艾比湖流域土壤有机质与影响因素响应系研究
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摘要
土壤有机质（SOM）是陆地生态系统中碳循环的重要源和汇，是土壤的一个重要组成，它能够提高土壤的肥力和生产力并能够固定碳应对大气CO2浓度的升高。土壤有机质含量是评价生态恢复效果、抑制碳排放和保持土壤质量的重要依据，土壤有机质的变化在土地荒漠化、草场退化和区域生态环境恶化等广受关注的环境问题中起着关键作用。鉴于土壤有机质的重要性，研究土壤有机质与影响因素空间分布特征及其响应关系是十分必要的。
目前，土壤有机质与影响因素响应关系的分析方法局限于经典统计分析工具，未能将土壤有机质与诸多影响因素置于多元统计分析框架下，并且通常假定响应关系是线性和空间平稳的。但大量研究显示土壤有机质及影响因素一般均具有空间变异性，因而土壤有机质与影响因素的响应关系亦可能具有空间变异性，即空间非平稳性和非线性特征。尽管有个别文献报告了相关成果，但研究缺乏系统性。
本论文以艾比湖流域为例，将土壤有机质与土壤质地类型、植被群落类型、土壤剖面深度、土壤酸碱性、电导率和重金属含量为影响因素构建了艾比湖流域土壤有机质响应关系的多元统计分析框架，揭示土壤有机质的非线性和空间非平稳性，为其他区域的研究提供参考和借鉴。首先，分析艾比湖流域土壤有机质在水平和垂直空间分布的变异特征；其次，建立土壤有机质与土壤因子的多元线性回归模型及空间展开模型，旨在多元线性框架下分析土壤有机质对土壤因子的线性响应关系和空间变化特征。然后，以探索土壤有机质与土壤因子的非线性响应关系为目的，建立可加模型；最后，利用空间变系数模型研究了土壤有机质与土壤因子的空间非平稳性。
本文的主要结论如下：
(1)艾比湖流域土壤有机质在水平和垂直空间分布不均匀，具有空间异质性。随着土壤深度变化,土壤有机质分布呈现不同的空间变异特征，流域内1—80cm呈现孔穴特征，但在81—120cm土壤有机质含量变化较为连续，呈现出流域东西两端高中间低；流域内土壤有机质沿土壤深度垂直分布模式表现出分异特征，流域中部SOM随土壤深度增加而降低，但在流域东部和西部SOM随土壤深度增加呈升高趋势；经验正交函数分解（EOF）方法得到，艾比湖流域东部和西部有机质含量随着土壤深度增加而呈现线性增加，但在流域中部土壤有机质含量沿土壤深度方向呈线性递减的趋势，并以60cm土壤深度为阈值产生分异。
（2）土壤酸碱性、电导率与土壤有机质含量线性负相关，而土壤重金属与有机质含量呈现线性正相关，并且在不同土壤深度影响程度有差异；土壤有机质与土壤因子的响应关系具有显著非线性特征，并且不同土壤深度的非线性模式变化不同；土壤有机质含量与土壤因子的回归关系是空间非平稳的，而且不同土壤深度的空间非平稳特征亦有差异。
（3）模型残差的全局和局部诊断结果说明，线性回归模型拟合残差具有显著的非线性和空间相关性；可加模型的拟合残差不具有显著的非线性；空间展开模型和空间变系数模型拟合残差的空间相关性均不显著。多元线性回归模型拟合效果较差，可加模型能够有效地探索变量间非线性响应关系；空间变系数模型能够充分揭示变量间响应关系的空间变异性；此外，基于局部线性拟合方法的空间变系数模型还能够有效探索变量的局部非线性响应关系。
（4）艾比湖流域生态景观破碎化、斑块化、干旱区植被根际沉积特点以及土壤剖面成土演化过程的空间异质性，主导土壤有机质在浅层和深层土壤呈现出不同模式的空间分异特征。
论文在分析方法和建模方面主要有以下创新：
（1）在土壤要素垂直分布相关性和变异性分析方法方面提出了垂直滞后相关系数以测度土壤要素沿土壤深度垂直方向相关性的强弱。
（2）以局部回归技术为基础建立了土壤有机质与土壤因子的可加模型和空间变数模型，旨在分析空间变量回归关系的非线性和空间非平稳性。
（3）应用空间数据模型残差的局部回归分析方法，诊断残差序列的趋势性和非线性；利用局部线性方法拟合残差的非参数回归模型，并以残差序列光滑曲线为依据，评价线性回归模型、空间展开模型、可加模型和空间变系数模型在艾比湖流域土壤有机质与土壤因子非线性响应关系方面的分析效果。
Soil organic matter (SOM) is an important source and sink of carbon cycling interrestrial ecosystems, which is an important attribute of the soil, it can improve soilfertility and productivity and fixed carbon response to elevated atmospheric CO2concentration. SOM content is an important basis for evaluation of ecologicalrestoration effect, to curb carbon emissions and maintain soil quality. And it is plays akey role in desertification, grassland degradation and regional ecological environmentdeteriorating in environmental issues of wide public concern. In view of theimportance of soil organic matter, it is very necessary to study the spatial distributioncharacteristics and response relationship between soil organic matter and influencingfactors.
