滩涂围垦农田土壤饱和导水率的影响因素及转换函数研究

确定苏北沿海滩涂围垦农田耕层土壤饱和导水率的影响因素,构建适合该区的土壤转换函数,是研究该区田间土壤水盐运动和盐渍化防控的重要前提。本文在该区典型地块实测土壤饱和导水率和相关土壤基本理化性质,探讨了该区土壤饱和导水率的剖面分布特点,对影响饱和导水率的土壤基本性质进行了主成分分析,并建立了用于该区饱和导水率间接估算的土壤转换函数。结果表明:滩涂围垦农田土壤饱和导水率随剖面深度增加呈表土层高、亚表层低、底土层又升高的趋势,20~40 cm土层饱和导水率最小,介于2.75~6.73 cm·d-1,属低透水强度;土壤容重随剖面深度增加表现出与饱和导水率相反的变化特点。除了容重、孔隙度、质地等物理因素外,土壤肥力、盐分等化学性质也是影响饱和导水率的重要因素;影响滩涂围垦农田土壤饱和导水率的因素可由持水特性、盐碱状况、养分特征和土壤质地4个主成分反映,其累计贡献率达78.17%。在Vereecken转换函数中引入土壤盐分后可提高预测精度,修正函数Vereecken_1是最适合滩涂围垦农区土壤、具有最佳预测精度的转换函数。本文构建的土壤转换函数,可通过较易获得的砂粒、黏粒、容重、盐分和有机质对耕层土壤饱和导水率进行较高精度的预测,其结果可为滩涂盐渍化农区田间尺度土壤饱和导水率间接估算以及水盐运动数值模拟提供支持。     

常熟地区水稻土饱和导水率的间接方法研究

作为直接试验的一种替代方法,利用土壤基本物理性质通过土壤转换函数预测饱和导水率简便易行,成本低廉,并且预测精度能满足实际研究的需要。本研究利用目前得到较多应用的9种基于多元回归分析建立的转换函数来构建、校正预测土壤饱和导水率的经验公式,并与人工神经网络方法相比较。结果表明,人工神经网络总体预测效果要优于基于多元回归分析建立的转换函数,并且Cosby(1984)在输入参数较少的基础上预测饱和导水率精度最高。本文以Cosby(1984)预测常熟水稻土壤饱和导水率,进一步利用GIS的空间描述能力与函数的定量分析能力,得到区域尺度饱和导水率的分布状况,为该地区区域尺度数值模拟的运行提供基础参数支持。     

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。     

灰色关联及非线性规划法构建传递函数估算黑土水力参数

土壤水分特征曲线和饱和导水率是重要的水力参数,为了简便准确获取这些参数,以松嫩平原黑土区南部为研究区域,采集136个采样点土样用于测定不同土层土壤水分特征曲线、饱和导水率以及土壤理化性质,并运用灰色关联分析确定影响土壤水力参数的主要土壤理化性质,采用非线性规划构建土壤分形维数、有机质、干容重、土壤颗粒组成与土壤水分特征曲线、饱和导水率之间的土壤传递函数,并通过与现有土壤传递函数对比分析进行精度验证。结果表明:1)土壤分形维数是估算土壤水分特征曲线模型参数和饱和导水率的主要参数之一,同时,干容重和有机质含量也在不同土层土壤传递函数中起到重要的作用;2)通过验证分析,不同土层各参数平均绝对误差接近于0,均方根误差值也都较小,其中在不同土层土壤传递函数估算的土壤含水率均方根误差分别为0.022、0.017cm~3/cm~3;3)对比分析其他已存的土壤水分特征曲线和饱和导水率的土壤传递函数,该文构建的土壤传递函数均方根误差值均较小,决定系数值都在0.66以上,表明估算精度较高,均好于其他方法估算精度,具有良好的区域适应性。综上,所构建的土壤水分特征曲线和饱和导水率土壤传递函数可以用于松嫩平原黑土区土壤水力参数估算。     

