利用大气二氧化碳和羰基硫浓度评估陆地生态系统模型碳通量模拟的不确定性

生态系统模型是模拟全球陆地生态系统碳循环的重要工具,但是其在全球不同区域的模拟存在很大的不确定性。如何评估陆地生态系统模型的不确定性是一项重要的研究。以北美地区为例,利用8个高塔观测站点同步获取的大气CO₂和羰基硫(OCS)浓度数据,结合WRF-STILT大气粒子扩散模型,评估了CASA-GFED3、SiB3和SiBCASA三种陆地生态系统模型模拟总初级生产力(GPP)和净生态系统CO₂交换(NEE)通量的不确定性。结果表明,SiB3模型能很好地模拟北美陆地生态系统GPP和NEE的季节变化时相和幅度,在3种模型中具有最佳的模拟能力;CASA-GFED3模型模拟的NEE季节变化较为理想、但对生长季GPP的模拟存在较大的误差,SiBCASA模型在模拟冬季晚期和春季早期的NEE和GPP时表现较不理想。研究证明了大气CO₂和OCS在评估陆地生态系统模型碳通量模拟的不确定性中的作用,为利用大气CO₂和OCS观测数据优化计算陆地生态系统光合和呼吸碳通量提供了理论支撑。     

基于叶面积指数估算植被总初级生产力

长时间序列的陆地碳通量数据在全球生态环境变化研究中具有重要意义。采用MODIS GPP(Gross Primary Productivity)算法,基于GIMMS LAI3g,MODIS15和Improved-MODIS15三种叶面积指数(LAI),估算了全球2000至2010年的植被总初级生产力(GPP)。该估算的GPP数值经过全球20个通量站点的验证,并结合MODIS17分析了它们在时空变化上的异同。结果表明:(1)4种GPP精度如下:GPP_(MOD17)GPP_(impro_MOD15)GPP_(LAI3g)GPP_(MOD15)。(2)4种GPP整体上具有一致的季节波动,冬季和夏季整体好于春季和秋季。GPP_(LAI3g)的4个季节精度较相近,而GPP_(MOD17)除了春秋季外其它季节都较好。(3)GPP_(LAI3g)在中等GPP值分布区的估值相对较高,其全球总GPP大体为(117±1.5)Pg C/a,GPP_(MOD17)和GPP_(impro_MOD15)相近且都低于该值。(4)GPP_(LAI3g)和GPP_(impro_MOD15)在大约63.29%的陆面上呈显著(P0.05)的正相关关系,它们和GPP_(MOD17)在LAI不确定性小的地区呈显著的正相关关系。GPP_(LAI3g)和GPP_(MOD15)正相关分布面积占比为40.61%。     

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。     

三江源地区人工草地的生态系统CO2净交换、总初级生产力及其影响因子

为了揭示三江源区垂穗披碱草(Elymus nutans)人工草地生态系统(100°26′-100°41′ E, 34°17′-34°25′ N, 海拔3 980 m)的净生态系统CO₂交换(NEE), 该研究利用2006年涡度相关系统观测的数据分析了该人工草地的NEE, 总初级生产力(GPP)、生态系统呼吸(R_eco)以及R_eco/GPP的变化特征及其影响因子。CO₂日最大吸收值为6.56 g CO₂·m^-2·d^-1, 最大排放值为4.87 g CO₂·m^-2·d^-1。GPP年总量为1 761 g CO₂·m^-2, 其中约90%以上被生态系统呼吸所消耗, CO₂的年吸收量为111 g CO₂·m^-2。5月的R_eco/GPP略高于生长季的其他月份, 为90%; 6月R_eco/GPP比值最低, 为79%。生态系统的呼吸商(Q₁₀)为4.81, 显著高于其他生态系统。该研究表明: 生长季的NEE主要受光量子通量密度(PPFD)、温度和饱和水汽压差(VPD)的影响, 生态系统呼吸则主要受土壤温度的控制。     

珊瑚礁生态系统初级生产力研究进展

珊瑚礁生态系统由珊瑚礁生物群落及其周围的海洋环境共同组成。该生态系统具有很高的生产力和生物多样性而引起科学家的重视 ,特别是高初级生产力。初级生产力的贡献者包括底栖植物、浮游植物、共生藻和自养细菌等。初级生产力的测定方法较多 ,各有利弊 ,通常采用 1 4C同位素法。在初级生产力中 ,新生产力更引起科学界关注。对于新生产力的测定 ,主要应用 1 5N示踪法 ,采用“f”比或 Redfield比值来估算。为了减少误差 ,一般同时使用几种方法。光是影响初级生产力的主要因素 ,而对新生产力构成限制的主要因素是氮源。珊瑚礁生态系统初级生产力研究较多 ,但新生产力却很少。未来科学界研究重点在于珊瑚礁生态系统初级生产力和新生产力的动力学效应     

