不同土壤转换函数预测砂土非饱和导水率的对比分析

以现有的4种土壤转换函数(Pedotransfer Functions, PTFs)(包括ROSETTA、RAWLS、CAMPBELL和VAUCLIN)为例,将其用于土壤水力性质数据库UNSODA中56个典型砂土样本的非饱和导水率预测,对4种PTFs的预测结果进行了误差分析,探讨了饱和导水率对非饱和导水率预测的影响。结果表明,当同种PTFs预测的饱和导水率作为输入时,在4种PTFs中VAUCLIN的预测精度最高,其次为ROSETTA和RAWLS,CAMPBELL的误差最大;当实测饱和导水率作为输入时,RAWLS、CAMPBELL和ROSETTA的预测精度有了不同程度提高,但VAUCLIN的预测误差反而有所增大;饱和导水率对非饱和导水率预测的影响较大,实测饱和导水率作为输入时CAMPBELL预测的非饱和导水率与实测值最为接近。     

土壤饱和导水率的多尺度预测模型与转换关系

运用联合多重分形方法研究不同土层土壤饱和导水率与土壤基本物理特性的多尺度相关性,建立不同土层土壤饱和导水率的多尺度预测模型,构建不同土层土壤饱和导水率之间的转换关系。结果表明:不同土层土壤饱和导水率与土壤基本物理特性的相关程度排序不同;在单一尺度和多尺度上,0~20 cm土层土壤饱和导水率与土壤基本物理特性的相关程度排序相同,20~40 cm土层土壤饱和导水率与土壤基本物理特性的相关程度排序不同;土壤饱和导水率多尺度预测模型的预测精度较高,0~20 cm土层和20~40 cm土层拟合值的均方根误差分别为0.035 0和0.029 0;0~20 cm土层和20~40 cm土层土壤饱和导水率转换关系的计算精度较高,拟合值的均方根误差为0.037 5。     

基于PCA-GWR的包气带不同土层饱和导水率的传递函数及其回归克里金估计

饱和导水率是重要的土壤水力学参数,对渗流和溶质运移研究起着至关重要的作用.土壤传递函数可以代替大规模采样和室内外试验间接预测该参数,但由于土壤的空间变异性以及方法的局限性等原因,以往的传递函数往往精度有限.本文以黄河下游沿岸兰考县闫楼乡包气带不同土层为研究对象,基于64个钻孔数据,将土壤粒径分布（黏粒、粉粒和砂粒含量）、土壤结构分形维数、干容重、总孔隙度、pH值、有机质和电导率等9个基本理化特性参数作为影响因素,基于逐步回归、主成分回归和主成分-地理加权回归（PCA-GWR）三种方法,分别对研究区包气带不同土层的饱和导水率进行回归分析,比较精度后基于PCA-GWR对饱和导水率进行地理加权回归克里金插值.结果显示,除表层粉土以外,PCA-GWR法的预测精度有显著优势.不同土层传递函数和饱和导水率分布的差异性表明,由于黄河下游地区快速多变的沉积过程、黄河的决口改道以及人类活动等因素,包气带土层相变剧烈,土壤饱和导水率在平面上亦具有明显的非平稳特征.而局部地质作用和人类活动是第二层粉土层回归克里金估计结果欠佳的深层原因.     

应用连续混合器模型模拟非饱和土壤盐分淋洗过程

尽管连续混合器模型已经广泛应用于土壤中溶质迁移转化规律计算,但却仅限于饱和土壤条件。通过采用Kostiakov公式描述非稳定入渗过程,并结合活塞假设计算入渗过程中土壤水分的运动和分布规律,将连续混合器模型扩展应用于非饱和土壤的盐分淋洗过程计算,并求出了模型的解析解。分析发现,在一定的土壤质地、初始含水率剖面和初始含盐量剖面下,模型的计算结果会受到土层划分厚度的影响,而合理的土层厚度又与饱和导水率和溶质弥散系数之间存在量纲一的函数关系。当土层厚度选取合理时,连续混合器模型与HYDRUS-1D的计算结果十分接近,采用该模型模拟试验土壤盐分淋洗过程,显示了较好的计算精度。     

