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1.
利用美国蒙大拿大学运用生物地球化学模式(Biome-BGC)估算出的净初级生产力产品(MOD17A3),研究吴起县2000—2013年植被净初级生产力(net primary productivety,NPP)的变化特征。结果表明:吴起县2000—2013年年均NPP变化范围在156.92~275.37gC·m-2·a-1,平均值为218.27gC·m-2·a-1,14a植被NPP在波动中呈显著增加趋势(P0.01);全县85.93%面积的NPP变化百分率在10%~50%,年均NPP变化百分率-10%的区域主要分布在吴起县县城周边,年均NPP变化百分率为-10%~0%的区域主要分布在吴起县西北部地区铁边城镇及王坬子乡等地,年均NPP变化百分率为0%~10%的区域主要分布在吴起县城以北及以西地区,植被NPP变化百分率50%的区域分布在吴起县南部白豹乡、楼坊坪乡及长官庙乡部分地区。  相似文献   

2.
三江源区是我国乃至亚洲重要的水源地,是高寒生态系统的脆弱区和敏感区。植被净初级生产力(Net Primary Productivity,NPP)是评价生态环境状况的重要指标。利用1961—2014年三江源区18个气象站的气象观测资料、11个监测点的草地生物量观测资料以及中国地区气候变化预估数据集的全球气候模式加权平均集合数据,通过5种估算植被NPP气候模型的对比验证,筛选出适用性好、精度高的模型构建该区植被NPP估算模型,并进行植被NPP的时空变化特征及对气候变化的响应分析。结果表明:周广胜模型对三江源区的植被NPP模拟结果有效且精度最高,故选用该模型模拟三江源区植被NPP。1961—2014年,三江源区植被NPP呈从东南向西北逐渐降低的空间分布特征,平均值为59.59 gC·m~(-2),其中黄河源区植被NPP的年际及空间波动高于长江源区和澜沧江源区;近54 a植被NPP整体呈显著增加趋势,但不同区域变化幅度有所差异。气温是影响三江源区植被NPP增加的主要气象因素;未来90 a三江源区植被NPP仍呈现持续增加态势。  相似文献   

3.
《内蒙古气象》2022,(1):7-11
为研究通辽市植被生态质量变化特征,文章利用MODIS归一化植被指数(NDVI)和气象监测资料,分析了通辽市2000—2019年生长季植被覆盖度、净初级生产力(NPP)以及植被生态质量指数变化特征,并分析了三者与气候因子的相关关系,以期为建设“生态通辽”提供决策依据。结果表明:(1)研究区生长季植被覆盖度平均值从2000年的36.26%上升至2019年的49.57%,平均每年增加0.54%;(2)研究区植被NPP平均值从2000年的198.04 gC·m-2增加到2019年的404.54 gC·m-2,平均每年增加8.63 gC·m-2;(3)研究区植被生态质量指数平均值由2000年40.28增加到2019年的70.87,平均每年增加1.25,2019年植被生态质量达到2000年以来最好;(4)研究区生长季植被覆盖度、植被NPP、植被生态质量指数与生长季平均气温呈不显著的负相关,与降水量呈极显著的正相关,表明降水量是研究区植被生态质量显著正向因子。  相似文献   

4.
我国南水北调东线地区陆地植被NPP变化特征   总被引：7，自引：0，他引：7       下载免费PDF全文
基于EOS/MODIS（TERRA）卫星遥感资料,讨论中国南水北调东线地区陆地植被年均净初级生产力（NPP）的变化特征。结果表明,2000-2004年该地区的陆地植被年均NPP的变化范围为0～1494 g/(m2·a),5 a平均值为395.06 g/(m2·a)。对不同植被的年均NPP分析表明,常绿阔叶林的NPP最大,草地最小。气温是影响该地区陆地植被NPP变化的主要因素,未来南水北调东线地区地表水资源的减少不会对陆地植被的生长产生明显影响。  相似文献   

5.
青藏高原1981～2000年植被净初级生产力对气候变化的响应   总被引：11，自引：3，他引：8  
基于分辨率为0.1°×0.1°的植被、土壤和气象数据,利用大气-植被相互作用模型（AVIM2）模拟研究了青藏高原1981～2000年植被净初级生产力（NPP）对气候变化的响应。结果表明：青藏高原近20年自然植被（森林、草地和灌木）受气温和降水量增加的影响,NPP总量呈现上升趋势。灌木和森林NPP总量分别以每年1.14％和0.88％的速度增加,均达到统计上的显著性水平。草地NPP上升趋势不如灌木和森林显著。降水量变化对森林和草地NPP的影响高于气温变化对它们的影响,而降水量变化对灌木的影响则小于气温变化影响。总的区域平均来看,尽管1981～2000年青藏高原年平均净辐射通量略有降低,但由于平均气温以0.058 ℃·a-1的速率增加,且降水量略有增长,降水量与气温的共同作用使得青藏高原植被NPP总量呈上升趋势。  相似文献   

