Jan 2016



Forest GPP Calculation Using Sap Flow and Water Use Efficiency Measurements

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This is a protocol to evaluate gross primary productivity (GPP) of a forest stand based on the measurements of tree’s sap flow (SF), 13C derived water use efficiency (WUE), and meteorological (met) data. GPP was calculated from WUE and stomatal conductance (gs), the later obtained from SF up-scaled from sampled trees to stand level on a daily time-scale and met data. WUE is obtained from 13C measurements in dated tree-ring wood and/or foliage samples. This protocol is based on the recently published study of Klein et al., 2016.

Keywords: Trees (树木), Ecosystem carbon flux (生态系统碳通量), Transpiration (蒸腾), Stable isotopes (稳定同位素)


Forests play a major role in the terrestrial carbon cycle through CO2 assimilation and respiration, as well as on the Earth climate by influencing atmosphere CO2 concentration (Luyssaert et al., 2007; Bonan, 2008; Canadell and Raupach, 2008; Reichstein et al., 2013). Gross primary productivity (GPP), plants’ carbon uptake through photosynthesis, is the ultimate source of organic material in land biosphere in general and in food production in particular.

GPP at the ecosystem scale is mainly derived using the eddy covariance (EC) technique, as the difference between EC-measured net ecosystem CO2 exchange (NEE) and the daily inferred ecosystem respiration (Re). The latter one is obtained by extrapolating measured night-time NEE, which equals to ecosystem respiration, Re, to daytime based on empirical equations of Re response to temperature and soil humidity (Aubinet et al., 2000; Baldocchi, 2003; Reichstein et al., 2005; Grunzweig et al., 2009). However, the EC approach has several limitations and uncertainties. Its application is limited to relatively large homogeneous and flat terrains. EC technique is critically dependent on factors such as the system deployment in field (e.g., height above the canopy), on atmospheric conditions (e.g., turbulence conditions, advections) (Aubinet et al., 2000), on corrections applied to data processing programs and algorithms. A common way to assess EC measurements reliability is through the evaluation of the ‘energy closure’ over the measured ecosystem. This test indicates in most sites an energy gap of 20% or more (Foken, 2008). Empirical extrapolation of the night-time NEE measurement to approximate daytime Re has by itself significant uncertainties (e.g., Van Gorsel et al., 2009). NEE measurements provide a whole ecosystem flux only, thus explicit carbon uptake by trees cannot be distinguished from others ecosystem layers. EC measurements are also relatively expensive.

The current protocol describes an alternative method for calculation of forest trees GPP based on measurements of air temperature and humidity, trees’ sap flow (SF) rate and intrinsic water use efficiency (WUEi, the ratio A/gs), where A is the rate of photosynthetic CO2 assimilation and gs is stomatal conductance. This protocol is based on the recently published study of Klein et al., 2016. WUEi is a parameter characterizing plant species, with a seasonal variation (Seibt et al., 2008; Klein et al., 2013 and 2016). It can be calculated from the carbon isotope ratio (δ13C) in the assimilated carbon of plant tissues (Farquhar and Richards, 1984). This is combined with sap flow measurements, which can be obtained easier and at a lower cost than EC measurements, and often have less spatial limitations (independent on ecosystem footprint, topography, homogeneity, etc.), and enable GPP estimation in complex ecosystems. The method is applicable to calculate GPP of woody vegetation and can be considered as total ecosystem GPP if the contribution of understory layer to the total ecosystem GPP can be neglected (e.g., dry environments) or independently assessed. Note that using this approach can also be applied to archived SF and 13C data to reconstruct past variations in GPP, as long as these data are available.

Materials and Reagents

  1. Forest or woody stand, which can be assumed to be homogenous to facilitate scaling up to stand level
    Note: If stand is composed of considerably different plots and species, the method can be applied separately for each plot. The measurements are conducted on living trees. No reagents are used in this procedure.


