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辽东栎林净初级生产力对气候变化情景响应的模拟与分析   总被引:3,自引:0,他引:3  
利用生态系统模型BIOME-BGC模拟北京东灵山地区辽东栎(Suercus liaotungensis Koidz)林生态系统净初级生产力(net primary productivity,NPP)对全球气候变化潜在响应。首先,BIOME-BGC模型模拟的土壤含水量和NPP与该地区实际测量结果、其他模型模拟研究进行比较分析,结果显示BIOME-BGC能较好的模拟辽东栎林生态系统净生产力。然后,利用BIOME-BGC模拟辽东栎林生态系统NPP对不同气候变化的响应。结果表明:(1)NPP对温度变化不敏感,而对降水和CO_2变化极为敏感;(2)温度、降水和CO_2对NPP的影响并没有显示出交互作用。  相似文献
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Detailed information from the Swedish National Forest Inventory was used to simulate the carbon balance for Sweden by the process-based model Biome-BGC. A few shortcomings of the model were identified and solutions to those are proposed and also used in the simulations. The model was calibrated against CO2 flux data from 3 forests in central Sweden and then applied to the whole country divided into 30 districts and 4 age classes. Gross primary production (GPP) ranged over districts and age classes from 0.20 to 1.71 kg C m−2 y−1 and net ecosystem production (NEP) ranged from −0.01 to 0.44. The 10- to 30-year age class was the strongest carbon sink because of its relatively low respiration rates. When the simulation results were scaled up to the whole country, GPP and NEP were 175 and 29 Mton C y−1, respectively, for the 22.7 Mha of forests in Sweden. A climate change scenario was simulated by assuming a 4°C increase in temperature and a doubling of the CO2 concentration; GPP and NEP then increased to 253 and 48 Mton C y−1, respectively. A sensitivity analysis showed that at present CO2 concentrations NEP would peak at an increase of 5°C for the mean annual temperature. At higher CO2 levels NEP showed a logarithmic increase.  相似文献
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利用模拟退火算法优化Biome-BGC模型参数   总被引:1,自引:1,他引:0  
生态过程模型建立在明确的机理之上,能够较好地模拟陆地生态系统的行为和特征,但模型众多的参数,成为模型具体应用的瓶颈。本文以Biome-BGC模型为例,采用模拟退火算法,对其生理、生态参数进行优化。在优化过程中,先对待优化参数进行了选择,然后采取逐步优化的方法进行优化。结果表明,使用优化后的参数,模型模拟结果与实际观测更为接近,参数优化能有效地降低模型模拟的不确定性。文中参数优化的过程和方法,可为生态模型的参数识别和优化提供一种实例和思路,有助于生态模型应用区域的扩展。  相似文献
4.
利用生态系统模型BIOME-BGC模拟北京东灵山地区辽东栎(Suercus liaotungensis Koidz)林生态系统净初级生产力(net primary productivity,NPP)对全球气候变化潜在响应.首先,BIOME-BGC模型模拟的土壤含水量和NPP与该地区实际测量结果、其他模型模拟研究进行比较分析,结果显示BIOME-BGC能较好的模拟辽东栎林生态系统净生产力.然后,利用BIOME-BGC模拟辽东栎林生态系统NPP对不同气候变化的响应.结果表明:(1)NPP对温度变化不敏感,而对降水和CO2变化极为敏感;(2)温度、降水和CO2对NPP的影响并没有显示出交互作用.  相似文献
5.
张廷龙  孙睿  张荣华  张蕾 《生态学杂志》2013,24(10):2746-2754
 模型模拟和站点观测是陆地生态系统水、碳循环研究最主要的两种手段,但各有优势和不足,若二者相互结合,则能更准确地反映生态系统水、碳通量的动态变化.数据同化为模型与观测结合提供了一条有效的途径.本文采用哈佛森林环境监测站相关数据,利用集合卡曼滤波同化算法,将实测叶面积指数(LAI)和遥感LAI同化进入Biome BGC模型中,对该地区水、碳通量进行模拟.结果表明:与未同化模拟相比,将1998、1999和2006年实测LAI数据同化后,模型模拟碳通量(NEE)与通量观测NEE的决定系数(R2)平均提升8.4%;蒸散发(ET)的R2平均提升10.6%;NEE的绝对误差和(SAE)和均方根误差(RMSE)平均下降17.7%和21.2%,ET的SAE和RMSE平均下降26.8%和28.3%.将2000—2004年MODIS LAI 产品与模型同化后,NEE、ET模拟值与观测值间的R2分别提升7.8%和4.7%;NEE的SAE和 RMSE分别下降21.9%和26.3%,ET的SAE和 RMSE分别下降24.5%和25.5%.无论实测LAI还是遥感观测LAI,同化进入模型都能不同程度地提高水碳通量的模拟精度.  相似文献
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Biome-BGC模型通过耦合植被、土壤与大气间的水分与CO2交换过程,实现植被生产力的模拟,但土壤水平衡模块的不够完善,导致在长时间无降水情况下植被生产力模拟存在较大误差.针对这一问题,本文对Biome-BGC模型中土壤水分胁迫气孔导度方程、蒸散计算公式及土壤水分流失过程等3方面进行了改进和调整,利用改进的Biome-BGC模型模拟美国哈佛森林地区蒸散、植被生产力,并与地面通量观测值进行了比较.结果表明,改进后模拟精度有明显的提高,蒸散、植被生态系统生产力(NEE)与观测值间的决定系数分别由0.483和0.658提高到0.617和O.813,蒸散逐年均方根误差平均下降了48.7%,NEE逐年误差平方和平均下降了39.8%.改进后的模型模拟结果更接近观测值.  相似文献
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In this study, the BIOME-BGC model, a biogeochemical model, was used and validated to estimate GPP (Gross Primary Productivity) and NPP (Net Primary Productivity) of Pinus elliottii forest in red soil hilly region and their responses to inter-annual climate variability during the period of 1993–2004 and climate change scenarios in the future. Results showed that the average total GPP and NPP were 1941 g C m?2a?1 and 695 g C m?2a?1, and GPP and NPP showed an increasing trend during the study period. The precipitation was the key factor controlling the GPP and NPP variation. Scenario analysis showed that doubled CO2 concentration would not benefit for GPP and NPP with less than 1.5% decrease. When CO2 concentration fixed, GPP responded positively to precipitation change only, and temperature increase by 1.5°C with precipitation increase, while NPP responded positively to precipitation change only. When CO2 concentration was doubled and climate was changed, GPP and NPP responded positively to precipitation change, and GPP also responded positively to temperature increase by 1.5°C with precipitation change.  相似文献
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