基于VPM模型的甘肃省植被总初级生产力动态变化

基于光能利用率模型(vegetation photosynthesis model,VPM)对2005-2017年甘肃省植被总初级生产力(gross primary productivity,GPP)进行估算,并对植被GPP的时空变化特征、不同植被区划的GPP变化趋势以及影响因素等进行探究.结果表明:(1)2005-2...     

基于遥感和过程模型的亚洲东部陆地生态系统初级生产力分布特征

利用美国环境预测中心的再分析气象资料和由GIMMS NDVI 资料生成的叶面积指数对BEPS生态模型进行驱动,模拟分析了2000-2005年亚洲东部地区总初级生产力（GPP）和总净初级生产力（NPP）的时空变化特征.在进行区域模拟计算前,使用15个站点不同生态系统的GPP观测数据及1300个样点的NPP观测数据对模型进行验证.结果表明： BEPS模型能较好地模拟不同生态系统的GPP和NPP变化,模拟的GPP与观测数据之间的R2为0.86～0.99,均方根误差（RMSE）为0.2～1.2 g C·m-2·d-1;BEPS模拟值能够解释78%的年NPP变化,其RMSE为118 g C·m-2·a-1.2000-2005年,亚洲东部地区GPP和NPP总量平均值分别为21.7和10.5 Pg C·a-1.NPP和GPP具有相似的时空变化特征.研究期间,NPP总量的变化范围为10.2～10.7 Pg C·a-1, 变异系数为2.2%.NPP由东南向西北显著减少,高值区〖JP2〗（>1000 g C·m-2·a-1）出现在东南亚海岛国家,我国的西北干旱沙漠地区为低值区（<30 g C·m-2·a-1）,〖JP〗其空间格局主要由气候因子决定.不同国家的人均NPP差异很大,其中,蒙古最高,达70217 kg C·a-1,远高于中国的人均NPP（1921 kg C·a-1）,印度的人均NPP最小,为757 kg C·a-1.     

基于FORCCHN模型的中国典型森林生态系统碳通量模拟

森林生态系统是陆地碳循环的重要组成部分,其固碳能力显著高于其他陆地生态系统,研究森林生态系统碳通量是认识和理解全球变化对碳循环影响的关键。碳循环模型是研究森林生态系统碳通量有效工具。以长白山温带落叶阔叶林、千烟洲亚热带常绿针叶林、鼎湖山亚热带常绿阔叶林和西双版纳热带雨林等4种中国典型森林生态系统为研究对象,利用涡度相关2003-2012年观测数据,评估FORCCHN模型对生态系统呼吸（ER）,总初级生产力（GPP）,净生态系统生产力（NEP）的模型效果。结果表明：（1） FORCCHN模型能够较好的模拟中国4种典型森林生态系统不同时间尺度的碳通量。落叶阔叶林和常绿针叶林ER和GPP的逐日变化模拟效果较好（ER的相关系数分别为0.94和0.92,GPP的相关系数分别为0.86和0.74）;（2）4种森林生态系统碳通量季节动态模拟值和观测值显著相关（P<0.01）,ER、GPP、NEP的观测值和模拟值的R²分别为0.77-0.93、0.54-0.88和0.15-0.38;模型可以很好地模拟森林生态系统不同季节碳汇（NEP>0）,碳源（NEP<0）的变化规律;（3）4种森林生态系统碳通量模拟值与观测值的年际变化有很好的吻合度,但在数值大小上存在差异,模型高估了常绿阔叶林的ER和GPP,略微低估了其他3种森林生态系统ER和GPP。     

基于涡度相关法的农田生态系统碳通量研究进展

涡度相关法作为国际上公认的碳通量测定的标准方法,在农田生态系统碳通量研究领域具有广阔的空间。本研究旨在总结涡度相关法的农田生态系统碳通量最新研究成果,为涡度相关法与农田生态系统碳通量研究提供参考。文章综述了国内外基于涡度相关技术的农田生态系统碳通量研究现状,重点总结了其在时间变化、驱动因子、生产力模型、数据处理等方面的最新研究成果。经了解发现涡度相关技术对农田生态系统碳通量的时间变化特征研究较多,且众多研究结果表明在单一种植模式下农田碳通量的日变化和季节变化呈显著单峰"U"型趋势,在多熟种植模式下季变化呈"W"型,但对种植模式的碳通量研究缺乏区域代表性;同时驱动因子研究集中在温度、光照、水分等环境因素对农田碳通量的影响方面,对环境因子与农艺措施之间的关系研究较少;且对于通量夜间数据的处理和无效、缺失数据的剔除与插补缺乏统一的标准。因此本文认为区域典型种植模式的长期定位监测、多因子协同分析、数据质量监控等方面具有较大研究空间。     

