气候变化对长白山阔叶红松林冠层蒸腾影响的模拟

应用基于过程的碳水耦合多层模型对长白山阔叶红松林冠层蒸腾量进行了模拟和模型验证,并模拟了冠层蒸腾量对未来气候变化的响应.结果表明:多层模型可以较好地模拟长白山阔叶红松林冠层蒸腾量,模拟值与涡动相关技术观测的实测值拟合较好.冠层蒸腾对气候变化响应的模拟显示,气温升高,潜热通量(LE)增加;土壤含水量减少,LE减少;大气CO2浓度增加,LE减少.在研究假定的气候变化情景下,LE对0～20 cm土壤含水量减少10%、CO2浓度增加190μmol·mol-1的联合变化的响应最敏感,对气温增加3.6℃、土壤含水量减少10%的联合变化的响应不敏感.     

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。     

长白山阔叶红松林能量平衡和蒸散

利用开路涡动相关系统的连续观测结果,分析了长白山阔叶红松林2008年能量平衡各分量和蒸散量的特征,并对生长季和非生长季能量各分量和蒸散量的差异进行了比较.结果表明:该观测系统能量闭合度为72%,处于国际同类观测的中等水平;能量各分量日、季差异显著,生长季森林生态系统最主要的能量支出项为潜热通量,约占可用能量的66%,非生长季最主要的能量支出项为感热通量,约占可用能量的63%.长白山阔叶红松林2008年蒸散量为484.7 mm,占同期降水量(558.9 mm)的87%,证实森林蒸散耗水是我国北方温带森林最主要的水分支出项.     

森林蒸散模型参数的确定

以长白山阔叶红松林为研究对象,利用长白山阔叶红松林气象观测塔安装的常规气象梯度观测系统、开路涡动相关系统的观测数据,依据空气动力学基本理论与能量平衡方程建立了森林蒸散机理模型,确定了模型参数,即风速廓线稳定度订正函数φ_m、温度廓线稳定度订正函数φ_h和零平面位移高度d.其中,研究地的零平面位移高度d为17.8m,是平均冠层高度(26m)的0.68.同时,给出了φ_m和φ_h随梯度理查逊数R_i变化的数学表达式.     

森林蒸散模型参数的确定

以长白山阔叶红松林为研究对象,利用长白山阔叶红松林气象观测塔安装的常规气象梯度观测系统、开路涡动相关系统的观测数据,依据空气动力学基本理论与能量平衡方程建立了森林蒸散机理模型,确定了模型参数,即风速廓线稳定度订正函数ψm、温度廓线稳定度订正函数ψh和零平面位移高度d。其中,研究地的零平面位移高度d为17．8m,是平均冠层高度(26m)的0．68。同时,给出了ψm和ψh随梯度理查逊数Ri变化的数学表达式。     

长白山阔叶红松林退化生态系统的土壤呼吸作用

选择处于全球变化中国东北样带东部典型生态系统的长白山阔叶红松林作为研究区,采用动态气室-CO₂红外分析法测定了森林生态系统不同退化阶段的土壤呼吸作用.结果表明：在生长季,长白山阔叶红松林不同退化阶段的土壤呼吸动态变化呈单峰型曲线,在7-8月达到最大值;不同退化阶段林地土壤呼吸大小顺序为：杨桦林＞蒙古栎林＞阔叶红松林＞硬阔叶林＞裸地.其中,杨桦林和蒙古栎林样地的碳释放量分别为对照阔叶红松林的1.4和1.3倍,硬阔叶林和裸地的碳释放量分别为对照阔叶红松林的88%和78%.     

3PG碳生产模型在长白山阔叶红松林总初级生产力估算中的应用

利用ChinaFLUX长白山站阔叶红松林的通量观测数据以及同期卫星遥感数据,对3PG模型中的植被光合模型(VPM)、光能利用率模型(EC-LUE)、陆地生态系统模型(TEM)、卡内基-埃姆斯-斯坦福方法模型(CASA)4种模型进行参数重组,通过对比通量观测值与估算值的均方根误差、决定系数及平均误差确定模型的最适合参数;并利用实测的通量观测数据对优化后的模型进行拟合度验证,以提高其估算长白山阔叶红松林总初级生产力(GPP)的准确性.结果表明: 采用温度、增强植被指数、地表水分指数分别表征原模型中的温度限制因子、光合有效辐射吸收比例、水分限制因子估算长白山阔叶红松林GPP时,结果最优,优化后模型的精度(R²=0.948,RMSE=0.035 mol·m^-2·month^-1)明显优于原模型(R²=0.854,RMSE=0.177 mol·m^-2·month^-1),且能够有效改善原模型生长季明显高估的现象;通过敏感性分析可知,温度是对GPP估算不确定性影响最大的参数,其次为增强型植被指数和光合有效辐射,地表水分指数最小,且变量间的交互作用对GPP估算不确定性也存在影响.     

