, 雷相东Effects of climate change on net primary productivity in Larix olgensis plantations based on process modeling
XIEYa-Lin, LEIXiang-Dong通讯作者:
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温室效应引起的气候变化影响着森林生态系统的结构和功能。在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1。东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1。降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(贾建英和郭建平, 2011)。气温升高及降水减少逐步加剧了东北地区的暖干化发展态势, 这必然引起森林生态系统结构及功能的变化, 进而影响到森林碳平衡及生产力水平。植被净初级生产力(NPP)是表征生态系统固定大气CO2能力的关键变量, 是评价植物群落在自然环境条件下的生产能力及可持续发展潜力的重要指标(董丹和倪健, 2011)。用基于过程模型的方法研究NPP, 在近10年来得到快速发展(Turner et al., 2006; Shvidenko et al., 2008; H?rk?nen et al., 2010), 其中FORECAST模型(王伟峰等, 2016)、3-PG模型(Landsberg & Waring, 1997)、CASA模型(尹锴等, 2015)、MODIS模型(Coops et al., 2009; 孙成明等, 2015)、BIOME-BGC模型(曾慧卿等, 2008; 苏薇等, 2012; 何丽鸿等, 2016)已被广泛地应用于研究森林生态系统NPP对气候变化的响应。研究表明气候变化由诸多环境因子(如大气组成成分、温度、降水、氮沉降以及土地利用等)共同决定。这些环境因子的共同作用对森林NPP会产生显著的影响, 但影响的方向和程度并不相同, 存在较大的不确定性(González-García et al., 2016)。源于大气污染的氮沉降会刺激缺氮林区林分生产力增加。对不同地区、不同树种而言, 大气CO2浓度、降水、温度的增加或减少对生产力的影响并不同(Medlyn et al., 2011)。
3-PG模型是目前应用最广的过程模型。它由Landsberg和Waring于1997年开发, 是以月为时间尺度, 以林分为空间尺度, 同时结合气象因子、立地条件、经营措施、树种特性来预测林分生产力、生物量分配、种群动态和土壤水分平衡的模型, 也是一个考虑了实际环境条件的完整的森林碳分配与平衡模型(Sands & Landsberg, 2002)。它可以准确地预测人工林的生产力及环境变化和营林措施对生产力的影响。虽然3-PG模型活跃于国外森林生长动态研究领域, 但是目前3-PG模型在我国应用较少(花利忠等, 2007; 刘坤等, 2015)。
长白落叶松(Larix olgensis)是北方和山地寒温带干燥寒冷气候条件下最具有代表性的森林植被。因其易栽植、生长快等优点, 在东北地区人工林中得到广泛应用, 成为中国重要的商业性用材树种(吴正方等, 2003)。据统计, 仅吉林省就有长白落叶松人工林37万hm2, 约占全省人工林总面积的65%以上(陈传国等, 1986)。因此, 长白落叶松人工林生态系统的NPP变化将对我国的森林碳储量产生重要影响, 估算该区域NPP的动态变化将有助于揭示整个森林生态系统碳循环过程。孙志虎(2012)等基于FORECAST模型对长白落叶松林NPP开展了研究, 但因局限在局部尺度, 并未模拟未来气候变化对NPP的影响。未来气候变化如何影响区域长白落叶松的生产力, 尚不清楚。
本文以长白落叶松为研究对象, 对3-PG模型进行本地参数化及验证, 基于吉林省30块固定样地, 模拟了长白落叶松人工林NPP在一个轮伐期(40年)内随林龄的动态变化, 以及在不同气候情景条件下, NPP的变化情况, 并模拟分析CO2浓度、降水量及温度变化对长白落叶松人工林NPP的影响, 以评价该模型对长白落叶松生长模拟的适用性, 并为应对气候变化条件下的落叶松人工林经营管理提供科学依据。
1 材料和方法
1.1 研究区概况
研究区位于吉林省, 数据来源于吉林省第六次、第七次和第八次森林资源清查固定样地, 为长白落叶松人工纯林。共30块固定样地, 在四平、临江、白山、龙井、辽源、舒兰、长春、汪清、和龙、通化林区均有分布, 具有一定的代表性。研究区域及样地位置详见图1。林区多位于长白山山脉的中低丘陵区, 属温带大陆性季风气候, 土壤类型主要是暗棕壤和棕壤。样地均为矩形, 面积0.06 hm2。每次调查的因子包括每木胸径、林分平均高、林龄及样地环境因子(海拔、坡向、坡位、土壤类型、质地和厚度等)。样地生物量通过已经建立的长白落叶松生物量方程获得(陈传国等, 1986), NPP通过生物量计算得到(Zhou et al., 2002)。第六次森林资源清查时样地基本概况详见附录I。
显示原图|下载原图ZIP|生成PPT图1采样点的分布。
-->Fig. 1Location of the sampling plots.
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气象数据由中国气象数据网(http://data.cma.cn/)中距离调查样地最近的10个气象台站获取, 样点距离气象台站的平均距离为42.7 km, 最小距离为 21.3 km, 最大距离为60.7 km。由于样点到气象台站的距离差异不明显, 采用气象站点提供的原始观测数据。驱动数据包括1980-2015年间的月平均最高气温、月平均最低气温、月降水量、月蒸散量、月霜冻日数、月降雨日数和月短波辐射等。
1.2 未来气候变化情景设置
为了预估未来气候变化对长白落叶松林生态系统NPP的影响, 本研究选用《长期气候变化—— IPCC第五次评估报告解读》(董思言和高学杰, 2014)中的最新排放情景, 即高排放(RCP 8.5)、中等排放(RCP 6.0)和低排放(RCP 2.6)作为未来主要的气候变化情景。各气候排放情景变化模式见表1。为了研究CO2浓度、温度、降水量对NPP的单独影响及其交互作用, 根据表1的排放情景, 组合设计了8种气候情景(表2), 以1986-2005年间气候变化为基准, 分别模拟在2081-2100年间RCP 8.5、RCP 6.0及RCP 2.6排放情景下, 长白落叶松人工林生态系统NPP对气候变化的响应。Table 1
表1
表1三种最新排放情景变化模式
Table 1Pattern of changes under three latest climate emission scenarios
| 排放情景 Emission scenarios | CO2浓度 CO2 concentration (mg·L-1) | CO2浓度基准值 CO2 concentration reference value (mg·L-1) | 气温增加 Air temperature increment (℃) | 气温增加基准值 Air temperature increment reference value (℃) | 降水量增加 Precipitation increment (%) | 降水量增加基准值 Precipitation increment reference value (%) |
|---|---|---|---|---|---|---|
| RCP 2.6 | 440-480 | 460 | 0.3-1.7 | 1.0 | 0.3-5.1 | 2.7 |
| RCP 6.0 | 510-570 | 540 | 1.4-3.1 | 2.3 | 1.4-9.3 | 5.4 |
| RCP 8.5 | 560-630 | 595 | 2.6-4.8 | 3.7 | 2.6-14.4 | 8.5 |
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Table 2
表2
表2不同未来气候变化情景设置
Table 2Different climate change scenarios
| 气候变化情景 Climate change scenarios | 排放情景 Emission scenarios | CO2浓度 CO2 concentration | 气温 Air temperature | 降水量 Precipitation |
|---|---|---|---|---|
| C0T0P0 | 不变 No change | 不变 No change | 不变 No change | |
| RCP 2.6 | 460 mg·L-1 | 不变 No change | 不变 No change | |
| C1T0P0 | RCP 6.0 | 540 mg·L-1 | 不变 No change | 不变 No change |
| RCP 8.5 | 595 mg·L-1 | 不变 No change | 不变 No change | |
| RCP 2.6 | 不变 No change | +1.0 ℃ | 不变 No change | |
| C0T1P0 | RCP 6.0 | 不变 No change | +2.3 ℃ | 不变 No change |
| RCP 8.5 | 不变 No change | +3.7 ℃ | 不变 No change | |
| RCP 2.6 | 不变 No change | 不变 No change | +2.7% | |
| C0T0P1 | RCP 6.0 | 不变 No change | 不变 No change | +5.4% |
| RCP 8.5 | 不变 No change | 不变 No change | +8.5% | |
| RCP 2.6 | 不变 No change | +1.0 ℃ | +2.7% | |
| C0T1P1 | RCP 6.0 | 不变 No change | +2.3 ℃ | +5.4% |
| RCP 8.5 | 不变 No change | +3.7 ℃ | +8.5% | |
| RCP 2.6 | 460 mg·L-1 | +1.0 ℃ | 不变 No change | |
| C1T1P0 | RCP 6.0 | 540 mg·L-1 | +2.3 ℃ | 不变 No change |
| RCP 8.5 | 595 mg·L-1 | +3.7 ℃ | 不变 No change | |
| RCP 2.6 | 460 mg·L-1 | 不变 No change | +2.7% | |
| C1T0P1 | RCP 6.0 | 540 mg·L-1 | 不变 No change | +5.4% |
| RCP 8.5 | 595 mg·L-1 | 不变 No change | +8.5% | |
| RCP 2.6 | 460 mg·L-1 | +1.0 ℃ | +2.7 % | |
| C1T1P1 | RCP 6.0 | 540 mg·L-1 | +2.3 ℃ | +5.4 % |
| RCP 8.5 | 595 mg·L-1 | +3.7 ℃ | +8.5 % |
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1.3 3-PG模型
1.3.1 模型简介3-PG模型的关键就在于通过一系列动态方程模拟太阳辐射的逐级递减、林分冠层吸收的碳分配和水分循环与利用, 进而模拟在全球变暖、大气CO2浓度上升、土壤肥力下降的潜在影响下森林生态系统NPP的年际变化(图2)。3-PG模型针对生物量积累及分配有相应的模块, 它主要是通过各种修正因子(蒸腾修正因子fD、土壤水分修正因子fΘ、温度修正因子fT、霜冻修正因子fF、养分修正因子fN、林龄修正因子fage以及光能利用率)计算林分的NPP, 并模拟养分在林木根、茎、叶间的分配, 从而调控林木的生长。此外, 模型基于Beer-Lambert日光消减规律模拟林分冠层吸收的光合有效辐射(PAR), 根据林分土壤水贮量变化模拟了森林每月水量平衡, 它主要包括大气降水、地下水、人工灌溉和林分蒸散与土壤水之间的动态变化。
显示原图|下载原图ZIP|生成PPT图23-PG模型原理(改自Sands和Landsberg (2002))。
-->Fig. 2Principles of 3-PG model (based on
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1.3.2 模型参数
3-PG模型运行需要的数据包括样地的气候数据、立地数据、林分初始数据和参数。本研究使用的长白落叶松生理生态参数和初始数据详见表3。参数主要通过以下5种方法获取: 1)样地实测得到, 主要是易于获取的参数; 2)查阅相关文献资料获得; 3)参照相似树种类推得到; 4)模型默认值; 5)优化调整确定。在了解参数的生物学意义及其对模型输出结果的具体影响(敏感性等级)的基础上, 根据模型的预测结果, 在参数允许的范围内, 系统、客观地进行校正, 以实现模型预测结果与相应实测数据的最佳拟合。主要检查参数值的选取及所有模型的输出是否符合生物学原理; 并用独立样本进行验证, 观察预测效果是否令人满意。文中的本地化参数由第六次、第七次森林资源清查数据校准拟合获取, 将第八次森林资源清查数据作为验证数据。模型参数的详细介绍见文献(Feikema et al., 2010)。
Table 3
表3
表3长白落叶松人工林3-PG模型参数和初始数据
Table 33-PG model parameters and the initial values for Larix olgensis plantations
| 参数 Parameter | 值 Value | 分类 Category | 来源 Source |
|---|---|---|---|
| 生物量的分配关系和比例 Allometric relationships and partitioning | |||
| 胸径2 cm树叶与干分配比 Foliage: stem partitioning ratio when DBH = 2 cm | 1.00 | A | 本文拟合 Fitted in this study |
