Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado

Eddy covariance records hold great promise for understanding the processes controlling the net ecosystem exchange of CO₂ (NEE). However, NEE is the small difference between two large fluxes: photosynthesis and ecosystem respiration. Consequently, separating NEE into its component fluxes, and determining the process‐level controls over these fluxes, is a difficult problem. In this study, we used a model‐data synthesis approach with the Simplified PnET (SIPNET) flux model to extract process‐level information from 5 years of eddy covariance data at an evergreen forest in the Colorado Rocky Mountains. SIPNET runs at a twice‐daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a soil moisture submodel that models both evaporation and transpiration. By optimizing the model parameters before evaluating model‐data mismatches, we were able to probe the model structure independent of any arbitrary parameter set. In doing so, we were able to learn about the primary controls over NEE in this ecosystem, and in particular the respiration component of NEE. We also used this parameter optimization, coupled with a formal model selection criterion, to investigate the effects of making hypothesis‐driven changes to the model structure. These experiments lent support to the hypotheses that (1) photosynthesis, and possibly foliar respiration, are down‐regulated when the soil is frozen and (2) the metabolic processes of soil microbes vary in the summer and winter, possibly because of the existence of distinct microbial communities at these two times. Finally, we found that including water vapor fluxes, in addition to carbon fluxes, in the parameter optimization did not yield significantly more information about the partitioning of NEE into gross photosynthesis and ecosystem respiration.     

基于模型数据融合的千烟洲亚热带人工林碳水通量模拟

人工林生态系统是我国森林生态系统的重要组成部分,在全球碳平衡中的作用越来越受到重视.利用千烟洲亚热带人工针叶林通量观测站的碳水通量和气象观测数据,通过模型数据融合方法对碳水循环过程模型——SIPNET模型关键参数进行反演,模拟了2004-2009年千烟洲人工林生态系统的碳水通量.结果表明:仅用碳通量观测数据优化模型参数时,净生态系统碳交换量(NEE)模拟效果较好(R2=0.934),而生态系统蒸散(ET)模拟效果较差(R2=0.188);同时用碳水通量观测数据优化时,NEE模拟效果稍差(R2=0.929),但ET模拟效果显著提升(R2=0.824),说明利用碳水通量观测数据同时优化,SIPNET模型才能较好地模拟试验站点碳水通量.在此基础上,开展了人工林生态系统碳通量对降水变化响应的敏感性分析,发现降水量减少对光合作用的影响比对呼吸作用的影响更为强烈,且碳水通量同时参与优化时模型才能较好地模拟碳通量随降水减少而快速降低的趋势,表明如果不能同时利用碳水通量进行参数优化,模型无法正确揭示生态系统碳循环对降水变异的响应.     

Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF-TV tower using SiB2.5

Three years of meteorological data collected at the WLEF‐TV tower were used to drive a revised version of the Simple Biosphere (SiB 2.5) Model. Physiological properties and vegetation phenology were specified from satellite imagery. Simulated fluxes of heat, moisture, and carbon were compared to eddy covariance measurements taken onsite as a means of evaluating model performance on diurnal, synoptic, seasonal, and interannual time scales. The model was very successful in simulating variations of latent heat flux when compared to observations, slightly less so in the simulation of sensible heat flux. The model overestimated peak values of sensible heat flux on both monthly and diurnal scales. There was evidence that the differences between observed and simulated fluxes might be linked to wetlands near the WLEF tower, which were not present in the SiB simulation. The model overestimated the magnitude of the net ecosystem exchange of CO₂ in both summer and winter. Mid‐day maximum assimilation was well represented by the model, but late afternoon simulations showed excessive carbon uptake due to misrepresentation of within‐canopy shading in the model. Interannual variability was not well simulated because only a single year of satellite imagery was used to parameterize the model.     