6.
利用2000—2015年MOD17A3数据和气象站点资料,分析呼伦贝尔市NPP的时空变化特征及其对气候变化的响应情况。研究表明,呼伦贝尔市平均植被NPP为261.02 gC/(m~2·a),总体呈自西向东依次递增的分布格局。NPP的年际变化呈波动增长趋势,平均变化率为5.51gC/(m~2·a),线性增长达到显著的区域主要位于呼伦贝尔草原、大兴安岭南部林地和大兴安岭与松嫩平原过渡的耕地。16个气象站周边的NPP与各站年降水量均呈正相关,且除莫力达瓦达斡尔族自治旗(简称莫旗)外均通过了0.01水平的显著性检验,NPP与年平均气温均呈负相关,但除海拉尔区外均未通过显著性检验,NPP与日照时数正、负相关的台站同时存在。由此可知,降水是影响呼伦贝尔市NPP变化的主要因素。  相似文献   

7.
2000-2016年赤水河流域植被生态质量变化分析   总被引：1，自引：0，他引：1       下载免费PDF全文
为研究中国西部赤水河流域植被生态质量变化特征,本文基于MODIS归一化植被指数（NDVI）和气象监测资料,获取了中国西部赤水河流域2000-2016年植被覆盖度、净初级生产力（NPP）以及植被生态质量指数变化数据,并对17年期间植被生态质量时空变化进行分析。结果表明:（1）植被覆盖度均值从2000年的55.4%提高到了2016年的67.4%,覆盖度呈平均每10年增加6.8%的变化趋势;（2）植被NPP均值从2000年的864 gC/m2提高到了2016年的1024 gC/m2,NPP呈平均每10年增加63 gC/m2的变化趋势;（3）近年来植被生态质量显著提高,2016年植被生态质量为2000年以来最好,植被生态质量指数高达83.7。  相似文献   

8.
季劲钧  黄玫  刘青 《气象学报》2005,63(3):257-266
应用大气植被相互作用模式(AVIM)模拟了内蒙古半干旱草原的净初级生产力和生物量。在此基础上,通过气温和降水变化的敏感性控制试验探讨了气候变化对草地初级生产力的影响机理。研究表明,无论是降水或温度的变化对草地的生产力都有显著影响。降水增加,生产力增加。而温度增加,生产力下降。气候变化对生产力影响的机理是:降水增加改善了土壤的水分供给条件,增强了光合速率,从而提高了生产力。温度增高,一方面可以增加光合速率,另一方面却使蒸散加强,土壤变干,光合速率下降,而后一作用过程在半干旱地区大于前者,因而温度增高使生产力下降。单一气候因子敏感性试验表明,温度增高或降低2℃,年净初级生产力(NPP)变化约20%,中纬度半干旱草地地上生物量可以改变30%以上。降水量变化50%,年NPP改变37%,地上生物量将改变近30%。  相似文献   

9.
以位于青藏高原与黄土高原及陇南山地过渡带的甘南藏族自治州为例,基于考虑土壤冻融界面变化的陆面过程模式研究了1979-2012年冻土变化及水资源与生态系统碳通量对气候变化的响应。结果表明,甘南州气候态多年冻土面积约1. 5×104km2,季节性冻土约占2. 5×10~4km~2,多年冻土最大融化深度呈增加趋势,季节冻土最大冻结深度逐渐减少,整体上冻土正随着气温上升逐步退化;尽管降雨有所增加,而气温上升引起的蒸散发增加也可能是产流减少的原因之一,其中多年冻土区更为敏感,水热变化增减率较季节冻土区大;生态系统碳循环方面,北部主要表现为碳源,南部则表现为碳汇,升温促进植被生长,使得进入生态系统的碳呈略微增加的趋势,尽管总初级生产力(GPP)与净初级生产力(NPP)呈增长趋势,但植被碳利用效率逐步减小,表明气候变化背景下生态系统固碳能力有所退化;最后经多元回归分析可知,气候变化在多年冻土区可以解释66%的NPP变化与31%的生态系统净交换量(NEE)变化,而在季节冻土区则能解释45%的净初级生产力变化。  相似文献   