| 胸径20 cm树叶与干分配比 Foliage: stem partitioning ratio when DBH = 20 cm | 0.5 | A | 本文拟合 Fitted in this study |
| 干生物量与胸径关系中常数值 Constant in the stem biomass and DBH relationship | 0.007 3 | A | 本文拟合 Fitted in this study |
| 干生物量与胸径关系中幂值 Power in the stem biomass and DBH relationship | 3.409 | A | 本文拟合 Fitted in this study |
| 净初级生产量分配给根的最大值 Maximum fraction of net primary productivity to roots | 0.95 | A | 本文拟合 Fitted in this study |
| 净初级生产量分配给根最小值 Minimum fraction of net primary productivity to roots | 0.5 | A | 本文拟合 Fitted in this study |
| 气温修正因子 Air temperature modifier | |||
| 生长最低气温 Minimum air temperature for growth (℃) | -25 | L | Xu et al., 2012 |
| 生长最适气温 Optimum air temperature for growth (℃) | 17 | L | Sun et al., 2009 |
| 生长最高气温 Maximum air temperature for growth (℃) | 27 | L | Xu et al., 2012 |
| 霜冻修正因子 Frost modifier | |||
| 每次霜冻导致生产力流失天数 Production lost days per frost day (d) | 1 | C | 默认参数 Default parameters |
| 冠层结构和过程 Canopy structure and process | |||
| 比叶面积 Specific leaf area (SLA) | |||
| 年龄为0时比叶面积 Specific leaf area at age 0 (m2·kg-1) | 12.93 | L | Song & Sun, 2012 |
| 成熟叶比叶面积 Specific leaf area for mature leaves (m2·kg-1) | 5 | L | Song & Sun, 2012 |
| 年龄为(SLA0 + SLA1)/2比叶面积 Age at which specific leaf area = (SLA0 + SLA1)/2 | 8 | L | Song & Sun, 2012 |
| 光截获 Light interception | |||
| 消光系数 Extinction coefficient | 0.5 | L | Amichev et al., 2011 |
| 郁闭度年龄 Age at canopy cover (a) | 5 | L | Gonzalez-Benecke et al., 2014 |
| 从林冠降水蒸发的最大比例 Maximum proportion of rainfall evaporated from canopy | 0.15 | C | 默认参数 Default parameters |
| 最大降水截留时叶面积指数 Leaf area index for maximum rainfall interception | 5 | C | 默认参数 Default parameters |
| 光合生产和呼吸 Photosynthesis production and respiration | |||
| 冠层量子效率 Canopy quantum efficiency (mol·mol-1) | 0.035 | L | Ma et al., 2008 |
| 净初级生产力/总初级生产力 Ratio of net primary productivity to gross primary productivity | 0.47 | L | Liu et al., 2015 |
| 树枝在干中的比例 Fraction of stem biomass as branch and bark | 0.15 | L | |
| 林分初生时树枝占干生物量的比例 Fraction of branch and bark at age = 0 | 0.15 | L | Coops & Waring, 2011 |
| 林分成熟时树枝占干生物量的比例 Fraction of branch and bark for mature stands | Coops & Waring, 2011 | ||
| 树枝占平均值时的林龄 Age at which fraction = (Branch and bark fraction at age = 0+Branch and bark fraction for mature stands)/2 | 1.5 | L | Coops & Waring, 2011 |
| 立地初始化条件 Stand initialization | |||
| 初始种植年 Years of initial plantation | 1973-1983 | M | 本研究测定 Measurements in this study |
| 初始密度 Initial stocking (trees·hm-2) | 3300 | M | 本研究测定 Measurements in this study |
| 海拔 Altitude (m) | 230-751 | M | 本研究测定 Measurements in this study |
| 纬度 Latitude (°) | 41.61-43.88 | M | 本研究测定 Measurements in this study |
| 肥力等级 Fertility rating | 0.7 ± 0.1 | M | 本研究测定 Measurements in this study |
| 土壤质地类型 Soil texture | Clay loam | M | 本研究测定 Measurements in this study |
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1.3.3 模型评价
通过将样地实测数据(yi)与3-PG模型的模拟值($\hat{y}_i$)进行回归分析, 计算模型的决定系数(R2)、平均误差(ME)、平均相对误差(MRE)、均方误(RMSE)、相对均方误差(RRMSE)来评估模型的预测能力。
式中: i为样本号,n为样本数, yi代表实测数据, $\hat{y}_i$代表模型模拟值。
1.4 3-PG模型参数敏感性分析
通过参数敏感性分析可以找到模型的关键和敏感参数, 为模型的准确预测和应用提供依据。主要是选取模型参数调整中不同取值对模型预测结果影响较明显的参数来进行。方法是在模型的其他运行参数不变的情况下, 仅通过改变以上参数的取值大小, 对30块固定样地的NPP进行模拟, 分析不同参数取值模拟结果的差异显著性(t检验)。2 结果和分析
2.1 模型验证
通过收集吉林省10个林区共30块长白落叶松人工林样地(林龄在8-44年) 1999-2013年的实测调查数据, 计算得到长白落叶松林净初级生产力数值。基于3-PG模型模拟30块长白落叶松人工林样地NPP, 再对两者进行回归分析得到以下结果(图3): 模型模拟NPP与样地实测NPP呈极显著相关关系(R2 = 0.86, p < 0.001), ME为-8.52 g·m-2·a-1, MRE为-1.50%, RMSE为65.08 g·m-2·a-1, RRMSE为11.26%。总体来看, 模型模拟NPP的大小和范围与实测数值基本相似, 变化趋势基本相同, 模拟效果整体较好。因此, 3-PG模型对长白落叶松人工林NPP的估算较准确且具有较高的统计可靠性。
显示原图|下载原图ZIP|生成PPT图3基于3-PG模型模拟30块长白落叶松林样地净初级生产力(NPP)与实测NPP的比较。图中三角代表样点的净初级生产力值, 黑线代表线性回归线, 灰线代
-->Fig. 3Comparisons between simulated net primary productivity (NPP) by 3-PG model and the measured data for the 30 sample plots in Larix olgensis plantations. Triangles mean net primary productivity (NPP) values. The black line means linear regression line, and gray line means 1:1 positive linear regression line.
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本研究将吉林省第六次、第七次森林资源清查数据作为校参数据, 在模型拟合的过程中, 获取最优参数值。将第八次森林资源清查数据作为验证数据, 验证3-PG模型对长白落叶松人工林NPP的拟合效果, 校参数据与验证数据的误差分析如表4。可以看出, 校参数据和验证数据的结果有较好的一致性, 相对误差均在5%以内, 较好地模拟了长白落叶松人工林的生产力变化。因此, 3-PG模型可以准确地模拟长白落叶松的生长和生产力。
Table 4
表4
表4校参数据与验证数据的误差比较
Table 4The comparison of errors between calibration data and validation data
| 指标 Indicator | 校参数据 Calibration data | 验证数据 Validation data |
|---|---|---|
| R2 | 0.870 5 | 0.848 9 |
| p | <0.05 (n = 60) | <0.05 (n = 30) |
| 平均误差 ME (g·m-2·a-1) | -9.568 | -6.422 |
| 平均相对误差 MRE (%) | -1.655 | -1.163 |
| 均方误 RMSE (g·m-2·a-1) | 67.794 | 60.399 |
| 相对均方误差 RRMSE (%) | 11.533 | 10.809 |
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2.2 长白落叶松林NPP年际和月际变化模拟
为进一步分析长白落叶松人工林的生产力动态变化, 本研究基于3-PG模型的立地初始化参数、气象数据对选取的10个林区长白落叶松人工林NPP在一个轮伐期(40年)内的变化趋势进行了模拟(图4)。图4A为30块长白落叶松人工林样地NPP的平均变化趋势, 可以发现: 在幼龄阶段, 随着林龄的增大, NPP快速增加, 到中龄阶段达到最大值(815.6 g·m-2·a-1), 之后随年龄增加逐渐减小, 这符合树木生长的生理学特点。分区域模拟(图4B)发现: 所有林区的NPP水平遵循先增加后减小的规律, 辽源、汪清、龙井、白山林区的NPP变化趋势相似, 先增加较快, 然后逐渐降低, 峰值较明显。舒兰、四平、通化、和龙、长春林区的NPP变化趋势相似, 先缓慢增加, 达到最大值后有个平稳期, 然后缓缓降低, 峰值不明显。临江林区的NPP水平最高, 最大值达1 200.16 g·m-2·a-1。这些差异可能是由于各林区的立地因子(气候条件)不同所导致, 说明3-PG模型的模拟结果既有较好的生物合理性, 又灵敏地反映了区域差异。图5是10个林区在1999-2013年间各月的平均气温、降水量及NPP变化情况。可以看出研究区内各月平均气温及降水量变化趋势相似, NPP呈先增大后减小的趋势。在所选林区内, 全年降水量及平均气温普遍不高。降水量较小的月份(如1月、2月、12月), 降水量均小于10 mm。7月降水量最大, 为171 mm, 8月次之, 全年降水量变幅在15.8%-95.6%之间。长白落叶松人工林NPP的主要积累时期集中在生长季(4-9月), 而11-3月研究区内温度较低、降水量少、霜冻期长, 落叶松人工林生长较慢, NPP水平较低。5、6月是落叶松人工林的营养生长期, 随着温度的升高、降水量的增加, 6月落叶松人工林生长最快, NPP增幅最大; 7月太阳辐射充足、水热条件充分, 适合植被的生长, NPP在整个生长季达到最大; 此后, 随着降水量的减小、温度的降低、冠层吸收的太阳辐射量少, 落叶松人工林NPP迅速下降, 到1月份降到最低。研究区全年气温普遍不高, 在7-8月达到峰值, 约23.5 ℃。由图5可以看出: NPP的月际变化与温度及降水量变化趋势相似, 它们之间存在一定相关性。
显示原图|下载原图ZIP|生成PPT图4基于3-PG模型模拟30块长白落叶松林样地一个轮伐期净初级生产力(NPP)变化趋势。
-->Fig. 4Changes in net primary productivity (NPP) simulated by 3-PG model for the 30 sample plots over a rotation period in Larix olgensis plantations.
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显示原图|下载原图ZIP|生成PPT图5研究区1999-2013年间月平均气温(曲线)、月降水量(柱状图)及相应净初级生产力(NPP)的变化(平均值±标准偏差)。
-->Fig. 5Changes in monthly mean temperature (curves), precipitation (bar charts) and net primary productivity (NPP) in the study area during 1999-2013 (mean ± SD).