林窗模型的几个基本问题的研究:I.模拟样地面积的效应

以中国东北长白山阔叶红松林为例,应用林窗模型NEWCOP探索了不同模拟样地面积对林窗模型输出结果的影响.结果表明,模拟样地面积大小变化可影响模拟出的森林群落的树种组成和模拟样地的林窗出现周期,通过应用这一特点确定了阔叶红松林的林窗面积为400～800m².     

SIMULATING SEASONAL AND INTERANNUAL VARIATIONS OF ECOSYSTEM EVAPOTRANSPIRATION AND ITS COMPONENTS IN INNER MONGOLIA STEPPE WITH VIP MODEL

 利用内蒙古羊草草原(Leymus chinensis)生态系统通量观测站的气象数据、野外实测和MODIS叶面积指数(Leaf area index, LAI), 应用基于生态系统过程的VIP(Vegetation interface process)模型, 以半小时为步长, 模拟分析了羊草草原生态系统2003～2005年(分别为平水年、平水年和干旱年)蒸散及其分量的变化过程。通过与通量数据对比, VIP模型能够很好地模拟羊草草原生态系统的蒸散过程(R² = 0.80), 在峰值大小和变化趋势上, 模拟值与实测值有较好的一致性。模拟结果显示: 3年蒸散量分别为337、338和223 mm; 在降水相对充沛的2003和2004年, 蒸腾量为192和171 mm, 而降水相对较少的2005年, 蒸腾量仅为96 mm; 年平均蒸腾和蒸发对蒸散的贡献基本持平; 生长季蒸散占全年的83%, 6月开始, 蒸腾大于蒸发, 蒸散和蒸腾的月总值均在7、8月达到最大值,两月蒸散占全年的43%。LAI是影响蒸散的主要因素, 其次是降水, 而净辐射对蒸散的影响较小。在生长季, 蒸发的季节变化平缓, 蒸散的差异主要体现在蒸腾的差异。     

中国东北地区阔叶红松林与兴安落叶松林的碳通量特征及其影响因子比较

北半球中高纬度的森林生态系统在全球碳循环过程中扮演着非常重要的角色。基于中国东北地区阔叶红松林与兴安落叶松林2007年和2008年2a生长季的涡度相关通量资料及气象观测资料,比较分析了两类生态系统的碳通量特征及其环境控制因子。结果表明:研究期间,阔叶红松林与兴安落叶松林都表现为碳吸收,强度分别为199gCm-2(阔叶红松林2a生长季平均值)与49gCm-2(兴安落叶松林2008年生长季);阔叶红松林碳吸收强度在生长季的大部分时段都大于兴安落叶松林。半小时尺度上,两类生态系统的呼吸作用均与10cm土壤温度呈显著的指数相关,兴安落叶松林生态系统呼吸的温度敏感性(Q10=3.44)显著大于阔叶红松林(Q10=1.90);日尺度上,阔叶红松林与兴安落叶松林碳释放/吸收的转变临界温度为10℃左右。研究期间,兴安落叶松林生态系统的水分利用效率高于阔叶红松林生态系统。     

基于Shuttleworth-Wallace模型的科尔沁沙地流动半流动沙丘蒸散发模拟

陆面蒸散发在气候调节和维持区域水量平衡中起关键作用.量化蒸散发及其各组分项,对深刻揭示干旱半干旱地区的生态水文过程具有重要意义.本研究基于科尔沁沙地流动半流动沙丘2017年生长季气象监测系统的原位监测数据,利用Shuttleworth-Wallace(S-W)模型对沙丘蒸散发进行模拟,在此基础上,对蒸散各组分进行拆分,并利用涡度相关对模拟蒸散发值进行验证.结果表明: 整个生长季模型模拟蒸散发值为308 mm,涡度相关实测值为296 mm,偏差较小,证明S-W模型适用于该地区的蒸散发模拟.蒸散发整体呈生长旺盛期>生长后期>生长初期,分别为192、71和45 mm,分别占总量的62.3%、23.1%和14.6%.日尺度上模型模拟值与实测蒸散发值一致性较高,模型模拟精度大体表现为: 晴天>阴天>雨天,且阴雨天模型模拟值较涡度相关实测值偏低.经拆分,土壤蒸发和植被蒸腾分别为176和132 mm,分别占总量的57.1%和42.9%,表明沙地水分利用效率较低.持续干旱和降水后,蒸散发规律明显不同,且土壤蒸发对降水的敏感性强于植被蒸腾.     

