基于大气CO2浓度升高对植被最大光能利用率的模拟及CASA模型的改进
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1.河南大学地理与环境学院;2.西北农林科技大学水土保持研究所

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X173 ???????

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(232300421245);国家自然科学(41807066,42371223)


Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO2 Concentrations
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School of Geography and Environment, Henan University

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    摘要:

    [目的] 为了在大气CO2浓度不断升高的背景下准确估算植被净初级生产力(NPP),[方法]在Carnegie-Ames-Stanford Approach(CASA)模型中引入CO2浓度因子,来模拟不同植被类型的最大光能利用率,并在此基础上对改进后模型的NPP估计潜力进行探索。[结果] 2000—2020年间,年平均大气CO2浓度在中国陆地范围都表现为显著升高趋势,平均增幅约为2.14 μmol·mol-1·a-1 ;纳入CO2胁迫因子后,植被类型按最大光能利用率从大到小排序为农田>常绿阔叶林>混交林>针叶林>灌丛>湿地>草地(1.85,1.69,1.30,0.87,0.52,0.40 ,0.40 gC·MJ-1);CASA_CO2模型对NPP的估算精度相比于原CASA模型得到提高,CASA_CO2模型在植被水平和综合水平的NPP模拟值与实测值之间的R2分别提升-0.1%~7.2%和0.5%,RMSE分别降低0.3%~9.2%和0.7%,年NPP估算结果的RMSE降低1.9%;CASA_CO2模型改善了CASA模型对整体NPP的低估,由10.62%降至9.81%,CASA_CO2模型对春季、秋季和冬季的整体NPP分别低估了5.11%,2.72%,2.51%,夏季高估了0.53%。[结论] 在模型中考虑CO2浓度变化对植被的影响,可以提高NPP估算的精度。

    Abstract:

    [Objective] In order to accurately estimate net primary productivity (NPP) of vegetation under the context of rising atmospheric CO2 concentrations, [Methods] a CO2 concentration factor was introduced into the Carnegie-Ames-Stanford Approach (CASA) model to simulate the maximum light use efficiency of different vegetation types. Based on this, the potential of the improved model for NPP estimation was explored. [Results] From 2000 to 2020, the annual average atmospheric CO2 concentration in China's terrestrial areas showed a significant increasing trend, with an average increase of approximately 2.14 μmol·mol?1·a?1. After incorporating the CO2 stress factor, the vegetation types were ranked by maximum light use efficiency in the following order: cropland > evergreen broadleaf forest > mixed forest > needleleaf forest > shrub > wetland > grassland (1.85, 1.69, 1.30, 0.87, 0.52, 0.40, 0.40 gC·MJ?1). The estimation accuracy of NPP by the CASA_CO2 model was improved compared to the original CASA model. The R2 between the simulated and measured NPP values at the vegetation level and the comprehensive level increased by -0.1% to 7.2% and 0.5%, respectively, and RMSE decreased by 0.3% to 9.2% and 0.7%, respectively. The RMSE of annual NPP estimation results decreased by 1.9%. The CASA_CO2 model improved the underestimation of overall NPP by the CASA model, reducing it from 10.62% to 9.81%. The CASA_CO2 model underestimated the overall NPP for spring, autumn, and winter by 5.11%, 2.72%, and 2.51%, respectively, while overestimating it for summer by 0.53%. [Conclusion] Considering the impact of CO2 concentration changes on vegetation in the model can improve the accuracy of NPP estimation.

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  • 收稿日期:2024-05-14
  • 最后修改日期:2024-06-10
  • 录用日期:2024-06-25
  • 在线发布日期: 2024-09-10
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