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2.3 参数敏感性分析
在进行模型参数调整时, 我们发现一些参数的不同取值对模型预测有较大影响, 如林分生长最适温度(Topt)、林分比叶面积达(SLA0 + SLA1)/2时的林龄(tSLA)和每次霜冻导致生产力流失天数(kF)。为检验模型的应用效果及了解模型参数的取值变化对3-PG模型模拟结果的影响, 本研究选取以上3个参数进行参数敏感性分析。通过查阅文献得知, 长白落叶松人工林Topt约为17 ℃ (孙志虎, 2009)。本文在运用模型进行NPP预测时, 参数Topt取值均为17 ℃。为探讨Topt对3-PG模型模拟长白落叶松人工林NPP的影响, 本研究基于最适温度设置了4个等级, 分别是10.2 ℃ (-40%)、13.6 ℃ (-20%)、20.4 ℃ (+20%)、23.8 ℃ (+40%)。从图6可以看出: 温度升高对模型预测的负效应大于温度降低对模型预测的正效应, 温度因子对模型预测NPP的影响随时间先增加后降低。过高(23.8 ℃)或过低(10.2 ℃)的温度都会显著影响长白落叶松人工林生物量积累(n = 40, p < 0.05), 小幅度的温度改变对模型预测的影响不显著(n = 40, p > 0.05)。在未来, 气温升高意味着全球变暖加剧, 适当的增加温度(0-2 ℃)会加快林分的生长速率, 提高NPP水平。而当温度增加超过5 ℃时, 将导致林分生长速率减缓。
tSLA在一定程度上反映了叶片截获光的能力及在强光下的自我保护能力, 往往与植物的生长和生存有密切的联系。通过查阅文献得知: 长白落叶松人工林的比叶面积一般在12.23-13.63之间, 比叶面积达1/2时林龄大约在5年(宋林和孙志虎, 2012)。本研究设定了4个林龄等级探讨参数tSLA取值对模型预测的影响, 它们分别是: 3 (-40%)、4 (-20%)、6 (+20%)、7 (+40%), 括号中的百分数是指在默认值的基础上改变的百分比, 负值表示减少, 正值表示增加。从图6可以发现: 参数tSLA取值显著影响长白落叶松幼龄林和中龄林的NPP水平(n = 40, p < 0.05), 且tSLA取值与模型模拟值成正比; 对近熟林之后的阶段影响不显著(n = 40, p > 0.05), 且tSLA取值与模型模拟值成反比。
显示原图|下载原图ZIP|生成PPT图6参数林分生长最适温度(Topt)、林分比叶面积达一定比例时林龄(tSLA)、霜冻导致生产力损失天数(kF)取值变化对长白落叶松人工林模拟净初级生产力(NPP)的影响。
-->Fig. 6The influences of optimum temperature for growth (Topt), age at which specific leaf area= (SLA0 + SLA1)/2) (tSLA) and days of production loss due to frost (kF) on simulated net primary productivity (NPP) in Larix olgensis plantations.
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kF模型默认取值为1。为探索霜冻现象对模型预测的影响, 本研究假定4个等级: 0.6 (-40%)、0.8 (-20%)、1.2 (+20%)、1.4 (+40%), 括号中的百分数是指在默认值的基础上改变的百分比, 负值表示减少, 正值表示增加。3-PG模型理论认为, 大多数树种在霜冻来临时即停止光合作用。细胞低温结冰后将造成细胞生理干旱、叶绿体机械损伤以及细胞通透性改变, 从而保卫细胞运动变慢直至气孔关闭。从图6可以看出: 参数kF取值与模型模拟值成反比, 且模拟结果之间差异显著(n = 40, p < 0.05)。
2.4 未来气候情景下的NPP变化
以1986-2005年气候为基准, 基于3-PG模型模拟预测在2081-2100年间大气CO2浓度、降水量和温度变化的情况下, 各林区长白落叶松人工林NPP的相对变化情况(图7)。在代表浓度路径(RCP)为2.6、6.0及8.5的排放情景下, 单独CO2浓度升高而温度和降水不变(C1T0P0)时, 长白落叶松林NPP分别增加6.24%-58.17% (平均值39.22%)、20.94%-72.52% (平均值50.80%)和44.35%-76.85% (平均值60.61%)。可见, CO2浓度增加有利于长白落叶松林NPP的积累, 且CO2浓度越高, NPP增加的幅度越大。在CO2浓度和降水量不变、温度分别升高1 ℃、2.3 ℃和3.7 ℃ (C0T1P0)时, 长白落叶松林NPP较原来(C0T0P0)降低了0.12%-20.98% (平均值10.28%)、11.91%-40.42% (平均值24.57%)和31.14%-59.59% (平均值45.35%)。可见, 单独温度升高不利于长白落叶松林NPP的积累。原因是温度升高加速了植被的光合作用, 枯枝落叶分解加快, 干物质积累减少。此外, 温度升高易引起土壤水分快速蒸发, 植物干旱缺水不利于NPP的积累。单独降水量增加(C0T0P1)时, 各林区内长白落叶松人工林NPP都有不同程度的增加, 增加幅度均小于10%。可见, 降水的单独增加有利于长白落叶松林NPP的积累, 原因在于大气降水能够缓解水分胁迫, 从而促进植物吸收养分。当温度和降水量同时增加(C0T1P1)时, 长白落叶松林NPP分别减小2.59%-9.91% (平均值5.96%)、7.83%-29.09% (平均值16.89%)和24.69%-50.97% (平均值36.88%), NPP减小的幅度要大于降水量单独增加时NPP增加的幅度。可见, 温度和降水的协同增加不利于NPP的积累。
在CO2浓度增加、温度升高、降水不变(C1T1P0)的情况下, 长白落叶松林NPP较原来(C0T0P0)增加了6.12%-37.19% (平均值28.94%)、9.03%-32.10% (平均值26.23%)和13.21%-17.26% (平均值15.26%); 在CO2浓度增加、温度不变、降水量增加(C1T0P1)的情况下, 长白落叶松林NPP分别增加了8.95%-69.24% (平均值44.54%)、25.02%-83.85% (平均值58.48%)和49.80%-88.47% (平均值69.08%); 当大气CO2浓度、温度及降水量同时增加(C1T1P1)时, 长白落叶松林NPP分别增加34.26%、33.91%及23.73%。可见, CO2浓度、降水和温度的协同增加有利于长白落叶松林NPP的积累, 且大气CO2浓度及降水量的协同增加对NPP的正效应大于温度升高对NPP的负效应。
显示原图|下载原图ZIP|生成PPT图7在RCP 2.6、RCP 6.0和RCP 8.5排放情景下长白落叶松人工林模拟净初级生产力(NPP)的相对变化(平均值±标准偏差)。C, CO2; P, 降水; T, 气温。1, 改变; 0, 不变。
-->Fig. 7Relative changes in simulated net primary productivity (NPP) for Larix olgensis plantations under RCP 2.6, RCP 6.0 and RCP 8.5 scenarios (mean ± SD). C, CO2; P, precipitation; T, air temperature. 1, change; 0, no change.
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3 讨论
3.1 长白落叶松人工林NPP的模拟
本研究基于30块固定样地, 首次运用3-PG过程模型探索了吉林省四平、临江、白山等地10个林区长白落叶松人工林NPP水平, 并模拟分析一个轮伐期(40年)内NPP的变化。发现3-PG模型模拟的NPP与样地实测NPP极显著相关(R2 = 0.86, p < 0.001), ME为-8.52 g·m-2·a-1, MRE为-1.50%, RMSE为65.08 g·m-2·a-1, RRMSE为11.26%, 可见模型对NPP的估算较准确, 且具有一定的统计可靠性。预测的NPP变动范围是272.79-844.80 g·m-2·a-1, 平均值为578.12 g·m-2·a-1, 与何丽鸿等(2016)的结果(286.60-566.27 g·m-2·a-1, 平均值为477.74 g·m-2·a-1)接近。误差产生的原因可能有: 长白落叶松属落叶树种, 参数设置仅考虑了树种的年凋落速率, 并未针对特定的季节设置落叶参数; 气象数据采用距离样地最近的气象站数据; 模型本身并未考虑自然灾害(如风害、病虫害、大气污染等)对林分生长的影响, 然而这些因素会在很大程度上限制林分的正常生长; 树木的生理生态学过程十分复杂, 到目前为止, 我们对许多过程知之甚少; 在小尺度范围内, 尤其是在山区, 林地的生长环境(如海拔、地形、地貌等)会不同程度地影响林分NPP的大小及分配, 而模型在模拟时并未充分考虑这些因素。这些方面将在后续的工作中完善, 以更准确地模拟林分生产力。NPP随年龄增加的变化趋势表现为先增加较快, 达到最大值后, 逐渐降低。因此, 3-PG模型的模拟结果具有较好的生物合理性, 并能灵敏地反映不同地区的差异。在大尺度上NPP的空间分布及时间序列上的动态变化不可能通过实测的方法获得, 但机理模型与遥感反演手段的结合应用可以获得NPP的时空变化及分布特征。因此模型就成为研究NPP时空动态不可或缺的手段(Wang et al., 2013)。上述研究结果说明3-PG过程模型在模拟吉林省长白落叶松林NPP的时空动态上有着良好的潜力。考虑到这一区域在气候变化和森林碳汇研究中的重要性, 有必要进一步加强这方面的研究。未来, 我们将在扩大样本量的基础上, 尝试3-PG过程模型与遥感反演技术的结合, 模拟抚育间伐和施肥等营林措施的影响。
3.2 参数敏感性分析
本研究发现: tSLA、kF和Topt是3-PG模型的重要参数。它们不同程度地影响着模型对长白落叶松人工林NPP的预测。这与以往的部分研究结果一致, 如Zhao等(2009)基于3-PG模型较好地模拟了中国杉木NPP随林龄的动态变化, 证实kF及Topt是影响3-PG模型预测的重要参数。Potithep和Yasuoka (2011)研究发现: 最大树冠量子效率、平均温度、Topt是影响落叶阔叶林生物量生产和分配的主要参数。Paul等(2007)基于3-PG模型较好地完成对桉树(Eucalyptus robusta)胸径的模拟分析, 发现Topt、NPP分配到根的比例及叶片凋落速率是影响模型预测林分胸径的重要参数。3.3 长白落叶松人工林NPP对气候变化的响应
温度和降水作为两大主要的环境因子, 对陆地生态系统植被的生长和碳积累有重要的影响。本研究发现: 长白落叶松人工林NPP与生长季的降水量正相关, 与生长季温度负相关, 这与吴玉莲等 (2014)、冯险峰等(2014)的结论基本一致。3-PG模型模拟结果表明: 温度及降水对长白落叶松人工林的影响不同。具体表现为: 单独温度升高时, 长白落叶松林NPP较原来有所降低。温度升高对NPP的影响表现为先增加后降低, 且温度升高对NPP的负效应大于温度降低对NPP的正效应。单独降水量增加时, 长白落叶松林NPP有小幅度增加。诸多****的研究证实影响森林生态系统NPP水平及格局的主导因素是降水量(王玉辉等, 2001; 苏薇等, 2012), 本文的研究结果表明, 降水量较多的月份, NPP积累也相对较大。同时, 温度和降水的协同增加不利于NPP的积累。除了温度和降水的变化, 大气CO2浓度增加是气候变化的另一个重要方面。关于CO2浓度的增加对森林NPP的影响, 一直都存在较大的争议(Peng et al., 2009)。本文的研究结果表明: 单独CO2浓度升高有利于长白落叶松林NPP的积累, 且CO2浓度越高, NPP增加的幅度越大。CO2浓度每增加1 mg·L-1, NPP增加2.6-3.5 g·m-2·a-1。这与一些研究结果(王秀云等, 2011; Chen et al., 2016; 何丽鸿等, 2016)一致。
综上所述, 在未来RCP2.6、RCP6.0和RCP8.5排放情景下, 当CO2浓度、降水量及温度同时升高时, 长白落叶松林NPP积累增加, 且CO2浓度及降水量对落叶松人工林NPP的正效应大于温度升高对其产生的负效应。诸多研究结果证实, 未来气候变化将导致我国东北地区森林的NPP明显增加(Peng et al., 2009, Peng & Dan, 2015; Hao et al., 2016)。
4 结论
通过本地参数化后的3-PG模型模拟的长白落叶松林NPP为272.79-844.80 g·m-2·a-1, 与基于样地实测的NPP具有很好的一致性。同时, 3-PG模型能够较好地模拟长白落叶松林NPP随林龄的动态变化, NPP呈现先增加后减小的规律。温度、降水和CO2浓度影响长白落叶松的NPP。长白落叶松林NPP对生长季的温度、降水的响应明显不同, 而这种差异将对长白落叶松林应对未来气候变化产生重要影响。在未来CO2浓度、温度及降水同时增加的情景下, 长白落叶松林NPP明显增加。单独增加温度会减小长白落叶松林NPP, 而降水及CO2浓度增加能够在一定程度上促进NPP的增加, 但降水的正效应明显弱于温度的负效应。参数敏感性分析表明: Topt、tSLA、kF是3-PG模型的关键参数。研究结果可为应对未来气候变化的长白落叶松经营管理提供依据。Appendix I
附录I
附录I 长白落叶松人工林样地概况
Appendix I Basic information of the Larix olgensis plantation stands
| 样地号 Plot No. | 海拔 Elevation (m) | 土壤深度 Soil depth (cm) | 坡向 Aspect (°) | 坡位 Position | 坡度 Slope (°) | 林龄 Stand age (a) | 林分密度 Stand density (stems?hm-2) |
|---|---|---|---|---|---|---|---|
| 四平-1 Siping-1 | 260 | 30 | 6 | 2 | 4 | 31 | 950 |
| 四平-2 Siping-2 | 319 | 30 | 9 | 6 | 1 | 25 | 1 783 |
| 四平-3 Siping-3 | 280 | 35 | 8 | 2 | 10 | 30 | 1 383 |
| 临江-1 Linjiang-1 | 876 | 30 | 9 | 6 | 3 | 13 | 1 950 |
| 临江-2 Linjiang-2 | 676 | 40 | 2 | 2 | 8 | 16 | 1 000 |
| 临江-3 Linjiang-3 | 880 | 26 | 4 | 3 | 5 | 27 | 983 |
| 白山-1 Baishan-1 | 600 | 15 | 2 | 4 | 10 | 8 | 183 |
| 白山-2 Baishan-2 | 510 | 25 | 8 | 3 | 5 | 8 | 133 |
| 白山-3 Baishan-3 | 680 | 25 | 1 | 2 | 16 | 16 | 533 |
| 龙井-1 Longjin-1 | 660 | 25 | 3 | 2 | 32 | 28 | 633 |
| 龙井-2 Longjin-2 | 630 | 45 | 2 | 2 | 6 | 12 | 633 |
| 龙井-3 Longjin-3 | 630 | 29 | 7 | 3 | 15 | 11 | 333 |
| 辽源-1 Liaoyuan-1 | 300 | 37 | 4 | 5 | 5 | 24 | 633 |
| 辽源-2 Liaoyuan-2 | 380 | 35 | 1 | 2 | 8 | 34 | 1 983 |
| 辽源-3 Liaoyuan-3 | 380 | 20 | 3 | 3 | 10 | 25 | 1 500 |
| 和龙-1 Helong-1 | 510 | 45 | 2 | 2 | 14 | 17 | 1 050 |
| 和龙-2 Helong-2 | 500 | 32 | 2 | 4 | 11 | 16 | 3 516 |
| 和龙-3 Helong-3 | 751 | 40 | 8 | 4 | 10 | 11 | 1 000 |
| 舒兰-1 Shulan-1 | 230 | 51 | 8 | 4 | 10 | 17 | 1 800 |
| 舒兰-2 Shulan-2 | 280 | 51 | 8 | 4 | 6 | 25 | 1 617 |
| 舒兰-3 Shulan-3 | 310 | 51 | 6 | 4 | 15 | 17 | 2 150 |
| 通化-1 Tonghua-1 | 580 | 42 | 2 | 4 | 20 | 16 | 900 |
| 通化-2 Tonghua-2 | 710 | 50 | 2 | 2 | 17 | 17 | 2 267 |
| 通化-3 Tonghua-3 | 512 | 50 | 4 | 3 | 20 | 15 | 900 |
| 汪清-1 Wangqing-1 | 390 | 40 | 5 | 3 | 3 | 17 | 2 250 |
| 汪清-2 Wangqing-2 | 510 | 51 | 8 | 4 | 5 | 20 | 950 |
| 汪清-3 Wangqing-3 | 390 | 40 | 5 | 3 | 3 | 18 | 2 241 |
| 长春-1 Changchun-1 | 338 | 30 | 5 | 1 | 5 | 26 | 1 367 |
| 长春-2 Changchun-2 | 290 | 32 | 8 | 4 | 20 | 19 | 1 567 |
| 长春-3 Changchun-3 | 240 | 35 | 9 | 1 | 0 | 26 | 1 050 |
Slope aspect: 1, northern slope; 2, northeastern slope; 3, eastern slope; 4, southeastern slope; 5, southern slope; 6. southwestern slope; 7, western slope; 8, northwestern slope; 9, no slope direction.Slope position: 1, ridge; 2, up slope position; 3, middle slope position; 4, lower slope position; 5, valley; 6, flat ground.
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| [1] | An important requirement for commercialization of willow biomass production in short-rotation crop (SRC) plantations is the reliable and cost-efficient estimation of biomass yield. Predictions and simulations of willow stand biomass have been problematic due to issues with modeling the multi-stem growth form of willow. The aim of this paper was to develop a new approach for managing allometric measurements from multi-stemmed willow for stand growth simulations. The 3PG model (Physiological Principles in Predicting Growth) was parameterized for willow and was used for biomass yield simulation for an entire 22-yr cycle (seven 3-yr rotations) of willow in SRC plantations. The multi-stemmed growth form was transformed into a single-stem modeling form by deriving whole plant willow allometric relationships using detailed stem-level measurements of basal area, stem biomass and volume. 3PG model predictions for plant diameter, height, biomass, and stand biomass and volume were within the 95% confidence range of mean plot values. Model simulations showed that after seven 3-yr rotations only 20% of planted cuttings would survive (a decrease from 15,152 to 3022 plantsha 611 ), but stand volume would increase continuously with each subsequent rotation. 3PG predictions for cumulative (for 22yr) aboveground biomass was 272Mgha 611 and mean annual yield was 12Mgha 611 yr 611 , comparing favorably with other findings. To our knowledge, this work is the first where the 3PG model was calibrated and used for willow species. Once parameterized for a specific willow clone, 3PG can predict biomass accumulation for any agricultural land in North America using only available soil and climate data. |