基于贝叶斯模型平均的蒸散遥感产品集成——以三江源国家公园为例

蒸散发(ET)是陆表水热过程的一个基础通量,不同模型基于的概念、假设、应用尺度等诸多差异给ET的准确模拟带来了多种不确定性。本研究以三江源国家公园为例,应用贝叶斯模型平均(BMA)方法,通过通量塔观测值对模型进行训练,并综合PT-JPL、ARTS-GIMMS3g、ARTS-MODIS、MOD16和SSEBo 5个模型结果,以提高ET的估测精度。结果表明: 5个模型结果可以捕捉海北高寒草地通量塔观测ET的季节变化,可解释观测ET季节变异的64%～86%,均方根误差(RMSD)的范围为0.47～0.76 mm·(8 d)^-1;基于BMA得到的ET的解释能力提高至89%,RMSD降低至0.43 mm·(8 d)^-1。2003—2015年,三江源国家公园地表ET总体呈不显著增加的趋势,在全区尺度上,温度和降水对蒸散的影响不显著;但在长江源园区,降水和气温对其影响达到显著水平。气温和降水对蒸散发有积极的影响,但不同园区之间的地理差异导致蒸散发也出现不同的变化趋势。本研究为其他多源数据的集成分析提供了方法参考,所集成的蒸散数据可以有效降低原各自模型的不确定性,为区域水热变化研究提供了更为精确的数据基础。这对于更好地认识气候变化背景下的水循环过程具有重要意义。     

Characteristics of canopy evapotranspiration from a small heterogeneous grassland using thermal imaging

We used thermal imaging in conjunction with the eddy covariance technique to characterize canopy evapotranspiration (ET) from a small heterogeneous grassland. We compared ET estimated by a simple soil–vegetation–atmosphere transfer (SVAT) at field scale (a few 100 m²) with that estimated by the eddy covariance method. These two independent estimates of ET showed a good correlation when the flux source area was the same. However, whereas the eddy covariance method yielded integrated results over a large, variable landscape area, the SVAT model primarily yielded values reflecting just the grassland area. We estimated mapped transpiration (Tr) at a point scale (1 m²) and showed that Tr increased linearly with increasing leaf area index (LAI). Although stomatal conductance of C₃ plants was appreciably larger than that of C₄ plants at the leaf scale, this difference was not reflected in Tr at the canopy scale. Tr may be more sensitive to aerodynamic conditions (wind speed and radiation) or environmental heterogeneity (soil–water) than stomatal conductance. The SVAT model clarified variations in the spatial distribution of Tr over a heterogeneous grassland.     

Modelling Seasonal and Inter-annual Variations in Carbon and Water Fluxes in an Arid-Zone <Emphasis Type="Italic">Acacia</Emphasis> Savanna Woodland, 1981–2012

Changes in climatic characteristics such as seasonal and inter-annual variability may affect ecosystem structure and function, hence alter carbon and water budgets of ecosystems. Studies of modelling combined with field experiments can provide essential information to investigate interactions between carbon and water cycles and climate. Here we present a first attempt to investigate the long-term climate controls on seasonal patterns and inter-annual variations in water and carbon exchanges in an arid-zone savanna-woodland ecosystem using a detailed mechanistic soil–plant–atmosphere model (SPA), driven by leaf area index (LAI) simulated by an ecohydrological model (WAVES) and observed climate data during 1981–2012. The SPA was tested against almost 3 years of eddy covariance flux measurements in terms of gross primary productivity (GPP) and evapotranspiration (ET). The model was able to explain 80 and 71% of the variability of observed daily GPP and ET, respectively. Long-term simulations showed that carbon accumulation rates and ET ranged from 20.6 g C m^?2 mon^?1 in the late dry season to 45.8 g C m^?2 mon^?1 in the late wet season, respectively, primarily driven by seasonal variations in LAI and soil moisture. Large climate variations resulted in large seasonal variation in ecosystem water-use efficiency (eWUE). Simulated annual GPP varied between 146.4 and 604.7 g C m^?2 y^?1. Variations in annual ET coincided with that of GPP, ranging from 110.2 to 625.8 mm y^?1. Annual variations in GPP and ET were driven by the annual variations in precipitation and vapour pressure deficit (VPD) but not temperature. The linear coupling of simulated annual GPP and ET resulted in eWUE having relatively small year-to-year variation.     