多孔介质非饱和导水率预测的分形模型

多孔介质非饱和导水率是地下水污染预测与评价的重要参数。根据分形几何的基本原理和方法,推导出了与Campbell经验公式在形式上完全一致的多孔介质非饱和导水率的预测公式。公式中的幂指数为介质孔隙分维和随机行走分维的函数,分别体现了多孔介质的静态性质与动态性质对其中水分运动的影响,但静态性质的影响是主要的,即导水率主要受多孔介质的结构控制。根据文献中报道的大量数据,利用笔者推导的预测公式计算得到的幂指数的统计值与试验测定的幂指数的统计值基本一致,说明推导的理论公式预测多孔介质非饱和导水率是较为可靠的。     

黄土高原北部坡面尺度土壤饱和导水率分布与模拟

为探明黄土高原北部坡面土壤饱和导水率(K_s)空间分布特征,为土壤水文过程模拟与预测提供理论依据,采用经典统计学和地统计学的空间变异分析方法,分析了坡面尺度土壤K_s的空间变异特征,并用一阶自回归状态空间模型对K_s的空间分布进行了模拟.研究区坡面尺度K_s的变异为中等程度变异,具有中等程度空间依赖性,变程为42 m.K_s与容重、砂粒、粉粒和黏粒含量在不同滞后距离下均具有自相关关系和交互相关关系.容重和土壤颗粒是影响坡面K_s空间分布的主要因素.状态空间模拟结果表明,基于容重和土壤颗粒的状态空间方程可以很好地解释坡面K_s的变异状况(R2>0.9).一阶自回归状态空间模型可用于田间条件下坡面尺度K_s分布特征的预测.     

基于地统计学方法的降水空间插值研究

为了实现离散降水数据到连续降水数据的插值,以内蒙古自治区多年(1950～2000年)平均降水量为例,讨论了不同空间插值方法的优劣,发现基于地统计方法的Kriging插值具有最佳的插值效果.进一步对比Ordinary Kriging和CoKriging插值方法的4种不同的半变异函数模型,结果表明利用指数半变异函数的CiKriging插值方法具有最小的预测误差.地统计方法对于预测降水具有重要的价值,而不同的地区具有各自适合的模型,理解该方法的数学原理和操作过程,对相关研究具有借鉴意义.     

植被退化对高寒土壤水文特征的影响

在黄河源冻土严重退化地区,采用选择典型区域和样地进行实验和模型模拟的方法,对不同植被退化特征条件下高寒土壤的水分特征曲线、土壤饱和导水率、土壤入渗及土壤水分进行研究.结果表明:Gradner和Visser提出的经验方程θ=AS-B对该地区土壤水分特征曲线有良好的模拟性;不同植被盖度条件下土壤的饱和导水率和土壤入渗有明显的区别,表层0～10cm范围内,黑土滩的饱和导水率和入渗强度最强,30cm以下土层中土壤饱和导水率、入渗强度以及土壤含水量几乎不受植被的影响.随植被退化表层土壤含水量出现明显降低,退化越严重,水分流失越多,最多时能达到38.6%,植被根系最发达的10～20cm范围的土壤含水量流失对高寒草甸土壤环境影响最大,水分流失导致退化草甸恢复难度较大.通过比较研究,在黄河源地区考斯加科夫(Kostiakov)入渗公式f(t)=at-b更适用于该研究区域高寒草甸土壤水分入渗过程的研究.     

非饱和流节点间导水率估计及达西平均分析

针对由Gardner-Russo函数描述的特征土壤,在双饱和度坐标系中,探讨了常规节点间导水率估计与量纲一水平达西平均(k*_H)和量纲一垂直达西平均(k*_V)的差异.k*_H是相邻两节点饱和度的函数,而k*_V也是空间步长(Δx)和特征参数(α)的函数.对水平非饱和流,算术平均高估导水率,而几何与调和平均低估导水率;从相对误差空间分布看,几何平均优于算术和调和平均.对垂直非饱和流,随Δx(或α)的增大,常规估计的相对误差空间顺(或逆)时针旋转并缩小,从而降低模型准确性.k*_H等值线具有对称性,而k*_V不具对称性;仅当Δx或α足够小时,k*_H才趋近于k*_V.因此,为提高非饱和流数值模拟的准确性,必须采用精确的达西平均来计算节点间导水率.     