Using ecosystem CO2 measurements to estimate the timing and magnitude of greenhouse gas mitigation potential of forest bioenergy

Forest bioenergy opportunities may be hindered by a long greenhouse gas (GHG) payback time. Estimating this payback time requires the quantification of forest‐atmosphere carbon exchanges, usually through process‐based simulation models. Such models are prone to large uncertainties, especially over long‐term carbon fluxes from dead organic matter pools. We propose the use of whole ecosystem field‐measured CO₂ exchanges obtained from eddy covariance flux towers to assess the GHG mitigation potential of forest biomass projects as a way to implicitly integrate all field‐level CO₂ fluxes and the inter‐annual variability in these fluxes. As an example, we perform the evaluation of a theoretical bioenergy project that uses tree stems as bioenergy feedstock and include multi‐year measurements of net ecosystem exchange (NEE) from forest harvest chronosequences in the boreal forest of Canada to estimate the time dynamics of ecosystem CO₂ exchanges following harvesting. Results from this approach are consistent with previous results using process‐based models and suggest a multi‐decadal payback time for our project. The time for atmospheric carbon debt repayment of bioenergy projects is highly dependent on ecosystem‐level CO₂ exchanges. The use of empirical NEE measurements may provide a direct evaluation of, or at least constraints on, the GHG mitigation potential of forest bioenergy projects.     

An improved analysis of forest carbon dynamics using data assimilation

There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process‐based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques – which combine stock and flux observations with a dynamic model – improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3‐year period, and include eddy flux and soil CO₂ efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)=?251±197 g C m^?2 over the 3 years, compared with an estimate of ?419±29 g C m^?2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5 g C m^?2 day^?1, but the uncertainty on assimilated estimates averaged 0.47 g C m^?2 day^?1, and only exceeded 0.5 g C m^?2 day^?1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long‐running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote‐sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis – which might be provided indirectly by remotely sensed data – improves the analysis of NEE.     

NEE观测误差分布类型对陆地生态系统机理模型参数估计的影响——以长白山温带阔叶红松林为例

基于观测数据的陆地生态系统模型参数估计有助于提高模型的模拟和预测能力,降低模拟不确定性.在已有参数估计研究中,涡度相关技术测定的净生态系统碳交换量(NEE)数据的随机误差通常被假设为服从零均值的正态分布.然而近年来已有研究表明NEE数据的随机误差更服从双指数分布.为探讨NEE观测误差分布类型的不同选择对陆地生态系统机理模型参数估计以及碳通量模拟结果造成的差异,以长白山温带阔叶红松林为研究区域,采用马尔可夫链-蒙特卡罗方法,利用2003～2005年测定的NEE数据对陆地生态系统机理模型CEVSA2的敏感参数进行估计,对比分析了两种误差分布类型(正态分布和双指数分布)的参数估计结果以及碳通量模拟的差异.结果表明,基于正态观测误差模拟的总初级生产力和生态系统呼吸的年总量分别比基于双指数观测误差的模拟结果高61～86 g C m-2 a-1和107～116 g C m-2 a-1,导致前者模拟的NEE年总量较后者低29～47 g C m-2 a-1,特别在生长旺季期间有明显低估.在参数估计研究中,不能忽略观测误差的分布类型以及相应的目标函数的选择,它们的不合理设置可能对参数估计以及模拟结果产生较大影响.     

生态系统光合与呼吸拆分的同位素理论、方法和应用进展

生态系统光合和呼吸是构成净生态系统CO₂交换量(NEE)的重要组分。涡度相关技术可直接观测生态系统NEE,并通过建立温度回归或光响应曲线等函数将NEE统计拆分为生态系统光合和呼吸,但是存在自相关和高估白天呼吸等问题。稳定同位素红外光谱技术的进步使高时间分辨率大气CO₂及其稳定碳同位素组成(δ¹³C)的连续观测成为可能,与涡度相关技术观测的NEE数据相结合,可实现昼夜和季节尺度生态系统光合和呼吸拆分。本文系统阐述了生态系统光合与呼吸的同位素通量拆分方法的基本理论与假设,阐述了同位素通量观测技术的发展及其应用进展,综述了同位素通量拆分理论解析生态系统光合与呼吸过程的新机制认识,最后总结并展望了同位素通量拆分理论的不确定性以及开展多种拆分方法综合比较的必要性。     