| [2] | , 长白落叶松(Larix olgensis Henvy)是我国东北北部、东部主要造林树种之一。据统计,仅吉林省就有落叶松人工林37万公顷,约占余省人工林总面积的65%以上。目前,这些人工林除少数可进行主伐利用外,大多数林分正处在中、幼龄阶段,其生长发育状况,受生态因子的制约十分突出。要充分发挥落叶松速生优势,必须找出影响其生长的主导因子,并采取合理的经营措施。为此,笔者采用典型相关分析方法,应用TQ-16型电子计算机,对影响吉林省内长白落叶松人工林生长的诸多生态因子进行了研究。 |
| [3] | Climate change, particularly the combined effects of elevated CO 2 and temperature, is likely to alter the internal nitrogen (N) cycle of agricultural ecosystems. However, little is known about such phenomena in paddy soils, which are expected to expand in the near future due to population increase. A 15 N tracer study, with soil taken from field manipulation treatments, showed that elevated CO 2 , either alone or combined with elevated temperature, stimulated the mineralization of labile organic N 37-fold but decreased the mineralization of recalcitrant organic N. In contrast, elevated temperature alone accelerated the mineralization of recalcitrant organic N approximately 2-fold but had no effect on the mineralization of labile organic N. Ammonium immobilization increased under elevated CO 2 and elevated temperature. Gross and net NO 3 61 NO 3 61 mathContainer Loading Mathjax production decreased under elevated CO 2 and the combined treatments, whereas elevated temperature caused an increase in both rates. Dissimilatory reduction of NO 3 61 NO 3 61 mathContainer Loading Mathjax to NH 4 + NH 4 + mathContainer Loading Mathjax increased under elevated CO 2 but decreased with elevated temperature. Our findings suggest that progressive N limitation can be alleviated by increasing gross N transformation rates under each climate change treatment and that counteraction will dominate the interactive responses of CO 2 and temperature. Because we expect a concomitant increase in both CO 2 and temperature, we only expect minor effects of these particular factors arising as a result of climate on soil N dynamics in paddy soils. |
| [4] | Gross primary production (GPP), the photosynthetic uptake of carbon, is an important variable in the global carbon cycle. Although continuous measurements of GPP are being collected from a network of micro-meteorological towers, each site represents a small area with records available for only a limited period. As a result, GPP is commonly modeled over forested landscapes as a function of climatic and soil variables, often supplemented with satellite-derived estimates of the vegetation's light-absorbing properties. Since the late 1990s, a number of models have been developed to provide seasonal and annual estimates of GPP across much of the Earth. Each model, however, contains different underlying assumptions and requires different amounts of data. As a result, predictions vary, sometimes significantly. In this paper we compare modeled estimates of GPP for forested areas across the U.S.A. derived from: NASA's MODIS Product (MOD17); the C-Fix model using SPOT-VGT satellite-derived vegetation data; and the Physiological Principles Predicting Growth from Satellites (3-PGS) model, a process-based model that requires information on both climate and soil properties. The models predicted average ecoregion values of forest GPP between 9.8 and 14.1MgC ha 1 y 1 across the United States. 3-PGS predicted the lowest values while the C-Fix model, which included a CO 2 fertilization factor, produced the highest estimates. In the western part of the country, estimates of GPP within a given ecoregion varied by as much as 50%, whereas in the northeast, where topography and climate are less extreme, variation in GPP was less than 10%. Within ecoregions, 3PGS predicted the most variation, reflecting its sensitivity to variation in soil properties. We conclude that where model predictions disagree, an opportunity is presented to evaluate underlying assumptions through sensitivity analyses, additional data collection and where more detailed study is warranted. |
| [5] | Lodgepole pine ( Pinus contorta Dougl.) is a widely distributed species in the Pacific Northwest of North America. The extent that the current distribution of this species may be altered under a changing climate is an important question for managers of wood supply as well as those interested in conservation of subalpine ecosystems. In this paper, we address the question, how much might the current range of the species shift under a changing climate? We first assessed the extent that suboptimal temperature, frost, drought, and humidity deficits affect photosynthesis and growth of the species across the Pacific Northwest with a process-based model (3-PG). We then entered the same set of climatic variables into a decision-tree model, which creates a suite of rules that differentially rank the variables, to provide a basis for predicting presence or absence of the species under current climatic conditions. The derived decision-tree model successfully predicted weighted presence and absence recorded on 12,660 field survey plots with an accuracy of ~70%. The analysis indicated that sites with significant spring frost, summer temperatures averaging <15$\circ$C and soils that fully recharged from snowmelt were most likely to support lodgepole pine. Based on these criteria, we projected climatic conditions through the twenty-first century as they might develop without additional efforts to reduce carbon emissions using the Canadian Climate Centre model (CGCM2). In the 30-year period centered around 2020, the area suitable for lodgepole pine in the Pacific Northwest was projected to be reduced only slightly (8%). Thereafter, however, the projected climatic conditions appear to progressively favor other species, so that by the last 30 years of twenty-first century, lodgepole pine could be nearly absent from much of its current range. We conclude that process-based models, because they are highly sensitive to seasonal variation in solar radiation, are well adapted to identify the importance of different climatic variables on photosynthesis and growth. These same variables, once indentified, and run through a decision-tree model, provide a reasonable approach to predict current and future patterns in a species distribution. |
| [6] | , 基于SPOT NDVI遥感数据并结合数字高程模型、气象数据和植被参数,利用实测植被生产力计算和修正最大光能利用率,通过改进CASA过程模型,估算了中国西南喀斯特地区1999—2003年的植被净第一性生产力(NPP)。结果表明:(1)改进后的CASA模型模拟的植被NPP与实测值相关性显著,可较好用于西南喀斯特植被的NPP估算;(2)西南8省市区1999—2000年喀斯特和非喀斯特植被的NPP有轻度增加,但空间变化不显著,2001年低值区范围增加,2002年NPP高值区的范围明显扩大,随后在2003年又降低,但仍高于2001年;(3)5a间西南喀斯特地区年NPP的变化范围是381.7—439.9 gC m-2 a-1,平均值为402.34 gC m-2 a-1,逐年NPP波动中呈现总体增长趋势,平均增加值为9.93 gC m-2 a-1,5a总增加量为11TgC,但非喀斯特地区的年NPP平均值和增加值都大于喀斯特地区;(4)5a间喀斯特地区的热带森林、亚热带森林、灌丛和草地的逐年NPP均小于非喀斯特地区,温带森林和农业植被则相反;这6种典型植被年NPP均呈增加趋势,热带森林的增加值最大,草地最小,非喀斯特地区植被NPP的增长趋势相似,但每种植被的年NPP增加值均大于喀斯特地区。西南喀斯特地区植被NPP的时空变化与气温、降水和太阳辐射的变化有关,而喀斯特植被NPP低于非喀斯特地区,则主要由喀斯特地区水分匮缺、土壤贫瘠等恶劣条件而抑制植物生长造成的。 |
| [7] | , IPCC第五次评估报告(AR5)~①中关于长期气候变化的预估主要基于全球耦合模式比较计划第五阶段(CMIP5)的46个地球系统模式结果,在对模式、情景及不确定性介绍的基础上,给出了21世纪及其后更远时期的气候变化预估结果。与第四次评估报告(AR4)及全球耦合模式比较计划第三阶段(CMIP3)不同的是,AR5预估所使用的温室气体排放情景为典型浓度路径(RCP,AR4主要使用的是SRES),但在相似温室气体浓度的情况下,两者给出的未来气候变化结果差别不大。 |
| [8] | Forest plantations for wood production are an increasingly important land use in southern Australia, and there are potentially important hydrologic consequences of what is mostly a change in land use from agriculture to silviculture. An ability to predict, with some degree of accuracy, the impact of plantation expansion on surface water and groundwater resources is essential. A validated process-based modelling approach, integrating the many interacting environmental and management factors which may influence plantation growth and transpiration, can be used for this purpose. The 3PG forest growth model has been evaluated for a number of species from widely differing climate and site conditions. While growth predictions have been validated, little attention has been given to testing the accuracy of the transpiration predictions or the model's representation of the water balance. We enhanced the 3PG forest growth model (known as 3PG+) and then integrated it into the Catchment Analysis Tool (CAT), so that it now interfaces with a more detailed multi-layered, daily time step representation of the soil water balance. Simulated transpiration using 3PG+ in CAT was compared with field measurements in 30 plots (across 15 sites) representing 5 common plantation species ( Eucalyptus globulus , E. nitens , E. grandis , E. regnans and Pinus radiata ) across ages 2–31 years. Mean daily plot transpiration during the measurement periods ranged between 0.4 and 4.202mm02day 611 (average 2.002mm02day 611 ). Simulated mean daily plot transpiration using 3PG+ in CAT for Eucalyptus was good (coefficient of efficiency02=020.80; R 2 02=020.81). While the model tended to slightly under-predict transpiration at higher measured rates (>3.502mm02day 611 ), predictions at monthly timescales had acceptable accuracy. The integration of 3PG+ into CAT resulted in an improvement in accuracy and applicability of CAT, and provides for the spatial application of 3PG+ across diverse and mixed land use catchments for investigation into carbon and water movement in forest systems. |
