土壤水分特征曲线Gardner模型参数预报研究  

Study on Nonlinear Prediction of Gardner Model Parameters of Moisture Characteristic Curve

作　　者：李浩然 樊贵盛[1] LIHaoran;FAN Guisheng(College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

机构地区：[1]太原理工大学水利科学与工程学院,山西太原030024

出　　处：《人民黄河》2019年第4期149-152,158,共5页Yellow River

基　　金：国家自然科学基金资助项目(40671081);山西省农田节水技术开发服务推广项目

摘　　要：为实现基于土壤基本理化参数的土壤水分特征曲线预测,以黄土高原地区农田土壤为研究对象,进行土壤水分特征曲线的系列试验,并配套测定了土壤基本理化参数,获取了Gardner模型参数与土壤基本理化参数间一一对应关系的数据样本。在分析研究各土壤理化参数与Gardner模型参数间单因素关系的基础上,创建了以土壤黏粒含量、粉粒含量、干密度、有机质含量为输入变量的水分特征曲线Gardner模型参数多元非线性预报模型。结果表明:以土壤黏粒含量、粉粒含量、干密度、有机质含量为输入因子,对Gardner模型参数进行预测是可行的,建模样本的拟合相对误差平均值小于11%,用检验样本进行预测的相对误差平均值小于10%,预测精度较高。In order to predict the soil moisture characteristics based on the soil basic physical and chemical parameters, based on the study of farmland soil in the Loess Plateau area, the soil moisture characteristic curve series experiment was carried out, and the basic physical and chemical parameters were measured. The data samples of Gardner model parameters and soil basic physical and chemical parameters were obtained. Based on the analysis of the single factor influence and relationship between the soil physical and chemical parameters and the Gardner model parameters, a multi parameter nonlinear prediction model of soil moisture characteristic curve Gardner model with soil clay content, silt content, bulk density and organic matter content as input variables was established. The results show that it is feasible to predict the parameters of Gardner model with soil clay content, powder content, bulk density and organic matter content as the input factor. The relative error between the predicted value and the measured value is 11% or less, the average relative error of the test sample is 10% or less and the prediction accuracy is better. The research results can provide theoretical and technical support for the convenient acquisition of soil moisture characteristics curve.

关 键 词：土壤水分特征曲线 非线性模型 Gardner模型 参数预测 样本检验 

分 类 号：S152[农业科学—土壤学] TV93[农业科学—农业基础科学]