预测土壤水力性质的形态学网络模型应用研究

土壤水力性质是研究非饱和带中水分和溶质运移的重要参数 ,可以用孔隙网络模型进行预测。通常采用的网络模型中的参数是任意指定的 ,无法真实反映土壤孔隙空间的形态特征。本文采用了一种基于孔隙形态学的网络模型来预测土壤的水力性质 ,即通过图像分析来直接测定孔隙的大小分布及其连通性 ,并将其结合到网络模型中 ,最大程度地再现了三维的土壤孔隙结构。本文根据河南封丘地区采集的砂壤土样本图像分析结果 ,采用形态学网络模型预测了其水力性质 ,同时也进一步评价了这种模型的优缺点及其应用前景。     

基于CT图像的土壤孔隙结构三维重建及水力学性质预测

为了更好地了解土壤孔隙结构对水分运动过程的影响机制,该文利用黄淮海平原原状潮土CT扫描图像,通过数字图像分析和计算机重建技术对孔隙结构进行三维重建,根据图像分析获得的孔隙大小分布和连通性等形态学参数建立了用于描述孔隙尺度结构特征对水分运动影响机制的网络模型,据此预测了样本尺度（样本体积为385.84 cm3）的土壤水力学性质。结果表明,模型预测的水力学性质和实测值基本吻合,变化趋势基本一致,二者的决定系数达0.94以上。结果表明相关网络模型可以较好地模拟孔隙尺度的水分运动过程,可用于预测土壤的非饱和水力学性质。     

水蚀风蚀交错带土壤剖面水力学性质变异

土壤剖面水力学性质的确定是土壤水分动态预测的基础。该文在水蚀风蚀交错区六道沟流域分别对居于坡中和坡上两块样地160 cm土层不同深度未扰动土壤的水分特征曲线进行了测定，将Van Genuchtens水分特征曲线模式与Mualem导水模式相结合，确定了两样地土壤剖面的水力学参数，对水力学参数在剖面的变化进行了分析。结果表明，土壤剖面饱和含水率、滞留含水率、进气吸力倒数和孔隙大小分布因子沿剖面变化不大，滞留含水率、进气吸力倒数属于中等程度变异，饱和含水率和孔隙大小分布指标属于弱变异，但经方差检验均不显著，说明该地区160 cm土壤剖面可以处理成均质剖面。     

基于BP神经网络的土壤水力学参数预测

为了获取区域土壤水分和溶质运移模拟所需的土壤水力学参数,利用黄淮海平原曲周县的试验资料建立基于BP神经网络的土壤转换函数模型。本文采用土壤粒径分布、容重、有机质含量等土壤基本理化性质,来预测土壤饱和导水率Ks、饱和含水量sθ、残余含水量θr、以及van Genuchten公式参数α、n的对数形式ln(α)和ln(n),并与多元线性逐步回归方法进行比较。t检验结果表明,BP神经网络训练和预测得到的模拟值与实测值之间吻合很好,该方法具有较高的预测精度。通过对平均相对误差的比较,得出在粒径分布的基础上增加容重、有机质含量等输入项目,可以提高部分土壤水力学参数的预测精度,而有些参数的预测精度反而降低。以误差平方和为标准的比较结果表明,BP神经网络模型的预测效果总的来看要优于多元线性回归法。     

基于支持向量机的土壤水力学参数预测

为了分析支持向量机在土壤水力学参数预测方面的效果,应用支持向量机构建用于预测土壤水力学参数的土壤传递函数,以土壤粒径分布、容重、有机质含量等土壤理化性质为输入项,分别预测土壤饱和导水率、饱和含水率、残余含水率,以及van Genuchten公式参数的对数形式。结果表明预测值和实测值不存在显著性差异,用支持向量机预测土壤水力学参数是可行的。不同输入项处理的预测分析表明,输入项为粒径分布、粒径分布和容重、粒径分布和有机质含量3种情况的预测效果差异不明显,而输入项为粒径分布、容重和有机质含量时预测效果优于前3种情况。支持向量机在预测土壤水力学参数方面的效果要优于多元线性逐步回归模型,而与BP神经网络模型相比不具有明显好的预测效果。     