Contribution of volatile organic compound fluxes to the ecosystem carbon budget of a poplar short‐rotation plantation

Biogenic volatile organic compounds (BVOCs) are major precursors of both ozone and secondary organic aerosols (SOA) in the troposphere and represent a non‐negligible portion of the carbon fixed by primary producers, but long‐term ecosystem‐scale measurements of their exchanges with the atmosphere are lacking. In this study, the fluxes of 46 ions corresponding to 36 BVOCs were continuously monitored along with the exchanges of mass (carbon dioxide and water vapor) and energy (sensible and latent heat) for an entire year in a poplar (Populus) short‐rotation crop (SRC), using the eddy covariance methodology. BVOC emissions mainly consisted of isoprene, acetic acid, and methanol. Total net BVOC emissions were 19.20 kg C ha^?1 yr^?1, which represented 0.63% of the net ecosystem exchange (NEE), resulting from ?23.59 Mg C ha^?1 yr^?1 fixed as CO₂ and 20.55 Mg C ha^?1 yr^?1 respired as CO₂ from the ecosystem. Isoprene emissions represented 0.293% of NEE, being emitted at a ratio of 1 : 1709 mol isoprene per mol of CO₂ fixed. Based on annual ecosystem‐scale measurements, this study quantified for the first time that BVOC carbon emissions were lower than previously estimated in other studies (0.5–2% of NEE) on poplar trees. Furthermore, the seasonal and diurnal emission patterns of isoprene, methanol, and other BVOCs provided a better interpretation of the relationships with ecosystem CO₂ and water vapor fluxes, with air temperature, vapor pressure deficit, and photosynthetic photon flux density.     

Carbon isotopes in terrestrial ecosystem pools and CO2 fluxes

Stable carbon isotopes are used extensively to examine physiological, ecological, and biogeochemical processes related to ecosystem, regional, and global carbon cycles and provide information at a variety of temporal and spatial scales. Much is known about the processes that regulate the carbon isotopic composition (delta(13)C) of leaf, plant, and ecosystem carbon pools and of photosynthetic and respiratory carbon dioxide (CO(2)) fluxes. In this review, systematic patterns and mechanisms underlying variation in delta(13)C of plant and ecosystem carbon pools and fluxes are described. We examine the hypothesis that the delta(13)C of leaf biomass can be used as a reference point for other carbon pools and fluxes, which differ from the leaf in delta(13)C in a systematic fashion. Plant organs are typically enriched in (13)C relative to leaves, and most ecosystem pools and respiratory fluxes are enriched relative to sun leaves of dominant plants, with the notable exception of root respiration. Analysis of the chemical and isotopic composition of leaves and leaf respiration suggests that growth respiration has the potential to contribute substantially to the observed offset between the delta(13)C values of ecosystem respiration and the bulk leaf. We discuss the implications of systematic variations in delta(13)C of ecosystem pools and CO(2) fluxes for studies of carbon cycling within ecosystems, as well as for studies that use the delta(13)C of atmospheric CO(2) to diagnose changes in the terrestrial biosphere over annual to millennial time scales.     

Characteristics of net ecosystem carbon dioxide exchange(NEE) from August to October of Alpine meadow on the Tibetan Plateau,China

The Alpine meadow is one of the vegetation types widely distributed on the Tibetan Plateau in China with an area of about 1.2 million square kilometers.The Damxung rangeland station,located in the hinterland of the Tibetan Plateau,is covered with an typical vegetation.The continuous carbon flux data (from August to middle October,2003) measured with the open-path eddy covariance system was used to analyze the diurnal variation pattern of net ecosystem carbon dioxide exchange (NEE) and its relationship with the environmental factors,such as photosynthetically active radiation (PAR),precipitation,and temperature.Results showed that NEE presented obvious diurnal variation pattern with single-peak of diurnal maximum carbon assimilation at 11:00-12:00 (local time) with an CO2.m-2-s-1.During the daytime,NEE fitted fairly well with PAR in a rectangular hyperbola function with the apparent the night-time,NEE showed a good exponential relation with the soil temperature at 5 cm depth.     