  1. Tape measure of 2-3 m or caliper for tree diameter at breast height (approximately at 1.3 m, DBH, cm) distribution measurements
  2. A total station theodolite or at least 30-50 m long tape-measure and compass for establishing a sample plot(s) with known area in order to get stand density
  3. Sap flow sensors of any suitable type (e.g., EMS Brno, model: THB sensors ; or ICT International, model: HFD8-100 ; or UP, model: TDP sensors , etc.)
    Note: The type of sensors depends on tree diameter, financial possibilities, power supply options etc. The most simple, TDP sensors can be manufactured in the institute workshop (see description, e.g., in Lu et al., 2004).
  4. Dendrometers of any type for obtaining of seasonal DBH growth curve (e.g., EMS Brno, model: DRL26C or Natkon, model: Point Dendrometers [Oetwil am See, Switzerland])
  5. Thermometer and air moisture meter or automatic meteorological station for continuous data recording
  6. Datalogger for storing sap flow and met’ data (if no own logger is provided by SF and met’ sensors suppliers), e.g., (Campbell Scientific, model: CR1000 ). Most of factory-made sap flow sensors are supplied with own loggers (e.g., EMS Brno, Czech Republic)
  7. Incremental borer. Short, 5.15’’ borers would usually fit for obtaining wood formed in the recent decade or period of interest
  8. Equipment for tree rings analysis
    Note: Best would be professional dendrometry desk station. Microtome can also help for intra-annual slicing. But the wider the tree-rings are, the easier it is to slice them with scalpel alone.
  9. Laboratory for δ13C analysis
    Note: This can be done in house isotope ratio mass spectrometry, or by commercial IRMS service labs.


Note: The technology of SF measurements and up-scaling from measuring point to tree is not discussed in this protocol. The theory and practice of SF measurements is discussed, e.g., in Čermák et al., 2004; Lu et al., 2004; Tatarinov et al., 2005, as well as in user guides of particular SF sensors. Furthermore, it is assumed that continuous data of SF of single trees in L h-1 tree-1 are available. Similarly, the protocol does not discuss δ13C isotopic analysis of wood material.

  1. Select representative sample plot, measure its area and DBH of all sample trees within the plot. For small trees or shrubs instead of breast height it is possible to measure diameter at lower level, e.g., at 10 cm. In any case, diameter should be measured and SF sensors should be installed below the first living branch (for shrubs, when branching starts from the ground, each branch should be considered as separate stem). The plot size depends on woody plant species variability, stand density and DBH variability, with more sparse and variable stands requiring larger plot area. We recommend that the plot should include about 50-150 trees in order to get reliable DBH distribution. For multi-species stands the researcher should first evaluate the possible contribution of each species to the total stand GPP (e.g., its proportion of the total stand basal area/LAI/crown area, taking into account, if possible, its photosynthesis rate relatively to other species, on the basis of data from literature or chamber measurements) before deciding how to distribute sensors among species (taking into account financial limitations of total amount of sensors). However, this is a special topic beyond the scope of this protocol. For more details about sampling size see, e.g., Kish, 1965.
  2. Select sample trees for SF measurement within the plot according to DBH distribution. Sample trees should be representative of the stand and include the entire DBH range. The total number of sample trees depends on the DBH range, stand species composition, desired precision and financial constraints. J. Cermak proposed to select sample trees using so called quantiles of total method, when total set of trees at sample plot is sorted according to a certain biometrical parameter B (usually DBH or basal area) in ascending order and cumulative B is calculated simultaneously. See details on sampling strategy, e.g., in Čermák et al., 2004 and 2014. Good description of sampling strategy is also given in ICT booklet Sap flow installation scenarios.
    Install SF sensors on selected sample trees. Installation procedure is described in user guides of particular sensors. See more information, e.g., in Čermák et al., 2004 and 2014; Paudel et al., 2013.
  3. Install dendrometers on the trees which will be used for δ13C sampling. The number of trees for δ13C sampling depends on the level of variance in the plot. For statistical reasons, n = 30 is usually used. But often not all SF sensors are functional and hence we ended up using a lower number for our analysis. But 30 trees is a good recommendation. Optimally, one should use the same trees as for SF measurements, and these must represent the plot. If possible, stem growth should be monitored in all study trees. This is feasible using simple girth-tapes which hare monitored at least once a week.
  4. Measure SF and met data continuously (1/2 hourly) during the study period. Meteorological data (air temperature, Ta, and moisture, RH) should be best measured in the middle of the canopy foliage layer.
  5. Take plant material samples for 13C analysis
    1. For further GPP calculation, SF and WUEi values for the same time periods are required. Consequently, the time of formation of the plant organs samples for 13C analysis should be known at the best time resolution (monthly or weekly, then the values should be interpolated in order to get daily values for the whole period of analysis), as it determines the time resolution of WUEi calculation.
    2. Time resolution can be obtained in several ways.
      1. Wood samples should be taken from tree rings. Identifying the period of formation of specific tree-ring sub-sections could be allowed by calibration to an empirical stem growth curve from contemporary dendrometer data. Points along this curve (y-axis) were then applied to the observed growth curve to extract the estimated date of formation (x-axis) (Klein et al., 2016, see Figure 1).
      2. In some ecosystems, the period of stem growth can be lagged after the period of foliage growth. For periods when no stem growth occurs, but foliage or shoots growth takes place, leaf or shoot samples should be taken (see Figure 1). Leaf or needle samples can be dated in the same way as tree ring samples, by means of regular measurements of needles or leaves length (see Klein et al., 2005).
      3. If there is a dormancy period (e.g., winter in boreal zone), no sampling is necessary, as no GPP occurred.

