Comparing global models of terrestrial net primary productivity (NPP): importance of vegetation structure on seasonal NPP estimates

Estimates of the seasonal absorbed fraction of photosynthetically active radiation (FPAR) and net primary productivity (NPP) are compared among four production efficiency models (PEMs) and seven terrestrial biosphere models simulating canopy development. In addition, the simulated FPARs of the models are compared to the FASIR-FPAR derived from NOAA-AVHRR satellite observations. All models reproduce observed summergreen phenology of temperate deciduous forests rather well, but perform less well for raingreen phenology of savannas. Some models estimate a much longer active canopy in savannas than indicated by satellite observations. As a result, these models estimate high negative monthly NPP during the dry season. For boreal and tropical evergreen ecosystems, several models overestimate LAI and FPAR. When the simulated canopy does respond to unfavourable periods, the seasonal NPP is largely determined by absorbed photosynthetically active radiation (APAR). When the simulated canopy does not respond to unfavourable periods, the light use efficiency (LUE) influences the seasonal NPP more. However, the relative importance of APAR and LUE can change seasonally.     

Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency

Twelve global net primary productivity (NPP) models were compared: BIOME3, CASA, CARAIB, FBM, GLO-PEM, HYBRID, KGBM, PLAI, SDBM, SIB2, SILVAN and TURC. These models all use solar radiation as an input, and compute either absorbed solar radiation directly, or the amount of leaves used to absorb solar radiation, represented by the leaf area index (LAI). For all models, we obtained or estimated photosynthetically active radiation absorbed by the canopy (APAR). We then computed the light use efficiency for NPP (LUE) on an annual basis as the ratio of NPP to APAR. We analysed the relative importance for NPP of APAR and LUE. The analyses consider the global values of these factors, their spatial patterns represented by latitudinal variations, and the overall grid cell by grid cell variability. Spatial variability in NPP within a model proved to be determined by APAR, and differences among models by LUE. There was a compensation between APAR and LUE, so that global NPP values fell within the range of ‘generally accepted values’. Overall, APAR was lower for satellite driven models than for the other models. Most computed values of LUE were within the range of published values, except for one model.     

Comparing global models of terrestrial net primary productivity (NPP): analysis of the seasonal atmospheric CO2 signal

Eight terrestrial biospheric models (TBMs) calculating the monthly distributions of both net primary productivity (NPP) and soil heterotrophic respiration (R_H) in the Potsdam NPP Model Intercomparison workshop are used to simulate seasonal patterns of atmospheric CO₂ concentration. For each model, we used net ecosystem productivity (NEP = NPP – R_H) as the source function in the TM2 atmospheric transport model from the Max-Planck Institute for Meteorology. Comparing the simulated concentration fields with detrended measurements from 25 monitoring stations spread over the world, we found that the decreasing seasonal amplitude from north to south is rather well reproduced by all the models, though the amplitudes are slightly too low in the north. The agreement between the simulated and observed seasonality is good in the northern hemisphere, but poor in the southern hemisphere, even when the ocean is accounted for. Based on a Fourier analysis of the calculated zonal atmospheric signals, tropical NEP plays a key role in the seasonal cycle of the atmospheric CO₂ in the whole southern hemisphere. The relatively poor match between measured and predicted atmospheric CO₂ in this hemisphere suggests problems with all the models. The simulation of water relations, a dominant regulator of NEP in the tropics, is a leading candidate for the source of these problems.     

四川植被净第一性生产力(NPP)对全球气候变化的响应

根据全球气候变化的趋势 ,利用生态信息系统 (EIS)技术 ,采用植被净第一性生产力模型 ,并结合海拔因素 ,模拟了四川植被净第一性生产力在未来气候 5种水热条件下空间分布格局的变化趋势。结果表明 ,当前四川植被的净第一性生产力 (NPP)从总体上沿东南向西北呈逐渐递减趋势。植被净第一性生产力与降水量呈明显正相关关系 ,二者曲线比较近似。与可能蒸散率呈明显负相关关系 ,与海拔关系比较复杂。在盆地内 ,NPP值主要取决于降水量的多少。在盆地向高原过渡地区和高山高原地区 ,植被净第一性生产力主要取决于可能蒸散率的大小。随着全球气候的变化 ,四川省的植被净第一性生产力将沿东南至西北方向发生面积和值的推移。当温度升高 2 5℃ ,降水量增加 10 %时 ,四川省的植被净第一性生产力将增加13 76 % ,随着降水量增加到 2 0 % ,其值将进一步升高 ,达到 10 92 2TDM·hm-2 ·年 -1。当温度升高 4℃ ,降水量增加 10 %时 ,四川省的植被净第一性生产力将增加 18 2 9% ,随着降水量减少到P 10 %时 ,其值将逐渐减少到 9 5 30TDM·hm-2 ·年-1。     