大孔隙对农田耕作层饱和导水率的影响

介绍了土壤中大孔隙流的研究,并用一种简单的方法对土壤内的大孔隙和中小孔隙的饱和导水率进行分析,同时讨论了农田耕作层大孔隙流和Darcy流饱和导水率的变异情况。     

非饱和水分运动参数空间变异与最优估值研究

以野外砂壤土30m×30m空间的实测土壤参数试验为基础,通过对实测数据的统计分析得到饱和水力传导度KS及容水度C遵从正态分布,而孔隙大小分布参数α遵从对数正态分布。应用变差函数对参数KS和α的空间结构进行分析得到两参数的空间结构可用球状变差函数模型进行描述。根据Kriging最优估值理论,分别对两参数空间的值进行了估计,结果表明本研究的野外实测试验点的数目减少26%即可满足精度要求,同时与克立格方法相比采用协克立格方法对参数lnα进行估计,其估计方差平均减少6.87%。     

Improved hydrograph prediction through subsurface characterization: conditional stochastic hillslope simulations

Subsurface heterogeneity is one of the largest sources of uncertainty associated with saturated hydraulic conductivity. Recent work has demonstrated that uncertainty in hydraulic conductivity can impart significant uncertainty in runoff generation processes and surface-water flow. Here, the role of site characterization in reducing hydrograph prediction bias and uncertainty is demonstrated. A fully integrated hydrologic model is used to conduct two sets of stochastic, transient simulation experiments comprising different overland flow mechanisms: Dunne and Hortonian. Conditioning hydraulic conductivity fields using values drawn from a simulated synthetic control case are shown to reduce both mean bias and variance in an ensemble of conditional hydrograph predictions when compared with the control case. The ensemble simulations show a greater reduction in uncertainty in the hydrographs for Hortonian flow. The conditional simulations predict surface ponding and surface pressure distributions with reduced mean error and reduced root mean square error compared with unconditional simulations. Uncertainty reduction in Hortonian and Dunne flow cases demonstrates different temporal signals, with more substantial reduction achieved for Hortonian flow.     

Study of the effect of seepage through the body of earth dam on its stability by predicting the affecting hydraulic factors using models of Brooks–Corey and van Genuchten (Case study of Nazluchay and Shahrchay earth dams)

Stability of an earth dam is affected by different factors of which the most important is seepage. One of the factors in defining the seepage rate is hydraulic conductivity coefficients in suctions and various moistures in earth dam body in which different models of soil–water retention curves are used to be defined. In this study, first the soil–water retention curves of van Genuchten and Brooks–Corey models are used to predict the soil–water retention of different body layers of 2 dams. Then, hydraulic conductivity coefficients are calculated through Mualem and Burdine’s models. Water seepage rate and the stability of dams were calculated in two phases: I. construction finalization prior to intaking water and II. rapid drawdown of water. Bishop and Morgenstern–Price methods are used to analyze the stability of dams. The stability and seepage results demonstrate that simultaneous use of Brooks–Corey’s soil–water retention curve with Mualem’s method results in the highest seepage rate in comparison with van Genuchten’s model in case of variable n, m and Burdine hydraulic conductivity. Safety factors achieved from both cases of construction finalization prior to intaking water and rapid drawdown of water demonstrated the accuracy and reliability of hydraulic coefficient prediction.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

库水升降条件下考虑饱和渗透系数空间变异性的白水河滑坡渗流变形分析

堆积层滑坡的岩土体渗透系数具有一定的不确定性,且渗透系数是饱和-非饱和渗流分析的重要参数,开展考虑其空间变异性的库岸堆积层滑坡渗流变形分析具有重要意义。以三峡库区中的白水河滑坡为研究对象,基于地面核磁共振技术获取的岩土体渗透系数,分析滑坡体渗透系数的空间变异特征,采用半变异函数方法求得滑坡体渗透系数的竖直波动范围,在此基础上建立渗透系数的非平稳随机场模型。以非侵入式随机有限元的方式开展库水升降两种工况下不确定模型与确定模型的流固耦合模拟,分析两种模型的渗流场、位移变形特征及其差异。结果表明：相比于确定模型,不确定模型孔压改变的滞后性更为明显,且库水下降工况下整体的变形更大,若忽略滑体渗透系数的非平稳空间变异特征将会低估滑坡的实际变形。     

