Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

Modeled responses of terrestrial ecosystems to elevated atmospheric CO2: a comparison of simulations by the biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP)

Although there is a great deal of information concerning responses to increases in atmospheric CO₂ at the tissue and plant levels, there are substantially fewer studies that have investigated ecosystem-level responses in the context of integrated carbon, water, and nutrient cycles. Because our understanding of ecosystem responses to elevated CO₂ is incomplete, modeling is a tool that can be used to investigate the role of plant and soil interactions in the response of terrestrial ecosystems to elevated CO₂. In this study, we analyze the responses of net primary production (NPP) to doubled CO₂ from 355 to 710 ppmv among three biogeochemistry models in the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): BIOME-BGC (BioGeochemical Cycles), Century, and the Terrestrial Ecosystem Model (TEM). For the conterminous United States, doubled atmospheric CO₂ causes NPP to increase by 5% in Century, 8% in TEM, and 11% in BIOME-BGC. Multiple regression analyses between the NPP response to doubled CO₂ and the mean annual temperature and annual precipitation of biomes or grid cells indicate that there are negative relationships between precipitation and the response of NPP to doubled CO₂ for all three models. In contrast, there are different relationships between temperature and the response of NPP to doubled CO₂ for the three models: there is a negative relationship in the responses of BIOME-BGC, no relationship in the responses of Century, and a positive relationship in the responses of TEM. In BIOME-BGC, the NPP response to doubled CO₂ is controlled by the change in transpiration associated with reduced leaf conductance to water vapor. This change affects soil water, then leaf area development and, finally, NPP. In Century, the response of NPP to doubled CO₂ is controlled by changes in decomposition rates associated with increased soil moisture that results from reduced evapotranspiration. This change affects nitrogen availability for plants, which influences NPP. In TEM, the NPP response to doubled CO₂ is controlled by increased carboxylation which is modified by canopy conductance and the degree to which nitrogen constraints cause down-regulation of photosynthesis. The implementation of these different mechanisms has consequences for the spatial pattern of NPP responses, and represents, in part, conceptual uncertainty about controls over NPP responses. Progress in reducing these uncertainties requires research focused at the ecosystem level to understand how interactions between the carbon, nitrogen, and water cycles influence the response of NPP to elevated atmospheric CO₂. Received: 13 December 1996 / Accepted: 20 November 1997     

Net primary productivity mapped for Canada at 1-km resolution

Aim To map net primary productivity (NPP) over the Canadian landmass at 1‐km resolution. Location Canada. Methods A simulation model, the Boreal Ecosystem Productivity Simulator (BEPS), has been developed. The model uses a sunlit and shaded leaf separation strategy and a daily integration scheme in order to implement an instantaneous leaf‐level photosynthesis model over large areas. Two key driving variables, leaf area index (every 10 days) and land cover type (annual), are derived from satellite measurements of the Advanced Very High Resolution Radiometer (AVHRR). Other spatially explicit input data are also prepared, including daily meteorological data (radiation, precipitation, temperature, and humidity), available soil water holding capacity (AWC) and forest biomass. The model outputs are compared with ground plot data to ensure that no significant systematic biases are created. Results The simulation results show that Canada’s annual net primary production was 1.22 Gt C year^?1 in 1994, 78% attributed to forests, mainly the boreal forest, without considering the contribution of the understorey. The NPP averaged over the entire landmass was ~140 g C m^?2 year^?1 in 1994. Geographically, NPP varied greatly among ecozones and provinces/territories. The seasonality of NPP is characterized by strong summer photosynthesis capacities and a short growing season in northern ecosystems. Conclusions This study is the first attempt to simulate Canada‐wide NPP with a process‐based model at 1‐km resolution and using a daily step. The statistics of NPP are therefore expected to be more accurate than previous analyses at coarser spatial or temporal resolutions. The use of remote sensing data makes such simulations possible. BEPS is capable of integrating the effects of climate, vegetation, and soil on plant growth at a regional scale. BEPS and its parameterization scheme and products can be a basis for future studies of the carbon cycle in mid‐high latitude ecosystems.     

