基于大气CO

郑泽豪; 张乐涛; 王清源; 魏仪媛; 何梦真; 卫颖颖

doi:10.13870/j.cnki.stbcxb.2024.05.029

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基于大气CO

更新时间：2025-02-18

- 基于大气CO
- Simulation of Vegetation Maximum Light Use Efficiency and Improvement of the CASA Model Based on Elevated Atmospheric CO
- 水土保持学报 2024年38卷第5期页码：305-314
- 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：
  
  [ "郑泽豪(1999—),男,在读硕士研究生,主要从事生态系统固碳研究。E-mail:zhengzehao163@163.com" ]
- 基金信息：
  
  河南省自然科学基金项目(232300421245);国家自然科学基金项目(41807066,42371223)
- DOI：10.13870/j.cnki.stbcxb.2024.05.029
  中图分类号： X173
- 纸质出版：2024
- 稿件说明：
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郑泽豪, 张乐涛, 王清源, 等. 基于大气CO[J]. 水土保持学报, 2024,38(5):305-314.

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.
郑泽豪, 张乐涛, 王清源, 等. 基于大气CO[J]. 水土保持学报, 2024,38(5):305-314. DOI： 10.13870/j.cnki.stbcxb.2024.05.029.

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

references

王苗苗,王绍强,陈斌,等.2020—2050年CO

施肥效应促进全球陆地生态系统碳吸收[J].生态学报,2023,43(6):2408-2418. WANG M M, WANG S Q, CHEN B, et al. CO

fertilization promoting the carbon uptake of global terrestrial ecosystems in 2020—2050[J].Acta Ecologica Sinica,2023,43(6):2408-2418.

WU J, GUAN K Y, HAYEK M, et al. Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales[J].Global Change Biology,2017,23(3):1240-1257.

DE KAUWE M G, KEENAN T F, MEDLYN B E, et al. Satellite based estimates underestimate the effect of CO

fertilization on net primary productivity[J].Nature Climate Change,2016,6:892-893.

ZHENG Y, SHEN R Q, WANG Y W, et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982—2017[J].Earth System Science Data,2020,12(4):2725-2746.

SUN C M, ZHONG X C, CHEN C, et al. Evaluating the grassland net primary productivity of Southern China from 2000 to 2011 using a new climate productivity model[J].Journal of Integrative Agriculture,2016,15(7):1638-1644.

RUNNING S W, THORNTON P E, NEMANI R, et al. Global terrestrial gross and net primary productivity from the earth observing system[M]//SALA O E, JACKSON R B, MOONEY H A, et al. Methods in Ecosystem Science. New York: Springer,2000:44-57.

MONTEITH J L. Solar radiation and productivity in tropical ecosystems[J].Journal of Applied Ecology,1972,9(3):747-766.

LV Y L, CHI H, SHI P C, et al. Phenology-based maximum light use efficiency for modeling gross primary production across typical terrestrial ecosystems[J].Remote Sensing,2023,15(16):e4002.

TURNER D P, URBANSKI S, BREMER D, et al. A cross-biome comparison of daily light use efficiency for gross primary production[J].Global Change Biology,2003,9(3):383-395.

RUIMY A, SAUGIER B, DEDIEU G. Methodology for the estimation of terrestrial net primary production from remotely sensed data[J].Journal of Geophysical Research:Atmospheres,1994,99(D3):5263-5283.

GOETZ S J, PRINCE S D. Remote sensing of net primary production in boreal forest stands[J].Agricultural and Forest Meteorology,1996,78(3/4):149-179.

高德新,王帅,李琰,等.植被光能利用率:模型及其不确定性[J].生态学报,2021,41(14):5507-5516. GAO D X, WANG S, LI Y, et al. Light use efficiency of vegetation:Model and uncertainty[J].Acta Ecologica Sinica,2021,41(14):5507-5516.

YUAN W, ZHENG Y, PIAO S, et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth[J]. Science Advances,2019,5(8):e1396.

SWINNEN E, VAN H R, EERENS H, et al. Algorithm theoretical basis document: dry matter productivity (DMP). . https://land.copernicus.eu/en.