        Figure 1. Scheme of high-resolution ring width development and leaf/needle elongation with time, and the sample slices dating. Example from Yatir semi-arid pine forest, Israel (see Maseyk et al., 2008)

  6. 13C analysis should be made on the plant samples (tree ring sections and/or leaf samples) and attributed to particular time intervals.

Data analysis

  1. The estimation of GPP is based on the well-established physiological relationship: WUEi = A/gs (μM CO2 mol-1 H2O). Assuming that for a good approximation, stand assimilation - Astand = GPP (in reality, A = GPP - RL; where RL is daytime leaf respiration and is usually a minor component of Re) and GPP is usually estimated using nighttime EC measurements extrapolated to the daytime (Reichstein et al., 2005). We use the following main estimate for GPP at time period t:

    GPP (t) = WUEi(t)/gs(t) (1)

    which can be solved by obtaining independent estimates of WUEi and gs
  2. WUEi is estimated from δ13C measurements of organic material averaged from all samples taken at the same growing time interval t, using the following equation (adapted from Farquhar and Richards 1984; Seibt., 2008):

    WUEi(t) = Ca(t)/r x {[b - Δ - pr x (Γ*/Ca(t))]/[b - a + (b - am) x (gs/(r x gi))]} (2)

    Ca is the atmospheric CO2 concentration in ppm (continuously measured on site, as it is not varying or taken as period average from the nearby station where it is measured),
    r is the ratio of the diffusivities of CO2 and water vapor in air (1.6),
    a, am, b and pr are the leaf-level discriminations against 13C in the diffusion through the stomata (4.4‰), during dissolution and liquid phase diffusion (1.8‰), in biochemical CO2 fixation (29‰), and in photo-respiratory CO2 release (8‰), respectively,
    Δ is the tree discrimination against 13C,
    Γ* is the temperature-dependent CO2 compensation point of ca. 30-45 ppm at our site (Maseyk et al., 2008),
    gs/gi is the ratio between stomatal and internal conductance to CO2 respectively (0.5 according to Maseyk et al., 2011). The values above are valid for plants in general, but they can vary, and the best approach would be to test it for the species of study or use best estimate for the most phylogenetically close species. The tree discrimination against 13C (Δ) is calculated as follows:

    Δ = (δ13Ca - δ13Co)/(1 + δ13Co) (3)

    where, δ13C = (δ13C/δ12C)sample/(δ13C/δ12C)reference - 1 and the reference is Vienna-PDB (Coplen, 1994), and subscripts a and o stand for atmospheric air (at annual resolution) and the organic material (tree-ring or needle), respectively. WUEi from Eq.3 is representative for the period of δ13C signal deposition (depending on time resolution of plant sample formation dating; Ca should be averaged for the same period). WUEi values for intermediate days between δ13C measurements could be interpolated.
  3. Stomatal conductance gs(t) for Eq.1 (as daytime average) can be calculated from measured stand transpiration and continuously monitored VPD(t) (average daytime, daily) using the general relationship (Beer et al., 2009):

    gs(t) = T(t)/VPD(t) (4)

    T(t) was measured as sap flow (SF) of individual trees integrated over the daily cycle (to overcome possible time offsets between SF and T, which can reach up to a few hours, e.g., under dry conditions) and up-scaled to stand level as described below. Vapor pressure deficit (VPD) can be calculated as follows (see, e.g., Monteith and Unsworth, 2007):

    where, VPD(t) is in Pascals and RH (in %) and Ta (in °C) are air humidity and temperature, respectively. VPD is daytime averaged since CO2 uptake is restricted to daytime hours only. If other conditions preventing CO2 uptake for the particular species/region are known (too high or too low VPD or air temperature etc.), the VPD records corresponding to such conditions should be also excluded from averaging.
    As mentioned above, we assume that although a time offset between SF and T of up to few hours can occur on the daily scale, stand transpiration total T is equal to total daily stand sap flow total (SFstand). As GPP is restricted to daytime, T should be restricted to same period, thus since water storage depleting and refilling acquired daily at a tree level, but night time T is negligible it is assumed that daily SF equals daytime T. Several possible SF up-scaling procedures from tree to stand level are available. (1) Dependence of tree SF on DBH can be derived by means of linear or nonlinear regression (where SF(t) is expressed in L tree-1 day-1) for each particular day and then

    T(t) = SFstand(t) = ∑kSF(t, Dk) × nk      (6)

    T(t) is expressed in mm day-1,
    Dk and nk are mean DBH of DBH class k and stocking density (trees ha-1) of trees of this diameter class (Čermák et al., 2004). (2) Another option is to calculate sap flux density per unit sapwood cross-section area averaged among sample trees, (SFD(t))  (in cm3 cm-2 day-1) and then multiply it by total stand sapwood area (∑SWA, in cm2 ha-1, see Klein et al., 2014):

    In addition, an evaluation of sapwood depth of the particular tree species is required, which can be measured separately or taken from the literature (see, e.g., Cermak and Nadezhdina, 1998; Gebauer et al., 2008). Then SWA of a particular tree can be calculated as follows:

    where, db and dSW are bark and sapwood widths, respectively. Then stand SWA can be calculated as the sum of sapwood areas of all trees on the sample plot divided by plot area.
    We recommend to use the first method if there is expressed dependence of SFD on DBH and the second one if such dependence is missing (Tatarinov et al., 2015).
    Finally, substituting WUEi from Eq.2 and gs calculated in daily scale by Eq.4 using Eqs. 5-8 into Eq.1 gives us daily GPP total.
    The total analysis scheme is presented in Figure 2. An example of the protocol application is described by Klein et al., 2016.

    Figure 2. Diagram of the protocol of forest GPP Calculation Using Sap Flow and Water Use Efficiency


  1. Weaknesses and uncertainties
    1. Sap flow measurements have their own sources of uncertainties related, e.g., to sapwood depth, SF radial profile, SF variation around stem, changing of sapwood physical properties with shrinkage/swelling etc. Preliminary information of sapwood depth is usually necessary for sensors installation. These data are generally species-specific and can be found in the literature. The problems and tips of sap flow measurements are discussed, e.g., in Čermák et al., 2004 and 2014; Paudel et al., 2013.
    2. The method is applicable for plants where sap flow sensors can be applied, i.e., mainly woody plants. Generally, it could be fully grown trees, but also young trees or shrubs, but this may require particular type of sensors (see, e.g., at http://emsbrno.cz/p.axd/en/Sap.Flow.small.stems.html). The method is not applicable for grasslands and small shrubs. Whereas in the forests with negligible understory layer the method should give total ecosystem GPP evaluation, in the presence of significant understory layer the result should be interpreted as GPP of trees only.
    3. Another limitation of the method concerns the time of formation of plant material used for the 13C analysis (which depends on the temporal resolution of measurement plus the slicing resolution and using standard techniques is weekly at best). The method is inapplicable for periods with no detectable growth of woody tissues or foliage observed.
    4. Additional source of uncertainty is the variability of Ta and RH within canopy, both, vertically and radially (with the distance from leaf surface), which influences VPD estimation. Generally, VPD at the leaf surface should be somewhat lower than VPD in the canopy air, which could lead to a certain overestimation of gs and consequently to the underestimation of GPP.
    5. Finally, the possible errors due to approximation of GPP = Astand should be assessed (which usually are negligible).