10.
基于卫星遥感数据和气象数据,采用距平分析、趋势分析、相关性分析等方法,分析了2000—2021年江西省植被生态质量时空变化特征,及其与气温、降水、日照等气候因子的关系。结果表明：1） 自2000年以来江西省植被生态质量整体改善明显,植被净初级生产力和生态质量指数呈明显上升趋势,年平均分别增加3.92 gC/m2和0.4,尤其2011年以后植被净初级生产力和生态质量指数处于较高水平,其中2018年最佳。2） 江西省植被生态指标低值区域位于城区周边,以及由长江和江西五大河流域的泥沙沉积形成的、以鄱阳湖为中心的冲积平原,中值区域位于中南部丘陵,高值区域分布于省境边陲山脉。3） 江西省植被生态指标与年降水量、年平均气温呈显著相关关系,与日照时数相关性不显著。与气候因子的相关性呈现地域差异,南部区域受气温影响较为明显,而中部盆地和东北区域受降水量影响更为明显。  相似文献   

11.
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12.
Grassland is one of the most widespread vegetation types worldwide and plays a significant role in regional climate and global carbon cycling. Understanding the sensitivity of Chinese grassland ecosystems to climate change and elevated atmospheric CO2 and the effect of these changes on the grassland ecosystems is a key issue in global carbon cycling. China encompasses vast grassland areas of 354 million ha of 17 major grassland types, according to a national grassland survey. In this study, a process-based terrestrial model the CENTURY model was used to simulate potential changes in net primary productivity (NPP) and soil organic carbon (SOC) of the Leymus chinensis meadow steppe (LCMS) under different scenarios of climatic change and elevated atmospheric CO2. The LCMS sensitivities, its potential responses to climate change, and the change in capacity of carbon stock and sequestration in the future are evaluated. The results showed that the LCMS NPP and SOC are sensitive to climatic change and elevated CO2. In the next 100 years, with doubled CO2 concentration, if temperature increases from 2.7-3.9˚C and precipitation increases by 10% NPP and SOC will increase by 7-21% and 5-6% respectively. However, if temperature increases by 7.5-7.8˚C and precipitation increases by only 10% NPP and SOC would decrease by 24% and 8% respectively. Therefore, changes in the NPP and SOC of the meadow steppe are attributed mainly to the amount of temperature and precipitation change and the atmospheric CO2 concentration in the future.  相似文献   

