Quantification of soil respiration in forest ecosystems across China

被引:39
|
作者
Song, Xinzhang [1 ]
Peng, Changhui [2 ,3 ]
Zhao, Zhengyong [2 ]
Zhang, Zhiting [4 ]
Guo, Baohua [5 ]
Wang, Weifeng [6 ]
Jiang, Hong [1 ]
Zhu, Qiuan [3 ]
机构
[1] Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Nurturing Stn, Linan 311300, Peoples R China
[2] Univ Quebec, Dept Biol Sci, Inst Environm Sci, Montreal, PQ H3C 3P8, Canada
[3] Northwest Agr & Forest Univ, Coll Forestry, Lab Ecol Forecasting & Global Change, Yangling 712100, Peoples R China
[4] Hebei North Coll, Zhangjiakou 075000, Peoples R China
[5] Int Ctr Bamboo & Rattan, Beijing 100102, Peoples R China
[6] McGill Univ, Dept Geog, Montreal, PQ H3A 0B9, Canada
关键词
Soil CO2 flux; Temperature sensitivity; Artificial neural network model; Climate change; Carbon cycle; TEMPERATURE SENSITIVITY; ORGANIC-CARBON; CO2; VARIABILITY; STORAGE; MODEL;
D O I
10.1016/j.atmosenv.2014.05.071
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We collected 139 estimates of the annual forest soil CO2 flux and 173 estimates of the (210 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 33.65 t CO2 ha(-1) year(-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 Q(10) value of 1.28 was lower than the world average (1.4-2.0). The Q(10) values derived from the soil temperature at a depth of 5 cm 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. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:546 / 551
页数:6
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