Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO

ZHENG Zehao; ZHANG Letao; WANG Qingyuan; WEI Yiyuan; HE Mengzhen; WEI Yingying

doi:10.13870/j.cnki.stbcxb.2024.05.029

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Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO

更新时间：2025-02-18

- Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO
- Vol. 38, Issue 5, Pages: 305-314(2024)
- 作者机构：
  
  1. 河南大学地理与环境学院, 环境与规划国家级实验教学示范中心,河南,开封,475004
  2. 西北农林科技大学水土保持研究所, 陕西杨凌,712100
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2024.05.029
  CLC： X173
- Published：2024
- 稿件说明：
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ZHENG Zehao, ZHANG Letao, WANG Qingyuan, et al. Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO[J]. 2024, 38(5): 305-314.
DOI：

ZHENG Zehao, ZHANG Letao, WANG Qingyuan, et al. Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO[J]. 2024, 38(5): 305-314. DOI： 10.13870/j.cnki.stbcxb.2024.05.029.

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)。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

年平均大气CO2浓度在中国陆地范围均表现为显著升高趋势

平均增幅为2.14 μmol/(mol·a);纳入CO2胁迫因子后

植被类型按最大光能利用率从大到小为农田＞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 g/MJ);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%~3.7%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5.11%

2.72%

2.51%

夏季高估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/(mon·a). 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 g/MJ). 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 3.7% 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.

关键词

Keywords

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