| [9] | , 植被净第一性生产力（NPP）不仅是表征植被活动和生态过程的关 键参数，而且是判定生态系统碳汇和反映生态系统对全球变化响应的主要因子。当前,模型模拟成为大尺度NPP研究的主要手段，而在众多NPP估算模型中，过 程模型逐渐趋于主导地位。虽然目前有关NPP的研究有很多，但还没有关注于大尺度上应用的过程模型及其模拟的NPP对全球变化的响应。因此本文主要侧重于 NPP 过程模型在区域及全球尺度上的应用，具体包含以下内容，①进一步将区域及全球尺度的NPP过程模型分为静态植被模型和动态植被模型。②阐明这些模型间存在 的区别与联系。③归纳出NPP过程模型在区域及全球尺度上应用的3大挑战：时空尺度转换、多源数据的获取与融合以及模型模拟结果的验证与评价，并根据其解 决方案总结出通用的模型应用框架。④从气候变化、大气成分变化和土地利用/土地覆盖变化3个方面探讨NPP对全球变化的响应机制，以期找到NPP变化的规 律与模式。最后根据NPP模型的发展对未来区域及全球尺度的NPP过程模型进行展望，认为未来模型的综合性将更高，机理性也将更强，同时与全球变化研究结 合得更加紧密，且基于多个已有模型的混合模型也是未来NPP模型发展的一个重要方向。此外，本文认为对NPP模拟结果的尺度效应研究也是未来NPP研究的 热点之一。 |
| [10] | The forest simulation model, 3-PG, has been widely applied as a useful tool for predicting growth of forest species in many countries. The model has the capability to estimate the effects of management, climate and site characteristics on many stand attributes using easily available data. Currently, there is an increasing interest in estimating biomass and assessing the potential impact of climate change for slash pine ( Pinus elliottii Engelm. var. elliottii ), a commercially important tree species of the southeastern U.S. The 3-PG model had not been previously parameterized for this species. Using data from the literature and long-term productivity studies, we parameterized 3-PG for slash pine stands, developing new functions for estimating biomass pools at variable starting ages, canopy cover dynamics, allocation dynamics, density-independent tree mortality and the fertility rating. The model was tested against data from measurement plots covering a wide range of stand characteristics (age, productivity and management), distributed within and beyond the natural range of the species, including stands in Uruguay, South America. Across all tested sites, estimations of survival, basal area, height, volume and above-ground biomass agreed well with measured values. The bias was small and generally less than 7%. This paper reports the first set of 3-PG parameter estimates for slash pine, showing new methodologies to determine important estimates. The model can be applied to stands growing over a large geographical area and across a wide range of ages and stand characteristics. |
| [11] | Abstract The feasibility of using plantation-grown biomass to fuel bioenergy plants is in part dependent on the ability to predict the capacity of surrounding forests to maintain a sustainable supply. In this study, the potential productivity of Eucalyptus nitens (Deane and Maiden) Maiden plantations grown for bioenergy in a region of north-west Spain was quantified using the 3-PG process-based model. The model was calibrated using detailed measurements from five permanent sample plots and validated using data from thirty-five additional permanent sample plots; both sets represented the variability of climate and soils of the region. Plot scale analysis showed that the model was able to reasonably estimate above-ground biomass and water use when compared with the observed data. Using a representative loam soil characteristic, a spatial analysis was then carried out to predict the potential productivity of E. nitens for bioenergy across a potential area for plantation establishment of 2550 km2 and to evaluate different management scenarios related to rotation length and stocking. An increase of only 1.9% in mean annual increment ( MAI ) of above-ground biomass ( W AGB) was found between stockings of 3000 and 5000 trees ha 1; for the lower stocking, MAI of W AGB increased 4% for rotation lengths between 6 and 8 years. Production was reduced by low summer rainfall and to a lesser extent by high summer and low winter temperatures, and vapour pressure deficit. Above-ground biomass production was higher by around 12% when average rather than actual climate data were applied. The information from this study can be used to optimize forest management, determine regional relative potential productivity and contribute to decision-making for bioenergy production from E. nitens plantations in north-west Spain. |
| [12] | The method was tested against data from permanent sample plots of the Finnish National Forest Inventory (NFI) from years 1985 and 1995. The results indicate that the approach produces realistic short-term estimations for stem growth. At the stand level the model was nearly unbiased (2.1% underestimation), with RMSE of 34% and R 2 of 0.52, and it provided a clearly better fit than a simple linear prediction of stem growth from the estimated GPP. More importantly, we showed in a model comparison that in the present data set our model provided results of similar accuracy as a well-established empirical tree-level growth model. |
| [13] | The issue of climate change has become an increasing concern for the international community. At the same time, climate change and, particularly, global warming affects the amount of energy required in China and the structure of this demand, thereby influencing the amount of CO 2 (carbon dioxide) emissions produced. To investigate the possible influence of climate change on China's CO 2 emissions, this paper utilizes the degree-day method to calculate the heating degree days (HDD) and cooling degree days (CDD) in 29 provinces in China from 1995 to 2011. A series of appropriate econometric models are subsequently estimated to measure the impact of climate change on CO 2 emissions per capita. To enhance the explanatory power of the estimation results, several indicators, such as income per capita, industry structure, urbanization rate and population density, are incorporated in the estimation equations as control variables. The estimation results indicate that the feedback effect of climate change on China's carbon emissions is statistically significant but not large in magnitude. During the sample period between 1995 and 2011, approximately 1.687% of China's increased total CO 2 emissions could be attributed to climate change. Moreover, the impacts of climate change on different regions of China are notably different. For example, the influence of HDD on CO 2 emissions per capita are most significant in the middle and southern regions, while the impacts of CDD are most significant in the eastern and northern regions. However, there is no obvious evidence that climate change has affected CO 2 emissions in the western region. |
| [14] | , 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. |
| [15] | , 3-PG模型是一个应用气候、立地条件、经营措施和树木生理特性来模拟森林生长的机理模型,在国外被广泛应用于森林经营.为了准确、快速预测速生尾叶桉人工林生产力,该文运用3-PG模型对我国广东省雷州半岛上广泛种植的尾叶桉的生长规律进行研究.用纪家林场尾叶桉标准地4年的观测数据来校正模型参数,用河头林场尾叶桉生长数据来验证模型在新立地条件下的性能.模型校正结果中,林分材积、树高和胸径的模拟平均精度都超过92%,相关系数超过0.93;除树根外林分生物量和叶面积指数的模拟精度都超过83%;观测的树根生物量比模型值偏低40%,主要原因是树根系统非常庞大复杂,远远超出了我们所能挖掘的深度,因此测定粗根和细根的生物量都会偏低.模型验证结果中,林分材积、树高和胸径的平均模拟精度都超过94%,相关系数达到0.98以上.研究结果表明:3-PG模型是预测桉树人工林生产力的一种有效工具,一旦模型被参数化便可应用于不同地区,而且其模拟精度和可靠性是令人满意的. |
| [16] | , 利用东北三省70个基本气象站1961-2006年逐日平均气温,最高气温,最低气温,降水量,日照时数资料,分析了东北地区近46年的气候变化特征。结果表明：全区近46年平均气温线性增温速率为0.36℃/10 a,其中冬季增温最显著,其次为春季,秋季,夏季,地域特征表现为黑龙江省气温升高幅度最大,其次为吉林省,辽宁省。以1987年为界划为两个时期,此前为冷期,此后为暖期,1980年代末发生了显著升温过程,此后一直为持续升温。全区最低气温增温速率为0.51℃/10a,最高气温为0.24℃/10a,最低气温的增温速率是最高气温增温速率的2倍左右。降水量变化不明显,整体上有减少的趋势,日照时数整体呈现出逐渐减少趋势,减少速率为0.11h/10a。 |
| [17] | Copyright (c) 1997 Elsevier Science B.V. All rights reserved. This paper describes a stand growth model, based on physiological processes, which incorporates a number of steps and procedures that have allowed considerable simplification relative to extant process-based models. The model, called 3-PG (use of Physiological Principles in Predicting Growth), calculates total carbon fixed (gross primary production; P |
| [18] | , |
| [19] | , 采用LI-6400便携式光合测定系统测定了黑龙江省佳木斯市孟 家岗林场不同立地条件、不同林龄组长白落叶松的光-光合速率曲线.结果表明,长白落叶松最大净光合速率、光补偿点、光饱和点和表观量子效率受针叶朝向(阳 生叶和阴生叶)、立地条件和林龄的影响而表现出不同:除近熟林立地指数为15的阳生叶和阴生叶在最大净光合速率和光补偿点方面存在显著差异外,其余林龄相 同立地指数的阳生叶和阴生叶在最大净光合速率、光补偿点、光饱和点和表观量子效率方面均无显著差异;幼龄林、中龄林和近熟林的阳生叶和阴生叶在最大净光合 速率、光补偿点、光饱和点和表观量子效率方面受立地条件的影响均不显著;阳生叶和阴生叶在最大净光合速率、光补偿点、光饱和点和表观量子效率方面受林龄的 影响均不显著. |
| [20] | Abstract Climate change is highly likely to impact on forest productivity over the next century. The direction and magnitude of change are uncertain because many factors are changing simultaneously, such as atmospheric composition, temperature, rainfall, and land use. Simulation models have been widely used to estimate how these interacting factors might combine to alter forest productivity. Such studies have used many different types of models with different underlying assumptions. To evaluate predictions made by such studies, it is essential to understand the type of model and the assumptions used. In this article, we provide a checklist for use when evaluating modeled estimates of climate change impacts on forest productivity. The checklist highlights the assumptions that we believe are critical in determining model outcomes. Models are classified into different general types, and assumptions relating to effects of atmospheric CO 2 concentration, temperature, water availability, nutrient cycling, and disturbance are discussed. Our main aim is to provide a guide to enable correct interpretation of model projections. The article also challenges modelers to improve the quality of information provided about their model assumptions. WIREs Clim Change 2011 2 332~355 DOI: 10.1002/wcc.108 For further resources related to this article, please visit the WIREs website |
