对较高, 中部的吉林地区和东部的延边地区次之, 西部的白城地区、松原地区等地呈负增长。     

天山森林生态系统碳储量格局及其影响因素

科学地估算亚洲中部天山雪岭杉(Picea schrenkiana)生态系统碳密度与碳储量是评价新疆森林碳汇潜力、评估森林在减缓大气CO₂浓度上升、应对气候变化等方面功能的关键, 对干旱区森林生态系统的保育和可持续发展具有重要意义。该文基于在天山雪岭杉林区布设的70个野外样地调查数据, 结合新疆森林资源连续清查数据, 全面估算了天山雪岭杉生态系统的碳密度和碳储量, 分析了其分布格局与影响因素。结果表明: 天山雪岭杉不同龄组叶、枝、干和根的含碳率变化不显著, 其乔木层平均含碳率为49%, 而林下植被(凋落物、草本等)平均含碳率仅为42%。雪岭杉森林生态系统单位面积生物量为187.98 Mg·hm^-2, 其中乔木层生物量占生态系统总生物量的98.93%。乔木层各组分生物量大小为: 干>根>枝>叶, 而各龄组生物量排序为: 成熟林>中龄林>近熟林>过熟林>幼龄林。雪岭杉生态系统碳密度为544.57 Mg·hm^-2, 碳储量为290.84 Tg C, 其中植被碳密度为92.57 Mg·hm^-2, 植被碳储量为53.14 Tg C, 土壤碳密度为452.00 Mg·hm^-2, 土壤碳储量为237.70 Tg C。天山雪岭杉生态系统碳密度分异与不同林区林带垂直宽度变化具有很高的相关性, 其生态系统碳密度西高东低的分布格局和它所处的环境因子西优东劣的变异是相一致的, 即不同的环境因素组合是造成天山雪岭杉生态系统碳密度差异的主要原因。     

Present status and rate of carbon sequestration of forest vegetation in Jilin Province,Northeast China

Aims
Forests represent the most important component of the terrestrial biological carbon pool and play an important role in the global carbon cycle. The regional scale estimation of carbon budgets of forest ecosystems, however, have high uncertainties because of the different data sources, estimation methods and so on. Our objective was to accurately estimate the carbon storage, density and sequestration rate in forest vegetation in Jilin Province of China, in order to understand the role of the carbon sink and to better manage forest ecosystems.
Methods
Vegetation survey data were used to determine forest distribution, size of area and vegetation types regionally. In our study, 561 plots were investigated to build volume-biomass models; 288 plots of shrubs and herbs were harvested to calculate the biomass of understory vegetation, and samples of trees, shrubs and herbs were collected to analyze carbon content. Carbon storage, density and sequestration rate were estimated by two forest inventory data (2009 and 2014), combined with volume-biomass models, the average biomass of understory vegetation and carbon content of vegetation. Finally, the distribution patterns of carbon pools were presented using ArcGIS soft ware.
Important findings
Understory vegetation biomass overall was less than 3% of the tree layer biomass, varying greatly among different forest types and even among the similar types. The carbon content of trees was between 45.80%-52.97%, and that of the coniferous forests was higher than that of the broadleaf forests. The carbon content of shrub and herb layers was about 39.79%-47.25% and 40%, respectively. Therefore, the vegetation carbon conversion coefficient was 0.47 or 0.48 in Jilin Province, and the conventional use of 0.50 or 0.45 would cause deviation of ±5.26%. The vegetation carbon pool of Jilin Province was at the upper range of regional carbon pool and had higher capacity of carbon sequestration. The value in 2009 and 2014 was 471.29 Tg C and 505.76 Tg C, respectively, and the total increase was 34.47 Tg C with average annual growth of 6.89 Tg C·a^-1. The corresponding carbon sequestration rate was 0.92 t·hm^-2·a^-1. The carbon density rose from 64.58 t·hm^-2 in 2009 to 66.68 t·hm^-2 in 2014, with an average increase of 2.10 t·hm^-2. In addition, the carbon storage of the Quercus mongolica forests and broadleaved mixed forests, accounted for 90.34% of that of all forests. The carbon increment followed the order of young > over-mature > near mature > middle-aged > mature forests. The carbon sequestration rate of followed the order of over-mature > young > near mature > middle-aged > mature forests. Both the carbon increment and the carbon sequestration rate of mature forests were negative. Furthermore, spatially the carbon storage and density were higher in the east than in the west of Jilin province, while the carbon increment was higher in northeast and middle east than in the west. The carbon sequestration rate was higher in Tonghua and Baishan in the south, followed by Jinlin in the middle and Yanbian in the east, while Baicheng and Songyuan, etc. in west showed negative values.     