基于过程模型的长白山自然保护区森林植被净第一性生产力空间尺度转换方法

净第一性生产力(Net Primary Productivity,NPP)是反映林分和生态系统结构特征的最重要的参数之一,其空间分布可为自然资源管理和可持续发展提供高度综合的信息,是全球变化和碳循环研究中的一个非常重要的组成部分。为了得到NPP的空间尺度转换算法,利用改进的北部生态系统生产力模拟模型(Improved Boreal Ecosystem Productivity Simulator,IBEPS),分别采用高分辨率(30m)的ETM 和低分辨率(1km)的MODIS遥感数据,模拟了长白山自然保护区不同分辨率森林植被的净第一性生产力。对模拟得到的30m分辨率的NPP结果,采用尺度上推(upscaling)的方法将其转换为1km分辨率的NPP结果(分布式的NPP,Distributed NPP),并将转换后的NPP与直接利用1km分辨率的遥感数据计算得到的NPP结果(聚合式的NPP,Lumped NPP)进行比较,以分布式模式计算的结果为基准,对聚合式的计算结果进行尺度效应校正。在对NPP进行尺度效应校正时,根据不同分辨率下土地覆盖类型(Land cover)的亚像元面积比信息,采用基于结构的分析方法对模拟的NPP结果进行校正,结果表明经过尺度效应校正后NPP的模拟精度要比原始的NPP计算精度有所提高,相关系数从校正前的0.898提高到0.960,标准偏差由校正前的49.84gC/m2减小到41.02gC/m2。采用这种方法可以很好的解决大尺度遥感应用的需要,为定量遥感分析和应用提供理论基础。     

Regional application of an ecosystem production model for studies of biogeochemistry in Brazilian Amazonia

The degree to which primary production, soil carbon, and trace gas fluxes in tropical forests of the Amazon are limited by moisture availability and other environmental factors was examined using an ecosystem modelling application for the country of Brazil. A regional geographical information system (GIS) serves as the data source of climate drivers, satellite images, land cover, and soil properties for input to the NASA Ames-CASA (Carnegie-Ames-Stanford Approach) model over a 8-km grid resolution. Simulation results lead us to hypothesize that net primary production (NPP) is limited by cloud interception of solar radiation over the humid north-western portion of the region. Peak annual rates for NPP of nearly 1.4 kg C m^–2 year^–1 are localized in the seasonally dry eastern Amazon in areas that we assume are primarily deep-rooted evergreen forest cover. Regional effects of forest conversion on NPP and soil carbon content are indicated in the model results, especially in seasonally dry areas. Comparison of model flux predictions along selected eco-climatic transects reveal moisture, soil, and land use controls on gradients of ecosystem production and soil trace gas emissions (CO₂, N₂O, and NO). These results are used to formulate a series of research hypotheses for testing in the next phase of regional modelling, which includes recalibration of the light-use efficiency term in NASA-CASA using field measurements of NPP, and refinements of vegetation index and soil property (texture and potential rooting depth) maps for the region.     

Estimating regional carbon stocks and spatially covarying edaphic factors using soil maps at three scales

Most estimates of regional and global soil carbon stocks are based on extrapolations of mean soil C contents for broad categories of soil or vegetation types. Uncertainties exist in both the estimates of mean soil C contents and the area over which each mean should be extrapolated. Geographic information systems now permit spatially referenced estimates of soil C at finer scales of resolution than were previously practical. We compared estimates of total soil C stocks of the state of Maine using three methods: (1) multiplying the area of the state by published means of soil C for temperate forests and for Spodosols; (2) calculating areas of inclusions of soil taxa in the 1:5,000,000 FAO/UNESCO Soils Map of the World and multiplying those areas by selected mean carbon contents; and (3) calculating soil C for each soil series and map unit in the 1:250,000 State Soil Geographic Data Base (STATSGO) and summing these estimates for the entire state. The STATSGO estimate of total soil C was between 23% and 49% higher than the common coarse scale extrapolations, primarily because STATSGO included data on Histosols, which cover less than 5% of the area of the state, but which constitute over one-third of the soil C. Spodosols cover about 65% of the state, but contribute less than 39% of the soil C. Estimates of total soil C in Maine based on the FAO map agreed within 8% of the STATSGO estimate for one possible matching of FAO soil taxa with data on soil C, but another plausible matching overestimated soil C stocks. We also compared estimates from the 1:250,000 STATSGO database and from the 1:20,000 Soil Survey Geographic Data Base (SSURGO) for a 7.5 minute quadrangle within the state. SSURGO indicated 13% less total soil C than did STATSGO, largely because the attribute data on depths of soil horizons in SSURGO are more specific for this locality. Despite localized differences, the STATSGO database offers promise of scaling up county soil survey data to regional scales because it includes attribute data and estimates of areal coverage of C-rich inclusions within map units. The spatially referenced data also permit examination of covariation of soil C stocks with soil properties thought to affect stabilization of soil C. Clay content was a poor predictor of soil C in Maine, but drainage class covaried significantly with soil C across the state.     

