青海湖流域泥炭湿地地气系统不同时间尺度上CO2交换特征

为了定量分析2017年青海湖流域泥炭湿地地气系统不同时间尺度上的碳交换的变化特征及影响机制, 利用涡动相关技术对其不同时间尺度上的碳通量进行了测定, 结果表明: 1)青海湖流域泥炭湿地地气系统在2017年表现为“碳源”, 全年合计排放209.312 gC·m–2。2)生态系统总初级生产量(GPP)和生态系统总呼吸(Re)年变化均呈倒V型, 而净生态系统碳交换(NEE)年内变化则呈双峰型。3)NEE和GPP 与各环境要素(气温、土壤温度、月平均降水量、土壤含水量)呈现负相关关系, 而Re与之呈显著的正相关关系(P<0.01)。4)NEE受温度因子影响较大, 主要受控于气温。5)GPP和Re与各水热因子都有较大的相关性, 但GPP受温度因子影响较显著, 而水、热季节变化及其协调程度对Re有更大的影响。     

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CO_2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CO_2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CO_2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CO_2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。     

数据处理方法不确定性对CO_2通量组分估算的影响

基于中国陆地生态系统通量观测研究网络(ChinaFLUX)4个站点(2个森林站和2个草地站)的涡度相关通量观测资料,分析了CO2通量数据处理过程中异常值剔除参数设置、夜间摩擦风速(u*)临界值(u*c)确定及数据插补模型选择对CO2通量组分估算的影响.结果表明:3种数据处理方法均对净生态系统碳交换量(NEE)年总量估算有显著影响,其中u*c确定是影响NEE估算的重要因子;异常值剔除、u*c确定及数据插补模型选择导致NEE年总量估算偏差分别为0.62～21.31 g C.m-2.a-1(0.84%～65.31%)、4.06～30.28 g C.m-2.a-1(3.76%～21.58%)和0.69～27.73 g C.m-2.a-1(0.23%～55.62%),草地生态系统NEE估算对数据处理方法参数设置更敏感;数据处理方法不确定性引起的总生态系统碳交换量和生态系统呼吸年总量估算相对偏差分别为3.88%～11.41%和6.45%～24.91%.     

川西贡嘎山峨眉冷杉成熟林生态系统CO2通量特征

成熟森林的碳收支对陆地生态系统碳循环研究具有重要意义。目前,我国关于西南亚高山暗针叶林成熟林碳通量的研究还相对较少,尚不明确对碳循环的作用。以涡度相关技术为基础,对川西贡嘎山东坡峨眉冷杉成熟林生态系统尺度的CO_2通量进行长期定位观测。利用2015年6月至2016年5月观测数据,分析了峨眉冷杉成熟林净生态系统CO_2交换量(NEE)、生态系统呼吸(Re)和总生态系统生产力(GPP)的季节变异特征及其源汇状况,并结合环境因子,分析CO_2通量的主要控制因子。结果表明:(1)峨眉冷杉成熟林NEE具有明显的日变化特征,呈现"U"形变化,白天为负值,夜间为正值,中午前后CO_2通量达到最大;各月间日平均NEE变化差异显著,NEE峰值最大出现在2015年6月(-0.64 mg CO_2m~(-2)s~(-1)),峰值最小出现在2016年1月(-0.08 mg CO_2m~(-2)s~(-1));日平均NEE由正值变为负值的时间夏季最早,冬季最晚,NEE由负值变为正值的时间冬季最早,夏季最晚。(2)峨眉冷杉成熟林NEE、Re和GPP具有明显的月变化。2015年6月和12月NEE分别达到最大值(-46.02 g C m~(-2)月~(-1))和最小值(-1.42 g C m~(-2)月~(-1));Re呈现单峰变化,最大和最小值分别出现在2015年6月(84.78 g C m~(-2)月~(-1))和2016年1月(12.82 g C m~(-2)月~(-1));GPP最大值和最小值分别出现在2015年6月(130.81 g C m~(-2)月~(-1))与2016年1月(16.15 g C m~(-2)月~(-1))。(3)空气温度(T_a)、5 cm土壤温度(T_(s5))和光合有效辐射(PAR)是影响峨眉冷杉成熟林CO_2通量的主要环境因子。T_a与CO_2通量呈指数相关(R~2=0.5283,P0.01);白天CO_2通量与PAR显著相关(R~2=0.4373,P0.01);夜晚CO_2通量与T_(s5)显著相关(R~2=0.4717,P0.01)。(4)全年NEE、Re和GPP分别为-241.87、564.81 g C m~(-2)和806.68 g C m~(-2),表明川西贡嘎山峨眉冷杉成熟林具有较强的碳汇功能。     