| [21] | In Australia, tools are required to assess likely commercial and environmental benefits of growing saw logs in regions of low-medium rainfall. The widely used 3-PG model was considered appropriate given that it has a relatively low demand for data and can be applied spatially. Two species of high potential for saw log production in such regions are Eucalyptus cladocalyx (sugar gum) and Corymbia maculata (spotted gum). For both of these species, we harvested 12 trees over a range of sizes (from 4 to 52 cm diameter at breast height) to derive 3-PG parameters, including stem density, specific leaf area, relationships between stem diameter and mass, and the branches and bark fraction. We also collated stem diameter data together with data on stocking and survival from 55 stands of E. cladocalyx , and 37 stands of C. maculata . By simulating growth at each of these sites using 3-PG, we calibrated parameters determining sensitivity to frost, optimum temperature for growth, and rates of litterfall to attain an efficiency of prediction of stem diameter of 80% for both species. It was also ensured that prediction of biomass of tree components were in the order of that calculated based on allometric relationships, although efficiencies of these predictions were particularly poor for foliage. We found 3-PG gave a 9 34% greater efficiency of prediction of stem diameter than when using stand age alone as an explanatory variable, indicating that even when only the most basic of site information is available (i.e. rainfall, temperature, solar radiation, number of frost days as well as some information regarding stocking and management), 3-PG can potentially account for some variations in growth across a wide range of sites. Accuracy of 3-PG predictions could be further improved by better quantifying site data (i.e. fertility rating, soil texture and available water) and improving model structure to better predict soil water availability, survival, and partitioning of biomass between tree components. |
| [22] | In this study, we report on the validation of process-based forest growth and carbon and nitrogen model of TRIPLEX against observed data, and the use of the model to investigate the potential impacts and interaction of climate change and increasing atmospheric CO 2 on forest net primary productivity (NPP) and carbon budgets in northeast of China. The model validation results show that the simulated tree total volume, NPP, total biomass and soil carbon are consistent with observed data across the Northeast of China, demonstrating that the improved TRIPLEX model is able to simulate forest growth and carbon dynamics of the boreal and temperate forest ecosystems at regional scale. The climate change would increase forest NPP and biomass carbon but decrease overall soil carbon under all three climate change scenarios. The combined effects of climate change and CO 2 fertilization on the increase of NPP were estimated to be 10~12% for 2030s and 28~37% in 2090s. The simulated effects of CO 2 fertilization significantly offset the soil carbon loss due to climate change alone. Overall, future climate change and increasing atmospheric CO 2 would have a significant impact on the forest ecosystems of Northeastern China. |
| [23] | Based on the simulations of the fifth phase of the Coupled Model Intercomparison Project (CMIP5), we estimated the response of net primary production (NPP) and net ecosystem production (NEP) to rising atmospheric CO 2 concentration and climate change on global and regional scales. The modeled NPP and NEP significantly increased by about 0.402Pg02C02yr 612 and 0.0902Pg02C02yr 612 , respectively, in response to the rising atmospheric CO 2 concentration. However, adverse trends of the two variables were driven by climate change on a global scale. Regarding the spatial pattern, the decreases were mainly located in tropical and temperate regions. Thus, the terrestrial carbon sink was accelerated not only by a rising atmospheric CO 2 concentration, but also by global warming at high latitude and altitude regions, e.g. Tibet and Alaska. Although the simulations indicated increases of NPP and NEP owing to the CO 2 fertilization effect, the strength of the trends significantly differed from the CMIP5 models. The enhanced trend in the terrestrial carbon sink simulated by MPI-ESM-LR was about 47 times larger than that simulated by CESM-BGC considering the CO 2 fertilization effect. Differences in the modeled responses of NPP and NEP resulted from the various processes of the land surface component accounting for the nitrogen limitation effect and plant functional types (PFTs). We also found that the difference in the accelerating terrestrial carbon loss forced by global warming between CMIP5 models, ranged between 6.002Tg02C02yr 612 in CESM-BGC and 52.702Tg02C02yr 612 in MPI-ESM-LR. Such a divergence was partially responsible for the difference in the simulated climate between the CMIP5 models: the difference in increasing temperature was about 1.402K. |
| [24] | The physiological principles predicting growth (3-PG) model is generally used to estimate gross primary productivity (GPP) in forest plantations. All existing parameter values in the 3-PG model for GPP estimation have been set as the standard values for eucalyptus and pine plantations. We propose that the 3-PG model can be applied to deciduous broadleaf forests dominated by Betula platyphylla via appropriate parameterization of their structure and functions. The allometric relationships between stem biomass and stem diameter, and between foliage biomass and stem biomass, were determined for the biomass partitioning ratio. Additionally, a temperature modifier was considered appropriate because it affected canopy quantum efficiency. After parameterization, the model showed a good correlation between the estimated results and the data from experimental plots in central and northern Japan. At both sites, GPP peaked around August and was 0 during the winter, when the canopy is bare of leaves. Furthermore, a sensitivity analysis was conducted to determine the most influential parameter relative to the output. GPP was sensitive to changes in canopy quantum efficiency and optimum temperature. Among the meteorological data used, solar radiation and temperature had great impacts on GPP, therefore, these parameters should be carefully considered to produce accurate results. |
| [25] | The following conclusions are drawn: 3-PG can provide a good simulation of future growth of intensively-managed, fertilised stands of E. globulus if the model is initialised with observed biomass data at some age around or following canopy closure. If the model is initialised with typical seedling biomass at planting, 3-PG adequately predicts stem growth rate but not canopy LAI. Further development of 3-PG should take into account possible environmental effects on litterfall, the effects of partial canopy closure during early canopy development, and the prediction of mortality prior to the onset of self-thinning. |
| [26] | No Abstract available for this article. |
| [27] | , 通过测定三江平原丘陵区13 ～40年生长白落叶松人工林中55株标准木的针叶生物量,建立了单本针叶生物量模型,结合每木检尺结果和比叶面积,估算和建立了不同立地条件(立地指数 11～17)、不同林龄(13 ～40a)和不同密度(556 ～3122株·hm-2)人工林的针叶生物量、叶面积指数和叶面积指数模型,给出了植被冠层分析仪(LAI-2000)间接测定叶面积指数时的校正系数和 校正系数模型.结果表明:长白落叶松比叶面积为12.93m2·kg-1,95％的区间估计为[12.23,13.63]m2·kg-1,单木针叶生物量 模型为幂函数模型,叶面积指数为5.76 ～11.04,针叶生物量为4455.52～8 535.69 kg·hm-2；LAI-2000测得的叶面积指数为1.77～4.02,低于实测叶面积指数,LAI-2000测量长白落叶松人工林叶面积指数的校正系 数为1.45 ～3.63.林龄、密度、立地条件及其综合作用能够影响长白落叶松人工林叶面积指数和LAI-2000测量叶面积指数时的校正系数,它们能够解释叶面积指 数和校正系数变异的99.9％. |
| [28] | , 应用BIOME-BGC模型模拟估算了1974—2010年间北京百花山华北落叶松林的净初级生产力（NPP）,并分析了不同CO2浓度和气候变化情景对NPP的影响。结果表明：模型模拟出的NPP总体上高于样地实际的测定值,平均值相差范围为-13.61%～23.55%,表现出数值的波浪形年际变化,年际变动率达4.65%;相对于温度变化,降水量是控制华北落叶松林NPP年际变化的主要气候因子;华北落叶松林NPP对单独的CO2浓度加倍、降水增加表现出正向响应,而单独的温度增加不利于华北落叶松林NPP的积累;CO2浓度加倍、降水增加和温度增加三因子共同作用有促进华北落叶松NPP增加的作用,各因子之间表现出较强的交互作用。 |
| [29] | Soil fertility is an important component of forest ecosystems, yet evaluating soil fertility remains one of the least understood aspects of forest science. We hypothesized that the fertility rating (FR) used in the model 3-PG could be predicted from site index (SI) for loblolly pine in the southeastern US and then developed a method to predict FR from SI to test this hypothesis. Our results indicate that FR values derived from SI when used in 3-PG explain 89% of the variation in loblolly pine yield. The USDA SSURGO dataset contains SI values for loblolly pine for the major soil series in most of the counties in the southeastern US. The potential of using SI from SSURGO data to predict regional productivity of loblolly pine was assessed by comparing SI values from SSURGO with field inventory data in the study sites. When the 3-PG model was used with FR values derived using SI values from SSURGO database to predict loblolly pine productivity across the broader regions, the model provided realistic outputs of loblolly pine productivity. The results of this study show that FR values can be estimated from SI and used in 3-PG to predict loblolly pine productivity in the southeastern US. |