Remotely Sensed Interannual Variations and Trends in Terrestrial Net Primary Productivity 1981–2000

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Niño season and increased in La Niña seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.     

考考赖沟流域植被生产力模拟

 该文应用气象数据、土壤物理属性实测数据、土壤水分分布式动态模型和植被表面净辐射模型的模拟结果,利用多元线性回归分析方法,建立 了植被净第一性生产力模型,实现了鄂尔多斯高原东部砂质荒漠化地区考考赖沟流域尺度上30 m×30 m空间分辨率的植被生产力精确模拟,并 且用植被生产力的野外实测数据对模拟结果进行了验证表明: 实测值与模拟值在固定沙丘、半固定沙丘和样线2上都达到0.05显著性相关水平; 不同位置的实测植被生 产力数据多分布在1∶1直线附近,模拟值与实测值吻合较好;植被生产力实测值与模拟值的相对误差范围为3.22%～ 6.27%,偏斜度范围在-12.84%～4.43%。该文的研究方法可以为流域尺度上植被生产力的精确模拟提供借鉴和参考。     

中国东北样带植被净初级生产力时空动态遥感模拟

 中国东北样带(Northeast China Transect, NECT)是中纬度半干旱区的国际地圈-生物圈计划(IGBP)陆地样带之一, 是全球变化研究的 重要手段与热点。该研究应用生态系统碳循环过程CASA(Carnegie-Ames-Stanford Approach)模型分析了NECT从1982~1999年植被净初级生产力 (Net primary productivity, NPP)的时空变异及其影响因子。结果表明, 1) 1982~1999年NECT植被NPP为58 ~ 811 g C·m^–2·a^–1, 平均为426 g C·m^–2·a^–1, 大体上呈现由东向西逐渐递减的趋势; 2)研究时段内NECT的总NPP变异范围是0.218 ~ 0.325 Pg C, 平均为0.270 Pg C (1 Pg = 10¹⁵ g); 3) NECT的总NPP在过去18年内整体呈显著性增加趋势, 其中从1982~1990年样带NPP呈显著性增加趋势, 而后期1991~1999样带NPP没 有显著性变化趋势; 4)沿NECT不同植被类型对气候变化的响应特征是不同的, 在研究时段内, 农田、典型草原和草甸草原表现出最大的NPP增加 量, 而典型草原、荒漠草原对气候变化表现出高的敏感性; 5) NECT植被NPP的空间分布格局是由年降水量的分布格局所决定, 而NPP的时间变异 则由年降水量、年太阳总辐射的变化所影响驱动。     

利用CASA模型估算我国植被净第一性生产力

基于地理信息系统和卫星遥感应用技术,利用CASA模型估算了我国1997年植被净第一性生产力及其分布。结果表明：1997年我国植被净第一性生产力为1．95PgC,约是世界陆地植被年净第一性生产力的4．0％;我国植被净第一性生产力的主要分布趋势是从东南沿海向西北逐渐减小,其中海南岛南部、云南西南部、青藏高原东南部的热带雨林和季雨林地区植被年净第一性生产力最大,达900gC.m^2.a^-1以上,而西部塔克拉玛干沙漠地区植被年净第一性生产力最小,不足10gC.m^-2.a^-1。     

Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m^-2 d^-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m^-2 d^-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.     

Historical and projected carbon balance of mature black spruce ecosystems across North America: the role of carbon–nitrogen interactions