13.
A high resolution global model of the terrestrial biosphere is developed to estimate changes in nitrous oxide (N2O) emissions from 1860–1990. The model is driven by four anthropogenic perturbations, including land use change and nitrogen inputs from fertilizer, livestock manure, and atmospheric deposition of fossil fuel NO x . Global soil nitrogen mineralization, volatilization, and leaching fluxes are estimated by the model and converted to N2O emissions based on broad assumptions about their associated N2O yields. From 1860–1990, global N2O emissions associated with soil nitrogen mineralization are estimated to have decreased slightly from 5.9 to 5.7 Tg N/yr, due mainly to land clearing, while N2O emissions associated with volatilization and leaching of excess mineral nitrogen are estimated to have increased sharply from 0.45 to 3.3 Tg N/yr, due to all four anthropogenic perturbations. Taking into account the impact of each perturbation on soil nitrogen mineralization and on volatilization and leaching of excess mineral nitrogen, global 1990 N2O emissions of 1.4, 0.7, 0.4 and 0.08 Tg N/yr are attributed to fertilizer, livestock manure, land clearing and atmospheric deposition of fossil fuel NO x , respectively. Consideration of both the short and long-term fates of fertilizer nitrogen indicates that the N2O/fertilizer-N yield may be 2% or more.C. NBM Definitions AET mon (cm H2O) = monthly actual evapotranspiration - AET ann (cm H2O) = annual actual evapotranspiration - age h (years) = stand age of herbaceous biomass - age w (years) = stand age of woody biomass - atmblc (gC/m2/month) = net flux of CO2 from grid - biotoc (gC/g biomass) = 0.50 = convert g biomass to g C - beff h = 0.8 = fraction of cleared herbaceous litter that is burned - beff w = 0.4 = fraction of cleared woody litter that is burned - bfmin = 0.5 = fraction of burned N litter that is mineralized or converted to reactive gases which rapidly redeposit. Remainder assumed pyrodenitrified to N2. + N2O - bprob = probability that burned litter will be burned - burn h (gC/m2/month) = herbaceous litter burned after land clearing - burn w (gC/m2/month) = woody litter burned after land clearing - cbiomsh (gC/m2) = C herbaceous biomass pool - cbiomsw (gC/m2) = C woody biomass pool - clear (gC/m2/month) = woody litter C removed by land clearing - clearn (gN/m2/month) = woody litter N removed by land clearing - cldh (month–1) = herbaceous litter decomposition coefficient - cldw (month–1) = woody litter decomposition coefficient - clittrh (gC/m2) = C herbaceous litter pool - clittrw (gC/m2) = C woody litter pool - clph (month–1) = herbaceous litter production coefficient - clpw (month–1) = woody litter production coefficient - cnrath (gC/gN) = C/N ratio in herbaceous phytomass - cnrats (gC/gN) = C/N ratio in soil organic matter - cnratt (gC/gN) = average C/N ratio in total phytomass - cnratw (gC/gN) = C/N ratio in woody phytomass - crod (month–1) = forest clearing coefficient - csocd (month–1) = actual soil organic matter decompostion coefficient - decmult decomposition coefficient multiplier; natural =1.0; agricultural =1.0 (1.2 in sensitivity test) - fertmin (gN/m2/month) = inorganic fertilizer input - fleach fraction of excess inorganic N that is leached - fligh (g Lignin/ g C) = lignin fraction of herbaceous litter C - fligw (g Lignin/ g C) = 0.3 = lignin fraction of woody litter C - fln2o = .01–.02 = fraction of leached N emitted as N2O - fnav = 0.95 = fraction of mineral N available to plants - fosdep (gN/m2/month) = wet and dry atmospheric deposition of fossil fuel NO x - fresph = 0.5 = fraction of herbaceous litter decomposition that goes to CO2 respiration - fresps = 0.51 + .068 * sand = fraction of soil organic matter decomposition that goes to CO2 respiration - frespw = 0.3 * (* see comments in Section 2.3 under decomposition) = fraction of woody litter decomposition that goes to CO2 respiration - fsoil = ratio of NPP measured on given FAO soil type to NPFmiami - fstruct = 0.15 + 0.018 * ligton = fraction of herbaceous litter going to structural/woody pool - fvn2o = .05–.10 = fraction of excess volatilized mineral N emitted as N2O - fvol = .02 = fraction of gross mineralization flux and excess mineral N volatilized - fyield ratio of total agricultural NPP in a given country in 1980 to total NPPmiami of all displaced natural grids in that country - gimmob h (gN/m2/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of herbaceous litter - gimmob s (gN/m2/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of soil organic matter - gimmob w (gN/m2/month) = gross immobilization of inorganic N into microbial biomass due to decomposition of woody litter - graze (gC/m2/month) = C herbaceous biomass grazed by livestock - grazen (gN/m2/month) = N herbaceous biomass grazed by livestock - growth h (gC/m2/month) = herbaceous litter incorporated into microbial biomass - growth w (gC/m2/month) = woody litter incorporated into microbial biomass - gromin h (gN/m2/month) = gross N mineralization due to decomposition and burning of herbaceous litter - gromin s (gN/m2/month) = gross N mineralization due to decomposition of soil organic matter - gromin w (gN/m2/month) = gross N mineralization due to decomposition and burning of woody litter - herb herbaceous fraction by weight of total biomass - leach (gN/m2/month) = leaching (& volatilization) losses of excess inorganic N - ligton (g lignin-C/gN) = lignin/N ratio in fresh herbaceous litter - LP h (gC/m2/month)= C herbaceous litter production - LP (gC/m2/month) = C woody litter production - LPN h (gN/m2/month) = N herbaceous litter production - LPN W (gN/m2/month) = N woody litter production - manco2 (gC/m2/month) = grazed C respired by livestock - manlit (gC/m2/month) = C manure input (feces + urine) - n2oint (gN/m2/month) = intercept of N2O flux vs gromin regression - n2oleach (gN/m2/month) = N2O flux associated with leaching and volatilization of excess inorganic N - n2onat (gN/m2/month) = natural N2O flux from soils - n2oslope slope of N2O flux vs gromin regression - nbiomsh (gN/m2) = N herbaceous biomass pool - nbiomsw (gN/m2) = N woody biomass pool - nfix (gN/m2/month) = N2 fixation + natural atmospheric deposition - nlittrh (gN/m2) = N herbaceous litter pool - nlittrw (gN/m2) = N woody litter pool - nmanlit (gN/m2/month) = organic N manure input (feces) - nmanmin (gN/m2/month) = inorganic N manure input (urine) - nmin (gN/m2) = inorganic N pool - NPP acth (gC/m2/month)= actual herbaceous net primary productivity - NPP actw (gC/m2/month) = actual woody net primary productivity - nvol (gN/m2/month) = volatilization losses from inorganic N pool - plntnav (gN/m2/month)= mineral N available to plants - plntup h (gN/m2/month) = inorganic N incorporated into herbaceous biomass - plntup w (gN/m2/month) = inorganic N incorporated into woody biomass - precip ann (mm) = mean annual precipitation - precip mon (mm) = mean monthly precipitation - pyroden h (gN/m2/month) = burned herbaceous litter N that is pyrodenitrified to N2 - pyroden w (gN/m2/month) = burned woody litter N that is pyrodenitrified to N2 - recyc fraction of N that is retranslocated before senescence - resp h (gC/m2/month) = herbaceous litter CO2 respiration - resp s (gC/m2/month) = soil organic carbon CO2 respiration - resp w (gC/m2/month) = woody litter CO2 respiration - sand sand fraction of soil - satrat ratio of maximum NPP to N-limited NPP - soiloc (gC/m2) = soil organic C pool - soilon (gN/m2) = soil organic N pool - temp ann (°C) = mean annual temperature - temp mon (°C) = mean monthly temperature Now at the NOAA Aeronomy Laboratory, Boulder, Colorado.  相似文献   

