基于BIOME-BGC模型的长白落叶松林净初级生产力模拟参数敏感性

基于植被生理生态过程的模型包含较多参数,合理的参数取值能够极大地提高模型的模拟能力.参数敏感性分析可以全面分析模型参数对模拟结果的影响程度,在筛选模型敏感参数过程中起到重要作用.本研究以模拟吉林省汪清林业局长白落叶松林净初级生产力（NPP）为例,分析了BIOME-BGC模型的参数敏感性.首先利用样地实测NPP数据与模拟值进行对比分析,检验模型对长白落叶松林NPP的模拟能力;然后利用Morris法和EFAST法筛选出BIOME-BGC模型中对长白落叶松林NPP影响较大的敏感参数.在此基础上,通过EFAST法对所有筛选出的参数进行定量的敏感性分析,计算了敏感参数的全局敏感性指数、一阶敏感性指数和二阶敏感性指数.结果表明： BIOME-BGC模型能够较好地模拟研究区内长白落叶松林NPP的变化趋势;Morris法可以在样本量较少的情况下实现对BIOME-BGC模型敏感参数的筛选,而EFAST法可以定量分析BIOME-BGC模型中单个参数以及不同参数之间交互作用对模拟结果的影响程度;BIOME-BGC模型中对长白落叶松林NPP影响较大的敏感参数为新生茎与叶片的碳分配比和叶片碳氮比,且二者之间的交互作用明显大于其他参数之间的交互作用.     

生态模型的灵敏度分析

灵敏度分析用于定性或定量地评价模型参数误差对模型结果产生的影响,是模型参数化过程和模型校正过程中的有用工具,具有重要的生态学意义．灵敏度分析包括局部灵敏度分析和全局灵敏度分析．局部灵敏度分析只检验单个参数的变化对模型结果的影响程度;全局灵敏度分析则检验多个参数的变化对模型运行结果总的影响,并分析每一个参数及其参数之间相互作用对模型结果的影响．目前,在对生态模型的灵敏度分析中,越来越倾向于使用全局灵敏度分析的方法．但国内仍多采用局部灵敏度分析方法,很少采用全局灵敏度分析方法．文中详细论述了局部灵敏分析和全局灵敏度分析的主要方法(一次变换法、多元回归法、Morris法、Sobol’法、傅里叶幅度灵敏度检验法和傅里叶幅度灵敏度检验扩展法),希望能为国内生态模型的发展提供一个比较完善的灵敏度分析方法库．结合国内外的灵敏度分析发展现状,指出联合灵敏度研究、灵敏度共性研究及空间直观景观模型的灵敏度分析将为生态模型灵敏度分析研究中的热点和难点．     

基于全局灵敏度分析的浒苔生长影响参数研究

近年来,以浒苔为主的绿潮灾害频发。构建了浒苔生长模型,以定量分析浒苔生长过程,探索浒苔爆发机理。参数不确定性是模型不确定性的主要来源,以参数灵敏度分析为基础的参数优化有利于提高模型精度,采用Morris方法对模型涉及的主要参数进行了全局灵敏度分析,以研究浒苔生长的主要影响参数。不同于其他有关大型绿藻的模型模拟,模型同时考虑了幼体浒苔和成熟浒苔的生物量变化,并修正了营养盐限制函数以及温度计算函数。全局性的参数灵敏度分析结果表明,最适温度(T_(opt))、光合作用最适光强(Is)、最大发芽率(G_(max))、浒苔生长所需的氮含量的半饱和系数(kqn)、最大氮摄取率(V_(maxn))这5个参数在浒苔生长模型中具有较大灵敏性。其中,T_(opt)影响最大,Is和V_(maxn)其次,说明浒苔生长主要受温度光照和氮含量限制。相较于局部灵敏度分析仅关注单个参数变化、依赖于初值选取等缺陷,全局灵敏度分析同时从各个参数的取值范围上分析参数对模型结果的影响,能揭示参数之间相互作用的影响。此外,灵敏度较大的参数往往和其他参数之间存在较大相关性。     

Seasonal and interannual variations of ecosystem photosynthetic characteristics in a semi-arid grassland of Northern China