1980—2013年中国陆地生态系统总初级生产力对干旱的响应特征

干旱事件通过影响陆地生态系统的组成、结构和功能显著改变整个陆地生态系统碳循环。陆地生态系统总初级生产力(GPP)是全球陆地碳通量中最大的组成部分,反映了陆地生态系统的生产力水平。本研究利用基于过程模型模拟的GPP数据(DLM GPP)、基于通量观测升尺度的GPP数据(FLUXCOM GPP)和标准化降水蒸散指数(SPEI),量化分析了1980-2013年中国陆地生态系统GPP和干旱的时空格局,讨论了不同时间尺度上GPP对干旱的响应特征。结果表明:1980-2013年,两种不同GPP数据在中国地区呈现的时间变化趋势的空间分布格局较为一致,上升趋势主要分布在西南地区,下降趋势主要分布在东北大部分地区;中国干旱面积的长期时间变化趋势略有下降,其中干旱化趋势主要位于秦岭淮河以南地区,而西北内陆地区则呈现明显的湿润化趋势;时间尺度上,GPP与SPEI年际变化格局基本吻合,1986、1997、2001和2011年等干旱年份的GPP显著降低;空间尺度上,北方大部分地区的GPP与SPEI呈正相关,南方大部分地区呈负相关,干旱对GPP的影响在半干旱地区表现更加明显;GPP对干旱的响应格局与选取干旱指数的时间尺度密切相关,而且不同方式估算的GPP对干旱响应和敏感度存在差异。因此,未来需进一步改进GPP模型和方法,增加观测站点,提高GPP估算的精确性。     

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。     

黄河三角洲芦苇湿地生态系统碳、水热通量特征

利用涡度相关法对黄河三角洲芦苇湿地生态系统进行了连续两年的通量观测,对2009—2010年生长季芦苇湿地的净生态系统碳交换量(NEE),感热通量(Hs)和潜热通量(LE)数据进行了分析。结果表明,在日尺度上,芦苇湿地NEE日变化特征表现为两个CO2吸收高峰,分别出现在11:00和16:00左右,其特点是在午间出现了碳交换通量的降低。CO2吸收的日最大值在两个生长季出现的时间有所不同,分别出现在2009年7月(-0.30 mg CO2m-2s-1)和2010年6月(-0.37 mg CO2m-2s-1)。CO2排放的日最大值两个生长季均出现在9月,分别为0.19和0.25 mg CO2m-2s-1。Hs和LE的日动态均为单峰型,极值都出现在中午前后,生长季生态系统的能量消耗主要以潜热为主,且在日尺度上,热通量和NEE有显著的负相关关系。在季节尺度上,芦苇湿地生长季具有明显的碳汇功能,2009年生长季生态系统白天固定354.63 g CO2/m2,同时期夜间释放159.24 g CO2/m2,净CO2吸收量为-195.39 g CO2/m2。2009年整个生长季生态系统总初级生产力(GPP)为-651.13 g CO2/m2,生态系统呼吸(Re)为455.74 g CO2/m2,系统表现为碳汇。路径分析表明:光合有效辐射(PAR)显著影响NEE的日动态(R2=0.46—0.84),而NEE的季节动态主要受土壤温度的影响,降水和PAR的影响次之。     

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.     

青海湖北岸草甸草原CO2通量年际动态及其驱动机制

青藏高原草甸草原是生态系统中重要的植被类型,准确评估高寒草甸草原生态系统碳源汇状况及碳储量变化尤为重要。基于涡度相关系统观测,分析了2009年至2016年8年期间青海湖北岸草甸草原环境因子以及碳通量的变化特征,运用结构方程模型(SEM)分析环境因子对总初级生产力(GPP)、净生态系统CO₂交换量(NEE)、生态系统呼吸(Re)的调控机制。结果表明：2009—2016年8年NEE日均值在-2.02—0.88 gC m^-2 d^-1之间,5—9月NEE为负值,表现为碳吸收,雨热同期的6、7、8月是CO₂净吸收最强的时期,平均每月吸收CO₂ 39.85 gC m^-2 month^-1,NEE负值日数约占全年的48%,10月—翌年4月为正值,表现为碳释放,初春3月和秋末11月是CO₂净释放最强的时期;Re日均值为1.69 gC m^-2 d^-1,受季节温度的影响,呈夏季强,冬季弱的态...     

