Lund-Potsdam-Jena Dynamic Global Vegetation Model(LPJ)模型是由Lund University,Pots-dam Climate Research Centre和Max-Planck-Institute for Biogeochemistry,Jena联合开发的植被动态模型,该模型以气候、土壤质地和CO2数据作为...Lund-Potsdam-Jena Dynamic Global Vegetation Model(LPJ)模型是由Lund University,Pots-dam Climate Research Centre和Max-Planck-Institute for Biogeochemistry,Jena联合开发的植被动态模型,该模型以气候、土壤质地和CO2数据作为输入模拟植被与环境的碳水交换过程。为了利用高时间分辨率的输入数据,对模型的代码进行了改写。张掖绿洲盈科站位于干旱区绿洲典型的农田生态系统。应用其观测资料作为LPJ的输入,模拟了张掖绿洲制种玉米的碳水通量,并用涡动观测到的潜热和CO2通量验证蒸散发和NEE的模拟结果。结果表明LPJ模型能够较好地模拟制种玉米与环境之间的碳水交换,蒸散发(ET)的模拟值与观测值的R2为0.8;NEE的模拟值与观测值的R2为0.79。展开更多
It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the curre...It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling （HASM）, proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country＇s total forest vegetation carbon stock respectively. Compared with the former value （1975-1995）, it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink.展开更多
利用1971—2006年环杭州湾地区25个气象站的降水、温度和云量资料及全球CO2年平均体积分数资料,采用LPJ全球动态植被模式(Lund-Potsdam-Jena Dynamic Global Vegetation Model),通过模拟环杭州湾地区的植被年净初级生产力(Annual Net Pr...利用1971—2006年环杭州湾地区25个气象站的降水、温度和云量资料及全球CO2年平均体积分数资料,采用LPJ全球动态植被模式(Lund-Potsdam-Jena Dynamic Global Vegetation Model),通过模拟环杭州湾地区的植被年净初级生产力(Annual Net Primary Productivity,ANPP),分析了该地区ANPP的变化特征,并探讨了植被ANPP变化的可能原因。结果表明:1)就环杭州湾地区,36a间植被ANPP均表现出不同程度的增加,尤其以嘉兴市北部、绍兴市东部较明显;全区平均增加速率为1.5243g·m-2·a-2;2)通过多元线性回归分析发现,环杭州湾地区平均云量与植被ANPP的关系最为密切,偏相关系数为-0.5175,而温度、降水与植被ANPP的关系不明显;同时,植被ANPP对气候变化的响应存在一定的地域性差异;3)在全区平均情况下,36a间由温度下降、降水增加、云量减小、CO2体积分数升高引起的植被ANPP变化趋势分别为-0.0813、-0.0171、0.7601、0.8673g·m-2·a-2,其对应的贡献率分别为-5.18%、-1.09%、48.38%、55.21%。由此可见,该地区植被ANPP变化的主要强迫因子是CO2体积分数和云量,而降水变化对植被ANNP的变化作用不大。展开更多