徐珂,邢艳秋,常晓晴.亚热带常绿针叶林光能利用率模型优化与总初级生产力估算[J].森林工程,2021,37(5):28-36. XU K, XING Y Q, CHANG X Q. Model optimization and GPP estimation of light energy utilization in subtropical evergreen coniferous forest[J].Forest Engineering,2021,37(5):28-36.

王彦伟,罗维均,陈佳,等.2015—2019年贵州普定自然恢复样地碳水通量观测数据集[J].中国科学数据,2023,8(3):78-89. WANG Y W, LUO W J, CHEN J, et al. A dataset of carbon and water fluxes in the sample plots of natural regeneration at Puding Station of Guizhou Province(2015—2019)[J].China Scientific Data,2023,8(3):78-89.

朱文泉,潘耀忠,何浩,等.中国典型植被最大光利用率模拟[J].科学通报,2006,51(6):700-706. ZHU W Q, PAN Y Z, HE H, et al. Simulation of maximum light utilization efficiency of typical vegetation in China[J].Chinese Science Bulletin,2006,51(6):700-706.

YANG D, XU X L, XIAO F J, et al. Improving modeling of ecosystem gross primary productivity through re-optimizing temperature restrictions on photosynthesis[J].Science of the Total Environment,2021,788:e147805.

KENDALL M. Rank correlation methods[J].British Journal of Psychology,1990,25(1):86-91.

POTTER C S, RANDERSON J T, FIELD C B, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data[J].Global Biogeochemical Cycles,1993,7(4):811-841.

BAO S N, IBROM A, WOHLFAHRT G, et al. Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level[J].Agricultural and Forest Meteorology,2022,326:e109185.

朱安然,孙睿,王梦佳.植被光能利用率遥感估算[J].遥感学报,2021,25(6):1227-1243. ZHU A R, SUN R, WANG M J. Estimation of light use efficiency by using remote sensing data[J].National Remote Sensing Bulletin,2021,25(6):1227-1243.

ZHANG Y H, HU Z W, WANG J Z, et al. Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation[J].International Journal of Applied Earth Observation and Geoinformation,2023,121:e103360.

RUNNING S W, ZHAO M. MOD17 user’s guide. Daily GPP and annual NPP (MOD17A2/A3) products NASA earth observing system MODIS land algorithm. . http://modis.gsfc.nasa.gov.

LIU L Y, LIU X J, CHEN J D, et al. Estimating maize GPP using near-infrared radiance of vegetation[J].Science of Remote Sensing,2020,2:e100009.

CHEN S Y, SUI L C, LIU L Y, et al. Effect of the partitioning of diffuse and direct APAR on GPP estimation[J].Remote Sensing,2022,14(1):e57.

HE M Z, JU W M, ZHOU Y L, et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity[J].Agricultural and Forest Meteorology,2013,173:28-39.

ZHANG H L, BAI J, SUN R, et al. An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors[J].Agricultural and Forest Meteorology,2023,332:e109359.

徐静馨,郑有飞,王圣,等.O

胁迫下冬小麦总初级生产力的损耗模拟[J].环境科学,2019,40(10):4725-4732. XU J X, ZHENG Y F, WANG S, et al. Simulated ozone damage on gross primary productivity (GPP) in a winter wheat field[J].Environmental Science,2019,40(10):4725-4732.

KING D A, TURNER D P, RITTS W D. Parameterization of a diagnostic carbon cycle model for continental scale application[J].Remote Sensing of Environment,2011,115(7):1653-1664.

XU Z Z, ZHOU G S. Effects of water stress and nocturnal temperature on carbon allocation in the perennial grass,

Leymus chinensis

.Physiologia Plantarum,2005,123(3):272-280.

PEI Y Y, DONG J W, ZHANG Y, et al. Evolution of light use efficiency models:Improvement,uncertainties,and implications[J].Agricultural and Forest Meteorology,2022,317:e108905.

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基于大气CO

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

DOI：10.13870/j.cnki.stbcxb.2024.05.029

摘要

Abstract

关键词

Keywords

references