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基于Biome-BGC模型的千烟洲森林水分利用效率研究

温永斌 韩海荣 程小琴 李祖政

温永斌, 韩海荣, 程小琴, 李祖政. 基于Biome-BGC模型的千烟洲森林水分利用效率研究[J]. 北京林业大学学报, 2019, 41(4): 69-77. doi: 10.13332/j.1000-1522.20190001
引用本文: 温永斌, 韩海荣, 程小琴, 李祖政. 基于Biome-BGC模型的千烟洲森林水分利用效率研究[J]. 北京林业大学学报, 2019, 41(4): 69-77. doi: 10.13332/j.1000-1522.20190001
Wen Yongbin, Han Hairong, Cheng Xiaoqin, Li Zuzheng. Forest water use efficiency in Qianyanzhou based on Biome-BGC model, Jiangxi Province of eastern China[J]. Journal of Beijing Forestry University, 2019, 41(4): 69-77. doi: 10.13332/j.1000-1522.20190001
Citation: Wen Yongbin, Han Hairong, Cheng Xiaoqin, Li Zuzheng. Forest water use efficiency in Qianyanzhou based on Biome-BGC model, Jiangxi Province of eastern China[J]. Journal of Beijing Forestry University, 2019, 41(4): 69-77. doi: 10.13332/j.1000-1522.20190001

基于Biome-BGC模型的千烟洲森林水分利用效率研究

doi: 10.13332/j.1000-1522.20190001
基金项目: 国家重点研发计划项目(2016YFD0600205),国家自然科学基金项目(31700372)
详细信息
    作者简介:

    温永斌。主要研究方向:森林生态学。Email:1744215892@qq.com 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    韩海荣,教授,博士生导师。主要研究方向:森林生态学。Email:hanhr@bifu.edu.cn 地址:同上

  • 中图分类号: S718.51+2.3

Forest water use efficiency in Qianyanzhou based on Biome-BGC model, Jiangxi Province of eastern China

  • 摘要: 目的为了探究全球气候变化背景下森林生态系统水分利用效率的影响因子及其对气候变化的响应。方法本文利用经PEST模型参数优化后的Biome-BGC模型,对千烟洲森林生态系统2000—2014(CK)年以及不同气候情景变化模式下的水分利用效率及其影响因子进行了探究。结果(1)千烟洲的年均温和最大叶面积指数同水分利用效率呈显著正相关关系(P < 0.01),降水同水分利用效率间的相关性不显著(P > 0.05)。(2)千烟洲不同情景模式水分利用效率的取值区间是2.09 ~ 3.71 g/kg、均值2.90 g/kg。(3)同CK(2.18 ~ 2.57 g/kg、均值2.38 g/kg)相比,千烟洲各情景模式下大气CO2浓度增加情景(2.38 ~ 3.41 g/kg、均值2.90 g/kg)、降水和大气CO2浓度同时增加情景(2.38 ~ 3.33 g/kg、均值2.86 g/kg)、气温和大气CO2浓度同时增加情景(2.58 ~ 3.71 g/kg、均值3.15 g/kg)、降水和气温同时增加(2.30 ~ 2.84 g/kg、均值2.57 g/kg)及降水、气温和大气CO2浓度同时增加情景(2.70 ~ 3.60 g/kg、均值3.15 g/kg)的水分利用效率差异显著。但是,降水增加情景(2.09 ~ 2.68 g/kg、均值2.39 g/kg)和气温增加情景(2.13 ~ 2.81 g/kg、均值2.47 g/kg)对水分利用效率的影响不显著。(4)降水和大气CO2浓度同时增加情景与大气CO2浓度增加情景的水分利用效率差异不显著,气温和大气CO2浓度同时增加情景与大气CO2浓度增加情景的水分利用效率差异显著。结论(1)千烟洲森林生态系统的水分利用效率受到气温和叶面积指数的影响,情景分析表明水分利用效率能很好的对气候变化做出响应。(2)降水、气温和大气CO2浓度对水分利用效率的影响存在耦合效应。(3)增温对水分利用效率的影响要大于降水。

     

  • 图  1  PEST模型参数优化流程图

    Figure  1.  Parameter optimization flowchart of PEST

    图  2  千烟洲2003—2005日GPP值

    GPP-BGC为Biome-BGC模型未进行参数优化模拟的日GPP,GPP-EC为涡度相关技术实测的日GPP,GPP-YHH为通过PEST模型进行参数优化后模拟而得的日GPP。GPP-BGC is a daily GPP for the Biome-BGC model without parameter optimization simulation, GPP-EC is a daily GPP measured by eddy correlation technology, GPP-YHH is a daily GPP that is simulated after parameter optimization through the PEST model.

    Figure  2.  Daily GPP of Qianyanzhou from 2003 to 2005

    图  3  千烟洲2003—2005日蒸散值

    ET-BGC为Biome-BGC模型未进行参数优化模拟的日ET,ET-EC为涡度相关技术实测的日ET,ET-YHH为通过PEST模型进行参数优化后模拟而得的日ET。ET-BGC is a daily ET for the Biome-BGC model without parameter optimization simulation, ET-EC is a daily ET measured by eddy correlation technology, ET-YHH is a daily ET that is simulated after parameter optimization through the PEST model.