利用土壤切片和数字图像方法土壤孔隙的垂直空间变异性

利用土壤切片和数字图像方法研究了两种土壤类型的也隙度在ｍｍ尺度上的垂直空间变异性。结果表明,土壤孔障度在ｍｍ尺度上存在较为明显的变异同的土壤变异性一定的差异;变异性与土壤孔隙呈反向关系,与工呈正向关系,在耕作期,变异性随呈现定一的波动趋势。     

土壤磷有效性及其与土壤性质关系的研究

选取我国14个不同地点土样,测定理化性质、全磷、速效磷、水溶性磷含量,采用通径分析研究土壤磷有效性与土壤性质的关系。结果表明,土壤磷有效性是各个土壤性质综合作用的结果。土壤CEC、有机碳、粘粒、砂粒、碳酸钙含量对土壤速效磷比例影响显著,土壤CEC、粘粒、砂粒含量对土壤速效磷比例贡献为正值,而土壤有机碳对土壤速效磷比例贡献为负值;土壤pH和CEC含量对土壤水溶性磷含量影响显著,土壤pH对水溶性磷比例贡献为正值,土壤CEC对土壤水溶性磷比例贡献为负值。本文所选土样基本符合土壤磷有效性与土壤性质之间通径分析的结果。     

基于决策树模型的土壤性质空间推断

许多水文、生态环境和土地管理规划的决策当中,都迫切需要空间连续的土壤性质信息来提高其建模和决策的精确性和可靠性。依靠传统土壤调查技术获得的土壤图则无法满足这种需求。本研究以浙江省龙游县研究区为例,利用第二次土壤普查的土壤性质数据,生成土壤性质-环境因子空间数据库。运用决策树建模方法将土壤性质含量与一些易于广泛观测的景观属性,包括地形、地质、土地利用和遥感影像建立联系,从而将有关土壤性质含量分布的知识转入一种清楚的、定量的、与环境因子相关联的规则系统中,并以此来预测研究区土壤性质的连续空间分布。研究结果表明,所建立的决策树模型可以解释75～81%的土壤性质空间变异。     

不同压实水平下铁尾矿砂和土壤的水力学特征比较

由于尾矿砂的不良结构和严重压实,水土流失严重,水分已成为其生态恢复的重要限制因素。因此以水分运移为主线,通过室内土柱模拟,研究铁尾矿砂和土壤在自然状态到最大压实状态间5个压实水平(铁尾矿砂1.50~1.70 g/cm^3,土壤1.30~1.50 g/cm^3)的水力学特征差异,为尾矿砂的合理改良提供依据。结果表明:铁尾矿砂自然容重1.50 g/cm^3的水分入渗能力低于土壤自然容重1.30 g/cm^3的水分入渗能力。随容重的增大,尾矿砂和土壤的水分运移特征均呈幂函数减小的趋势,但分别在1.60,1.40 g/cm^3处入渗能力明显降低。从水分参数入渗率、湿润锋距离、累计入渗量、饱和导水率、剖面水分分布整体来看,在较低容重范围内,土壤水分运移能力高于铁尾矿砂,但由于容重对土壤水分运移的影响大于铁尾矿砂,在较高容重范围内,土壤的水分运移能力则不如铁尾矿砂。铁尾矿砂和土壤的水分特征曲线形状也完全不同,铁尾矿砂呈现"上凸"形,表现为高吸力段缓低吸力段陡,土壤水分特征曲线均为"下凹"形,表现为高吸力段陡低吸力段缓。因此,尾矿砂中可以添加土壤或者类似土壤结构的基质来增强其不良的持水性和导水性,促进生态恢复。     

应用基于PLSR的土壤-环境模型预测土壤属性

土壤-环境模型对于正确理解土壤属性与环境因子间的关系,以及进行土壤属性预测与制图均具有重要的意义。研究区位于陕西省长武县内多年退耕还林还草沟壑区域,采集72个土壤表层样本,选择3/4的样本作为建模集,其余1/4的样本作为验证集;环境因子选择容易获取的地形因子和由遥感影像提取的植被因子和湿度因子,建立基于偏最小二乘回归(PLSR)的土壤-环境模型。结果表明:全氮、速效钾、全钾、有机质与环境因子间均有显著相关性;建立的PLSR模型可解释土壤属性的空间变异从23%(全氮)到27%(全钾);与逐步回归方法构建的模型相比,利用PLSR构建的土壤-环境模型可以更好地表征土壤属性与环境变量间的关系,拟合精度和预测精度也相对较高,说明PLSR建立的模型可以更好地应用于相似区域的土壤属性预测。     