Currently, SOM and its influencing factors analysis is limited to the classicalstatistical analysis tools, failed to soil organic matter and many influencing factors inthe multivariate statistical analysis framework, and revealed that response relationshipis linear and smooth space. It will provide refence and studies for other regions.However, a large number of studies have shown that soil organic matter and itsinfluencing factors are generally spatial variability, thus the response relationship ofsoil organic matter and soil factor may also have spatial variability, which is spatialnon-stationary and nonlinear. Although the individual reports in the literature relatedoutcomes, it is the lack of systematic research.
Ebinur Lake Basin soil organic matter content and soil texture type, vegetationcommunity types, the depth of the soil profile, soil pH, soil conductivity and soilheavy metal content and other factors response relationship of nonlinear and spatialnon-stationary would be included in multivariate statistical analysis framework. Firstly, I have analyzed the distribution of the horizontal and vertical space variabilityfor the Ebinur Lake watershed soil organic matter; Secondly, multiple linearregression model and spatial expand model have been made between soil organicmatter and soil factors, aimed to analysis the linear response and spatial variationcharacteristics of soil organic matter and soil factors with multivariate statisticalanalysis framework. Then, it is a purpose to explore the nonlinear responserelationship of soil organic matter and soil factors, and established the additive model;Finally, I have applied the spatially varying coefficient model to study spatialnon-stationary.
The main conclusions are as follows:
(1)The results show spatial variability of SOM content exhibits differentcharacteristics with changes in soil depth; SOM display a patchy pattern in the1–80cm soil layer in the Ebinur Lake Basin. However, the SOM content changes arerelatively continuous in the81–120cm soil layer, and the SOM content is higher in theeastern and western parts of the region than in the central region of the Ebinur LakeBasin. The spatial variation patterns satisfy the hole model in the1–80cm soil layer,while the exponent model is fitted well in the81–120cm soil layer in thesemi-variogram. Also, the results of the EOF analysis illustrate the vertical spatialdistribution of SOM shows different characteristics with the soil depth across theEbinur Lake Basin. The SOM content decreases as the soil depth increases in thecentral part of the Basin. However, the SOM content displays an increasing trend withincreasing soil depth in the eastern and western regions of the Ebinur Lake Basin. Inthe Ebinur Lake Basin, the patterns of spatial variation in the SOM are extremelysignificantly different between the shallow soil layers when compared to the deepersoil layers.
(2)The soil pH, conductivity and SOM are linear negatively correlated, and theheavy metal content and SOM are linear positively correlated, while the degree of correlation is varying across soil depths. The results of additive model analysis showthat there are nonlinear relationship between the SOM content and soil factors, andthe nonlinear patterns are dramatic changes in different soil depths. The regressionrelationships of SOM and soil factors are spatial non-stationary and the patterns arevarying across the soil depths.
(3)The global and local diagnostic results of the model residual indicate thatlinear regression model fitting residual with significant nonlinearity and spatialcorrelation. The fitting residual of Additive model residual was not significantnonlinear; and the fitting residual spatial correlation of spatially expand model andspatially varying coefficient model was not significant. Multiple linear regressionmodel are less effective, additive model was able to effectively explore the nonlinearresponse relationship between the variables; spatially varying coefficient model canfully reveal the spatial variability of response relationship; In addition, spatiallyvarying coefficient model based on local linear fitting method can effectively analysisthe local nonlinear response relationship of the variables.
(4)This characteristic is strongly relevant to the patches of the arid oasisecological landscape and the features of the vegetation rhizodeposition. Also, thespatial heterogeneity during evolution of the soil profiles in the Basin suggests asignificant restraining effect on vertical spatial variability of the SOM with soil depth.
Papers have some innovations in analytical methods and modeling:
(1) The correlation and variability analysis method of vertical distribution of thesoil elements proposed vertical lag correlation coefficient to measure the strength ofthe soil elements along the soil depth vertical direction correlation.
(2)Additive model and spatially varying coefficient model based on localregression techniques between SOM and soil factor objected to analyze nonlinear andspatial non-stationary of the regression relationship.