北方半干旱草原生态系统光合参数的季节和年际变异 生态系统表观量子效率(α)、最大光合速率(P_max)和暗呼吸速率(R_d)不仅反映了生态系统水平 光合生理特征,同时也是碳循环模型中光合过程模拟的关键参数。气候和植被因子都会影 响光合参数的季节和年际变异,但二者在光合参数调控过程中的相对贡献和作用途径尚不清晰。本研究基于连续12年(2006–2017)的涡度相关观测数据,分析了内蒙古半干旱典型草原光合参数的季节和年际变化规律;利用回归分析和结构方程模型(SEM)方法明晰了环境和生理调控的作用途径及相对贡献。结果发现,光合参数(α、P_max和R_d)均表现出单峰的季节变化趋势,并呈现明显的年际波动。温度(T_a)和土壤含水量(SWC)的变化共同影响光合参数的季节变化,而SWC主导了其年际变异。α和R_d的变化主要由T_a决定,而Pmax的变化主要受SWC的影响。SEM模型分析表明,除了直接作用外,环境因子主要通过影响冠层水平气孔导度(gc)对光合参数和碳同化生理过程进行调控。此外,叶面积指数对光合参数特别是Pmax的季节和年际变异起主要调控作用。以上结果明确了环境和植被共同决定了生态系统水平光合参数的季节和年际变异,并强调了在水分受限的草原生态系统中,植被生理调控在光合碳同化能力和碳汇功能评估中的重要作用。     

基于植株拓扑结构的生物量分配的玉米虚拟模型

依据植物结构—功能相互作用机理,建立了能模拟玉米生长发育与形态结构建成的虚拟模型。该模型的重要部分为基于植株拓扑结构的生物量分配模块。叙述了该模块的构建原理,以2000年田间试验数据提取了玉米的发育、生物量生产和生物量分配参数。模型模拟了2001年的玉米生长发育与生物量分配过程,模拟结果与田间试验结果比较吻合。应用该模型模拟了2001年玉米不同生育阶段植株的生物量分配和各器官生物量积累动态。     

Biome-BGC模型模拟阔叶红松林碳水通量的参数敏感性检验和不确定性分析

生态过程模型的发展为研究者在长时间序列和区域尺度的研究提供了便利, 但模型模拟的准确性受到模型自身结构、模型参数估计合理性的影响。敏感性分析能够定量或定性筛选出对模型模拟结果影响较大的敏感参数, 是模型参数校准过程中的重要工具, 也是建模和应用的先决条件。该文以阔叶红松林为研究对象, 采用全局敏感性分析方法——傅里叶幅度灵敏度检验扩展法(EFAST)对Biome-BGC模型的生理生态参数进行了敏感性分析, 分别分析了红松(Pinus koraiensis)和阔叶树的净初级生产力(NPP)、蒸散(ET)对参数变化的敏感性。结果表明: (1)模拟红松NPP的不确定性高于阔叶树, 但二者的模拟ET的不确定性均较小。阔叶树的NPP和ET对生理生态参数的敏感性总体上都小于红松。(2)无论是红松、阔叶或其他植被类型, 模拟NPP均表现出对叶片碳氮比、细根碳氮比、比叶面积(SLA)和冠层截留系数的敏感性, 这4个参数的高敏感性主要是由模型自身结构所决定的, 与植被类型和研究地区的关系较小。对模拟ET而言, 细根与叶片碳分配比、新茎与新叶碳分配比和SLA均是影响红松和阔叶树ET的敏感参数, 但红松ET主要受参数与参数间的二阶或多阶交互作用的间接影响, 而阔叶树ET则主要是受到敏感参数直接效应的影响。(3)除了上述影响红松和阔叶树碳水通量的共性参数外, 诸如核酮糖-1,5-二磷酸羧化酶中叶氮含量、叶片与细根周转率、所有叶面积与投影叶面积之比等也是对模拟结果有影响的重要参数, 但是其敏感程度随物种不同和研究区不同而不同, 所以这类参数可以根据具体情况进行参数本地化, 对于其他不敏感参数则可以采用模型缺省值。     

Testing parameter sensitivities and uncertainty analysis of Biome-BGC model in simulating carbon and water fluxes in broadleaved-Korean pine forests