Effects of wastewater application on saturated hydraulic conductivity of different soil textures

As metropolitan areas expand, the municipal and industrial uses of freshwater increase. Therefore, water resources for irrigation become limited and wastewater reuse for irrigation becomes a good alternative. For this purpose, the effects of suspended solids in wastewater on the soil physical properties, i.e., saturated hydraulic conductivity, K_s, have to be considered. The objectives of this research were to study the effects of applying freshwater and differently treated wastewater on K_s in the surface and subsurface layers of sandy‐loam, loam, and clay‐loam soils. This effect was studied by investigating the ratio of K_s for wastewater to K_s for fresh water in soil surface as K_r1 and in soil subsurface as K_r2. The results showed that the application of freshwater did not reduce the K_r1 considerably. However, the reduction in K_r1 mainly occurred in soil depth of 0–50 mm due to the application of wastewater. This effect is more pronounced in clay‐loam soil than in loam and sandy‐loam soils. It is concluded that application of wastewater with TSS (total suspended solid) of ≥ 40 mg L^–1 resulted in K_r1 reduction of >50% in different soil textures. However, the K_r2 reduction at soil depth of 100–300 mm is not considerable by application of wastewater for different soil textures. Further, it is concluded that less purified wastewater can be used in light‐texture soils resulting in less reduction in K_r1. Empirical models were developed for predicting the value of K_r1 as a function of amounts of wastewater application and TSS for different soil textures that can be used in management of wastewater application for preventing deterioration of soil hydraulic conductivity.     

基于地形属性使用回归, 克里格和人工神经网络方法估算饱和导水率空间分布

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.     

基于遗传算法的土壤水分运动参数识别

土壤水分运动参数的识别是研究土壤水分运动的基础。该文以反映土壤含水率实测值和计算值吻合程度的均方差最小为优化目标,以土壤导水率和扩散率经验参数上下限为约束条件,建立了土壤水分运动参数识别的优化计算模型。采用遗传算法和田间均质土壤一维非饱和运动数值计算相结合的方法,获得土壤导水率和扩散率经验参数最优值。经验证计算,土壤含水率实测值和计算值吻合程度较高,表明这一方法是可行的。     

植被群落演替对土壤饱和导水率的影响

土壤饱和导水率是表征土壤入渗能力的重要参数,对不同土地利用类型反应敏感。为了揭示植被演替对土壤剖面上饱和导水率的影响规律,采用恒定水头法测定了天童林区155 a植物群落演替序列60 cm深土壤剖面上的饱和导水率。结果表明,不同演替阶段饱和导水率均随土壤深度增加迅速降低,在0～20 cm土层内,各演替阶段饱和导水率均存在极显著差异,0～60 cm土层内饱和导水率的平均值从裸地、石栎+檵木灌丛、马尾松林、木荷+马尾松林、木荷林到栲树林升高极为显著,植物群落演替到灌丛阶段,平均饱和导水率已与裸地存在显著差异,演     

Effect of biochar application rate on soil physical and hydraulic properties of a sandy loam

Biochar is used as a soil amendment for improving soil quality and enhancing carbon sequestration. In this study, a loamy sand soil was amended at different rates (0%, 25%, 50%, 75%, and 100% v/v) of biochar, and its physical and hydraulic properties were analyzed, including particle density, bulk density, porosity, infiltration, saturated hydraulic conductivity, and volumetric water content. The wilting rate of tomato (Solanum lycopersicum) grown in soil amended with various levels of biochar was evaluated on a scale of 0–10. Statistical analyses were conducted using linear regression. The results showed that bulk density decreased linearly (R² = 0.997) from 1.325 to 0.363 g cm^?3 while the particle density decreased (R² = 0.915) from 2.65 to 1.60 g cm^?3 with increased biochar amendment, with porosity increasing (R² = 0.994) from 0.500 to 0.773 cm³ cm^?3. The mean volumetric water content ranged from 3.90 to 14.00 cm³ cm^?3, while the wilting rate of tomato ranged from 4.67 to 9.50, respectively, for the non-amended soil and 100% biochar-amended soil. These results strongly suggest positive improvement of soil physical and hydraulic properties following addition of biochar amendment.     