黄河小浪底人工混交林冠层CO2储存通量变化特征

基于黄河小浪底人工混交林2008年的CO2浓度和碳通量数据,分析了不同天气条件下CO2浓度在时间和空间上的变化特征,对比了CO2浓度廓线法和涡度相关法估算的CO2储存通量,研究了CO2储存通量的日、季变化特征。结果表明:人工混交林冠层上方月平均CO2浓度具有明显的季节变化规律。月平均CO2浓度最大值出现在3月(370μmol/mol),最低值出现在8月(347μmol/mol)。涡度相关法估算的CO2储存通量比廓线法所得结果偏低9%。生长季,冠层CO2储存通量和净生态系统碳交换量(NEE)日平均值分别为-0.0004和-0.091 mg CO2m-2s-1,冠层CO2储存通量在NEE中仅占0.4%。2008年CO2储存通量和NEE分别为-46.1、-1133 g CO2m-2a-1。在年尺度上,CO2储存通量占NEE的4.1%。因此,在日和年尺度上计算黄河小浪底人工混交林NEE时,CO2储存通量可以忽略。     

Wetland flux controls: how does interacting water table levels and temperature influence carbon dioxide and methane fluxes in northern Wisconsin?

Wetlands play a disproportionately large role in global terrestrial carbon stocks, and from 1 year to the next individual wetlands can fluctuate between carbon sinks and sources depending on factors such as hydrology, temperature, and land use. Although much research has been done on short-term seasonal to annual wetland biogeochemical cycles, there is a lack of experimental evidence concerning how the reversibility of wetland hydrological changes will influence these cycles over longer time periods. Five years of drought-induced declining water table at Lost Creek, a shrub fen wetland in northern Wisconsin, coincided with increased ecosystem respiration (R_eco) and gross primary production (GPP) as derived from long-term eddy covariance observations. Since then, however, the average water table level at this site has increased, providing a unique opportunity to explore how wetland carbon fluxes are affected by interannual air temperature differences as well as changing water table levels. Water table level, as measured by water discharge, was correlated with R_eco and GPP at interannual time scales. However, air temperature had a strong correlation with R_eco, GPP, and net ecosystem productivity (NEP) at monthly time scales and correlated with NEP at inter-annual time scales. Methane flux was strongly temperature-controlled at seasonal time scales, increasing an order of magnitude from April to July. Annual methane emissions were 51 g C m^?2. Our results demonstrate that over multi-year timescales, water table fluctuations can have limited effects on wetland net carbon fluxes and instead at Lost Creek annual temperature is the best predictor of interannual variation.     

Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits

The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties (EFPs). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity (GPP) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFPs and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site‐years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R² = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra‐ and interspecific trait variation on ecosystem functioning.     

The annual carbon budget of a French pine forest (Pinus pinaster) following harvest

Eddy covariance measurements of net ecosystem exchange (NEE) of carbon dioxide and sensible and latent heat have operated since clear felling of a 50‐year old maritime pine stand in Les Landes, in Southwestern France. Turbulent fluxes from the closed‐path system are computed via different methodologies, including those recommended from EUROFLUX (Adv. Ecol. Res. 30 (2000) 113; Agric. Forest Meteorol. 107 (2001a, b) 43 and 71), and sensitivity analysis demonstrates the merit of post‐processing for accurate flux calculation. Footprint modeling, energy balance closure, and empirical modeling corroborate the eddy flux measurements, indicating best reliability in the daytime. The ecosystem, a net source of atmospheric CO₂, is capable of fixing carbon during fair weather during any season due to the abundance of re‐growing species (mostly grass), formerly from the understorey. Annual carbon loss of 200–340 g m^?2 depends on the period chosen, with inter‐annual variability evident during the 18‐month measurement period and apparently related to available light. Empirical models, with weekly photosynthetic parameters corresponding to seasonal vegetation and respiration depending on soil temperature, fit the data well and allow partitioning of annual NEE into GPP and TER components. Comparison with a similar nearby mature forest (Agric. Forest Meteorol. 108 (2001) 183) indicates that clear‐cutting reduces GPP by two thirds but TER by only one third, transforming a strong forest sink into a source of CO₂. Likewise, the loss of 50% of evapotranspiration (by the trees) leads to increased temperatures and thus reduced net radiation (by one third), and a 50% increase in sensible heat loss by the clear cut.     