Carbon storage and potentials of the broad-leaved forest in alpine region of the Qinghai- Xizang Plateau,China

Aims
Our objective was to explore the vegetation carbon storages and their variations in the broad-leaved forests in the alpine region of the Qinghai-Xizang Plateau that includes Qinghai Province and Xizang Autonomous Region.
Methods
Based on forest resource inventory data and field sampling, this paper studied the carbon storage, its sequestration rate, and the potentials in the broad-leaved forests in the alpine region of the Qinghai-Xizang Plateau.
Important findings
The vegetation carbon storage in the broad-leaved forest accounted for 310.70 Tg in 2011, with the highest value in the broad-leaved mixed forest and the lowest in Populus forest among the six broad-leaved forests that include Quercus, Betula, Populus, other hard broad-leaved species, other soft broad-leaved species, and the broadleaved mixed forest. The carbon density of the broad-leaved forest was 89.04 Mg·hm^-2, with the highest value in other hard broad-leaved species forest and the lowest in other soft broad-leaved species forest. The carbon storage and carbon density in different layers of the forests followed a sequence of overstory layer > understory layer > litter layer > grass layer > dead wood layer, which all increased with forest age. In addition, the carbon storage of broad-leaved forest increased from 304.26 Tg in 2001 to 310.70 Tg in 2011. The mean annual carbon sequestration and its rate were 0.64 Tg·a^-1 and 0.19 Mg·hm^-2·a^-1, respectively. The maximum and minimum of the carbon sequestration rate were respectively found in other soft broad-leaved species forest and other hard broad-leaved species forest, with the highest value in the mature forest and the lowest in the young forest. Moreover, the carbon sequestration potential in the tree layer of broad-leaved forest reached 19.09 Mg·hm^-2 in 2011, with the highest value found in Quercus forest and the lowest in Betula forest. The carbon storage increased gradually during three inventory periods, indicating that the broad-leaved forest was well protected to maintain a healthy growth by the forest protection project of Qinghai Province and Xizang Autonomous Region.     

青藏高原高寒区阔叶林植被固碳现状、速率和潜力

为明晰青藏高原高寒区阔叶林植被碳储量现状及其动态变化特征, 利用森林资源清查数据和标准样地实测数据, 估算了青藏高原高寒区(青海和西藏两省区)阔叶林植被的碳储量、固碳速率和固碳潜力。结果表明: 2011年青藏高原高寒区阔叶林植被碳储量为310.70 Tg, 碳密度为89.04 Mg·hm^-2。六类阔叶林型(栎(Quercus)林、桦木(Betula)林、杨树(Populus)林、其他硬阔林、其他软阔林和阔叶混交林)中, 阔叶混交林的碳储量最大, 杨树林碳储量最小; 其他硬阔林碳密度最大, 其他软阔林碳密度最小。空间分配上碳储量和碳密度表现为: 乔木层>灌木层>凋落物层>草本层>枯死木层。不同龄级碳储量和碳密度总体表现为随林龄增加逐渐增大的趋势。阔叶林碳储量从2001年的304.26 Tg增加到2011年的310.70 Tg, 平均年固碳量为0.64 Tg·a^-1, 固碳速率为0.19 Mg·hm^-2·a^-1。不同林型固碳速率表现为其他软阔林最大, 其他硬阔林最小; 不同龄级表现为成熟林最大, 幼龄林最小。阔叶林乔木层固碳潜力为19.09 Mg·hm^-2, 且不同林型固碳潜力表现为栎林最大, 桦树林最小。三次调查期间阔叶林碳储量逐渐增加, 主要原因是近年来森林保护工程的开展使阔叶林生长健康良好。     

Carbon storage of the forests and its spatial pattern in Nei Mongol,China

Aims
Forest carbon storage in Nei Mongol plays a significant role in national terrestrial carbon budget due to its large area in China. Our objectives were to estimate the carbon storage in the forest ecosystems in Nei Mongol and to quantify its spatial pattern.
Methods
Field survey and sampling were conducted at 137 sites that distributed evenly across the forest types in the study region. At each site, the ecosystem carbon density was estimated thorough sampling and measuring different pools of soil (0-100 cm) and vegetation, including biomass of tree, grass, shrub, and litter. Regional carbon storage was calculated with the estimated carbon density for each forest type.
Important findings
Carbon storage of vegetation layer in forests in Nei Mongol was 787.8 Tg C, with the biomass of tree, litter, herbaceous and shrub accounting for 93.5%, 3.0%, 2.7% and 0.8%, respectively. Carbon density of vegetation layer was 40.4 t·hm^-2, with 35.6 t·hm^-2 in trees, 2.9 t·hm^-2 in litter, 1.2 t·hm^-2 in herbaceous and 0.6 t·hm^-2 in shrubs. In comparison, carbon storage of soil layer in forests in Nei Mongol was 2449.6 Tg C, with 79.8% distributed in the first 30 cm. Carbon density of soil layer was 144.4 t·hm^-2. Carbon storage of forest ecosystem in Nei Mongol was 3237.4 Tg C, with vegetation and soil accounting for 24.3% and 75.7%, respectively. Carbon density of forest ecosystems in Nei Mongol was 184.5 t·hm^-2. Carbon density of soil layer was positively correlated with that of vegetation layer. Spatially, both carbon storage and carbon density were higher in the eastern area, where the climate is more humid. Forest reserves and artificial afforestations can significantly improve the capacity of regional carbon sink.