预测天然文岩渠流域土壤饱和导水率的土壤转换函数方法比较研究

在天然文岩渠流域大量实测土壤剖面数据的基础上,评价了12种根据基本土壤性质预测不同层次土壤饱和水力传导率的土壤转换函数方法的效果,同时还探讨了多元回归和BP人工神经网络两种构建方法的适用性。结果表明：基于BP神经网络方法的土壤转换函数预测精度均显著优于根据多元回归建立的土壤转换函数,其中基于BP-ANN的Wosten1999函数对于表层和底层土壤预测精度最高,而Li2007方法对第二层土壤预测效果最好;不考虑分层因素时,基于BP-ANN的Wosten1999函数预测效果最好。此外还利用GIS空间插值,对天然文岩渠流域不同深度的土壤饱和导水率进行可视化表达,为模拟该地区的土壤水分运动提供参数支持。     

基于线性源法与图像处理的土壤饱和导水率快速测量方法

土壤饱和导水率是计算土壤剖面水通量以及设计灌溉和排水系统的重要参数,其测量准确与否直接影响各类水文和水动力学模型的预测精度。然而,现有土壤饱和导水率测定方法费时费力,给土壤水动力学研究工作带来了诸多不便。为此,该研究提出了一种基于线性源入流法与手机图像处理相结合的土壤饱和导水率快速测量方法。该方法首先利用手机拍照获取图像记录充分供水条件下线性水流在土壤表面扩散的过程,图像经处理后计算出土壤表面湿润面积及其随时间的变化关系,然后根据线性源入流法估算的土壤稳态入渗率来测得土壤饱和导水率,并与传统的定水头标准法测得的饱和导水率进行对比。结果表明：图像经畸变校正与二值化处理之后计算出栓皮栎林区土壤、油松林区土壤和砂壤土表面湿润面积与时间具有较好的幂指数关系,决定系数R2分别为0.994、0.995和0.998;在此基础上,采用线性源入流法测量栓皮栎林区土壤、油松林区土壤和砂壤土的稳态入渗率（即土壤饱和导水率）分别为23.40±1.21、23.86±1.83和22.99±2.26 mm/h,同时使用定水头标准法测量三种土样得到的饱和导水率分别为24.41±1.53、24.26±0.37和23.81±0.10 mm/h,与定水头标准法相比,该研究提出的土壤饱和导水率测量方法的相对误差分别为4.14%、1.64%和3.42%。可见,该研究提出的测定方法较为合理、简便、准确,可为获取土壤饱和导水率提供一种新的测量手段,后续研究会将该方法用于野外环境下土壤饱和导水率的原位测定,并验证该方法的准确性。     

Determination of unsaturated soil hydraulic properties at different applied tensions and water qualities

Irrigation with low-quality water may change soil hydraulic properties due to excessive electrical conductivity (EC_w) and sodium adsorption ratio (SAR_w). Field experiments were conducted to determine the effects of water quality (EC_w of 0.5–20 dS m^?1 and SAR_w of 0.5–40 mol^0.5 l^?0.5) on the hydraulic properties of a sandy clay loam soil (containing ～421 g gravel kg^?1 soil) at applied tensions of 0–0.2 m. The mean unsaturated hydraulic conductivity [K(ψ)], sorptive number (α) and sorptivity coefficient (S) varied with change in EC_w and SAR_w as quadratic or power equations, whereas macroscopic capillary length, λ, varied as quadratic or logarithmic equations. The maximum value of K(ψ) was obtained with a EC_w/SAR_w of 10 dS m^?1/20 mol^0.5 l^?0.5 at tensions of 0.2 and 0.15 m, and with 10 dS m^?1/10 mol^0.5 l^?0.5 at other tensions. Changes in K(ψ) due to the application of EC_w and SAR_w decreased as applied tension increased. Analysis indicated that 13.7 and 86.3% of water flow corresponded to soil pore diameters <1.5 and >1.5 μm, respectively, confirming that macropores are dominant in the studied soil. The findings indicated that use of saline waters with an EC of <10 dS m^?1 can improve soil hydraulic properties in such soils. Irrigation waters with SAR_w < 20 mol^0.5 l^?0.5 may not adversely affect hydraulic attributes at early time; although higher SAR_w may negatively affect them.