陆地植被净初级生产力计算模型研究进展

植被净初级生产力(NPP)研究是全球变化与陆地生态系统的核心内容之一。在回顾NPP模型研究的基础上,综合分析了气候模型、生态生理过程模型、光能利用率模型各自的优缺点,并对NPP模型研究做出展望。生态生理过程模型是当前陆地NPP估算研究的主要手段,而区域尺度转换则是它所面临的关键问题。近年来光能利用率模型已成为NPP估算的一种全新手段,它利用遥感所获得的全覆盖数据,使区域及全球尺度的NPP估算成为可能,但其生态学机理还有待于进一步研究。已有研究表明,“生态一遥感耦合模型”将是陆地NPP估算的主要发展方向,它融合了生态生理过程模型和光能利用率模型的优点,增强了NPP模型估算的可靠性和可操作性。     

广西植被净初级生产力(NPP)时空演变及主要影响因素分析

利用2001-2010年EOS/MODIS17A3卫星遥感资料,对广西植被净初级生产力(NPP)时空特征及其影响因素进行分析.结果表明：(1)NPP 表现出明显的年际变化,2005年植被年均 NPP 最小为625 gC??m-2??a-1,2003年最大,为714 gC??m-2??a-1,十年间广西植被年NPP平均值为662 gC??m-2??a-1;(2)不同植被类型NPP有较大差异,森林、灌木、农作物的NPP 平均值分别为834、614、517 gC??m-2??a-1;(3)十年间广西区年均NPP为显著下降趋势,且年均气温和降水对NPP时间变化作用显著,而日照时数对 NPP 时间变化的作用不显著;(4)广西区NPP空间格局形成主要影响因素为坡度,其次为经度,再次为地貌特征、纬度和降水;(5)非喀斯特区域北热带季雨林、南亚热带季雨林化/季雨化常绿阔叶林年均 NPP 大于喀斯特地区,相反,喀斯特地区中亚热带常绿阔叶林,农作物年均NPP大于非喀斯特地区.整体而言,广西非喀斯特地区植被NPP为683 gC??m-2??a-1,喀斯特地区植被NPP为620 gC??m-2??a-1.     

西辽河流域植被NPP时空分布特征及其影响因素研究

为研究西辽河流域植被生长特征及受气候变化的影响,该文以2000年—2015年MOD17A3的年均植被净初级生产力(NPP)数据、植被类型数据、土壤类型数据以及气温、降水资料为基础,利用GIS和RS技术,分析了西辽河流域植被净初级生产力时空格局、演变特征及驱动因子。结果表明:(1)西辽河流域近16年来植被NPP总量呈波动增加的趋势,变化范围为156.89～260.90 g C·m^-2·a^-1,平均值为219.76 g C·m^-2·a^-1,空间分布呈“边缘高、中间低”的特征; 植被NPP变化斜率为-16.53～16.65,95.74%的区域NPP呈增加趋势。(2)不同植被类型的NPP总量大小排序为草原>栽培植被>阔叶林>灌丛>草甸>针叶林; 西辽河流域固碳的植被类型主要是草原、栽培植被以及阔叶林,固碳能力较强的为针叶林。(3)生长在棕壤、褐土和潮土的植被年均NPP较高,生长在栗钙土和风沙土的植被年均NPP较低。(4)16年间植被NPP增长主要受降雨影响。气候暖-湿化及生态建设工程的实施,促进了西辽河流域植被的生长。以上研究结果为后期流域生态环境治理提供了科学依据及数据支持。     