The protocol was adapted from Klein et al., 2016. This research was supported by the Jewish National Fund (KKL-JNF), the C. Wills and R. Lewis program in Environmental Science, German Research Foundation (DFG) as part of the project ‘Climate feedbacks and benefits of semi-arid forests (CliFF)’ and Israeli Science Foundation (ISF, grant 673/12 ‘Estimating gross photosynthesis using coupled COS/CO2 measurements at the ecosystem scale’).


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这是一个根据树木流量(SF),超临界流量(WUE)和气象(met)的测量结果来评估林分总生产力(GPP)的方案,数据。从WUE和气孔导度(g s )计算GPP,后者是从日常时间尺度上从抽样树到定级的SF升高得到的,并且满足数据。 WUE是从日期的树木木材和/或树叶样品中的 13 C测量获得的。该协议基于最近发表的Klein等人的研究,2016年。

森林通过CO 2同化和呼吸以及通过影响CO 2浓度浓度在地球气候中对陆地碳循环发挥重要作用(Luyssaert et al。 2007; Bonan,2008; Canadell和Raupach,2008; Reichstein等人,2013)。总生产力(GPP),植物通过光合作用的碳吸收,是一般土地生物圈和特别是粮食生产中有机物质的最终来源。
 生态系统规模中的GPP主要是利用涡度协方差(EC)技术得出的,因为EC测量的净生态系统CO 2 交换(NEE)和每日推测的生态系统呼吸(Re)之间的差异, 。后者通过根据对温度和土壤湿度的响应的经验方程,将测量的夜间NEE(其等于生态系统呼吸,Re)推广到白天获得(Aubinet等人,2000; Baldocchi,2003; Reichstein等人,2005; Grunzweig等人,2009)。然而,EC方法有一些局限性和不确定性。其应用仅限于相对较大的均匀和平坦的地形。 EC技术在很大程度上依赖于诸如现场系统部署(例如,,高于冠层的高度),大气条件(例如,湍流条件,平流) (Aubinet等人,2000),关于应用于数据处理程序和算法的校正。评估EC测量可靠性的一个常见方法是通过评估测量生态系统的“能量关闭”。该测试表明大多数网站的能量差距在20%以上(Foken,2008)。夜间NEE测量的经验推断近似白天Re本身具有显着的不确定性(例如,Van Gorsel等人,2009)。 NEE测量仅提供整个生态系统通量,因此树木的明显碳摄取不能与其他生态系统层相区分。 EC测量也相对昂贵。
 目前的协议描述了一种用于计算森林树木GPP的替代方法,该方法基于空气温度和湿度,树木流量(SF)率和内在水分利用效率(WUEi,A g ),其中A是光合CO 2同化速率,g 是气孔导度。该协议基于最近发表的Klein等人,2016年的研究.WUEi是表征植物物种的参数,具有季节变化(Seibt等人,2008年) ; Klein等人,2013和2016)。可以从植物组织的同化碳中的碳同位素比(δ 13 C)计算(Farquhar和Richards,1984)。这与流量测量相结合,可以比EC测量更容易且成本更低,并且通常具有较少的空间限制(独立于生态系统足迹,地形,同质性,等等),以及在复杂的生态系统中实现GPP估计。该方法适用于计算木本植被的GPP,如果下层林分层对总生态系统GPP的贡献可以忽略(例如,干旱环境)或独立评估,则可以将其视为总体生态系统GPP。请注意,只要这些数据可用,使用此方法也可以应用于归档SF和 13 C数据来重建GPP中的过去变体。

