...
【24h】

Uncertainty of organic carbon dynamics in Tai-Lake paddy soils of China depends on the scale of soil maps

机译:中国大湖稻田土壤有机碳动力学的不确定性取决于土壤图谱的尺度

获取原文
获取原文并翻译 | 示例

摘要

Agro-ecosystem models have been widely used to quantify soil organic carbon (SOC) dynamics based on digital soil maps. However, most of the studies use soil data of single or limited choices of map scales, thus the influence of map scales on SOC dynamics has rarely been quantified. In this study, six digital paddy soils databases of the Tai -Lake region in China at scales of 1:50,000 (P005),1:200,000 (P02),1:500,000 (P05), 1:1,000,000 (P1), 1:4,000,000 (P4), and 1:14,000,000 (P14) were used to drive the DNDC (DeNitrification & DeComposition) model to quantify SOC dynamics for the period of 2001-2019. Model simulations show that the total SOC changes from 2001 to 2019 in the top layer (0-30 cm) of paddy soils using P005, P02, P05, P1, P4, and P14 soil maps would be 3.44, 3.71, 1.41, 2.01, 3.57 and 0.10 Tg C, respectively. The simulated SOC dynamics are significantly influenced by map scales. Taking the total SOC changes based on the most detailed soil map, P005, as a reference, the relative deviation of P02, P05, P1, P4, and P14 were 7.9%, 58.9%, 41.6%, 3.9%, and 97.0%, respectively. Such differences are primarily attributed to missing soil types and spatial variations in soil types in coarse -scale maps. Although the relative deviation of P4 soil map for the entire Tai -Lake region is the lowest, substantial differences (i.e., 22-1010%) exist at soil subgroups level. Overall, soil map scale of P02 provides best accuracy for quantifying SOC dynamics of paddy soils in the study region. Considering the soil data availability of. entire China, P1 soil map is also recommended.This study suggested how to select an appropriate scale of input soil data for modeling the carbon cycle of agro-ecosystems. (C) 2016 Elsevier B.V. All rights reserved.
机译:农业生态系统模型已被广泛用于基于数字土壤图来量化土壤有机碳(SOC)动态。但是,大多数研究使用的是单一或有限选择的地图比例尺的土壤数据,因此很少量化地图比例尺对SOC动力学的影响。在这项研究中,中国大湖地区的六个数字水稻土数据库分别以1:50,000(P005),1:200,000(P02),1:500,000(P05),1:1,000,000(P1),1:使用4,000,000(P4)和1:14,000,000(P14)来驱动DNDC(反硝化和分解)模型以量化2001-2019年期间的SOC动态。模型模拟表明,使用P005,P02,P05,P1,P4和P14的土壤图,从2001年到2019年,水稻土壤顶层(0-30 cm)的总SOC变化为3.44、3.71、1.41、2.01,分别为3.57和0.10 TgC。模拟的SOC动力学受地图比例显着影响。以最详细的土壤图(P005)为基础的总SOC变化,以P02,P05,P1,P4和P14的相对偏差为7.9%,58.9%,41.6%,3.9%和97.0%,分别。这种差异主要归因于缺少的土壤类型和粗略地图中土壤类型的空间变化。尽管整个太湖地区P4土壤分布图的相对偏差最小,但在土壤亚组水平上存在实质性差异(即22-1010%)。总体而言,P02的土壤图比例尺为定量研究区域内水稻土的SOC动态提供了最佳的准确性。考虑到土壤数据的可用性。在整个中国,也推荐使用P1土壤图。本研究建议如何选择适当规模的输入土壤数据来模拟农业生态系统的碳循环。 (C)2016 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号