中亚热带人工针叶林生态系统碳通量拆分差异分析

涡度通量观测可直接获取陆地生态系统与大气之间CO2净交换量(NEE),但深入认识碳循环过程和校验生态系统模型需要不同时间尺度总初级生产力(GPP)和生态系统呼吸(Re)等碳通量数据。利用中国陆地生态系统通量观测与研究网络(ChinaFLUX)中亚热带人工针叶林生态系统2003—2009年的涡度通量和气象观测数据,分析了两种NEE拆分方法对不同时间尺度GPP和Re评估的影响,结果表明:(1)两种拆分方法得到的生态系统碳通量组分(GPP和Re)的季节动态变化一致,都在生长季7、8月份达到峰值;(2)非线性回归模型拆分得到的全年Re和GPP相较于光响应曲线模型分别高出2%—28.6%和1.6%—23%,最大高出317.6 gC·m-2·a-1(2006年),逐月最大差值主要发生在8、9月份;(3)不同时间尺度上,两种方法拆分得到的GPP和Re之间差值的环境响应因子不同。在广泛采用非线性回归模型进行拆分时,如果当月光合有效辐射接近到905mol·m-2·月-1,月平均空气饱和水汽压差接近1.18 kPa时,需要考虑使用光响应曲线模型拆分该月通量,结合两种拆分方法以减小全年的误差。     

内蒙古克氏针茅草原生态系统-大气通量交换特征

基于内蒙古克氏针茅草原生态系统的涡动相关观测资料和小气候梯度系统观测资料,对研究区碳通量和水热通量的日、季动态进行了研究.结果表明：克氏针茅草原生态系统生长季的碳通量日动态呈U型曲线,即日出前释放CO₂,日出后开始吸收CO₂,正午前后达峰值,午后吸收CO₂减弱,日落后重新转为释放CO₂;9月白天CO₂吸收最为强烈,8月次之,10月最低.克氏针茅草原的感热和潜热通量的日动态均呈倒U型曲线,与碳通量日动态相反,即白天感热和潜热通量多为正值,夜间感热为负值,潜热接近于零;感热通量以5月最高,潜热通量以9月最高.冬季草地为弱碳源,CO₂通量较小,夏季表现为明显的碳汇.     

Influences of error distributions of net ecosystem exchange on parameter estimation of a process-based terrestrial model: A case of broad-leaved Korean pine mixed forest in Changbaishan, China

Model predictions can be improved by parameter estimation from measurements. It was assumed that measurement errors of net ecosystem exchange (NEE) of CO₂ follow a normal distribution. However, recent studies have shown that errors in eddy covariance measurements closely follow a double exponential distribution. In this paper, we compared effects of different distributions of measurement errors of NEE data on parameter estimation. NEE measurements in the Changbaishan forest were assimilated into a process-based terrestrial ecosystem model. We used the Markov chain Monte Carlo method to derive probability density functions of estimated parameters. Our results showed that modeled annual total gross primary production (GPP) and ecosystem respiration (Re) using the normal error distribution were higher than those using the double exponential distribution by 61–86 gC m^?2 a^?1 and 107–116 gC m^?2 a^?1, respectively. As a result, modeled annual sum of NEE using the normal error distribution was lower by 29–47 gC m^?2 a^?1 than that using the double exponential error distribution. Especially, modeled daily NEE based on the normal distribution underestimated the strong carbon sink in the Changbaishan forest in the growing season. We concluded that types of measurement error distributions and corresponding cost functions can substantially influence the estimation of parameters and carbon fluxes.     