The role of carbon (C) and nitrogen (N) interactions on sequestration of atmospheric CO₂ in black spruce ecosystems across North America was evaluated with the Terrestrial Ecosystem Model (TEM) by applying parameterizations of the model in which C–N dynamics were either coupled or uncoupled. First, the performance of the parameterizations, which were developed for the dynamics of black spruce ecosystems at the Bonanza Creek Long-Term Ecological Research site in Alaska, were evaluated by simulating C dynamics at eddy correlation tower sites in the Boreal Ecosystem Atmosphere Study (BOREAS) for black spruce ecosystems in the northern study area (northern site) and the southern study area (southern site) with local climate data. We compared simulated monthly growing season (May to September) estimates of gross primary production (GPP), total ecosystem respiration (RESP), and net ecosystem production (NEP) from 1994 to 1997 to available field-based estimates at both sites. At the northern site, monthly growing season estimates of GPP and RESP for the coupled and uncoupled simulations were highly correlated with the field-based estimates (coupled: R ²= 0.77, 0.88 for GPP and RESP; uncoupled: R ² = 0.67, 0.92 for GPP and RESP). Although the simulated seasonal pattern of NEP generally matched the field-based data, the correlations between field-based and simulated monthly growing season NEP were lower (R ² = 0.40, 0.00 for coupled and uncoupled simulations, respectively) in comparison to the correlations between field-based and simulated GPP and RESP. The annual NEP simulated by the coupled parameterization fell within the uncertainty of field-based estimates in two of three years. On the other hand, annual NEP simulated by the uncoupled parameterization only fell within the field-based uncertainty in one of three years. At the southern site, simulated NEP generally matched field-based NEP estimates, and the correlation between monthly growing season field-based and simulated NEP (R ² = 0.36, 0.20 for coupled and uncoupled simulations, respectively) was similar to the correlations at the northern site. To evaluate the role of N dynamics in C balance of black spruce ecosystems across North America, we simulated historical and projected C dynamics from 1900 to 2100 with a global-based climatology at 0.5° resolution (latitude × longitude) with both the coupled and uncoupled parameterizations of TEM. From analyses at the northern site, several consistent patterns emerge. There was greater inter-annual variability in net primary production (NPP) simulated by the uncoupled parameterization as compared to the coupled parameterization, which led to substantial differences in inter-annual variability in NEP between the parameterizations. The divergence between NPP and heterotrophic respiration was greater in the uncoupled simulation, resulting in more C sequestration during the projected period. These responses were the result of fundamentally different responses of the coupled and uncoupled parameterizations to changes in CO₂ and climate.     

Estimating net primary production of boreal forests in Finland and Sweden from field data and remote sensing

We calculated annual mean stem volume increment (AMSVI) and total litter fall to produce forest net primary production (NPP) maps at 1‐km² and half‐degree resolutions in Finland and Sweden. We used a multi‐scale methodology to link field inventory data reported at plot and forestry district levels through a remotely sensed total plant biomass map derived from 1‐km² AVHRR image. Total litter fall was estimated as function of elevation and latitude. Leaf litter fall, a surrogate for fine root production, was estimated from total litter fall by forest type. The gridded NPP estimates agreed well with previously reported NPP values, based on point measurements. Regional NPP increases from northeast to southwest. It is positively related to annual mean temperature and annual mean total precipitation (strongly correlated with temperature) and is negatively related to elevation at broad scale. Total NPP (TNPP) values for representative cells selected based on three criteria were highly correlated with simulated values from a process‐based model (CEVSA) at 0.5°× 0.5° resolution. At 1‐km² resolution, mean above‐ground NPP in the region was 408 g/m²/yr ranging from 172 to 1091 (standard deviation (SD) = 134). Mean TNPP was 563 (252 to 1426, SD = 176). Ranges and SD were reduced while the mean values of the estimated NPP stayed almost constant as cell size increased from 1‐km² to 0.5°× 0.5°, as expected. Nordic boreal forests seem to have lower productivity among the world boreal forests.     

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.     

基于CASA模型的内蒙古典型草原植被净初级生产力动态模拟

 植被净初级生产力及其对气候变化的响应研究是全球变化的核心内容之一。在利用内蒙古典型草原连续13年的地上生物量资料对基于遥感信息的生态系统碳循环过程CASA(Carnegie-Ames-Stanford Approach)模型验证的基础上, 分析了内蒙古典型草原1982~2002年植被净初级生产力(Net primary productivity, NPP)的时间变异及其影响因子。结果表明: 1) 1982~2002年21年间内蒙古典型草原的平均年NPP为290.23 g C·m^–2·a^–1, 变化范围为 145.80~502.84 g C·m^–2·a^–1; 2)内蒙古典型草原NPP呈增加趋势, 但没有达到显著性水平, 其中1982~1999年的18年间NPP呈现非常显著的增加趋势(p<0.01), NPP增加的直接原因是由于生长旺季生长本身增强所致; 3)内蒙古典型草原NPP与年降水量呈极显著的相关关系, 年降水量显著影响NPP的变异, 而NPP与年均温无显著相关关系。     

Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site‐level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite‐based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty‐five 1 km² cells at each site, with 8‐day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ～30 m resolution), and process‐based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m^?2 yr^?1 at an arctic tundra site to 550 g C m^?2 yr^?1 at a temperate deciduous forest site. There was not a consistent over‐ or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over‐ and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.     