关键字:树木, 生态系统碳通量, 蒸腾, 稳定同位素


  1. 森林或木质的立场,可以被认为是同质的,以便扩大到站立级别


  1. 胶卷尺寸为2-3米,厚度为乳胶高度(约1.3米,DBH,厘米)分布测量值的树径
  2. 全站经纬仪或至少30-50米长的卷尺和指南针,以建立已知区域的样地图,以获得站立密度
  3. 任何适合类型(例如的EMS流量传感器,型号:THB传感器或ICT International,型号:HFD8-100;或UP,型号:TDP传感器, )>
  4. 用于获得季节性DBH生长曲线的任何类型的硬度计(例如,EMS Brno,型号:DRL26C或Natkon,型号:Point Dendrometer [Oetwil am See,Switzerland])
  5. 温度计和空气湿度计或自动气象台,用于连续数据记录
  6. 数据记录器用于存储液流和遇到的数据(如果没有自己的记录器由SF和met'传感器供应商提供),例如(Campbell Scientific,型号:CR1000)。大多数工厂制造的液流传感器都配有自己的记录仪(例如,捷克共和国的EMS布尔诺)
  7. 增量钻头短的5.15"钻孔者通常适合获得最近十年或兴趣期间形成的木材
  8. 树环分析设备
  9. 实验室为δ 13 C分析


注意:本协议中没有讨论SF测量和从测量点到树的扩展的技术。 SF测量的理论和实践在例如Čermáket al。,2004; Lu et al。,2004; Tatarinov等,2005,以及特定SF传感器的用户指南。此外,假定在L h -1 tree -1 中的单个树的SF的连续数据是可用的。类似地,协议不讨论木材的同位素分析。

  1. 选择代表性样本图,测量其中所有样本树的面积和DBH。对于小树或灌木而不是乳房高度,可以在10厘米处测量较低水平的直径,例如,在任何情况下,应测量直径,并将SF传感器安装在第一个生活分支下方(对于灌木,当从地面开始分支时,每个分支应被视为单独的茎)。绘图尺寸取决于木本植物种类的变异性,立木密度和DBH变异性,更多的稀疏和可变的立场需要较大的地块面积。我们建议该情节应包括大约50-150棵树,以获得可靠的DBH分布。对于多品种,研究人员应首先评估每种物种对GPP总量的可能贡献(例如,其占总基础面积/LAI /冠面积的比例),同时考虑到,如果可能,其光合作用速率相对于其他物种,根据文献或室内测量数据),然后决定如何在物种间分配传感器(考虑到传感器总量的财务限制)。但是,这是超出本协议范围的特殊主题。有关采样大小的更多细节,请参见,例如,,Kish,1965.
  2. 根据DBH分布,在图中选择样品树进行SF测量。示例树应该代表展台,并包括整个DBH范围。样本总数取决于DBH范围,物种组成,所需精度和财务限制。 Cermak提出使用所谓的总方法的分位数来选择样本树,当样本图中的总体树根据某个生物参数B(通常为DBH或基础面积)按升序排序并且累积B被同时计算时。在2004年和2014年的Čermák等人,见"采样策略"中的采样策略,例如的详细信息。ICT小册子 Sap流安装场景。
    在选定的样品树上安装SF传感器。特定传感器的用户指南中介绍了安装步骤。在Čermák等人,2004年和2014年期间查看更多信息,例如。 Paudel等,2013年
  3. 在树木上安装树状体,将用于δ 13 C采样。 δ 13 C采样的树数取决于图中的方差水平。出于统计学原因,通常使用n = 30。但是,通常并不是所有的SF传感器都是功能性的,因此我们最终使用较低的数据进行分析。但30棵树是一个很好的推荐。最佳地,应该使用与SF测量相同的树,这些必须代表图。如果可能,应在所有研究树中监测茎生长。这是可行的,使用简单的环形磁带,每周至少监控一次。
  4. 在研究期间测量SF并连续满足数据(1/2小时)。气候数据(气温,气温,湿度,RH)应在冠层叶中层最好测量。
  5. 拿植物材料样品进行 13 C分析
    1. 对于进一步的GPP计算,需要相同时间段的SF和WUEi值。因此,应以最佳时间分辨率(每月或每周)知道植物器官样品的形成时间,以便得到整体的日常值分析时间),因为它决定了WUEi计算的时间分辨率。
    2. 时间分辨率可以通过几种方式获得。
      1. 木材样品应从树上取出。通过校准来自当代树状体数据的经验茎生长曲线,可以确定特定树环子切片的形成期。然后将沿该曲线(y轴)的点应用于观察到的生长曲线以提取估计的形成日期(x轴)(Klein等,2016,参见图1)。 br />
      2. 在一些生态系统中,茎叶生长期可能落后于叶面生长期。在没有茎生长发生的时期,但是发生叶子或枝条生长的时候,应该采取叶片或枝条样本(见图1)。叶或针样品的日期可以与树环样本相同,通过针或叶长度的常规测量(参见Klein等人,2005)。
      3. 如果没有休眠期(例如北部冬季),则不需要抽样,因为没有GPP发生。