Seasonal variability in net ecosystem carbon dioxide exchange over a young Switchgrass stand

Understanding carbon dynamics of switchgrass ecosystems is crucial as switchgrass (Panicum virgatum L.) acreage is expanding for cellulosic biofuels. We used eddy covariance system and examined seasonal changes in net ecosystem CO₂ exchange (NEE) and its components – gross ecosystem photosynthesis (GEP) and ecosystem respiration (ER) – in response to controlling factors during the second (2011) and third (2012) years of stand establishment in the southern Great Plains of the United States (Chickasha, OK). Larger vapor pressure deficit (VPD > 3 kPa) limited photosynthesis and caused asymmetrical diurnal NEE cycles (substantially higher NEE in the morning hours than in the afternoon at equal light levels). Consequently, rectangular hyperbolic light–response curve (NEE partitioning algorithm) consistently failed to provide good fits at high VPD. Modified rectangular hyperbolic light–VPD response model accounted for the limitation of VPD on photosynthesis and improved the model performance significantly. The maximum monthly average NEE reached up to ?33.02 ± 1.96 μmol CO₂ m^?2 s^?1 and the highest daily integrated NEE was ?35.89 g CO₂ m^?2 during peak growth. Although large differences in cumulative seasonal GEP and ER were observed between two seasons, total seasonal ER accounted for about 75% of GEP regardless of the growing season lengths and differences in aboveground biomass production. It suggests that net ecosystem carbon uptake increases with increasing GEP. The ecosystem was a net sink of CO₂ during 5–6 months and total seasonal uptakes were ?1128 ± 130 and ?1796 ± 217 g CO₂ m^?2 in 2011 and 2012, respectively. In conclusion, our findings suggest that the annual carbon status of a switchgrass ecosystem can be a small sink to small source in this region if carbon loss from biomass harvesting is considered. However, year‐round measurements over several years are required to assess a long‐term source‐sink status of the ecosystem.     

内蒙古温带荒漠草原生态系统水热通量动态

基于2008年全年内蒙古温带荒漠草原的水热通量观测数据,对荒漠草原水、热通量的日、季动态进行了分析.结果表明：温带荒漠草原感热通量和潜热通量的日动态均呈单峰型曲线,在12:00-13:30左右达最大值,其与地表净辐射的日变化趋势基本一致,但感热和潜热的峰值出现时间较地表净辐射峰值出现时间滞后约1 h;温带荒漠草原感热通量和潜热通量的日累积最大值分别为319.01和425.37 W·m^-2,分别出现在5月30日和6月2日;月均感热通量与潜热通量的最大值分别出现在5月和6月,最小值分别出现在1月和12月.研究区土壤含水量与降水的相关性较好,表层土壤含水量对降水的反应最敏感,深层土壤水分对降水的反应存在位相滞后.感热通量和潜热通量的季节动态与地表净辐射基本一致,均受降水影响.感热通量受地表净辐射的影响明显,而潜热通量对降水的反应较敏感,且土壤含水量在潜热通量中起主要作用.     

Endogenous circadian regulation of carbon dioxide exchange in terrestrial ecosystems

It is often assumed that daytime patterns of ecosystem carbon assimilation are mostly driven by direct physiological responses to exogenous environmental cues. Under limited environmental variability, little variation in carbon assimilation should thus be expected unless endogenous plant controls on carbon assimilation, which regulate photosynthesis in time, are active. We evaluated this assumption with eddy flux data, and we selected periods when net ecosystem exchange (NEE) was decoupled from environmental variability in seven sites from highly contrasting biomes across a 74° latitudinal gradient over a total of 36 site‐years. Under relatively constant conditions of light, temperature, and other environmental factors, significant diurnal NEE oscillations were observed at six sites, where daily NEE variation was between 20% and 90% of that under variable environmental conditions. These results are consistent with fluctuations driven by the circadian clock and other endogenous processes. Our results open a promising avenue of research for a more complete understanding of ecosystem fluxes that integrates from cellular to ecosystem processes.     

Leaf age affects the seasonal pattern of photosynthetic capacityand net ecosystem exchange of carbon in a deciduous forest