14.
The potential effects of climate change on net primary productivity (NPP) of U.S. rangelands were evaluated using estimated climate regimes from the A1B, A2 and B2 global change scenarios imposed on the biogeochemical cycling model, Biome-BGC from 2001 to 2100. Temperature, precipitation, vapor pressure deficit, day length, solar radiation, CO2 enrichment and nitrogen deposition were evaluated as drivers of NPP. Across all three scenarios, rangeland NPP increased by 0.26 % year?1 (7 kg C ha?1 year?1) but increases were not apparent until after 2030 and significant regional variation in NPP was revealed. The Desert Southwest and Southwest assessment regions exhibited declines in NPP of about 7 % by 2100, while the Northern and Southern Great Plains, Interior West and Eastern Prairies all experienced increases over 25 %. Grasslands dominated by warm season (C4 photosynthetic pathway) species showed the greatest response to temperature while cool season (C3 photosynthetic pathway) dominated regions responded most strongly to CO2 enrichment. Modeled NPP responses compared favorably with experimental results from CO2 manipulation experiments and to NPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS). Collectively, these results indicate significant and asymmetric changes in NPP for U.S. rangelands may be expected.  相似文献   

15.
利用6个地球系统模式模拟的植被净初级生产力（NPP）对1901～2005年NPP时空变化进行了研究,并结合气候因子分析了NPP的变化与气温和降水的关系。结果表明:（1）近百年来全球NPP呈现上升趋势,模式集合平均的趋势系数为0.88,通过了99.9%的信度检验;北半球的趋势比南半球明显。（3）近百年来800 g(C) m-2 a-1以上的NPP高值区主要分布在南美洲赤道地区、非洲赤道地区、中南半岛和印度尼西亚一带的热带雨林区;低值区主要分布在北半球高纬度地区、非洲北部地区、亚洲大陆干旱半干旱区以及青藏高原西北部地区。（3）全球NPP与气温百年演变在大部分地区主要为正相关关系,仅在赤道附近的南美洲、非洲以及印度地区为负相关关系,主要由于这些地区辐射是NPP的限制因子。全球NPP与降水的百年变化在大部分地区也主要是正相关关系,在非洲北部到西亚中亚的干旱半干旱地区为负相关关系。（4）6个地球系统模式在全球21个区域的大部分地区的NPP和气温降水的变化关系较为一致,西非地区不同模式变化不一致,NPP模拟的不确定性较大,其次是地中海地区。（5）东亚地区NPP与气候的百年演变同步并且相关性高,反映了强烈的植被大气相互作用过程。  相似文献   

