Simulation soil organic carbon change in China's Tai-Lake paddy soils

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• 作者：L.M. Zhang^a ; ^b ; D.S. Yu^a ; ^{dshyu@issas.ac.cn} ; X.Z. Shi^a ; S.X. Xu^a ; S.H. Wang^a ; S.H. Xing^b ; Y.C. Zhao^a
• 关键词：Soil organic carbon ; Tai-Lake region ; Paddy soil ; Denitrification&ndash ; decomposition (DNDC) ; 1 ; 50 ; 000 soil database
• 刊名：Soil & Tillage Research
• 出版年：2012
• 期刊代码：95_01671987
• 类别：abs
• 出版时间：May, 2012
• 卷：121
• 期：Complete
• 页码：1-9
• 文件大小：902 K

摘要

Regional soil organic carbon (SOC) modeling is the dominant approach for regional and global carbon cycling assessment, but the models are often applied to regions with high heterogeneity that are not adequately represented by the spatially limited soil data. This study used version 9.1 of the denitrification-decomposition (DNDC) model with the most detailed soil database for the paddy region of China. The database is a 1:50,000 record derived from 1107 paddy soil profiles with 52,034 polygons. The simulations suggested that the 2.3 Mha of paddy soils in the Tai-Lake region had a net sequestration of about 1.48 Tg C from 1982 to 2000, with the annual SOC change ranging from 鈭?5 to 92 kg C ha^鈭? y^鈭?. In general, paddy soils in the Tai-Lake region were a weak sink of atmospheric CO₂. Highest SOC loss (鈭?01 kg C ha^鈭? y^鈭?) was associated with the gleyed paddy soil subgroup. Highest SOC sequestration (205 kg C ha^鈭? y^鈭?) was associated with the submergenic paddy soil subgroup. On a regional basis, model simulations indicated a C loss in the polder region (鈭?9 kg C ha^鈭? y^鈭?), but this was offset by increases in the alluvial plain (104 kg C ha^鈭? y^鈭?), low mountainous and hilly region (87 kg C ha^鈭? y^鈭?), and Tai-Lake Plain (7 kg C ha^鈭? y^鈭?). At the administrative scale, SOC of most counties in Zhejiang Province decreased, while it increased in Jiangsu Province and Shanghai City. Overall, the SOC change in the Tai-Lake region was strongly influenced by paddy subgroup, sub-region, and administrative area, because of the high variability in soil properties. This emphasizes that the use of detailed soil data sets with high-resolution digital soil maps and robust soil profile data essential for creating accurate models of the soil carbon cycle.