Temporal trends in photosynthetic capacity are a critical factorin determining the seasonality and magnitude of ecosystem carbonfluxes. At a mixed deciduous forest in the south‐eastern United States (Walker Branch Watershed, Oak Ridge, TN, USA), we independently measured seasonal trends in photosynthetic capacity (using single‐leaf gas exchange techniques) and the whole‐canopycarbon flux (using the eddy covariance method). Soil respiration was also measured using chambers and an eddy covariance system beneath the canopy. These independent chamber and eddy covariance measurements, along with a biophysical model (CANOAK), areused to examine how leaf age affects the seasonal pattern of carbon uptake during the growing season. When the measured seasonality in photosynthetic capacity is representedin the CANOAK simulations, there is good agreement with the eddy covariance data on the seasonal trends in carbon uptake. Removing the temporal trends in the simulations by using the early season maximum value of photosynthetic capacity over the entire growing season over estimates the annual carbon uptake by about 300 g C m^?2 year^?1– halfthe total estimated annual net ecosystem exchange. Alternatively, use of the mean value of photosynthetic capacity incorrectly simulates the seasonality in carbon uptake by the forest. In addition to changes related to leaf development and senescence, photosynthetic capacitydecreased in the middle and late summer, even when leaf nitrogenwas essentially constant. When only these middle and late summer reductions were neglected in the model simulations, CANOAK still overestimated the carbon uptake by an amount comparable to 25% ofthe total annual net ecosystem exchange.     

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。     

基于模型数据融合的长白山阔叶红松林碳循环模拟

 充分、有效地利用各种陆地生态系统碳观测数据改善陆地生态系统模型, 是当前我国陆地生态系统碳循环研究领域亟待解决的重要问题之一。该研究以2003~2005年长白山阔叶红松林的6组生物计量观测数据和涡度相关技术测定的碳通量数据为基础, 利用马尔可夫链-蒙特卡罗方法对陆地生态系统模型的关键参数(即碳滞留时间)进行了反演, 进而预测了长白山阔叶红松林生态系统碳库、碳通量及其不确定性。反演结果表明, 长白山阔叶红松林叶凋落物和微生物碳的平均滞留时间最短, 为2~6个月; 其次是叶和细根生物量碳, 二者的平均滞留时间为1~2 a; 慢性土壤有机碳的平均滞留时间为8~16 a; 碳在木质生物量和惰性土壤有机质库中的滞留时间最长, 平均滞留时间分别为77~109 a和409~1 879 a。模拟结果显示, 碳库和累积碳通量模拟值的不确定性将随着模拟时间的延长而增大。当气温升高10%和20%时, 长白山阔叶红松林总初级生产力年总量将分别增加6.5%和9.9%, 净生态系统生产力(NEP)年总量的变化取决于土壤温度的变化。若土壤温度保持不变, NEP年总量将分别增加11.4%~21.9%和17.6%~33.1%; 若土壤温度也相应升高10%和20%, NEP年总量的增幅反而下降甚至低于原来的水平。假设气候和植被保持在2003~2005年的状态, 2020年长白山阔叶红松林NEP年总量为(163±12) g C·m^–2·a^–1, 土壤呼吸年总量为(721±14) g C·m^–2·a^–1。马尔可夫链-蒙特卡罗方法是反演模型参数、优化模拟结果和评估模拟结果不确定性的有效方法, 但今后仍需在惰性土壤碳滞留时间的估计、驱动数据和模型结构的不确定性分析、模型数据融合方法方面进行深入研究, 以进一步提高碳循环模拟的准确性。     

A new model of net ecosystem carbon exchange for the deciduous-dominated forest by integrating MODIS and flux data

The eddy covariance technique provides continuous measurements of plot-level net ecosystem carbon exchange (NEE) across a wide range of vegetation types. However, these NEE estimates only represent fluxes at the tower footprint scale. To quantify the NEE over regions or continents, flux tower measurements need to be up-scaled to large areas. In the present study, we propose a new NEE model solely based on Moderate Resolution Imaging Spectroradiometer data, including enhanced vegetation index (EVI), land surface water index (LWSI), land surface temperature (LST), and Terra nighttime LST′. Site-specific data from the deciduous-dominated Harvard Forest flux site were used. Analysis covered six years (2001-2006) of CO₂ flux data. The data of the first four years were used for model building and the rest as validation set. Compared with the model based solely on EVI, we also introduced LST and LSWI into the new model. The results showed that this method could further improve the precision (R² and RMSE reached 0.857 and 1.273, respectively) and generally capture the expected seasonal patterns of NEE.