16.
生态阈值现象普遍存在于自然系统中.气候变化幅度过大,超出了生态系统本身的调节和修复能力,生态系统的结构功能就会遭到破坏.新疆干旱区气候波动明显,该区草地生态系统对大气氮沉降和气候变化的响应是否存在阈值,有待深入研究.本文以天山北坡沿海拔梯度分布的四种草地类型（高山草甸（AM）、中山森林草地（MMFM）、低山干草原（LMDG）和平原荒漠草原（PDG））为研究对象,基于DNDC模型,揭示氮沉降及气候变化对天山北坡草地生态系统净初级生产力的影响.研究结果表明：1）草地净初级生产力 （NPP）对氮沉降增加的响应存在阈值,PDG、LMDG、MMFM和AM的响应阈值分别为20±5.77、60±26.46、50±15.28和30±11.55 kg·hm-2.2）四种草地类型的NPP从大到小依次为MMFM、LMDG、PDG和AM,水热条件是决定NPP的主要因素.3）PDG草地NPP对温度升高的响应存在阈值,而对于其他类型的草地,在目前的研究中尚未得出确切结论.4）PDG和LMDG草地NPP与降水有明显的正相关关系,而AM草地NPP的变化与降水变化呈负相关.不同草地类型对降水变化的敏感程度也有较大差异,PDG最大,其次是LMDG,之后是AM和MMFM.  相似文献   

17.
We assess the appropriateness of using regression- and process-based approaches for predicting biogeochemical responses of ecosystems to global change. We applied a regression-based model, the Osnabruck Model (OBM), and a process-based model, the Terrestrial Ecosystem Model (TEM), to the historical range of temperate forests in North America in a factorial experiment with three levels of temperature (+0 °C, +2 °C, and +5 °C) and two levels of CO2 (350 ppmv and 700 ppmv) at a spatial resolution of 0.5° latitude by 0.5° longitude. For contemporary climate (+0 °C, 350 ppmv), OBM and TEM estimate the total net primary productivity (NPP) for temperate forests in North America to be 2.250 and 2.602 × 1015 g C ? yr?1, respectively. Although the continental predictions for contemporary climate are similar, the responses of NPP to altered climates qualitatively differ; at +0 °C and 700 ppmv CO2, OBM and TEM predict median increases in NPP of 12.5% and 2.5%, respectively. The response of NPP to elevated temperature agrees most between the models in northern areas of moist temperate forest, but disagrees in southern areas and in regions of dry temperate forest. In all regions, the response to CO2 is qualitatively different between the models. These differences occur, in part, because TEM includes known feedbacks between temperature and ecosystem processes that affect N availability, photosynthesis, respiration, and soil moisture. Also, it may not be appropriate to extrapolate regression-based models for climatic conditions that are not now experienced by ecosystems. The results of this study suggest that the process-based approach is able to progress beyond the limitations of the regression-based approach for predicting biogeochemical responses to global change.  相似文献   

18.
Terrestrial carbon fluxes are an important factor in regulating concentrations of atmospheric carbon dioxide (CO2). In this study, we use a coupled climate model with interactive biogeochemistry to benchmark the simulation of net primary productivity (NPP) and its response to elevated atmospheric CO2. Short-term field experiments such as Free-Air Carbon Dioxide Enrichment (FACE) studies have examined this phenomenon but it is difficult to infer trends from only a few years of field data. Here, we employ the University of Victoria's Earth System Climate Model (UVic ESCM) version 2.8 to compare simulated changes in NPP due to an elevated atmospheric CO2 concentration of 550 ppm to observed increases in NPP of 23% ±2% from four temperate forest FACE studies between 1997 and 2002. We further compare two scenarios: elevated CO2 with climate change, and elevated CO2 without climate change, the latter being consistent with FACE methodology. In the climate change scenario global terrestrial and forest-only NPP increased by 24.5% and 27.9%, respectively, while these increases were 21.0% and 17.2%, respectively, in the latitude band most representative of the location of the FACE studies. In the scenario without climate change, terrestrial and forest-only NPP increased instead by 28.3% and 30.6%, respectively, while these increases were 24.3% and 14.4%, respectively, in the FACE latitudes. This suggests that the model may underestimate temperate forest NPP increases when compared to results from temperate forest FACE studies and highlights the need for both increased experimental study of other forest biomes and further model development.  相似文献   

19.
Using the regional terrestrial Net Primary Production (NPP) from different observations and models over China, we validated the NPP simulations and explored the relationship between NPP and climate variation at interannual and decadal scales in the Modified Sheffield Dynamic Global Vegetation Model (M-SDGVM) during 1981–2000. M-SDGVM shows agreement with the NPP data from 743 sites under the Global Primary Production Data Initiative (GPPDI). The spatial and the zonal averaged NPP of M-SDGVM agree well with ...  相似文献   

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