生态过程模型的发展为研究者在长时间序列和区域尺度的研究提供了便利, 但模型模拟的准确性受到模型自身结构、模型参数估计合理性的影响。敏感性分析能够定量或定性筛选出对模型模拟结果影响较大的敏感参数, 是模型参数校准过程中的重要工具, 也是建模和应用的先决条件。该文以阔叶红松林为研究对象, 采用全局敏感性分析方法——傅里叶幅度灵敏度检验扩展法(EFAST)对Biome-BGC模型的生理生态参数进行了敏感性分析, 分别分析了红松(Pinus koraiensis)和阔叶树的净初级生产力(NPP)、蒸散(ET)对参数变化的敏感性。结果表明: (1)模拟红松NPP的不确定性高于阔叶树, 但二者的模拟ET的不确定性均较小。阔叶树的NPP和ET对生理生态参数的敏感性总体上都小于红松。(2)无论是红松、阔叶或其他植被类型, 模拟NPP均表现出对叶片碳氮比、细根碳氮比、比叶面积(SLA)和冠层截留系数的敏感性, 这4个参数的高敏感性主要是由模型自身结构所决定的, 与植被类型和研究地区的关系较小。对模拟ET而言, 细根与叶片碳分配比、新茎与新叶碳分配比和SLA均是影响红松和阔叶树ET的敏感参数, 但红松ET主要受参数与参数间的二阶或多阶交互作用的间接影响, 而阔叶树ET则主要是受到敏感参数直接效应的影响。(3)除了上述影响红松和阔叶树碳水通量的共性参数外, 诸如核酮糖-1,5-二磷酸羧化酶中叶氮含量、叶片与细根周转率、所有叶面积与投影叶面积之比等也是对模拟结果有影响的重要参数, 但是其敏感程度随物种不同和研究区不同而不同, 所以这类参数可以根据具体情况进行参数本地化, 对于其他不敏感参数则可以采用模型缺省值。     

WOFOST模型在内蒙古河套灌区模拟玉米生长全程的适应性

在河套灌区引入成熟的作物模型并进行适应性验证,可为进一步开展玉米生长监测及估产提供依据和基础。本文利用河套灌区巴彦淖尔农业气象试验站2012年玉米观测数据,结合当地气象、土壤资料对荷兰瓦赫宁根大学开发的WOFOST模型进行参数校准,并利用2013年玉米观测数据和2001—2011年农业气象观测资料对模型的区域适用性进行验证,获得了玉米的基本作物参数,包括各发育阶段比叶面积、最大CO2同化率、单叶光能利用率等。结果表明:通过校准作物参数,WOFOST模型可以较好地模拟LAI扩展、生物量的动态积累过程,LAI、各器官生物量及最终产量的模拟值与实测值吻合较好;独立样本检验中,模型模拟LAI的绝对偏差平均值为0.75,叶生物量、茎生物量、贮存器官生物量、地上部总生物量、产量的归一化均方根误差分别为33%、26%、17%、18%和13%;模拟2001—2011年玉米产量的归一化均方根误差为7.5%。参数校准后的模型对LAI、各器官生物量、产量的模拟结果较为符合实际,WOFOST模型能够适用于河套地区玉米生产过程生理、生态因子诊断、评估等。     

基于器官生物量构建植株形态的玉米虚拟模型

探讨了基于玉米器官生物量模拟其形态的方法,并应用2000年田间试验数据提取了玉米节间、叶鞘和叶片的形态构建参数。基于玉米虚拟模型生物量分配模块模拟的器官生物量积累和建立的形态构建方法与提取的参数,模拟了2001年玉米不同生长阶段的器官形态,模拟结果与田间试验数据吻合较好。应用本模型实现了玉米生长过程中植株各个器官形态变化以及植株高度、叶面积动态的模拟,并实现了植株形态的可视化。     

SWMM模型参数局部灵敏度分析

利用Morris筛选法,分析了3场不同雨强降雨条件下,城市降雨径流模型SWMM的水文水力模块和水质模块的参数灵敏度。结果表明:对SWMM模型径流总量和径流峰值最敏感的参数依次是不透水面洼蓄量(destore-imperv)、管道曼宁系数(conduit roughness)和汇流区宽度(width-K),最大灵敏度分别为-0.329、-0.144、0.133和-0.294、0.171和0.143;对污染物总量敏感性较高的是路面和屋面的最大积累量(max buildup)、冲刷系数(coefficient)和冲刷指数(exponent)参数。雨强对于SWMM模型水文水力参数中的下渗参数的敏感性有较大影响,而对水质参数的敏感性影响较小;研究区的土地利用状况对参数敏感性也有较大的影响。水质参数总体的稳定性要高于水文水力参数。     

Structure is more important than physiology for estimating intracanopy distributions of leaf temperatures