Interannual variability in responses of belowground net primary productivity (NPP) and NPP partitioning to long‐term warming and clipping in a tallgrass prairie

The dynamics of belowground net primary productivity (BNPP) is of fundamental importance in understanding carbon (C) allocation and storage in grasslands. However, our knowledge of the interannual variability in response of BNPP to ongoing global warming is limited. In this study, we explored temporal responses of BNPP and net primary productivity (NPP) partitioning to warming and clipping in a tallgrass prairie in Oklahoma, USA. Infrared heaters were used to elevate soil temperature by approximately 2 °C since November 1999. Annual clipping was to mimic hay harvest. On average from 2005 to 2009, warming increased BNPP by 41.89% in the unclipped subplots and 66.93% in the clipped subplots, with significant increase observed in wet years. Clipping also had significant positive impact on BNPP, which was mostly found under warming. Overall, f_BNPP, the fraction of BNPP to NPP, increased under both warming and clipping treatments, more in dry years. Water availability (either precipitation or soil moisture) was the most limiting factor for both BNPP and f_BNPP. It strongly dominated the interannual variability in NPP, f_BNPP, and their responses to warming and clipping. Our results suggest that water availability regulates tallgrass prairie's responses to warming and land use change, which may eventually influence the global C cycle. With increasing variability in future precipitation patterns, warming effects on the vegetation in this region may become less predictable.     

内蒙古草原区植被净初级生产力及其与气候的关系

利用NOAA/AVHRR GIMMSNDVI数据、土地覆盖分类数据、气象数据等,基于改进的基于光能利用率的净初级生产力(Net Primary productivity,NPP)遥感估算模型对内蒙古草原区1982-2006年的NPP进行估算,并分别以年、季节和月为时间单位,计算基于像元的NPP与降水、温度之间的相关及偏相关系数,分析不同时间单位及尺度上NPP与气候的关系。结果表明,1982-2006年内蒙古草原区NPP总量呈波动增加的趋势,平均增加值为0.861Mt C/a。以年为时间单位,内蒙古草原区年NPP与降水的关系比较明显。以季节为时间单位,年际春季和夏季NPP与降水的关系比较明显,秋季二者关系相对较弱,春季和秋季NPP与温度的相关系数和偏相关系数空间格局比较一致,且相关性明显高于夏季。以月为时间单位的相关水平明显高于年际水平,多年平均年内月NPP与降水、温度的相关程度明显增强,除去降水的影响,月均温对NPP的影响明显下降,且空间格局也有明显的变化,说明以月为时间单位在年内尺度上降水对植被生长的影响比温度要大。而以4、7、10月份为例,在年际尺度上,虽然各月份NPP均受降水的影响较大,但与降水关系最为密切的是4月份和10月份NPP,与之相比,7月份NPP与温度的关系明显高于其他两月。     

基于校正的CASA模型NPP遥感估算及分析——以河西走廊为例

利用CASA模型估算植被净初级生产力(NPP)应用广泛,但其精度仍然有待提高。基于地理因子回归方法(AMMRR)和地表水分指数(LSWI)对CASA模型的两个关键参数:温度胁迫系数和水分胁迫系数进行校正,再估算NPP并分析了校正对植被NPP及各因子与NPP关系的影响。研究表明:(1)校正能有效提高CASA模型的估算精度,校正后NPP总量为34.29 TgC/a,原CASA模型高估了0.23 TgC/a。(2)研究不仅可以校正地形对NPP的影响,还可以校正平坦地形下人类活动区NPP的影响;在高海拔、地形起伏较大的区域以及人类活动地区,校正对NPP估算影响较大,绿洲区原模型存在高估。(3)校正对生长季的影响大于非生长季;坡度对NPP影响较大,坡度越大原模型高估越多;校正前高估了阳坡NPP,低估了阴坡NPP。     