China's crop productivity and soil carbon storage as influenced by multifactor global change

Much concern has been raised about how multifactor global change has affected food security and carbon sequestration capacity in China. By using a process‐based ecosystem model, the Dynamic Land Ecosystem Model (DLEM), in conjunction with the newly developed driving information on multiple environmental factors (climate, atmospheric CO₂, tropospheric ozone, nitrogen deposition, and land cover/land use change), we quantified spatial and temporal patterns of net primary production (NPP) and soil organic carbon storage (SOC) across China's croplands during 1980–2005 and investigated the underlying mechanisms. Simulated results showed that both crop NPP and SOC increased from 1980 to 2005, and the highest annual NPP occurred in the Southeast (SE) region (0.32 Pg C yr^?1, 35.4% of the total NPP) whereas the largest annual SOC (2.29 Pg C yr^?1, 35.4% of the total SOC) was found in the Northeast (NE) region. Land management practices, particularly nitrogen fertilizer application, appear to be the most important factor in stimulating increase in NPP and SOC. However, tropospheric ozone pollution and climate change led to NPP reduction and SOC loss. Our results suggest that China's crop productivity and soil carbon storage could be enhanced through minimizing tropospheric ozone pollution and improving nitrogen fertilizer use efficiency.     

Phylogenetics of the grass genus Ehrharta: evidence for radiation in the summer-arid zone of the South African Cape

Abstract Climatic and geological change may play a key role in stimulating biological radiations. Here, we use phylogenetic data to test whether the comparatively high diversity of ehrharteoid grasses in the Cape region of South Africa is the result of rapid radiation associated with the onset of a seasonally arid climate during the late Miocene. A phylogenetic hypothesis based on morphological and nucleotide sequence (nuclear ITS1 and plastid trn L-F) data confirms the monophyly of the African Ehrharta species and shows that the diversification of this lineage was centered in the Cape region. Sequence divergence data (ITS1 + trn L-F) indicate a pulse of rapid speciation, which may explain poor phylogenetic resolution within the African Ehrharta clade. Alternative calibrations yield a broad range of time estimates for the start and end of this radiation, most of which indicate a radiation inside the last 11 million years. A calibration based on the age of Ehrhartoideae suggests that radiation started 9.82 ± 0.20 million years ago and ended 8.74 ± 0.21 million years ago. Under alternative calibrations, estimated speciation rates during the period of radiation range between 0.87 and 4.18 species per million years. Parsimony optimization of habitat parameters reveals that radiation was correlated with the occupation of seasonally arid succulent karoo environments, wet heathy (fynbos) environments being ancestral. These data support earlier suggestions that late Miocene climatic change stimulated floristic radiation at the Cape, and highlight the potential importance of environmental change in powering diversification in continental floras.     

太阳辐射变化对亚热带人工常绿针叶林总初级生产力影响的模拟分析

太阳辐射是陆地生态系统碳水循环的能量来源。太阳辐射的变化对植被吸收大气CO₂具有重要影响。该文通过辐射观测数据建立散射辐射比例与晴空指数的关系, 结合生态过程模型(BEPS)和通量观测数据, 模拟分析了太阳辐射变化对千烟洲常绿针叶林总初级生产力(GPP)的影响。研究结果表明: 千烟洲森林生态系统的阴叶对年GPP总量的贡献达67%, 太阳辐射变化对阴叶光合作用的影响决定了冠层GPP的变化; 太阳辐射强度和分布的年际差异导致年GPP对太阳辐射变化的响应不同, 2003、2004和2005年太阳辐射分别变化-5.44%、-1.83%和6.26%, 可使千烟洲生态系统当年GPP总量达到最大值; 在季节上, 太阳辐射的增加会导致5-6月GPP上升, 7-9月GPP下降, 使年GPP变化程度降低; 在天尺度上, 晴空指数在0.43时, 太阳辐射变化对GPP的影响最小。     

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.