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO₂ between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO₂ release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO₂ fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NN_C‐part). NN_C‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R² > .94) and RECO (R² > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NN_C‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO₂ fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO₂ fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN_C‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.     

Carbon balance assessment of a Belgian winter wheat crop (Triticum aestivum L.)

The carbon balance of a winter wheat crop in Lonzée, Belgium, was assessed from measurements carried out at different spatial and temporal scales between November 2004 and August 2005. From eddy covariance measurements, the net ecosystem exchange was found to be ?0.63 kg C m^?2 and resulted from the difference between gross primary productivity (GPP) (?1.58 kg C m^?2) and total ecosystem respiration (TER) (0.95 kg C m^?2). The impact of the u_* threshold value on these fluxes was assessed and found to be very small. GPP assessment was partially validated by comparison with an estimation scaled up from leaf scale assimilation measurements. Soil respiration (SR), extrapolated from chamber measurements, was 0.52 kg C m^?2. Net primary productivity, assessed from crop sampling, was ?0.83 kg C m^?2. By combining these fluxes, the autotrophic and heterotrophic components of respiration were deduced. Autotrophic respiration dominated both TER and SR. The evolution of these fluxes was analysed in relation to wheat development.     

涡度相关法和静态箱/气相色谱法在生态系统呼吸观测中的比较

基于涡度相关法和静态箱/气相色谱法(箱式法)的碳通量观测数据,对比分析了两种方法在评价禹城冬小麦 夏玉米复种农田生态系统和海北高寒矮嵩草草甸生态系统呼吸中的差异.结果表明：在保证涡度相关法和箱式法观测数据质量的条件下,两种方法实时观测的夜间通量结果具有较好的一致性,相关系数达0.95～0.98;箱式法白天的观测结果与涡度相关法估算的白天生态系统呼吸值有较好的一致性,但前者普遍大于后者;两种方法测定生态系统呼吸日平均值的差异达极显著水平（P<0.01）,但二者的季节变化趋势较一致.在整个观测期内, 冬小麦-夏玉米复种农田观测箱内外平均温差为1.8 ℃,涡度相关法较箱式法测定的生态系统呼吸日平均值偏低30.3%;高寒矮嵩草草甸观测箱内外平均温差为1.9 ℃,涡度相关法较箱式法测定的生态系统呼吸日平均值偏低31.4%.两种方法对生态系统生长季呼吸日平均值测定结果的偏差高于非生长季.     

基于涡度协方差法和生理生态法对落叶松林CO2通量的初步研究

 该文利用涡度协方差法和生理生态学方法（不同分量的累积和）获得的通量观测数据,对老山落叶松(Larix gmelinii)林(45°20′N, 127°34 ′E)的碳收支进行了分析。通过对每0.5 h所测数据进行的分析表明,能量平衡达到75％,说明涡度协方差法适应于本站的研究。较阴天气情况 下,林分光照利用效率显著高于晴朗天气,可能归因于阴天较多的散射光。以单位土地面积计算发现,通过涡度协方差法计算的落叶松林生态 系统的总初级生产力在20～50 μmol•m^-2•s^-1之间,远高于冠层叶片的总光合速率9.8～23.4μmol•m^-2•s^-1 (平均值16.2μmol•m^-2•s^-1 ),而 当综合考虑冠层光合和林下植物光合作用时,两种方法测定结果吻合性较好,说明林下植物对落叶松林碳平衡有重要影响。在估计森林生态系 统呼吸方面,以有风夜晚净生态系统交换量（NEE）来代表生态系统呼吸总量（3～9μmol•m^-2•s^-1）低估了生态系统呼吸总量,粗略估计较生 理生态学方法（不同呼吸分量的累积和）低估了50%左右（14.2μmol•m^-2•s^-1）。结果发现两种方法在估计森林碳平衡方面存在一定的差异, 呼吸量的估计差异应是今后研究的重点。     