    Figure  3.  Daily evapotranspiration (ET) of Qianyanzhou from 2003 to 2005

    图  4  各情景模式年水分利用效率变化趋势

    CK是对照,P是年降雨量增加15%情景,T是气温升高4 ℃情景,C是大气CO2浓度加倍情景,PT是年降雨量增加15%同时气温增加4 ℃情景,PC是年降雨量增加15%同时大气CO2浓度加倍情景,TC是气温增加4 ℃同时大气CO2浓度加倍情景,PTC是年降雨量增加15%,气温升高4 ℃同时大气CO2浓度加倍情景。CK means control, P means annual precipitation increases by 15% scenario, T means temperature rises by 4 ℃ scenario, C means double the concentration of atmospheric carbon dioxide scenario, PT means annual precipitation increases by 15% and temperature increases by 4 ℃ scenario, PC means annual precipitation increases by 15% and double the concentration of atmospheric carbon dioxide scenario, TC means temperature increases by 4 ℃ and double the concentration of atmospheric carbon dioxide scenario, PTC means annual precipitation increases by 15%, temperature increases by 4 ℃ and double the concentration of atmospheric carbon dioxide scenario.

    Figure  4.  Trends of annual water use efficiency in different climate scenarios

    表  1  Biome-BG C模型优化前后参数对照表

    Table  1.   Biome-BG C model parameter comparison table before and after optimization

    生理参数 Physiological parameter   站点 Station
    千烟洲 Qianyanzhou
    缺省值 Default value 优化值 Optimization value
    年内叶和细根周转比 Annual leaf and fine root turnover fraction 0.500 0.546
    年内活木转化比 Annual live wood turnover fraction 0.700 0.764
    年度整株植株死亡率 Annual whole-plant mortality fraction 0.005 0.020
    年度植物火灾死亡率 Annual fire mortality fraction 0.002 0.018
    细根碳与叶碳分配比 New fine root C: new leaf C 1.000 0.802
    茎干碳与叶碳分配比 New stem C: new leaf C 1.000 1.470
    活木碳与总碳分配比 New live wood C: new total wood C 0.220 0.208
    粗根碳与茎干碳分配比 Coarse root C: stem C 0.300 0.362
    当前生长比例 Current growth proportion 0.500 0.416
    叶片碳氮比 C:N of leaves 42.000 37.632
    落叶碳氮比 C:N of leaf litter 49.000 54.683
    细根碳氮比 C:N of fine roots 42.000 39.624
    活木碳氮比 C:N of live wood 50.000 49.872
    死木碳氮比 C:N of dead wood 300.000 299.994
    冠层水分截留系数 Canopy water interception coefficient 0.041 0.041 3
    冠层消光系数 Canopy light extinction coefficient 0.700 0.701
    投影叶面积比 All-sided to projected leaf area ratio 2.000 1.997
    比叶面积 Specific leaf area 12.000 11.871
    阴阳叶比叶面积比 Ratio of shaded SLA:sunlit SLA 2.000 2.747
    Rubisco酶中叶氮质量分数 Fraction of leaf N in Rubisco 0.060 0.062
    最大气孔导度 Maximum stomatal conductance 0.005 0.005 4
    叶水势传导上限 Leaf water potential: start of conductance reduction − 0.600 − 0.582
    叶水势传导下限 Leaf water potential: complete conductance reduction − 3.900 − 3.784
    水汽压差限制传导上限 Vapor pressure deficit: start of conductance reduction 1 800.000 1 798.576
    水汽压差限制传导下限 Vapor pressure deficit: complete conductance reduction 4 100.000 4 097.758
    下载: 导出CSV

    表  2  年水分利用效率及其影响因子相关性分析

    Table  2.   Correlation analysis of annual water use efficiency and its impact factors

    千烟洲 Qianyanzhou    WUE
    降水量
    Precipitation (PRCP)
    0.136
    平均温度
    Average temperature (Tavg)
    0.268**
    最大叶面积指数
    Maximum leaf area index (maxLAI)
    0.605**
    注:**代表P < 0.01,表示极显著相关。Notes:**represents P < 0.01, indicating extremely significant correlation.
    下载: 导出CSV

    表  3  千烟洲各情景模式年水分利用效率方差分析

    Table  3.   Annual water use efficiency analysis of variance of various scenarios in Qianyanzhou

    CK P T C PT PC TC PTC
    CK 1.000
    P 0.034 1.000
    T 0.144 0.110 1.000
    C 0.448* 0.414* 0.304* 1.000
    PT 0.185* 0.150 0.041 0.263* 1.000
    PC 0.473* 0.439* 0.329* 0.025 0.288* 1.000
    TC 0.788* 0.753* 0.644* 0.340* 0.603* 0.315* 1.000
    PTC 0.801* 0.767* 0.657* 0.353* 0.617* 0.328* 0.014 1.000
    注:*代表P < 0.05,表示显著相关。Notes:*represents P < 0.05, indicating significant correlation.
    下载: 导出CSV
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  • 收稿日期:  2019-01-20
  • 修回日期:  2019-03-14
  • 刊出日期:  2019-04-01

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