热融湖塘对青藏高原土壤饱和导水率的影响及因素分析

为研究多年冻土区热融湖塘对湖岸生态水文过程的影响,该文基于湖岸不同迹地植被发育、导水性及土壤理化性质的分析,并结合土壤转换函数(pedo-transfer functions,PTFs),对土壤导水性及其影响因素进行研究。结果表明:热融湖塘的形成使土壤环境发生了重要演变,其中湖岸死根区土壤饱和导水率相比于未影响区域(110.88 cm/d)增加了70.1%之多,而其在盐渍化区域相比于未影响区域减少了33.8%,同时土壤饱和导水率随着坡度的增加而增强;通过比较ROSETTA、CAMPBELL和VAUCLIN 3种土壤转换函数的预测能力,发现VAUCLIN模型更适合于模拟青藏高原高寒草甸土壤饱和导水率。热融湖塘影响迹地对土壤饱和导水率的变化,是植被盖度、有机质含量、颗粒组成等因素耦合影响作用的结果,运用土壤转换函数对其进行预测时,须综合考虑以上因子。对热融湖塘不同迹地土壤水力参数的研究可为区域土壤侵蚀,产流模式及水文过程的研究提供理论基础。     

渝东北紫色土饱和导水率传递函数研究

为研究渝东北紫色土理化性质在垂直空间上的分布情况以及对饱和导水率的影响,进而建立饱和导水率与各理化性质间的关系函数,推求饱和导水率的传递函数,选择渝东北开州区、云阳县等7个区县内45个紫色土典型田块为研究区域,运用Excel 2013和Matlab 2015b软件统计分析后,利用多元非线性回归法推求并验证了渝东北紫色土饱和导水率传递函数模型和模型参数。研究表明:①研究区土壤饱和导水率变化范围在0.16～195.68 cm/d,变化范围广,空间变异系数大,变异性较强;同一采样点深度越大,饱和导水率越小;②土壤饱和导水率与有机质含量有显著的指数函数关系,与饱和含水量有较强的二次函数关系,与土壤容重和土壤颗粒的相关性不大;③本次试验建立的土壤饱和导水率传递函数模型及模型系数检验合格,预测值与实际测算值误差较小,精度良好,可用于渝东北紫色土饱和导水率的预测工作。     

应用土壤质地预测干旱区葡萄园土壤饱和导水率空间分布

田间表层土壤饱和导水率的空间变异性是影响灌溉水分入渗和土壤水分再分布的主要因素之一,研究土壤饱和导水率的空间变化规律,有助于定量估计土壤水分的空间分布和设计农田的精准灌溉管理制度。为了探究应用其他土壤性质如质地、容重、有机质预测土壤饱和导水率空间分布的可行性,试验在7.6 hm2的葡萄园内,采用均匀网格25 m×25 m与随机取样相结合的方式,测定了表层(0～10 cm)土壤饱和导水率、粘粒、粉粒、砂粒、容重和有机质含量,借助经典统计学和地统计学,分析了表层土壤饱和导水率的空间分布规律、与土壤属性的空间相关性,并对普通克里格法、回归法和回归克里格法预测土壤饱和导水率空间分布的结果进行了对比。结果表明:1)土壤饱和导水率具有较强的变异性,平均值为1.64 cm/d,变异系数为1.17;2)表层土壤饱和导水率60%的空间变化是由随机性或小于取样尺度的空间变异造成;3)土壤饱和导水率与粘粒、粉粒、砂粒和有机质含量具有一定空间相关性,而与土壤容重几乎没有空间相关性;4)在中值区以土壤属性辅助的回归克里格法对土壤饱和导水率的预测精度较好,在低值和高值区其与普通克里格法表现类似。研究结果将为更好地描述土壤饱和导水率空间变异结构及更准确地预测其空间分布提供参考。     