  6. 应对植物样本(树环节和/或叶子样本)进行C分析,并归因于特定的时间间隔。


  1. GPP的估计基于公认的生理关系:WUE = A/g (μMCO 2 mol -1 H 2 O)。假设为了很好的近似,立场同化 - 实际上,A = GPP-R L ,其中R L 是日间叶呼吸并且通常是Re的次要组分),并且通常使用外推到白天的夜间EC测量来估计GPP(Reichstein等人,2005)。我们在时间段t对GPP使用以下主要估计:

    GPP(t)= WUE i(t)/g(t)(1)

    这可以通过获得WUE< i>和<>的独立估计来解决。
  2. 使用以下等式(从Farquhar和Richards 1984改编而来),从在相同生长时间间隔t获取的所有样品平均的有机材料的δ 13 C测量估计WUE ; Seibt。,2008):

    (t)= C (t)/rx { (ΓΓΓΓΓpr pr pr pr pr>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> b - a +( b - a m )x (g s /(rxg i ))]}(2)

    是以ppm为单位的大气CO 2亚类浓度(在现场连续测量,因为它不变,取自测量的附近车站的平均时间),
    r是CO 2 2的扩散系数和空气中的水蒸汽(1.6)的比例,
    是通过气孔扩散(4.4‰),在溶解和液相扩散(1.8‰),在生化CO 2 固定中的 13 C的叶片鉴别(29‰),光呼吸CO 2次释放(8‰),
    Δ是与 13 C,
    的树歧视 Γ*是ca 的温度依赖的CO 2 补偿点。在我们的网站(Maseyk等人,2008),
    中为30-45ppm gs/gi分别是气孔和内部电导与CO 2之间的比率(0.5,根据Maseyk等人,2011)。上述值对植物一般有效,但它们可以变化,最好的方法是对其研究物种进行测试或对大多数系统发育密切物种进行最佳估计。对于 13 C(Δ)的树辨别如下计算:

    Δ=(δ 13 C a C )/(1 +δ 13 C o )(3)

    其中,δ 13 C =(δ 13 C /δ 12 C)子样品/(δ参考文献是Vienna-PDB(Coplen,1994),下标 a >和 o 分别代表大气(年分辨率)和有机材料(树环或针)。来自等式3的WUE i 代表δ 13信号沉积的时间(取决于植物样品形成约会的时间分辨率; C 应该在同一时期对 a 进行平均)。可以内插δ 13 C测量值之间的中间日期的WUE 值。
  3. 方程1(作为白天平均值)的气孔导度g(t)可以从测定的蒸腾量计算,并使用一般关系连续监测VPD(t)(平均白天,日) em> et al。,2009):

    (t)= T(t)/VPD(t)(4)