(3)Local regression analysis method of spatial data analysis model residuals had been used to diagnosis trend and nonlinear of the residuals, the paper applied locallinear method to fit nonparametric regression model of the residuals, and based on theresidual series of smooth curves had been used to evaluate the analysis effect of thenonlinear response relations on linear regression model, spatially expand model,additive model and spatially varying coefficient model between soil organic matterand soil factor in the Ebinur Lake Basin.
目前，土壤有机质与影响因素响应关系的分析方法局限于经典统计分析工具，未能将土壤有机质与诸多影响因素置于多元统计分析框架下，并且通常假定响应关系是线性和空间平稳的。但大量研究显示土壤有机质及影响因素一般均具有空间变异性，因而土壤有机质与影响因素的响应关系亦可能具有空间变异性，即空间非平稳性和非线性特征。尽管有个别文献报告了相关成果，但研究缺乏系统性。
本论文以艾比湖流域为例，将土壤有机质与土壤质地类型、植被群落类型、土壤剖面深度、土壤酸碱性、电导率和重金属含量为影响因素构建了艾比湖流域土壤有机质响应关系的多元统计分析框架，揭示土壤有机质的非线性和空间非平稳性，为其他区域的研究提供参考和借鉴。首先，分析艾比湖流域土壤有机质在水平和垂直空间分布的变异特征；其次，建立土壤有机质与土壤因子的多元线性回归模型及空间展开模型，旨在多元线性框架下分析土壤有机质对土壤因子的线性响应关系和空间变化特征。然后，以探索土壤有机质与土壤因子的非线性响应关系为目的，建立可加模型；最后，利用空间变系数模型研究了土壤有机质与土壤因子的空间非平稳性。
本文的主要结论如下：
(1)艾比湖流域土壤有机质在水平和垂直空间分布不均匀，具有空间异质性。随着土壤深度变化,土壤有机质分布呈现不同的空间变异特征，流域内1—80cm呈现孔穴特征，但在81—120cm土壤有机质含量变化较为连续，呈现出流域东西两端高中间低；流域内土壤有机质沿土壤深度垂直分布模式表现出分异特征，流域中部SOM随土壤深度增加而降低，但在流域东部和西部SOM随土壤深度增加呈升高趋势；经验正交函数分解（EOF）方法得到，艾比湖流域东部和西部有机质含量随着土壤深度增加而呈现线性增加，但在流域中部土壤有机质含量沿土壤深度方向呈线性递减的趋势，并以60cm土壤深度为阈值产生分异。
（2）土壤酸碱性、电导率与土壤有机质含量线性负相关，而土壤重金属与有机质含量呈现线性正相关，并且在不同土壤深度影响程度有差异；土壤有机质与土壤因子的响应关系具有显著非线性特征，并且不同土壤深度的非线性模式变化不同；土壤有机质含量与土壤因子的回归关系是空间非平稳的，而且不同土壤深度的空间非平稳特征亦有差异。
（3）模型残差的全局和局部诊断结果说明，线性回归模型拟合残差具有显著的非线性和空间相关性；可加模型的拟合残差不具有显著的非线性；空间展开模型和空间变系数模型拟合残差的空间相关性均不显著。多元线性回归模型拟合效果较差，可加模型能够有效地探索变量间非线性响应关系；空间变系数模型能够充分揭示变量间响应关系的空间变异性；此外，基于局部线性拟合方法的空间变系数模型还能够有效探索变量的局部非线性响应关系。
（4）艾比湖流域生态景观破碎化、斑块化、干旱区植被根际沉积特点以及土壤剖面成土演化过程的空间异质性，主导土壤有机质在浅层和深层土壤呈现出不同模式的空间分异特征。
论文在分析方法和建模方面主要有以下创新：
（1）在土壤要素垂直分布相关性和变异性分析方法方面提出了垂直滞后相关系数以测度土壤要素沿土壤深度垂直方向相关性的强弱。
（2）以局部回归技术为基础建立了土壤有机质与土壤因子的可加模型和空间变数模型，旨在分析空间变量回归关系的非线性和空间非平稳性。
（3）应用空间数据模型残差的局部回归分析方法，诊断残差序列的趋势性和非线性；利用局部线性方法拟合残差的非参数回归模型，并以残差序列光滑曲线为依据，评价线性回归模型、空间展开模型、可加模型和空间变系数模型在艾比湖流域土壤有机质与土壤因子非线性响应关系方面的分析效果。
Soil organic matter (SOM) is an important source and sink of carbon cycling interrestrial ecosystems, which is an important attribute of the soil, it can improve soilfertility and productivity and fixed carbon response to elevated atmospheric CO2concentration. SOM content is an important basis for evaluation of ecologicalrestoration effect, to curb carbon emissions and maintain soil quality. And it is plays akey role in desertification, grassland degradation and regional ecological environmentdeteriorating in environmental issues of wide public concern. In view of theimportance of soil organic matter, it is very necessary to study the spatial distributioncharacteristics and response relationship between soil organic matter and influencingfactors.