Spatiotemporal variations of T/ET (the ratio of transpiration to evapotranspiration) in three forests of Eastern China

Evapotranspiration (ET), which is comprised by evaporation from soil surface (E), transpiration (T) and evaporation from the intercepted water by canopy (EI), plays an important role in maintaining global energy balance and regulating climate. Quantifying the spatiotemporal variations of T/ET (the ratio of T to ET) can improve our understandings on the role of vegetation ecophysiological processes in climate regulation. Using eddy covariance measurements at three forest ecosystems (Changbaishan temperate broad-leaved Korean pine mixed forest (CBS), Qianyanzhou subtropical coniferous plantation (QYZ) and Dinghushan subtropical evergreen mixed forest (DHS)) in north–south transect of Eastern China (NSTEC), we run the revised Shuttleworth–Wallace model (S–W model), validated its performance with the water vapor fluxes measured at two layers, and quantified the spatiotemporal variations of T/ET. The S–W model performed well in simulating ET and T/ET. The mean value of annual T/ET at three forests during the observation period all exceeded 0.6. The diurnal variation of canopy stomal conductance (G_c) dominated that of T/ET. The seasonal dynamics of T/ET was mainly shaped by that of leaf area index (LAI), vapor pressure deficit (VPD) and air temperature (T_a) through altering G_c and the portion that the energy absorbed by canopy (P_EC) at temperate forest (CBS), while the seasonal dynamics of T/ET at subtropical forests (QYZ and DHS) were mainly affected by T_a, net radiation, VPD, and soil water content through altering G_c and soil surface conductance (G_s). The variation of mean annual G_c governed the interannual varaition and spatial variation of T/ET. Therefore, forests in Eastern China played an important role in regulating climate through T and G_c primarily affected the spatial and temproal variations of the role of forest T in regulating climate.     

A growing season climatic index to simulate gross primary productivity and carbon budget in a Tibetan alpine meadow

Accurate estimation of gross primary production (GPP) of ecosystem is needed to evaluate terrestrial carbon cycle at various spatial and temporal scales. Eddy covariance (EC) technique provides continuous measurements of net ecosystem CO₂ exchange (NEE) and can be used to separate GPP from NEE in real time series. However, seasonal and inter-annual variation and consequently ecosystem carbon budget is still very difficult to simulate from climatic and environment. To address this limitation, we develop a growing season indicator (GSI) based on low temperature and soil water stress to model and predict intra and inter-annual dynamic of gross primary productivity (GPP). Validation of this new index was conducted using continuous six-year consective EC measurement from 2004 to 2009 at a Tibetan alpine meadow. Simulated GPP agreed well with the observed GPP in terms of seasonal and inter-annual variation. The six-year correlation coefficients on seasonal scale between GSI and scalar GPP derived from EC reached more than 0.85 no matter in dry years or wet years. In addition, the temporal GPP estimation derived from GSI model was quite similar to those from observed values by EC measurement. Moreover, accumulated GSI values can predict annual variability of net ecosystem production (NEP). Higher yearly accumulated GSI corresponded to more annual NEP. When cumulative GSI arrived up to 92, the target ecosystem was a carbon sink. This is probably a threshold which Tibetan alpine meadow changes from carbon source to carbon sink. It is indicated that the GSI model is a simple, alternative approach to estimating GPP and has the potential to simulate spatial GPP in a larger scale. However, the performance of GSI model in other vegetation types or regions still needs a further verification.     