Estimating leaf temperature distributions (LTDs) in canopies is crucial in forest ecology. Leaf temperature affects the exchange of heat, water, and gases, and it alters the performance of leaf‐dwelling species such as arthropods, including pests and invaders. LTDs provide spatial variation that may allow arthropods to thermoregulate in the face of long‐term changes in mean temperature or incidence of extreme temperatures. Yet, recording LTDs for entire canopies remains challenging. Here, we use an energy‐exchange model (RATP) to examine the relative roles of climatic, structural, and physiological factors in influencing three‐dimensional LTDs in tree canopies. A Morris sensitivity analysis of 13 parameters showed, not surprisingly, that climatic factors had the greatest overall effect on LTDs. In addition, however, structural parameters had greater effects on LTDs than did leaf physiological parameters. Our results suggest that it is possible to infer forest canopy LTDs from the LTDs measured or simulated just at the surface of the canopy cover over a reasonable range of parameter values. This conclusion suggests that remote sensing data can be used to estimate 3D patterns of temperature variation from 2D images of vegetation surface temperatures. Synthesis and applications. Estimating the effects of LTDs on natural plant–insect communities will require extending canopy models beyond their current focus on individual species or crops. These models, however, contain many parameters, and applying the models to new species or to mixed natural canopies depends on identifying the parameters that matter most. Our results suggest that canopy structural parameters are more important determinants of LTDs than are the physiological parameters that tend to receive the most empirical attention.     

Estimation of carbon storage based on individual tree detection in <Emphasis Type="Italic">Pinus densiflora</Emphasis> stands using a fusion of aerial photography and LiDAR data

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.     

Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment

Tropical forest structural variation across heterogeneous landscapes may control above‐ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above‐ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one‐hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.     

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model‐building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base‐consumption were found low compared to the large uncertainty observed in the antibiotic and off‐gas CO₂ predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases ‐ meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass‐transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

Multivariate control of plant species richness and community biomass in blackland prairie

Recent studies have shown that patterns of plant species richness and community biomass are best understood in a multivariate context. The objective of this study was to develop and evaluate a multivariate hypothesis about how herbaceous biomass and richness relate to gradients in soil conditions and woody plant cover in blackland prairies. Structural equation modeling was used to investigate how soil characteristics and shade by scattered Juniperus virginiana trees relate to standing biomass and species richness in 99 0.25 m² quadrats collected in eastern Mississippi, USA. Analysis proceeded in two stages. In the first stage, we evaluated the hypothesis that correlations among soil parameters could be represented by two underlying (latent) soil factors, mineral content and organic content. In the second stage, we evaluated the hypothesis that richness and biomass were related to (1) soil properties, (2) tree canopy extent, and (3) each other (i.e. reciprocal effects between richness and biomass). With some modification to the details of the original model, it was found that soil properties could be represented as two latent variables. In the overall model, 51% and 53% of the observed variation in richness and biomass were explained. The order of importance for variables explaining variations in richness was (1) soil organic content, (2) soil mineral content, (3) community biomass, and (4) tree canopy extent. The order of importance for variables explaining biomass was (1) tree canopy and (2) soil organic content, with neither soil mineral content nor species richness explaining significant variation in biomass. Based on these findings, we conclude that variations in richness are uniquely related to both variations in soil conditions and variations in herbaceous biomass. We further conclude that there is no evidence in these data for effects of species richness on biomass.     

基于GreenLab原理构建油松成年树的结构-功能模型

 林木的结构-功能模型(functional-structural tree modeling, FSTMs)是基于器官级组件构建的将植物结构和功能结合起来的一类模型, 在应用于成年树时需要解决拓扑结构复杂性和年轮分配模式普适性的问题。该文以18年生和41年生的油松 (Pinus tabulaeformis)成年树为研究对象, 将GreenLab模型应用到成年树的模拟中。采用破坏性取样, 实测了2株油松成年树的形态结构, 利用子结构模型解决成年树拓扑结构复杂性的问题, 引入年轮影响系数λ, 将全局分配模式和Pressler模式结合起 来, 解决年轮分配模式在不同年龄和环境条件下不同的问题。模型的直接参数通过实测数据获得, 隐含参数利用非线性最小二乘法拟合反求获得。通过实测数据与模拟数据的对比、模拟数据与经验模型模拟数据的对比, 对模型的模拟效果进行了评估, 发现节间总重、针叶总重、树高、树干节间重观测值和模型模拟值建立的回归方程的决定系数为0.84–0.98, 结构-功能模型与经验模型对总生物量模拟的决定系数为0.95, 表明该模型能较真实地反映油松的结构和生长过程。     

ORCHIDEE‐STICS,a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology

Agro‐Land Surface Models (agro‐LSM) combine detailed crop models and large‐scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil–vegetation–atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro‐LSM ORCHIDEE‐STICS developed for sugarcane. Using the Morris method to identify the key parameters impacting LAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulating LAI are (i) the maximum predefined rate of LAI increase during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing their LAI, and a parameter defining a threshold for nitrogen stress on LAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model‐data RMSE, the figure of merit and a Bayesian quadratic model‐data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross‐validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling the LAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error in LAI simulation after the calibration is discussed.     

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species‐specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model‐data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.