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。     

青海三江源地区土壤水分常数转换函数的建立与比较

利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据,本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数,并对其预测精度进行了比较。结果表明:(1)回归分析方法总体预测效果比较理想,特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下,决定系数(R2)高达0.868;(2)BP神经网络方法的预测效果非常理想,各土壤水分常数平均误差和均方根误差都在4.685%以下,并且决定系数均在0.857以上;(3)Rosetta模型的预测效果相对较差,特别是饱和含水量和毛管持水量,平均误差(ME)和均方根误差(RMSE)相对较大,决定系数(R2)相对较小。3种方式中,BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳,回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型,Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。     

含岩屑紫色土水力特性及饱和导水率传递函数研究

紫色土中存在的岩石碎屑会对土壤的水力性质如饱和导水率、水分特征曲线产生显著影响。以两种不同母质发育的土壤(紫色页岩和紫色泥岩)为研究对象,设置0.25~2、2~5、5~10 mm三个岩屑粒径水平,0、30%、50%、70%、100%五个岩屑含量水平,采用压力膜仪法和定水头法分别测定水分特征曲线和土壤饱和导水率。利用BP神经网络,选择特定输入变量建立土壤饱和导水率传递函数PTF1和PTF2(PTF1的输入变量为岩屑含量、岩屑粒径、初始土壤容重和机械组成,PTF2的输入变量为岩屑含量、岩屑粒径、初始土壤容重、机械组成、进气压力值和S指数(土壤水分特征曲线拐点处斜率的绝对值))。结果表明:添加岩屑极大提高了土壤饱和导水率和S指数,并且随岩屑含量的增加而增加,相比之下,进气压力值随岩屑含量增加而减小,饱和导水率也随岩屑粒径的增加而增加,岩屑粒径从0.25~2 mm增加至5~10 mm,饱和导水率平均提高了2.3倍。岩屑粒径对进气压力值和S指数影响较小。PTF1和PTF2的几何平均数、几何标准偏差、均方根误差以及AIC指数分别为1.27、5.57、0.16、2.94和1.17、1.70、0.06、–53.28,PTF2的相关值均小于PTF1,表明PTF2模型的预测效果更好。综上所述,岩屑的存在显著影响了紫色土的水力特性,使饱和紫色土导水能力增加而保水能力减弱,利用神经网络所构建的传递函数PTF2可很好地实现含岩屑土壤饱和导水率的预测。     

Estimating the bulk density in 0– 20 cm of tilled soils in China’s Loess Plateau using support vector machine modeling

ABSTRACT

Pedotransfer functions (PTFs), as an indirect forecasting method, offer an alternative for labor-intensive bulk density (BD) measurements. In order to improve the forecasting accuracies, support vector machine (SVM) method was first used to develop PTFs for predicting BD. Cross-validation and grid-search methods were used to automatically determine the SVM parameters in the forecasting process. Soil texture and organic matter content were selected as input variables based on results of predecessors, coupled with gray correlation theory. And additional properties were added as inputs for improving PTF's accuracy and reliability. The performance of the PTF established by SVM method was compared with artificial neural network (ANN) method and published PTFs using two indexes: root-mean-square error (RMSE) and coefficient of determination(R²). Results showed that the average RMSE of published PTFs was 0.1053, and the R² was 0.4558. The RMSE of ANN–PTF was 0.0638, and the R² was 0.7235. The RMSE of SVM–PTF was 0.0558, and the R² was 0.7658. Apparently, the SVM–PTF had better performance, followed by ANN–PTF. Additionally, performances could be improved when accumulated receiving water was added as predictor variable. Therefore, the first application of SVM data mining techniques in the prediction of soil BD was successful, improved the accuracy of predictions, and enhanced the function of soil PTFs. The idea of developing PTFs using SVM method for predicting soil BD in the study area could provide a reference for other areas.     

