不同景观综合体土壤有机碳空间分异特征及驱动因素研究

Q2 Environmental Science
Chen-Chen Kong, Fang Yan, Wei-Rui Wang, Shi-Wen Zhang, Dan-Dan Guo, Shan Wang
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引用次数: 0

摘要

研究区域农用地土壤有机碳(SOC)的空间分异特征并分析其驱动因素,对于筛选农用地有机碳预测的辅助变量和准确预测土壤碳储量具有重要意义。本研究以北京市不同类型农用地景观复合体的有机碳为研究对象。利用北京市耕地质量长期定位监测数据和田间取样测试数据,探讨了不同景观综合体土壤有机碳含量及其储量的差异。利用多源和开源数据作为影响土壤有机碳空间分异的环境变量,通过GeoDetector和地理加权回归模型,探讨了不同景观复合体中土壤有机碳与气候、地形、土壤母质、土地利用和生物量因子之间的定量和空间关系。此外,我们构建了结构方程模型,从直接和间接的角度揭示了各个驱动因子对有机碳的影响途径。最终确定了研究区土壤有机碳的主要控制因素。结果表明:①研究区各类型景观综合体的ω(SOC)平均值为6.23 ~ 28.26 g·kg-1,变异系数为3.80% ~ 33.92%,具有空间异质性;②气候、地形、土壤母质、土地类型和生物量因子对有机碳的贡献均达到极显著水平(P <;0.01),各因子互作后对有机碳具有协同效应。各因子均能解释土壤有机碳在0.691 ~ 0.704范围内的空间变异,解释效度稳定。③温度、地形和土地类型对气候变化的直接影响非常显著(P <;0.01)。其中,温度与有机碳含量呈负相关。在不同土地类型中,林地景观复合体的有机碳含量较高,耕地景观复合体的有机碳含量较低。地形因子对土壤有机碳的直接影响最为显著(效应值= 0.698,P <;0.001),山地土壤有机碳含量较高,平原土壤有机碳含量较低,地形因素也可以通过温度和土地类型的差异间接影响土壤有机碳含量(P <;0.01)。土壤母质和归一化植被指数与地形因子呈显著相关,但对有机碳含量的直接影响不显著(P≥0.05)。总体而言,地形因素是影响研究区土壤有机碳空间异质性的重要因素。它可以作为一个关键的辅助因子,为准确评估研究区农用地土壤碳储量提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Spatial Differentiation Characteristics and Driving Factors of Soil Organic Carbon in Various Landscape Complexes].

Investigating the spatial differentiation characteristics of soil organic carbon (SOC) in regional agricultural land and analyzing its driving factors are important for screening auxiliary variables for SOC prediction in agricultural land and the accurate prediction of soil carbon stock. This study considered SOC in different types of agricultural land landscape complexes in Beijing as the research object. The differences in SOC content and its stock in different landscape complexes were explored based on the long-term positional monitoring data on the quality of cultivated land in Beijing and the field sampling and testing data. Utilizing multi-source and open-source data as environmental variables that affected SOC spatial differentiation, we explored the quantitative and spatial relationships between SOC and climate, topography, soil parent material, land use, and biomass factors in different landscape complexes through GeoDetector and geographically weighted regression modeling. Additionally, we constructed a structural equation model to reveal the pathways that influence each driving factor on SOC in terms of direct and indirect effects. Ultimately, the major controlling factors of SOC were identified in the study area. The results showed that: ① The mean values of ω(SOC) for various types of landscape complexes in the study area ranged from 6.23 to 28.26 g·kg-1, with a variation coefficient of 3.80% to 33.92%, showing spatial heterogeneity. ② Climate, topography, soil parent material, land type, and biomass factors contributed to SOC at highly significant levels (P < 0.01), and all factors were synergistic on SOC after their interaction. All factors could explain the spatial variation of SOC from 0.691 to 0.704, with stable explanatory validity. ③ Temperature, topography, and land type showed a highly significant direct effect (P < 0.01) on SOC in the study area. Among them, temperature was negatively correlated with SOC content. In different land types, SOC content was higher in landscape complexes located in forested land and lower in cultivated land. Topographic factors had the most excellent direct effect on SOC (effect value = 0.698, P < 0.001), with higher SOC content in the mountains and lower in the plains, and topographic factors could also exert an indirect effect on SOC through differences in temperature and land type (P < 0.01). Soil parent material and normalized difference vegetation index correlated significantly with topographic factors but had non-significant (P ≥ 0.05) direct effects on SOC content. Overall, topographic factors are essential factors influencing SOC spatial heterogeneity in the study area. It can be used as a critical cofactor to provide a reference for accurately assessing soil carbon stock on agricultural land in the study area.

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环境科学
环境科学 Environmental Science-Environmental Science (all)
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4.40
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