羊草群落水分状况的初步研究

本文采用笔者自行设计和组装的人工气候箱装置,对天然羊草(Aneurolepidium chinense)群落的水分状况进行了研究,结果表明,在生长季各时期的晴天条件下,羊草群落蒸腾、蒸散速率的日进程曲线均为双峰型。群落的蒸腾、蒸散速率与太阳总辐射强度和气温呈正相关,与空气相对湿度呈负相关。群落的无效水分散失比率与蒸腾速率呈负相关。群落中植物的蒸腾强度,以开花期最高,为1.156g／cm2(叶面积)／d;整个群落的蒸腾速率在种子蜡熟期达到最高值,为4861.07g／m2(地面)／d。群落的蒸散速率在6月份最高,达6454.36 g／m2／d。群落月蒸散、蒸腾耗水量的最大值分别出现在6月份和8月份,各为125.9mm和83.9mm。在生长季中,群落的总耗水量与总降水量基本相等,但二者的季节消长不同步。在植物生长发育早期的6月份,水分亏缺严重,使群落对后期充沛的降水不能有效利用,群落生产力低下。     

Construction of 3D human distal femoral surface models using a 3D statistical deformable model

Construction of 3D geometric surface models of human knee joint is always a challenge in biomedical engineering. This study introduced an improved statistical shape model (SSM) method that only uses 2D images of a joint to predict the 3D joint surface model. The SSM was constructed using 40 distal femur models of human knees. In this paper, a series validation and parametric analysis suggested that more than 25 distal femur models are needed to construct the SSM; each distal femur should be described using at least 3000 nodes in space; and two 2D fluoroscopic images taken in 45° directions should be used for the 3D surface shape prediction. Using this SSM method, ten independent distal femurs from 10 independent living subjects were predicted using their 2D plane fluoroscopic images. The predicted models were compared to their native 3D distal femur models constructed using their 3D MR images. The results demonstrated that using two fluoroscopic images of the knee, the overall difference between the predicted distal femur surface and the MR image-based surface was 0.16±1.16 mm. These data indicated that the SSM method could be a powerful method for construction of 3D surface geometries of the distal femur.     

Shortcomings of vitamin D-based model simulations of seasonal influenza

Seasonal variation in serum concentration of the vitamin D metabolite 25(OH) vitamin D [25(OH)D], which contributes to host immune function, has been hypothesized to be the underlying source of observed influenza seasonality in temperate regions. The objective of this study was to determine whether observed 25(OH)D levels could be used to simulate observed influenza infection rates. Data of mean and variance in 25(OH)D serum levels by month were obtained from the Health Professionals Follow-up Study and used to parameterize an individual-based model of influenza transmission dynamics in two regions of the United States. Simulations were compared with observed daily influenza excess mortality data. Best-fitting simulations could reproduce the observed seasonal cycle of influenza; however, these best-fit simulations were shown to be highly sensitive to stochastic processes within the model and were unable consistently to reproduce observed seasonal patterns. In this respect the simulations with the vitamin D forced model were inferior to similar modeling efforts using absolute humidity and the school calendar as seasonal forcing variables. These model results indicate it is unlikely that seasonal variations in vitamin D levels principally determine the seasonality of influenza in temperate regions.     

The Sensitivity of Evapotranspiration to Inter-Specific Plant Neighbor Interactions: Implications for Models

Evapotranspiration (ET) is an important water loss flux in ecosystem water cycles, and quantifying the spatial and temporal variation of ET can improve ecohydrological models in arid ecosystems. Plant neighbor interactions may be a source of spatial and temporal variation in ET due to their effects on the above- and belowground microclimate and increased water demand for transpiration. Over longer timescales (annual to multiple years), adjustments in plant physiological traits may occur in response to neighbor environments, potentially affecting the transpiration (T) component of ET. We used a dynamic soil water model to assess the sensitivity of ET and T estimates to neighbor effects on soil moisture via competition for water, aboveground microclimate effects via canopy shading, and physiological adjustments (specifically, root distribution, stomatal behavior, and canopy leaf area). We focus on a common desert shrub (Larrea tridentata) under different inter-specific neighbor environments and precipitation regimes. Neighbors impacted T of Larrea by as much as 75% at the patch scale (plant and surrounding soil) and 30% at the stand scale. Annual T estimates were highly sensitive to changes in soil moisture associated with competition for water, and the inclusion of physiological adjustments to neighbor environments significantly impacted seasonal T. Plant neighbor interactions can significantly influence ET and soil moisture, and their inclusion in models can help explain spatial and temporal variation in water fluxes in arid ecosystems. Furthermore, physiological adjustments to neighbor environments may be an important source of variation to include in models that operate over seasonal timescales or in studies focused on plant responses to precipitation under climate change.