| [30] | , |
| [31] | , 为了对东北地区东部落叶松人工 林的多代经营提供指导,以黑龙江省孟家岗林场的长白落叶松人工林为对象,采用森林生态系统经营管理模型FORECAST,从轮伐期长度、林地枯落物的管理 和采伐剩余物的处理方面,评价不同经营措施下落叶松人工林的生物量、养分动态和长期生产力。结果表明:常规森林利用方式下维持落叶松人工林长期生产力的轮 伐期应大于35a;落叶松林地枯落物的保留可以显著提高各种轮伐期长度时的林地生产力,短轮伐期时作用效果尤为明显;全面保留采伐剩余物可以维持不同轮伐 期条件下落叶松人工用材林的长期生产力。 |
| [32] | |
| [33] | Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites ~ often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites ~ often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters. |
| [34] | Abstract Net primary productivity (NPP) is an important component of the carbon cycle and a key indicator of ecosystem performance. The aim of this study is to construct a more accurate regional vegetation NPP estimation model and explore the relationship between NPP and climatic factors (air temperature, rainfall, sunshine hours, relative humidity, air pressure, global radiation, and surface net radiation). As a key variable in NPP modeling, photosynthetically active radiation (PAR) was obtained by finding a linear relationship between PAR and horizontal direct radiation, scattered radiation, and net radiation with high accuracy. The fraction of absorbed photosynthetically active radiation (FPAR) was estimated by enhanced vegetation index (EVI) instead of the widely used normalized difference vegetation index (NDVI). Stress factors of temperature/humidity for different types of vegetation were also considered in the simulation of light use efficiencies (LUE). The authors used EVI datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2011 and geographic information techniques to reveal NPP variations in Wuhan. Time lagged serial correlation analysis was employed to study the delayed and continuous effects of climatic factors on NPP. The results showed that the authors' improved model can simulate vegetation NPP in Wuhan ef-fectively, and it may be adopted or used in other regions of the world that need to be further tested. The results indicated that air temperature and air pressure contributed significantly to the interannual changes of plant NPP while rainfall and global radiation were major climatic factors influencing seasonal NPP variations. A significant positive 32-day lagged correlation was observed between seasonal variation of NPP and rainfall (P , 0.01); the influence of changing climate on NPP lasted for 64 days. The impact of air pressure, global radiation, and net radiation on NPP persisted for 48 days, while the effects of sunshine hours and air temperature on NPP only lasted for 16 and 32 days, respectively. |
| [35] | , 在全球气候变化背景下,科学的经营管理是人工林碳汇提升的主要途径。合理轮伐期从一定程度上反映了人工林集约经营的理念,是实现森林结构调整的主要影响因素之一。杉木（Cunninghamia lanceolata）多代连栽出现立地生产力下降与轮伐期的选择密切相关,开展不同轮伐期对杉木人工林碳固存影响的研究,可为其可持续经营提供理论依据。通过设置不同年龄序列的杉木人工林野外观测样地,应用野外观测数据对FORECAST模型进行验证,在此基础上模拟不同轮伐期对其碳固存的影响。结果表明：（1）短轮伐期（15年）在150年间的总固碳量较高,但固碳持久性较低,每个轮伐期之间的固碳量下降幅度较大,是一种不可持续的经营模式。（2）正常轮伐期（25年）和长轮伐期（50年）的总固碳量低于短轮伐期,但长轮伐期固碳持久性更强,有利于维持每个轮伐期内固碳量的稳定。（3）在好的立地条件下（立地指数（SI）=27）,轮伐期越短对地力消耗影响越大,为了碳固存的持久性,建议杉木人工林的生态轮伐期选择在25年以上。（4）应用FORECAST模型可以定量地评估人工林的固碳能力,且该固碳能力是基于不同经营管理措施下的可持续固碳能力。 |
| [36] | , |
| [37] | , 丰富的森林资源清查资料是了解各类森林材积准确信息的重要途径 ,如果能将这些资源用于估算森林生物量和生产力的动态变化 ,不仅对于科学地指导森林的经营管理 ,而且对于全球变化的研究 ,特别是区域尺度的生产力模型验证 ,都具有重要意义。根据我国落叶松 (L arix)林生物量和材积的实际调查资料 ,探讨了基于森林资源清查资料 (森林材积 V和林龄 A)估算森林生物量和生产力的方法 ,指出无论是人工林还是天然林 ,落叶松林的生物量与其蓄积量、生产力与其年均净生物生产量 (B/ A)和年均净蓄积生产量 (V/ A)均呈双曲线关系 ,但落叶松林的生产力与其生物量 (B)关系不明显 ,并分别建立了人工和天然落叶松林的相关模型 ;所建模型克服了将森林生物量与其蓄积量之比作为常数的不足 ,并考虑了林龄对于森林生产力的影响。 |
| [38] | , |
| [39] | , 根据东北地区生态气候环境和生物地理规律对Holdridge生命地带分类系统进行修正,将东北地区植被分为寒温带湿润森林、寒温带潮湿森林、温带湿润森林、暖温带湿润森林、温带半湿润森林草甸草原、温带半湿润草甸草原、温带半干旱典型草原、暖温带半湿润草甸草原和暖温带半干旱典型草原等9个生命地带并分析了其空间分布特征.运用大气环流模式分析东北地区由于温室气体增加导致的气候变化趋势.以此为基础评价东北地区植被分布的区域响应.全球气候变暖情景下,东北地区暖温带和温带范围明显扩大,而寒温带范围缩小甚至退出东北地区,植被分布界限显著北移;同时湿润区面积减少半湿润区和半干旱区扩大,导致森林面积缩小草原面积扩大. |
| [40] | , 以采穗圃中的采穗母株为研究对象,对日本落叶松、长白落叶松及其杂种进行了光响应曲线和CO2响应曲线的测定,通过估算光合参数,比较了它们的光合特性。结果表明:与日本落叶松相比,日本落叶松×长白落叶松杂种的最大净光合速率、表观量子效率和光合能力等与光合效率正相关的参数都较低,光强和CO2的利用范围也更窄,暗呼吸速率却更高,而羧化效率和光呼吸速率没有差别。与长白落叶松相比,尽管长白落叶松×日本落叶松杂种的暗呼吸速率较低,但其表观量子效率更低,CO2补偿点更高,而羧化效率、光呼吸速率、光补偿点没有差别。日本落叶松×长白落叶松杂种与长白落叶松×日本落叶松杂种相比,光呼吸速率和CO2补偿点稍高,羧化效率稍低,而表观量子效率、暗呼吸速率、光补偿点没有差别。因此,认为落叶松杂种的光合效率不具有超亲杂种优势。 |
| [41] | , 以北京为研究区,整合遥感数据、气象数据及其他多源辅助数据,基于改进的光能利用率(carnegie-ames-stanford approach,CASA)模型分析了2010年北京植被生态系统净初级生产力(net primary productivity,NPP)的时空分布格局及其主要影响因素。结果表明: 12010年北京NPP总量为5.5 TgC,其NPP的空间分布格局为北部和西部山区总量较高,平原区NPP总量较低; 2北京植被NPP的季节变化明显,夏季NPP最大,占全年的62%,冬季最小,仅占3%,春季和秋季分别占全年NPP总量的18%和17%; 3北京植被NPP受水分和热量条件限制,不同区域的主要限制因子不同,北部和西部山区自然植被受气温影响较大,平原区农作物生长更容易受降水影响,而在山区向平原过渡区域的植被受太阳辐射变化影响明显。 |
| [42] | , 应用生物地球化学模型BIOME-BGC模型估算了1993～2004年红壤丘陵区湿地松林总第一性生产力(GPP)、净第一性生产力(NPP),并分析GPP、NPP年际变化对气候的响应以及未来气候变化情景下GPP、NPP的响应。结果表明,湿地松林1993～2004年GPP、NPP的总量变化波动于1777～2160g Cm-2a-1之间和453～828gCm-2a-1之间,平均值分别为1941g Cm-2a-1和695gCm-2a-1。在研究时段内,GPP、NPP有缓慢增长趋势,GPP、NPP总量平均值从1990年代初期(1993～1996年)的1826、687gCm-2a-1上升到21世纪初期(2001～2004年)的2026、693gCm-2a-1。这主要是由于研究时段内GPP、NPP对降水缓慢增长的正响应造成的。未来气候变化情景分析表明,CO2浓度倍增不利于湿地松林GPP、NPP的增长,但均不超过1.5%。在CO2浓度不增加条件下,GPP正向响应了降水单独变化和温度升高1.5℃且降水增加情景,正向响应NPP的情景条件是降水的单独变化;当CO2浓度倍增和气候改变时,预测的GPP正向响应了降水的变化,同时正向响应了温度升高1.5℃且降水变化;正向响应NPP的情景条件是降水的变化。 |
| [43] | Chinese fir [( Cunninghamia lanceolata (Lamb.) Hook (Taxodiaceae)] plantations are helping to meet China's increasing demands for timber, while, at the same time, sequestering carbon (C) above and belowground. The latter function is important as a means of slowing the rate that CO 2 is increasing in the atmosphere. Available data are limited, however, and even if extensive, would necessitate consideration of future changes in climatic conditions and management practices. To evaluate the contribution of Chinese fir plantations under a range of changing conditions a dynamic model is required. In this paper, we report successful outcome in parameterizing a process-based model (3-PG) and validating its predictions with recent and long-term field measurements acquired from different ages of Chinese fir plantations at the Huitong National Forest Ecosystem Research Station. Once parameterized, the model performed well when simulating leaf area index (LAI), net primary productivity (NPP), biomass of stems ( W S ), foliage ( W F ) and roots ( W R ), litterfall, and shifts in allocation over a period of time. Although the model does not specifically include heterotrophic respiration, we made some attempts to estimate changes in root C storage and decomposition rates in the litterfall pool as well as in the total soil respiration. Total C stored in biomass increased rapidly, peaking at age 21 years in unthinned stands. The predicted averaged above and belowground NNP (13.8102t02ha 611 02a 611 ) of the Chinese fir plantations between the modeling period (from 4 to 21-year-old) is much higher than that of Chinese forests (4.8–6.2202t02ha 611 02a 611 ), indicating that Chinese fir is a suitable tree species to grow for timber while processing the potential to act as a C sequestration sink. Taking into account that maximum LAI occurs at the age of 15 years, intermediate thinning and nutrient supplements should, according to model predictions, further increase growth and C storage in Chinese fir stands. Predicted future increases (approximately 0–202°C) in temperature due to global warming may increase plantation growth and reduce the time required to complete a rotation, but further increases (approximately 2–602°C) may reduce the growth rate and prolong the rotational age. |
| [44] | Forest inventory data (FID) are important resources for understanding the dynamics of forest biomass, net primary production (NPP) and carbon cycling at landscape and regional scales, especially for complying with the Kyoto Protocol on greenhouse gas reduction and validating ecosystem dynamic models from regional and global scales. FID-based biomass and NPP estimation models for China' Larix forests are discussed in this paper. The results indicated that the relationships between biomass ( B) and its volume ( V), NPP and mean annual biomass increment ( B/ A) or mean annual volume increment ( V/ A) can be expressed as hyperbola curves for both natural and planted Larix forests. The relationship between NPP and its biomass is not linear, which is not the same with the former studies. These FID-based models take into account the change in the ratio of forest biomass to volume with stand age and the effect of stand age on forest NPP. The result also indicates that natural and planted forests should be treated separately when biomass and NPP of forest are estimated based on FID. |
2011
长白落叶松人工林的生长与生态因子相关关系的研究
2
1986
... 长白落叶松(Larix olgensis)是北方和山地寒温带干燥寒冷气候条件下最具有代表性的森林植被.因其易栽植、生长快等优点, 在东北地区人工林中得到广泛应用, 成为中国重要的商业性用材树种(
... 研究区位于吉林省, 数据来源于吉林省第六次、第七次和第八次森林资源清查固定样地, 为长白落叶松人工纯林.共30块固定样地, 在四平、临江、白山、龙井、辽源、舒兰、长春、汪清、和龙、通化林区均有分布, 具有一定的代表性.研究区域及样地位置详见
Enhanced gross nitrogen transformation rates and nitrogen supply in paddy field under elevated atmospheric carbon dioxide and temperature.
1
2016
... 除了温度和降水的变化, 大气CO2浓度增加是气候变化的另一个重要方面.关于CO2浓度的增加对森林NPP的影响, 一直都存在较大的争议(
Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States.