基于EALCO模型对中亚热带人工针叶林CO2通量季节变异的模拟

 EALCO模型是一个基于生理生态学过程,模拟生态系统下垫面与大气之间水、热和碳通量交换的综合模型。将该模型应用在亚热带常绿针叶林, 对其生态系统过程进行了模拟,以深入探讨季节性干旱对生态系统过程的影响。对EALCO模型进行了参数化与初始化并对模型的光合作用时段和 落叶机制进行了改进,以更好地模拟亚热带人工针叶林生态系统。千烟洲通量观测站自2002年底开始应用涡度相关技术对中亚热带人工针叶林 生态系统进行通量观测,该站点2003年经历了一次较严重的季节性干旱(由高温与少雨综合作用造成),降水量仅为多年平均值的65%,而2004年 的年降水量与多年平均值较为接近,利用该站点2003和2004年特殊的气候条件,使用其通量观测数据对模型的模拟效果进行检验。从模拟结果 的总体趋势来看,模型能较好地从半小时、日及年尺度上反映两年内土壤-植被-大气之间的碳交换状况。总初级生产力(Gross primary production, GPP)在一年中呈现单峰型变化,遇高温及干旱胁迫GPP值下降。由于受到干旱胁迫的影响,2003年GPP值比2004年偏低12.9%。模拟 结果显示,2003年GPP值比2004年偏低11.2%。观测数据与模拟结果均显示,水分胁迫期间净碳交换量(Net ecosystem production, NEP)模拟值 与实测值的日变化均呈现一种“偏态",即一天中生态系统碳交换量最大值出现在上午某一时刻,之后逐渐降低。 模拟结果显示,水分匮缺对 光合能力的影响比对生态系统呼吸作用的影响更为强烈,因而导致了净生态系统生产力的降低。进一步分析表明,水分匮缺期间,晴天正午之 前,深层土壤( >20 cm) 水分的匮缺抑制了光合作用能力,正午之后,高温与深层土壤水分匮缺共同削弱光合作用能力,影响各占一半。     

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.     

Seasonal and annual respiration of a ponderosa pine ecosystem

The net ecosystem exchange of CO₂ between forests and the atmosphere, measured by eddy covariance, is the small difference between two large fluxes of photosynthesis and respiration. Chamber measurements of soil surface CO₂ efflux (F_s), wood respiration (F_w) and foliage respiration (F_f) help identify the contributions of these individual components to net ecosystem exchange. Models developed from the chamber data also provide independent estimates of respiration costs. We measured CO₂ efflux with chambers periodically in 1996–97 in a ponderosa pine forest in Oregon, scaled these measurements to the ecosystem, and computed annual totals for respiration by component. We also compared estimated half-hourly ecosystem respiration at night (F_nc) with eddy covariance measurements. Mean foliage respiration normalized to 10 °C was 0.20 μmol m^–2 (hemi-leaf surface area) s^–1, and reached a maximum of 0.24 μmol m^–2 HSA s^–1 between days 162 and 208. Mean wood respiration normalized to 10 °C was 5.9 μmol m^–3 sapwood s^–1, with slightly higher rates in mid-summer, when growth occurs. There was no significant difference (P > 0.10) between wood respiration of young (45 years) and old trees (250 years). Soil surface respiration normalized to 10 °C ranged from 0.7 to 3.0 μmol m^–2 (ground) s^–1 from days 23 to 329, with the lowest rates in winter and highest rates in late spring. Annual CO₂ flux from soil surface, foliage and wood was 683, 157, and 54 g C m^–2 y^–1, with soil fluxes responsible for 76% of ecosystem respiration. The ratio of net primary production to gross primary production was 0.45, consistent with values for conifer sites in Oregon and Australia, but higher than values reported for boreal coniferous forests. Below-ground carbon allocation (root turnover and respiration, estimated as F_s– litterfall carbon) consumed 61% of GPP; high ratios such as this are typical of sites with more water and nutrient constraints. The chamber estimates were moderately correlated with change in CO₂ storage in the canopy (F_stor) on calm nights (friction velocity u* < 0.25 m s^–1; R² = 0.60); F_stor was not significantly different from summed chamber estimates. On windy nights (u* > 0.25 m s^–1), the sum of turbulent flux measured above the canopy by eddy covariance and F_stor was only weakly correlated with summed chamber estimates (R² = 0.14); the eddy covariance estimates were lower than chamber estimates by 50%.