为评估吉林省落叶松林的生产力现状并为我国森林生态系统生产力和植被监测研究提供基础数据,以吉林省落叶松林为研究对象,基于吉林省及其周边100 km范围内41个气象站点资料,采用LPJ-DGVM模型模拟了2000-2019年吉林省落叶松林近20年的净...为评估吉林省落叶松林的生产力现状并为我国森林生态系统生产力和植被监测研究提供基础数据,以吉林省落叶松林为研究对象,基于吉林省及其周边100 km范围内41个气象站点资料,采用LPJ-DGVM模型模拟了2000-2019年吉林省落叶松林近20年的净初级生产力,并采用线性回归趋势分析、变异系数、Hurst指数和相关性分析法对其时空变化、稳定性及其与气候因子的相关关系进行了分析。结果表明:(1)2000-2019年吉林省落叶松林年均净初级生产力(NPP)为592 g C m^(-2)a^(-1),年均增长率为2.81%,随时间推移呈现波动增长的趋势(β=14.55,R^(2)=0.784,P<0.01)。(2)NPP变异系数为0.07-2.33,均值为0.48,除幼龄林外,整体波动较小。Hurst指数介于0.441-0.849之间,均值为0.612,未来吉林省落叶松林NPP呈增加趋势。(3)吉林省落叶松林NPP存在明显的空间异质性,北部和南部区域NPP较高,是近20年NPP增长较快的区域。(4)2000-2019年吉林省落叶松林年均NPP与年总降水、生长季降水量之间均不显著(P>0.05),与年均温呈显著正相关(P<0.05),与生长季均温为极显著正相关(P<0.01),该阶段内温度比降水更能对吉林省落叶松林NPP的年际变化产生影响。LPJ模型模拟吉林省落叶松林2000-2019年NPP与样地实测值极显著相关(P<0.01),可以用于模拟吉林省落叶松林的NPP。展开更多
准确理解地质历史时期气候变化的现象和机制,对预测未来气候变化有重要的启示意义。末次间冰期早期是研究未来气候变化可参考的典型暖期。目前,基于气候模式模拟的末次间冰期早期温度低于气候记录重建的结果。这一现状的一个潜在原因在...准确理解地质历史时期气候变化的现象和机制,对预测未来气候变化有重要的启示意义。末次间冰期早期是研究未来气候变化可参考的典型暖期。目前,基于气候模式模拟的末次间冰期早期温度低于气候记录重建的结果。这一现状的一个潜在原因在于,这些气候模拟研究中采用的植被数据为工业革命前水平,忽略了植被动态对气候的反馈作用。本研究利用iLOVECLIM气候模式耦合植被模块VECODE和LPJ-GUESS开展末次间冰期早期(125 ka B. P.)植被动态对气候反馈作用的模拟分析。模拟结果显示,相比基于工业革命前植被条件模拟得到的温度水平,耦合动态植被模拟的全球气候更温暖,但仍略低于记录重建的温度。在大陆/亚大陆尺度,高纬和北非地区模拟的125 ka B. P.植被覆盖度明显高于工业革命前水平,增温幅度也显著高于其他地区;此外,末次间冰期早期北非植被覆盖对区域气温的正反馈通过增强的大气环流使低纬地区输送到高纬地区热量增加,从而表现出对全球气温的正反馈作用。展开更多
文摘Lund-Potsdam-Jena Dynamic Global Vegetation Model(LPJ)模型是由Lund University,Pots-dam Climate Research Centre和Max-Planck-Institute for Biogeochemistry,Jena联合开发的植被动态模型,该模型以气候、土壤质地和CO2数据作为输入模拟植被与环境的碳水交换过程。为了利用高时间分辨率的输入数据,对模型的代码进行了改写。张掖绿洲盈科站位于干旱区绿洲典型的农田生态系统。应用其观测资料作为LPJ的输入,模拟了张掖绿洲制种玉米的碳水通量,并用涡动观测到的潜热和CO2通量验证蒸散发和NEE的模拟结果。结果表明LPJ模型能够较好地模拟制种玉米与环境之间的碳水交换,蒸散发(ET)的模拟值与观测值的R2为0.8;NEE的模拟值与观测值的R2为0.79。
基金National High-tech R&D Program of the Ministry of Science and Technology of the People's Republic of China,No.2013AA122003National Key Technologies R&D Program of the Ministry of Science and Tech-nology of China,No.2013BACO3B05
文摘It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling （HASM）, proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country＇s total forest vegetation carbon stock respectively. Compared with the former value （1975-1995）, it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink.