中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据

该研究基于中国生态系统研究网络(CERN)数据对传统CASA模型进行优化,对比两叶模型与优化CASA模型在站点尺度和像元尺度对于8个典型生态站点的植被净初级生产力(NPP)估算精度,选择在像元尺度表现更好的优化CASA模型,结合中国土地覆被数据(ChinaCover)开展2000—2019年中国陆地植被NPP监测与分析。研究结果表明：(1)基于FY2D PAR的优化方案能够有效避免空间插值导致的不确定性问题,显著提高了PAR估算精度;(2)在站点尺度上,两叶模型用于估算典型森林、草地生态系统的NPP表现更好,而在像元尺度上优化CASA模型估算精度更高;(3)在全国尺度上,优化了最大光能利用率、水分胁迫系数以及光合有效辐射计算方法的CASA模型能够较好地模拟中国陆地植被NPP,近20年中国陆地植被NPP变化范围为2.703—2.882 PgC/a,在空间上呈西北低东南高的格局,在时间上呈现波动中缓慢增加的趋势。     

气候变化和人类活动对中国陆地生态系统总初级生产力的影响厘定研究

总初级生产力(GPP)是生态系统植被光合作用生成有机物的能力表征,是生态系统服务功能的基础,关系到区域社会经济可持续发展及区域生态安全。基于生态系统过程模型CEVSA2,应用中分辨率成像光谱仪(MODIS)卫星遥感的叶面积指数数据产品(MCD15A2H),以强迫法构建了遥感数据驱动的模型新版本——CEVSA-RS;基于CEVSA-RS模拟分析了气候变化和人类活动对中国陆地生态系统GPP时空变化的相对影响,从气候潜在总初级生产力(GPP_CL)和现实总初级生产力(GPP_RS)的大小和趋势两方面厘定了人类活动影响。2000至2017年全国平均潜在GPP(1016.36 gC m^-2a^-1)略高于对应现实GPP(962.85 gC m^-2a^-1),但存在明显的空间分异:长江以南大部、秦岭、太行山脉以东以及大兴安岭以东和长白山地区等森林植被覆盖区,现实GPP高于潜在GPP;而西部草地及灌丛等地区现实GPP低于潜在GPP。全国GPP呈显著增加趋势(P<0.05)...     

基于植被生产力的西南地区生态系统脆弱性特征

中国西南地区是全球生物多样性保护的重要地区之一.在全球气候变化背景下,该地区生态系统呈现出脆弱性增加的趋势.本研究基于生态系统总初级生产力(GPP),根据IPCC有关脆弱性的概念,计算西南地区生态系统的脆弱性,并分析了该区脆弱等级的空间分布格局,以及生态系统脆弱性与降水、温度、海拔、坡度和植被类型等因子间的相关性.结果表明: 西南地区生态系统脆弱性呈现由东南向西北逐渐增强的趋势,区域内多数地区为轻度、中度脆弱区(二者共占69%).脆弱等级随着区域内年平均降水量、多年平均温度的增加而减少,随着区域内海拔、坡度的增加而增加.西南喀斯特山区和西北山地农牧交错区呈现较高的脆弱性,更容易受气候变化或其他外界扰动的影响.针叶林、灌丛和草地的脆弱性相对较高,未来可能更容易受到气候变化的影响.     

Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years

The uncertainties of China's gross primary productivity (GPP) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year² from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.     

Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data

This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C₃ and C₄) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPAR_ps) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R² > 0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry‐grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R² > 0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPAR_ps) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPAR_ps at relatively high spatial resolution (20 m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution.     