Currently, SOM and its influencing factors analysis is limited to the classicalstatistical analysis tools, failed to soil organic matter and many influencing factors inthe multivariate statistical analysis framework, and revealed that response relationshipis linear and smooth space. It will provide refence and studies for other regions.However, a large number of studies have shown that soil organic matter and itsinfluencing factors are generally spatial variability, thus the response relationship ofsoil organic matter and soil factor may also have spatial variability, which is spatialnon-stationary and nonlinear. Although the individual reports in the literature relatedoutcomes, it is the lack of systematic research.
Ebinur Lake Basin soil organic matter content and soil texture type, vegetationcommunity types, the depth of the soil profile, soil pH, soil conductivity and soilheavy metal content and other factors response relationship of nonlinear and spatialnon-stationary would be included in multivariate statistical analysis framework. Firstly, I have analyzed the distribution of the horizontal and vertical space variabilityfor the Ebinur Lake watershed soil organic matter; Secondly, multiple linearregression model and spatial expand model have been made between soil organicmatter and soil factors, aimed to analysis the linear response and spatial variationcharacteristics of soil organic matter and soil factors with multivariate statisticalanalysis framework. Then, it is a purpose to explore the nonlinear responserelationship of soil organic matter and soil factors, and established the additive model;Finally, I have applied the spatially varying coefficient model to study spatialnon-stationary.
The main conclusions are as follows:
(1)The results show spatial variability of SOM content exhibits differentcharacteristics with changes in soil depth; SOM display a patchy pattern in the1–80cm soil layer in the Ebinur Lake Basin. However, the SOM content changes arerelatively continuous in the81–120cm soil layer, and the SOM content is higher in theeastern and western parts of the region than in the central region of the Ebinur LakeBasin. The spatial variation patterns satisfy the hole model in the1–80cm soil layer,while the exponent model is fitted well in the81–120cm soil layer in thesemi-variogram. Also, the results of the EOF analysis illustrate the vertical spatialdistribution of SOM shows different characteristics with the soil depth across theEbinur Lake Basin. The SOM content decreases as the soil depth increases in thecentral part of the Basin. However, the SOM content displays an increasing trend withincreasing soil depth in the eastern and western regions of the Ebinur Lake Basin. Inthe Ebinur Lake Basin, the patterns of spatial variation in the SOM are extremelysignificantly different between the shallow soil layers when compared to the deepersoil layers.
(2)The soil pH, conductivity and SOM are linear negatively correlated, and theheavy metal content and SOM are linear positively correlated, while the degree of correlation is varying across soil depths. The results of additive model analysis showthat there are nonlinear relationship between the SOM content and soil factors, andthe nonlinear patterns are dramatic changes in different soil depths. The regressionrelationships of SOM and soil factors are spatial non-stationary and the patterns arevarying across the soil depths.
(3)The global and local diagnostic results of the model residual indicate thatlinear regression model fitting residual with significant nonlinearity and spatialcorrelation. The fitting residual of Additive model residual was not significantnonlinear; and the fitting residual spatial correlation of spatially expand model andspatially varying coefficient model was not significant. Multiple linear regressionmodel are less effective, additive model was able to effectively explore the nonlinearresponse relationship between the variables; spatially varying coefficient model canfully reveal the spatial variability of response relationship; In addition, spatiallyvarying coefficient model based on local linear fitting method can effectively analysisthe local nonlinear response relationship of the variables.
(4)This characteristic is strongly relevant to the patches of the arid oasisecological landscape and the features of the vegetation rhizodeposition. Also, thespatial heterogeneity during evolution of the soil profiles in the Basin suggests asignificant restraining effect on vertical spatial variability of the SOM with soil depth.
Papers have some innovations in analytical methods and modeling:
(1) The correlation and variability analysis method of vertical distribution of thesoil elements proposed vertical lag correlation coefficient to measure the strength ofthe soil elements along the soil depth vertical direction correlation.
(2)Additive model and spatially varying coefficient model based on localregression techniques between SOM and soil factor objected to analyze nonlinear andspatial non-stationary of the regression relationship.
(3)Local regression analysis method of spatial data analysis model residuals had been used to diagnosis trend and nonlinear of the residuals, the paper applied locallinear method to fit nonparametric regression model of the residuals, and based on theresidual series of smooth curves had been used to evaluate the analysis effect of thenonlinear response relations on linear regression model, spatially expand model,additive model and spatially varying coefficient model between soil organic matterand soil factor in the Ebinur Lake Basin.
引文
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