基于不同PTF_S的流域尺度土壤持水特性空间变异性分析

利用点估计模型、线性回归模型、非线性回归模型和人工神经网络模型等四种PTFS分别预测大沽河流域90个土壤样本的田间持水量(θ-30 kPa)和凋萎含水量(θ-1 500 kPa),借助传统统计学和地统计学方法对其空间变异性进行了比较分析。传统统计学分析认为非线性回归模型预测的效果最好,无论是实测值还是估计值,所有土壤样本θ-30 kPa的变异系数总是小于θ-1 500 kPa,两者均属于中等变异性;地统计学分析表明实测值和预测值的θ-30 kPa和θ-1 500 kPa均存在不同程度的块金效应,且θ-30 kPa总是表现出较θ-1 500 kPa更强烈的空间相关性,通过分析θ-30 kPa和θ-1 500 kPa的半方差函数模型参数,发现人工神经网络模型最能真实地反映试验区土壤持水特性的空间变异性特征。

Spatial variability analysis of soil water retention capability at basin scale based on different PTFS

Field water retention capacities (θ_(-30 kPa)) and wilting coefficients (θ_(-1 500 kPa)) of ninety soil samples in the Dagu River Basin were predicted separately with four PTF_S, i.e. point regression method, linear regression method, nonlinear regression method and artificial neural network method, and their spatial variabilities were analyzed with the aid of traditional statistic and geostatistic methods. The traditional statistics revealed that the nonlinear regression method was the best with the variation coefficients of θ_(-30 kPa) of all the soil samples, being always less than θ_(-1 500 kPa), however, no matter measured or predicted values, both belonged to the category of moderate in spatial variability. The geostatistics also showed that both measured and predicted θ_(-30 kPa) and θ_(-1 500 kPa) demonstrated varied nugget effects, moreover, θ_(-30 kPa) always had stronger spatial dependence than θ_(-1 500 kPa) did. Analysis of the parameters of semi-variance model for θ_(-30 kPa) and θ_(-1 500 kPa) ultimately revealed that the artificial neural network model could most truthfully characterize spatial variability of the soil water retention capability in the experimental zone.