SongPengZhaoEtAl2014

Référence

Song, X., Peng, C., Zhao, Z., Zhang, Z., Guo, B., Wang, W., Jiang, H., Zhu, Q. (2014) Quantification of soil respiration in forest ecosystems across China. Atmospheric Environment, 94:546-551. (Scopus )

Résumé

We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the Q10 value (the temperature sensitivity) assembled from 90 published studies across Chinese forest ecosystems. We analyzed the annual soil respiration (Rs) rates and the temperature sensitivities of seven forest ecosystems, including evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), broadleaf and needleleaf mixed forests (BNMF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), bamboo forests (BF) and shrubs (SF). The results showed that the mean annual Rs rate was 33.65t CO2ha-1year-1 across Chinese forest ecosystems. Rs rates were significantly different (P<0.001) among the seven forest types, and were significantly and positively influenced by mean annual temperature (MAT), mean annual precipitation (MAP), and actual evapotranspiration (AET); but negatively affected by latitude and elevation. The mean Q10 value of 1.28 was lower than the world average (1.4-2.0). The Q10 values derived from the soil temperature at a depth of 5cm varied among forest ecosystems by an average of 2.46 and significantly decreased with the MAT but increased with elevation and latitude. Moreover, our results suggested that an artificial neural network (ANN) model can effectively predict Rs across Chinese forest ecosystems. This study contributes to better understanding of Rs across Chinese forest ecosystems and their possible responses to global warming. © 2014 Elsevier Ltd.

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@ARTICLE { SongPengZhaoEtAl2014,
    AUTHOR = { Song, X. and Peng, C. and Zhao, Z. and Zhang, Z. and Guo, B. and Wang, W. and Jiang, H. and Zhu, Q. },
    TITLE = { Quantification of soil respiration in forest ecosystems across China },
    JOURNAL = { Atmospheric Environment },
    YEAR = { 2014 },
    VOLUME = { 94 },
    PAGES = { 546-551 },
    NOTE = { cited By (since 1996)0 },
    ABSTRACT = { We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the Q10 value (the temperature sensitivity) assembled from 90 published studies across Chinese forest ecosystems. We analyzed the annual soil respiration (Rs) rates and the temperature sensitivities of seven forest ecosystems, including evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), broadleaf and needleleaf mixed forests (BNMF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), bamboo forests (BF) and shrubs (SF). The results showed that the mean annual Rs rate was 33.65t CO2ha-1year-1 across Chinese forest ecosystems. Rs rates were significantly different (P<0.001) among the seven forest types, and were significantly and positively influenced by mean annual temperature (MAT), mean annual precipitation (MAP), and actual evapotranspiration (AET); but negatively affected by latitude and elevation. The mean Q10 value of 1.28 was lower than the world average (1.4-2.0). The Q10 values derived from the soil temperature at a depth of 5cm varied among forest ecosystems by an average of 2.46 and significantly decreased with the MAT but increased with elevation and latitude. Moreover, our results suggested that an artificial neural network (ANN) model can effectively predict Rs across Chinese forest ecosystems. This study contributes to better understanding of Rs across Chinese forest ecosystems and their possible responses to global warming. © 2014 Elsevier Ltd. },
    AUTHOR_KEYWORDS = { Artificial neural network model; Carbon cycle; Climate change; Temperature sensitivity },
    CODEN = { AENVE },
    DOCUMENT_TYPE = { Review },
    DOI = { 10.1016/j.atmosenv.2014.05.071 },
    ISSN = { 13522310 },
    KEYWORDS = { Carbon dioxide; Climate change; Ecosystems; Global warming; Neural networks; Soils, Actual evapotranspiration; Artificial neural network modeling; Artificial neural network models; Carbon cycles; Mean annual precipitation; Mean annual temperatures; Soil CO; Temperature sensitivity, Forestry, artificial neural network; carbon cycle; carbon dioxide; carbon flux; climate change; evapotranspiration; forest ecosystem; forest soil; global warming; precipitation (climatology); soil respiration, artificial neural network; autotrophy; bamboo; carbon cycle; China; climate change; environmental temperature; evapotranspiration; evergreen; forest soil; greenhouse effect; latitude; microbial respiration; precipitation; priority journal; review; soil analysis; soil depth; soil quality; soil respiration; soil temperature; temperature sensitivity, Carbon; Carbon Dioxide; Ecosystems; Forestry; Neural Networks; Soil, China },
    SOURCE = { Scopus },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84901989085&partnerID=40&md5=129ccb03c9bbfe309603b72a1df037c0 },
}

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