江汉平原土壤饱和渗透系数变化规律及影响因素

目前对影响土壤饱和渗透系数规律的机理缺乏深刻认识.以不同沉积环境、不同岩性及不同土地利用方式的江汉平原汉江下游浅层土壤为对象,采用改进的TST-55型土壤渗透仪开展室内变水头达西实验.结果表明,研究区内土壤饱和渗透系数平均值为4.94×10-5cm/s,服从对数-正态分布;粉砂壤土冲积物和湖积物的饱和渗透系数平均值为3.53×10-5cm/s和1.98×10-5cm/s,粉砂质粘壤土中,两者分别为8.13×10-7cm/s和5.88×10-7cm/s,同一岩性冲积物的饱和渗透系数较湖积物大;冲积物中,砂壤土、粉砂壤土和粉砂质粘壤土饱和渗透系数平均值为1.98×10-5 cm/s、3.53×10-5 cm/s和8.13×10-7 cm/s,表现为砂壤土>粉砂壤土>粉砂质粘壤土;耕作土壤中,粉砂壤土、粉砂质粘壤土的饱和渗透系数平均值为3.75×10-5 cm/s和8.11×10-7cm/s,非耕作土壤中,两者分别为1.88×10-6cm/s、5.93×10-7cm/s,同一岩性耕作土壤饱和渗透系数较非耕作土壤大.      

Consolidation in spatially random unsaturated soils based on coupled flow‐deformation simulation

This paper integrates random field simulation of soil spatial variability with numerical modeling of coupled flow and deformation to investigate consolidation in spatially random unsaturated soil. The spatial variability of soil properties is simulated using the covariance matrix decomposition method. The random soil properties are imported into an interactive multiphysics software COMSOL to solve the governing partial differential equations. The effects of the spatial variability of Young's modulus and saturated permeability together with unsaturated hydraulic parameters on the dissipation of excess pore water pressure and settlement are investigated using an example of consolidation in a saturated‐unsaturated soil column because of loading. It is found that the surface settlement and the pore water pressure profile during the process of consolidation are significantly affected by the spatially varying Young's modulus. The mean value of the settlement of the spatially random soil is more than 100% greater than that of the deterministic case, and the surface settlement is subject to large uncertainty, which implies that consolidation settlement is difficult to predict accurately based on the conventional deterministic approach. The uncertainty of the settlement increases with the scale of fluctuation because of the averaging effect of spatial variability. The effects of spatial variability of saturated permeability k_sat and air entry parameters are much less significant than that of elastic modulus. The spatial variability of air entry value parameters affects the uncertainties of settlement and excess pore pressure mostly in the unsaturated zone. Copyright © 2016 John Wiley & Sons, Ltd.     

Environmental significance of magnetic properties of Gley soils near Rosslau (Germany)

Geophysical methods are widely applied to soils for resolving different tasks in precision agriculture, pollution evaluation, erosion estimation, etc. Environmental magnetic methods were applied in our study on a collection of soil samples from area near Rosslau (Germany), which was gathered on the basis of a field electromagnetic induction study. Magnetic laboratory analyses include magnetic susceptibility, isothermal remanent magnetization (IRM), anhysteretic remanent magnetization (ARM), thermomagnetic analyses for determination of the kind of magnetic minerals present in soils. The results reveal the presence of statistically significant inverse correlations between magnetic susceptibility, as well as remanent magnetizations ARM and IRM, and conductivity values. This maybe ascribed to influence of topography and water regime on the iron oxide forms in soil and the influence of soil moisture on soil conductivity. Magnetic measurements on soil cores showed close correspondence between soil horizons outlined in 1 m-long cores, and depth changes in mass-specific magnetic susceptibility. Existing relationships between magnetic characteristics, soil reaction pH, and nutrients’ content (total nitrogen, carbon and sulphur) have been explored by cluster analysis and general regression model statistics. The results reveal the presence of significant correlations between nutrients’ content, magnetic susceptibility and the grain size sensitive ratio ARM/χ. These are expressed by numerical equations, representing pedotransfer functions, which predict the content of Nitrogen, Carbon and Sulphur through combination of magnetic parameters and soil pH. The obtained pedotransfer functions for the particular case of Gley soils and Fluvisols studied could be used for application of magnetic methods in agricultural practice as a fast and inexpensive proxy evaluation of the content of these nutrition elements.