科尔沁沙丘-草甸相间地区表土饱和导水率的土壤传递函数研究

以科尔沁沙地典型坨-甸相间地区为研究区,野外布设240个采样点,对流动沙丘、半固定沙丘、固定沙丘、沙丘区杨树林、沙丘区耕地、低覆盖度草甸、高覆盖度草甸、草甸区耕地、撂荒地9种地貌类型下的表层土壤进行了采样,测定了其含水率、干容重、有机质、饱和导水率等理化特性,分析了不同地貌类型下表层土壤理化参数差异。选取Campbell、Cosby、Wosten等、Saxton等4种土壤饱和导水率传递函数,对该地区表土饱和导水率进行了预测。结果显示这几种土壤传递函数预测值与实测值偏差较大,相关系数均小于0.3,精度难以满足本地区应用。在此基础上,选取土壤容重、有机质含量、饱和含水率、平均粒径、粒径标准偏差5种土壤特性参数作为输入变量,采用主成分分析与非线性回归分析相结合的方法,重新建立了预测本地区表土饱和导水率的土壤传递函数,结果显示预测值与实测值相关系数为0.661,该传递函数可用于科尔沁沙地表层土壤饱和导水率的预测。     

Prediction of soil saturated water content using evolutionary polynomial regression (EPR)

The measurement of saturated water content (SWC) is necessary in the estimation of soil water retention and unsaturated hydraulic conductivity curves. In several studies, pedotransfer functions (PTFs) were developed to predict SWC. Among them, evolutionary polynomial regression (EPR) is one that can operate on large quantities of data in order to capture nonlinear and complex interactions between the variables of the system. In this study, the evolutionary data-mining technique was used to derive new PTFs and different methods were evaluated, such as the soil porosity method, Rosetta method, and others, for the estimation of SWC. For this purpose, 270 soil samples (3:1 ratio for development and validation) from three data sets were used. Among 190 PTFs provided by EPR, one equation with the highest accuracy and the least number of inputs was selected. The EPR predictions were compared with the experimental results as well as the PTFs proposed in previous studies. Comparison of the statistical indicators showed that the ‘proposed PTF’ and ‘porosity method’ are the best and worst methods for the prediction of SWC, respectively. Also, good predictions were achieved from the proposed approaches by the groups of Scheinost, Vereecken, and Williams.     

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。     

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.     

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (p > 0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE) between the measured and predicted parameter values. The R² and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies.     

Performance of pedotransfer functions in predicting soil water characteristics of soils in Norway

Abstract

Pedotransfer functions (PTFs), predicting the soil water retention curve (SWRC) from basic soil physical properties, need to be validated on arable soils in Norway. In this study we compared the performance of PTFs developed by Riley (1996), Rawls and Brakensiek (1989), Vereecken et al. (1989), Wösten et al. (1999) and Schaap et al. (2001). We compared SWRCs calculated using textural composition, organic matter content (SOM) and bulk density as input to these PTFs to pairs of measured water content and matric potential. The measured SWRCs and PTF input data were from 540 soil horizons on agricultural land in Norway. We used various statistical indicators to evaluate the PTFs, including an integrated index by Donatelli et al. (2004). The Riley PTFs showed good overall performance. The soil specific version of Riley is preferred over the layer specific, as the latter may introduce a negative change in water content with increasing matric potential (h). Among the parameter PTFs, Wösten's continuous PTF showed the overall best performance, closely followed by Rawls&B and Vereecken. The ANN-based continuous PTF of Schaap showed poorer performance than its regression based counterparts. Systematic errors related to both particle size and SOM caused the class PTFs to perform poorly; these PTFs do not use SOM as input, and are therefore inappropriate for soils in Norway, being highly variable in SOM. The PTF performance showed little difference between soil groups. Water contents in the dry range of the SWRC were generally better predicted than water contents in the wet range. Pedotransfer functions that included both SOM and measured bulk density as input, i.e. Wösten, Vereecken and Rawls&B, performed best in the wet range.