文摘利用1971—2006年环杭州湾地区25个气象站的降水、温度和云量资料及全球CO2年平均体积分数资料,采用LPJ全球动态植被模式(Lund-Potsdam-Jena Dynamic Global Vegetation Model),通过模拟环杭州湾地区的植被年净初级生产力(Annual Net Primary Productivity,ANPP),分析了该地区ANPP的变化特征,并探讨了植被ANPP变化的可能原因。结果表明:1)就环杭州湾地区,36a间植被ANPP均表现出不同程度的增加,尤其以嘉兴市北部、绍兴市东部较明显;全区平均增加速率为1.5243g·m-2·a-2;2)通过多元线性回归分析发现,环杭州湾地区平均云量与植被ANPP的关系最为密切,偏相关系数为-0.5175,而温度、降水与植被ANPP的关系不明显;同时,植被ANPP对气候变化的响应存在一定的地域性差异;3)在全区平均情况下,36a间由温度下降、降水增加、云量减小、CO2体积分数升高引起的植被ANPP变化趋势分别为-0.0813、-0.0171、0.7601、0.8673g·m-2·a-2,其对应的贡献率分别为-5.18%、-1.09%、48.38%、55.21%。由此可见,该地区植被ANPP变化的主要强迫因子是CO2体积分数和云量,而降水变化对植被ANNP的变化作用不大。
文摘为评估吉林省落叶松林的生产力现状并为我国森林生态系统生产力和植被监测研究提供基础数据,以吉林省落叶松林为研究对象,基于吉林省及其周边100 km范围内41个气象站点资料,采用LPJ-DGVM模型模拟了2000-2019年吉林省落叶松林近20年的净初级生产力,并采用线性回归趋势分析、变异系数、Hurst指数和相关性分析法对其时空变化、稳定性及其与气候因子的相关关系进行了分析。结果表明:(1)2000-2019年吉林省落叶松林年均净初级生产力(NPP)为592 g C m^(-2)a^(-1),年均增长率为2.81%,随时间推移呈现波动增长的趋势(β=14.55,R^(2)=0.784,P<0.01)。(2)NPP变异系数为0.07-2.33,均值为0.48,除幼龄林外,整体波动较小。Hurst指数介于0.441-0.849之间,均值为0.612,未来吉林省落叶松林NPP呈增加趋势。(3)吉林省落叶松林NPP存在明显的空间异质性,北部和南部区域NPP较高,是近20年NPP增长较快的区域。(4)2000-2019年吉林省落叶松林年均NPP与年总降水、生长季降水量之间均不显著(P>0.05),与年均温呈显著正相关(P<0.05),与生长季均温为极显著正相关(P<0.01),该阶段内温度比降水更能对吉林省落叶松林NPP的年际变化产生影响。LPJ模型模拟吉林省落叶松林2000-2019年NPP与样地实测值极显著相关(P<0.01),可以用于模拟吉林省落叶松林的NPP。
文摘准确理解地质历史时期气候变化的现象和机制,对预测未来气候变化有重要的启示意义。末次间冰期早期是研究未来气候变化可参考的典型暖期。目前,基于气候模式模拟的末次间冰期早期温度低于气候记录重建的结果。这一现状的一个潜在原因在于,这些气候模拟研究中采用的植被数据为工业革命前水平,忽略了植被动态对气候的反馈作用。本研究利用iLOVECLIM气候模式耦合植被模块VECODE和LPJ-GUESS开展末次间冰期早期(125 ka B. P.)植被动态对气候反馈作用的模拟分析。模拟结果显示,相比基于工业革命前植被条件模拟得到的温度水平,耦合动态植被模拟的全球气候更温暖,但仍略低于记录重建的温度。在大陆/亚大陆尺度,高纬和北非地区模拟的125 ka B. P.植被覆盖度明显高于工业革命前水平,增温幅度也显著高于其他地区;此外,末次间冰期早期北非植被覆盖对区域气温的正反馈通过增强的大气环流使低纬地区输送到高纬地区热量增加,从而表现出对全球气温的正反馈作用。