1982-2009年东北多年冻土区植被净初级生产力动态及其对全球变化的响应

东北多年冻土区作为高纬度寒区之一,对全球变化较敏感.本文基于AVHRR和MODIS两种遥感数据源的归一化植被指数,应用CASA模型对1982-2009年东北多年冻土区植被净初级生产力(NPP)进行模拟.结果表明:1982-2009年,东北多年冻土区年均气温、年太阳辐射总量和年日照时数显著上升,年降水量显著下降,CO2浓度及其年增长率显著增大;植被年NPP呈显著的先增加后降低趋势,变化分异节点在1998年.研究期间,东北多年冻土区植被年均NPP总量为623 g C·m-2,植被年NPP空间分布差异明显.降水是该区生长季植被生长的主要影响因子,植被NPP对气候变化响应的空间异质性明显.土地利用变化通过改变土地覆被状况使植被NPP发生变化,影响了植被NPP的时空分布特征,植被NPP与CO2浓度呈显著正相关.多年冻土退化对植被NPP的影响随着各区域环境的不同而有所差异.多年冻土区植被NPP与年均地温呈显著正相关,与年最大冻土深度呈负相关.     

1982-2009年东北多年冻土区植被净初级生产力动态及其对全球变化的响应

东北多年冻土区作为高纬度寒区之一,对全球变化较敏感.本文基于AVHRR和MODIS两种遥感数据源的归一化植被指数,应用CASA模型对1982-2009年东北多年冻土区植被净初级生产力（NPP）进行模拟.结果表明： 1982-2009年,东北多年冻土区年均气温、年太阳辐射总量和年日照时数显著上升,年降水量显著下降,CO₂浓度及其年增长率显著增大;植被年NPP呈显著的先增加后降低趋势,变化分异节点在1998年.研究期间,东北多年冻土区植被年均NPP总量为623 g C·m^-2,植被年NPP空间分布差异明显.降水是该区生长季植被生长的主要影响因子,植被NPP对气候变化响应的空间异质性明显.土地利用变化通过改变土地覆被状况使植被NPP发生变化,影响了植被NPP的时空分布特征.植被NPP与CO₂浓度呈显著正相关.多年冻土退化对植被NPP的影响随着各区域环境的不同而有所差异.多年冻土区植被NPP与年均地温呈显著正相关,与年最大冻土深度呈负相关.     

Effects of climate change on net primary productivity in Larix olgensis plantations based on process modeling

Aims Climate change has significant effects on net primary productivity (NPP) in forests, but there is a large uncertainty in the direction and magnitude of the effects. Process-based models are important tools for understanding the responses of forests to climate change. The objective of the study is to simulate changes in NPP of Larix olgensis plantations under future climate scenarios using 3-PG model in order to guide the management of L. olgensis plantations in the context of global climate change.Methods Data were obtained for 30 permanent plots of L. olgensis plantations in Siping, Linjiang, Baishan, etc. of Jilin Province, and a process model, 3-PG model, was applied to simulate changes in NPP over a rotation period of 40 years under different climate scenarios. Parameter sensitivity was also determined. Important findings The locally parameterized 3-PG model well simulates the changes in NPP against the measured NPP data, with values between 272.79-844.80 g·m^-2·a^-1 and both mean relative error and relative root mean square error within 12%. The NPP in L. olgensis plantations would increase significantly with increases in atmospheric CO₂ concentration, temperature and precipitation collectively. However, an increase in temperature alone would lead to a decrease in NPP, but increases in precipitation and atmospheric CO₂ concentration would increase NPP; the positive effect of increasing precipitation appears to be weaker than the negative effect of increasing temperature. Sensitivity analysis shows that the model performance is sensitive to the optimum temperature, stand age at which specific leaf area equals to half of the sum of specific leaf area at age 0 (SLA₀) and that for mature leaves (SLA₁), and days of production loss due to frost.     

陆地生态系统净第一性生产力对全球变化的响应

陆地生态系统的年净第一性生产力是每年植物通过光合作用固定的碳总量。随着全球变化发生,ＮＰＰ发生相应的变化。传统的方法预测ＮＰＰ的变化是利用气候和植被之间的局地关系建立回归模型,但用此方法预测ＮＰＰ的变化是有条件的。目前国际上出现了一种陆地生态系统的动态模型,它考虑了植物营养元素如氮的有效性,同时利用不同ＧＣＭｓ模型预测的气候因子的变化值和全球变化模拟研究的实验数据,预测全球ＮＰＰ的可能变化及区域分