多数据源土壤传递函数模型在水分模拟中的不确定性  

Soil Pedotransfer Function of Multiple Data Source and Its Uncertainty in Water Simulation

作　　者：王改改[1] 张玉龙[1] 虞娜[1] 

机构地区：[1]沈阳农业大学土地与环境学院,沈阳110866

出　　处：《农业机械学报》2012年第11期45-50,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点基础研究发展计划(973计划)资助项目(2011CB100502)

摘　　要：基于前馈神经网络的传递函数模型,采用4种不同的数据源预测土壤水分特征曲线,并借助误差统计指标、Hydrus-1D水动力学模型对传递函数模型的预测性能及其应用不确定性进行了分析。结果表明,预测土壤水吸力1 000、10 000、15 000 cm对应含水率θ1000、θ10000、θ15000时,相对于仅有颗粒组成变量的传递函数模型,增加容重和θ60(水吸力60 cm对应的含水率)传递函数模型的平均绝对误差降低了42.86%、23.87%、26.15%;增加θ60和θ15 000模型预测θ100、θ10000、θ15000的平均绝对误差比仅包含θ60模型降低了8.67%、16.96%、15.95%。将模型预测的van Genuchten参数应用于土壤水分模拟,增加θ60模型的平均绝对误差比以颗粒组成为输入变量模型的相应值降低了11.11%;相对于增加θ60的传递函数,额外再增加θ15000并未降低模型应用过程中的不确定性。Pedotransfer function was established to predict soil water retention curve by using the feed-forward neural networks methods. The prediction performance and application uncertainty of pedotransfer function were analyzed according to the error statistics index and Hydrus-lD water dynamics model. The results showed that mean absolute error value of θ1000, θ10000, θ15000 （soil water retention θ1000, θ10000, θ15000 at soil water suction equal to 1 000 cm, 10 000 cm and 15 000 cm, respectively） using pedotransfer functions with particle size distribution, bulk density, 06o as predictor was 42.86% ,23.87% and 26.15% lower than the value of PTF1 using particle size distribution as predictor. Mean absolute error of θ100,θ10000, θ15000 using pedotransfer function which added θ15000 as predictor was 8.67% ,16.96% and 15.95% lower than pedotransfer function using θ60 （ soil water θ60 at soil water suction equal to 60 cm） as predictor. The parameters of van Genuchten equation were predicted using pedotransfer function were used to simulate soil water, the MAE value of PTF using 06o as predictor was 11. 11% lower than PTF using particle size distribution as predictor. Therefore, adding additional θ15000 cannot reduce the uncertainty of pedotransfer function application comparing with the pedotransfer function using particle size, bulk density 060 as predictor.

关 键 词：土壤传递函数 数据源 不确定性 土壤水分特征曲线 

分 类 号：S152.3[农业科学—土壤学]