1
2009
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
A process-based approach to estimate lodgepole pine (Pinus contorta Dougl.) distribution in the Pacific Northwest under climate change.
2011
利用CASA模型模拟西南喀斯特植被净第一性生产力
1
2011
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
长期气候变化——IPCC第五次评估报告解读
1
2014
... 为了预估未来气候变化对长白落叶松林生态系统NPP的影响, 本研究选用《长期气候变化—— IPCC第五次评估报告解读》(
Validation of plantation transpiration in south-eastern Australia estimated using the 3-PG forest growth model.
1
2010
... 3-PG模型运行需要的数据包括样地的气候数据、立地数据、林分初始数据和参数.本研究使用的长白落叶松生理生态参数和初始数据详见
区域及全球尺度的NPP过程模型和NPP对全球变化的响应
2014
Parameterization of the 3-PG model for Pinus elliottii stands using alternative methods to estimate fertility rating, biomass partitioning and canopy closure.
2014
Application of a process-based model for predicting the productivity of Eucalyptus nitens bioenergy plantations in Spain.
1
2016
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
Estimating annual GPP, NPP and stem growth in Finland using summary models.
1
2010
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
The influence of climate change on CO2 (carbon dioxide) emissions: An empirical estimation based on Chinese provincial panel data.
1
2016
... 综上所述, 在未来RCP2.6、RCP6.0和RCP8.5排放情景下, 当CO2浓度、降水量及温度同时升高时, 长白落叶松林NPP积累增加, 且CO2浓度及降水量对落叶松人工林NPP的正效应大于温度升高对其产生的负效应.诸多研究结果证实, 未来气候变化将导致我国东北地区森林的NPP明显增加(
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
3
2016
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
... 本研究基于30块固定样地, 首次运用3-PG过程模型探索了吉林省四平、临江、白山等地10个林区长白落叶松人工林NPP水平, 并模拟分析一个轮伐期(40年)内NPP的变化.发现3-PG模型模拟的NPP与样地实测NPP极显著相关(R2 = 0.86, p < 0.001), ME为-8.52 g·m-2·a-1, MRE为-1.50%, RMSE为65.08 g·m-2·a-1, RRMSE为11.26%, 可见模型对NPP的估算较准确, 且具有一定的统计可靠性.预测的NPP变动范围是272.79-844.80 g·m-2·a-1, 平均值为578.12 g·m-2·a-1,
... 除了温度和降水的变化, 大气CO2浓度增加是气候变化的另一个重要方面.关于CO2浓度的增加对森林NPP的影响, 一直都存在较大的争议(
3-PG模型在华南尾叶桉人工林的应用研究
1
2007
... 3-PG模型是目前应用最广的过程模型.它由Landsberg和Waring于1997年开发, 是以月为时间尺度, 以林分为空间尺度, 同时结合气象因子、立地条件、经营措施、树种特性来预测林分生产力、生物量分配、种群动态和土壤水分平衡的模型, 也是一个考虑了实际环境条件的完整的森林碳分配与平衡模型(
东北地区近46年气候变化特征分析
1
2011
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning.
1
1997
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
基于3-PG模型的杉木人工林各器官生物量和LAI估算
1
2015
... 3-PG模型是目前应用最广的过程模型.它由Landsberg和Waring于1997年开发, 是以月为时间尺度, 以林分为空间尺度, 同时结合气象因子、立地条件、经营措施、树种特性来预测林分生产力、生物量分配、种群动态和土壤水分平衡的模型, 也是一个考虑了实际环境条件的完整的森林碳分配与平衡模型(
立地条件对长白落叶松光合特性的影响
2008
Forest productivity under climate change: A checklist for evaluating model studies.
1
2011
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
Calibration of the forest growth model 3-PG to Eucalyptus plantations growing in low rainfall regions of Australia.
2007
Quantifying the response of forest carbon balance to future climate change in Northeastern China: Model validation and prediction.
2
2009
... 除了温度和降水的变化, 大气CO2浓度增加是气候变化的另一个重要方面.关于CO2浓度的增加对森林NPP的影响, 一直都存在较大的争议(
... 综上所述, 在未来RCP2.6、RCP6.0和RCP8.5排放情景下, 当CO2浓度、降水量及温度同时升高时, 长白落叶松林NPP积累增加, 且CO2浓度及降水量对落叶松人工林NPP的正效应大于温度升高对其产生的负效应.诸多研究结果证实, 未来气候变化将导致我国东北地区森林的NPP明显增加(
Impacts of CO2 concentration and climate change on the terrestrial carbon flux using six global climate—Carbon coupled models.
1
2015
... 综上所述, 在未来RCP2.6、RCP6.0和RCP8.5排放情景下, 当CO2浓度、降水量及温度同时升高时, 长白落叶松林NPP积累增加, 且CO2浓度及降水量对落叶松人工林NPP的正效应大于温度升高对其产生的负效应.诸多研究结果证实, 未来气候变化将导致我国东北地区森林的NPP明显增加(
Application of the 3-PG model for gross primary productivity estimation in deciduous broadleaf forests: A study area in Japan.
2011
Parameterisation of 3-PG for plantation grown Eucalyptus globulus.
2
2002
... 3-PG模型是目前应用最广的过程模型.它由Landsberg和Waring于1997年开发, 是以月为时间尺度, 以林分为空间尺度, 同时结合气象因子、立地条件、经营措施、树种特性来预测林分生产力、生物量分配、种群动态和土壤水分平衡的模型, 也是一个考虑了实际环境条件的完整的森林碳分配与平衡模型(
... Principles of 3-PG model (based on
Net primary production of forest ecosystems of Russia: A new estimate.
1
2008
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
长白落叶松人工林叶面积指数测定
1
2012
... tSLA在一定程度上反映了叶片截获光的能力及在强光下的自我保护能力, 往往与植物的生长和生存有密切的联系.通过查阅文献得知: 长白落叶松人工林的比叶面积一般在12.23-13.63之间, 比叶面积达1/2时林龄大约在5年(
气候变化对北京山区华北落叶松林NPP影响研究
2
2012
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
... 温度和降水作为两大主要的环境因子, 对陆地生态系统植被的生长和碳积累有重要的影响.本研究发现: 长白落叶松人工林NPP与生长季的降水量正相关, 与生长季温度负相关, 这与吴玉莲等 (2014)、冯险峰等(2014)的结论基本一致.3-PG模型模拟结果表明: 温度及降水对长白落叶松人工林的影响不同.具体表现为: 单独温度升高时, 长白落叶松林NPP较原来有所降低.温度升高对NPP的影响表现为先增加后降低, 且温度升高对NPP的负效应大于温度降低对NPP的正效应.单独降水量增加时, 长白落叶松林NPP有小幅度增加.诸多****的研究证实影响森林生态系统NPP水平及格局的主导因素是降水量(
Determination of fertility rating (FR) in the 3-PG model for loblolly pine plantations in the Southeastern United States based on site index.
2
2015
... Parameters in 3-PG model are roughly divided into four categories (A, C, L and M). Category A means the parameters are adjustable; Category C means the parameters are common and can be applied to all tree species; Category L means the parameters are found from literatures; and Category M means the parameters can be calculated from measurements. The initial tree density, elevation, latitude and soil texture of sample plots are derived from forest resource inventory. The initial planting years of stands were estimated from forest age and investigation time. Fertility rating was converted from site index (
... 用于3-PG模型的参数大致可分为4个等级(A、C、L、M).A表示此类参数是可调整的; C表示此类参数是通用的, 可以运用在所有树种; L表示此类参数是通过查阅相关文献获得的数据; M表示此类参数是通过测量或间接推算所得数据.样地初始密度、海拔、纬度和土壤质地类型由森林资源清查一并获取.林分初始种植年由林龄及调查时间推算而来, 肥力等级由立地指数推算而来(
基于MODIS的中国草地NPP综合估算模型
1
2015
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
基于FORECAST模型的长白落叶松人工林经营措施对长期生产力的影响
2012
1
... 通过查阅文献得知, 长白落叶松人工林Topt约为17 ℃ (
Evaluation of MODIS NPP and GPP products across multiple biomes.
1
2006
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
Modeling regional vegetation NPP variations and their relationships with climatic parameters in Wuhan, China.
1
2013
... NPP随年龄增加的变化趋势表现为先增加较快, 达到最大值后, 逐渐降低.因此, 3-PG模型的模拟结果具有较好的生物合理性, 并能灵敏地反映不同地区的差异.在大尺度上NPP的空间分布及时间序列上的动态变化不可能通过实测的方法获得, 但机理模型与遥感反演手段的结合应用可以获得NPP的时空变化及分布特征.因此模型就成为研究NPP时空动态不可或缺的手段(
不同轮伐期对杉木人工林碳固存的影响
1
2016
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
不同密度长白落叶松林生物量与碳储量分布特征
1
2011
... 除了温度和降水的变化, 大气CO2浓度增加是气候变化的另一个重要方面.关于CO2浓度的增加对森林NPP的影响, 一直都存在较大的争议(
基于森林资源清查资料的落叶松林生物量和净生长量估算模式
1
2001
... 温度和降水作为两大主要的环境因子, 对陆地生态系统植被的生长和碳积累有重要的影响.本研究发现: 长白落叶松人工林NPP与生长季的降水量正相关, 与生长季温度负相关, 这与吴玉莲等 (2014)、冯险峰等(2014)的结论基本一致.3-PG模型模拟结果表明: 温度及降水对长白落叶松人工林的影响不同.具体表现为: 单独温度升高时, 长白落叶松林NPP较原来有所降低.温度升高对NPP的影响表现为先增加后降低, 且温度升高对NPP的负效应大于温度降低对NPP的正效应.单独降水量增加时, 长白落叶松林NPP有小幅度增加.诸多****的研究证实影响森林生态系统NPP水平及格局的主导因素是降水量(
长白山阔叶红松林净初级生产力对气候变化的响应: 基于BIOME-BGC模型的分析
2014
东北地区植被分布全球气候变化区域响应
1
2003
... 长白落叶松(Larix olgensis)是北方和山地寒温带干燥寒冷气候条件下最具有代表性的森林植被.因其易栽植、生长快等优点, 在东北地区人工林中得到广泛应用, 成为中国重要的商业性用材树种(
日本落叶松与长白落叶松及其杂种光合特性比较
2012
基于CASA模型的北京植被NPP时空格局及其因子解释
1
2015
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
基于BIOME-BGC模型的红壤丘陵区湿地松(Pinus elliottii)人工林GPP和NPP
1
2008
... 温室效应引起的气候变化影响着森林生态系统的结构和功能.在全球变暖的大背景下, 近30年来中国地表平均气温较以往有明显的增加, 增温速率为0.025 ℃·a-1.东北地区是全国增温最显著的一个区域, 增温速率为0.036 ℃·a-1.降水量整体上呈减少的趋势, 但变化不明显, 而吉林省总体降水略有增加的趋势(
Simulating age-related changes in carbon storage and allocation in a Chinese fir plantation growing in southern China using the 3-PG model.
1
2009
... 本研究发现: tSLA、kF和Topt是3-PG模型的重要参数.它们不同程度地影响着模型对长白落叶松人工林NPP的预测.这与以往的部分研究结果一致, 如
Estimating biomass and net primary production from forest inventory data: A case study of China’s Larix forests.
1
2002
... 研究区位于吉林省, 数据来源于吉林省第六次、第七次和第八次森林资源清查固定样地, 为长白落叶松人工纯林.共30块固定样地, 在四平、临江、白山、龙井、辽源、舒兰、长春、汪清、和龙、通化林区均有分布, 具有一定的代表性.研究区域及样地位置详见