Assessing canopy performance using carbonyl sulfide measurements

Carbonyl sulfide (COS) is a tracer of ecosystem photosynthesis that can advance carbon cycle research from leaf to global scales; however, a range of newly reported caveats related to sink/source strength of various ecosystem components hinder its application. Using comprehensive eddy‐covariance and chamber measurements, we systematically measure ecosystem contributions from leaf, stem, soil, and litter and were able to close the ecosystem COS budget. The relative contributions of nonphotosynthetic components to the overall canopy‐scale flux are relatively small (~4% during peak activity season) and can be independently estimated based on their responses to temperature and humidity. Converting COS to photosynthetic CO₂ fluxes based on the leaf relative uptake of COS/CO₂, faces challenges due to observed daily and seasonal changes. Yet, this ratio converges around a constant value (~1.6), and the variations, dominated by light intensity, were found unimportant on a flux‐weighted daily time‐scale, indicating a mean ratio of daytime gross‐to‐net primary productivity of ~2 in our ecosystem. The seasonal changes in the leaf relative uptake ratio may indicate a reduction in mesophyll conductance in winter, and COS‐derived canopy conductance permitted canopy temperature estimate consistent with radiative skin temperature. These results support the feasibility of using COS as a powerful and much‐needed means of assessing ecosystem function and its response to change.     

基于遥感总初级生产力的天山-塔里木绿洲地区生态系统脆弱性研究

生态系统的脆弱性是全球气候变化与可持续发展研究的核心问题,测定与评价脆弱性对认识生态系统的结构与功能至关重要。天山-塔里木绿洲地区包含着山地、荒漠和绿洲等多种类型的生态系统,存在着多个不同生态类型的交界过渡区。为定量评价该地区生态脆弱性,以植被总初级生产力这一生态系统重要的功能性指标为基础,对该地区生态系统脆弱性进行了计算和分级（不脆弱、轻度脆弱、中度脆弱、重度脆弱和极度脆弱）,并对研究区生态系统脆弱性的空间分布特征及其与环境因子的关系进行了分析与讨论。结果表明：（1）研究区生态系统脆弱性总体上表现出明显的空间分化格局,以中度和重度脆弱为主,极度脆弱的地区主要分布在南部的塔里木绿洲。（2）生态系统脆弱等级大体上随着区域内多年平均温度的升高而升高。受地表水灌溉的影响,生态系统脆弱性与降水量间并无明显趋势性规律。（3）研究区的生态系统脆弱等级随着区域内的平均海拔以及平均坡度的升高都呈现下降的趋势。受自然条件恶劣、过度放牧以及农田过度开垦的影响,目前该地区总体呈现脆弱性严重的状态。研究表明该地区应积极开展生态治理工作,合理规划生态功能关键区,保护好现有草原和湿地等易开垦地区,划定绿洲开发范围的"红线",限制农田的开垦,协调好塔里木河流域的水资源分配。研究为使用卫星遥感数据研究生态系统脆弱性提供了方法上的参考,为可持续发展和生态治理提供了科学依据。     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems

Aim The controls of gross radiation use efficiency (RUE), the ratio between gross primary productivity (GPP) and the radiation intercepted by terrestrial vegetation, and its spatial and temporal variation are not yet fully understood. Our objectives were to analyse and synthesize the spatial variability of GPP and the spatial and temporal variability of RUE and its climatic controls for a wide range of vegetation types. Location A global range of sites from tundra to rain forest. Methods We analysed a global dataset on photosynthetic uptake and climatic variables from 35 eddy covariance (EC) flux sites spanning between 100 and 2200 mm mean annual rainfall and between ?13 and 26°C mean annual temperature. RUE was calculated from the data provided by EC flux sites and remote sensing (MODIS). Results Rainfall and actual evapotranspiration (AET) positively influenced the spatial variation of annual GPP, whereas temperature only influenced the GPP of forests. Annual and maximum RUE were also positively controlled primarily by annual rainfall. The main control parameters of the growth season variation of gross RUE varied for each ecosystem type. Overall, the ratio between actual and potential evapotranspiration and a surrogate for the energy balance explained a greater proportion of the seasonal variation of RUE than the vapour pressure deficit (VPD), AET and precipitation. Temperature was important for determining the intra‐annual variability of the RUE at the coldest energy‐limited sites. Main conclusions Our analysis supports the idea that the annual functioning of vegetation that is adapted to its local environment is more constrained by water availability than by temperature. The spatial variability of annual and maximum RUE can be largely explained by annual precipitation, more than by vegetation type. The intra‐annual variation of RUE was mainly linked to the energy balance and water availability along the climatic gradient. Furthermore, we showed that intra‐annual variation of gross RUE is only weakly influenced by VPD and temperature, contrary to what is frequently assumed. Our results provide a better understanding of the spatial and temporal controls of the RUE and thus could lead to a better estimation of ecosystem carbon fixation and better modelling.