    其中,VPD(t)为Pascals,RH(以%为单位)和 (°C)为空气湿度和温度,分别。 VPD是白天平均的,因为CO 2摄取仅限于白天时间。如果特定物种/区域的CO 2摄取的其他条件是已知的(VPD或空气温度太高等)太高或太低),对应于这些条件的VPD记录也应该排除平均值。
    如上所述,我们假设尽管在日常尺度上可能发生SF和T之间的时间偏差高达几个小时,但是蒸腾总量T等于总日常总液流总量(SF )。由于GPP被限制在白天,T应该被限制在同一时期,因此由于每天在树木级别获得储水消耗和再填充,而夜间T可以忽略不计,所以假设每日SF等于白天T.几个可能的SF up-从树到平台的缩放程序是可用的。 (1)树SF对DBH的依赖可以通过线性或非线性回归(其中SF(t)以L树 -1 day -1 表示)对于每个特定的日子,然后

    (t)= SF (t)=Σ ( t D k )×n k       (6)

    T(t)表示为mm天 -1
    的DBH类别k和/或这种直径类别的树木的种群密度(树木ha -1 )(Čermák等人,2004)。 (2)另一个选择是计算采样树中平均每单位边材截面积的树液通量密度,(SFD(t))  (cm 3) cm -2 day -1 ),然后乘以总站立边材面积(Σ SWA ,以厘米 2 ha -1 ,参见Klein等人,2014年):

    此外,需要对特定树种的边材深度进行评估,这可以单独测量或从文献中获取(参见,例如,Cermak和Nadezhdina,1998; Gebauer et al。 ,,2008)。那么特定树的 SWA 可以计算如下:

    其中, b d SW 树皮和边材宽度分别。然后可以将SWA 计算为样本图上所有树木的边材面积除以绘图面积的总和。
    最后,用等式4用方程2代替方程2的每一个等级的WUE< i> i 5-8转入方程式1给我们每天GPP总计。



  1. 缺点和不确定性
    1. Sap流量测量有其自身的不确定性来源,例如,边坡深度,SF径向剖面,茎周围的SF变化,边缘物理性质随收缩/膨胀等的变化。 >。传感器安装通常需要边坡深度的初步信息。这些数据通常是物种特异性的,可以在文献中找到。在Čermák等人,2004和2014中讨论了液流测量的问题和提示,例如, Paudel等,2013年
    2. 该方法适用于可应用液流传感器的植物,即主要是木本植物。一般来说,它可以是完全生长的树木,也可以是幼树或灌木,但这可能需要特定类型的传感器(参见 eg 。在 http://emsbrno.cz/p.axd/en/Sap.Flow.small。 stems.html )。该方法不适用于草原和小灌木。而在具有可忽略的下层森林的森林中,该方法应该给出总体生态系统GPP评估,在存在显着的下层层的情况下,结果应该被解释为只有树木的GPP。
    3. 该方法的另一个限制涉及用于 13 C分析的植物材料的形成时间(其取决于测量的时间分辨率加上切片分辨率,并且每周最多使用标准技术)。该方法不适用于观察到木本组织或叶子无可见生长的时期。
    4. 额外的不确定性来源是在冠层内,纵向和径向两者(具有 与叶面的距离),影响VPD估计。一般来说,叶面上的VPD应略低于冠层空气中的VPD,这可能会导致g 的某些过高估计,从而导致GPP的低估。
    5. 最后,应该评估由于近似GPP = A 可能的误差(通常可以忽略)。


该协议由Klein等人于2016年进行了调整。该研究得到了犹太国家基金(KKL-JNF),德国研究基金会环境科学学院C.Wills和R. Lewis计划的支持(DFG)作为"半干旱森林气候反馈和利益(CliFF)"和以色列科学基金会(ISF,授权673/12"使用生态系统规模下的耦合COS/CO2测量估算总光合作用"项目的一部分)。


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引用:Tatarinov, F., Rotenberg, E., Yakir, D. and Klein, T. (2017). Forest GPP Calculation Using Sap Flow and Water Use Efficiency Measurements. Bio-protocol 7(8): e2221. DOI: 10.21769/BioProtoc.2221.

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