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Spatiotemporal Pattern of Soil Respiration of Terrestrial Ecosystems in China: The Development of a Geostatistical Model and Its Simulation

机译:中国陆地生态系统土壤呼吸的时空格局:地统计学模型的发展及其模拟

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摘要

Quantification of the spatiotemporal pattern of soil respiration (R_s at the regional scale can provide a theoretical basis and fundamental data for accurate evaluation of the global carbon budget This study summarizes the R_s data measured in China from 1995 to 2004. Based on the data, a new region-scale geostatistical model of soil respiration (GSMSR) was developed by modifying a global scale statistical model. The GSMSR model, which is driven by monthly air temperature, monthly precipitation, and soil organic carbon (SOC) density, can capture 64% of the spatiotemporal variability of soil R_s. We evaluated the spatiotemporal pattern of R_s in China using the GSMSR model. The estimated results demonstrate that the annual R_s in China ranged from 3.77 to 4.00 Pg C yr~(-1) between 1995 and 2004, with an average value of 3.84 ± 0.07 Pg C yr~(-1), contributing 3.92%-4.87% to the global soil CO_2 emission. Annual R_s rate of evergreen broadleaved forest ecosystem was 698 ± 11 g C m~(-2) yr~(-1), significantly higher than that of grassland (439 ± 7 g C m~(-2)yr~(-1) and cropland (555 ± 12 g C m~(-2)yr~(-1). The contributions of grassland, cropland, and foresrland ecosystems to the total R_s in China were 48.38 ± 0.35%, 22.19 ± 0.18%, and 20.84 ± 0.13%, respectively.
机译:定量分析土壤呼吸(R_s)的时空格局可以为准确评估全球碳预算提供理论依据和基础数据。本研究总结了1995年至2004年在中国测得的R_s数据。通过修改全球尺度统计模型,开发了新的区域尺度土壤呼吸地统计学模型(GSMSR),该模型由月气温,月降水量和土壤有机碳(SOC)密度驱动,可捕获64%我们用GSMSR模型评估了中国R_s的时空格局,估计结果表明,1995年至2004年间,中国年R_s在3.77至4.00 Pg C yr〜(-1)范围内,平均值为3.84±0.07 Pg C yr〜(-1),占全球土壤CO_2排放的3.92%-4.87%,常绿阔叶林生态系统的年R_s率为698±11 g C m〜(-2)。年〜(-1),显着高于草原(439±7 g C m〜(-2)yr〜(-1)和农田(555±12 g C m〜(-2)yr〜(-1) 。中国草地,农田和前陆生态系统对总R_s的贡献分别为48.38±0.35%,22.19±0.18%和20.84±0.13%。

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  • 来源
    《Environmental Science & Technology》 |2010年第16期|p.6074-6080|共7页
  • 作者单位

    Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;

    rnDepartment of Environmental Science and Technology, East China Normal University, Shanghai 200062, China;

    rnSynthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;

    rnSynthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;

    rnSynthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China Department of Biosystems Engineering and Soil Science, Institute for a Secure and Sustainable Environment, Center for Environmental Biotechnology, The University of Tennessee, Knoxville, Tennessee 37996-4134;

    rnSynthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;

    rnInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 入库时间 2022-08-17 14:04:02

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