文章摘要
林青,徐绍辉.基于Bayes理论的田间层状土壤水分运动参数识别及不确定性分析[J].水利学报,2018,49(4):428-438
基于Bayes理论的田间层状土壤水分运动参数识别及不确定性分析
Parameter identification and uncertainty analysis of soil water movement model in field layered soils based on Bayes Theory
投稿时间:2017-10-12  
DOI:10.13243/j.cnki.slxb.20170990
中文关键词: 层状土壤  Bayes理论  不确定性分析  参数后验分布  自适应差分演化
英文关键词: layered soil  Bayes Theory  uncertainty analysis  posterior distribution  DREAMZS
基金项目:国家自然科学基金项目(41571214);国家重点研发计划课题(2016YFC0402807);山东省自然科学基金项目(ZR2014DQ021)
作者单位E-mail
林青 青岛大学 环境科学与工程学院, 山东 青岛 266071  
徐绍辉 青岛大学 环境科学与工程学院, 山东 青岛 266071 shhxu@qdu.edu.cn 
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中文摘要:
      土壤水分运动参数是非饱和带水分及污染物运移研究的核心参数,根据点尺度土壤样本的室内稳态试验得到的水分运动参数往往不能准确反映天然条件下田间尺度土壤水分运动特征。本文基于为时2年的田间土壤含水量观测数据(2013年为率定期,2014年为验证期),通过土壤转换函数得到了VGM (van Genuchten-Mualem)模型水力参数的先验分布,建立了反演层状土壤持水和导水特征的贝叶斯模型,采用自适应差分演化(DREAMZS)的采样方法,结合Hydrus_1d模型,对田间尺度土壤水分含量预测模型进行优化及不确定性分析,获得了水分特征参数的后验分布,分析了最优参数组的模拟效果及模型预测的95%的置信区间。结果表明,基于DREAMZS采样的Bayes方法可以实现田间尺度层状土壤水分特征参数的率定及土壤水分动态的模拟预测。率定结果显示饱和导水率Ks最不敏感,饱和含水量θs最为敏感,可识别性较高,室内试验反演得到θs可用于田间土壤水分运动的模拟。随着土壤含水量模拟深度的增加,PUCI(单位平均相对宽度所包含的实测点数据比例)值越大,模型预测的性能越高。模拟结果的不确定性主要由模型结构所引起,所以对模型结构的修改完善是未来提高模型预测的关键。
英文摘要:
      Soil water movement parameters are the core parameters of water and pollutant migration in unsaturated zone. However, water movement parameters obtained from the indoor steady-state test of the soil samples at the point scale can't accurately reflect the soil water movement characteristics at the field scale under the natural occurring boundary conditions. A Bayesian inference for inversion of soil water retention and hydraulic parameters, based approach DREAMZS (Differential Evolution Adaptive Metropolis algorithm), was combined with Hydrus_1d to implement model optimization and uncertainty analysis on the data of soil moisture content in field observation (2013 for calibration and 2014 for validation). In order to get the prior information of the parameters in the van Genuchten-Mualem (VGM) model of the soil hydraulic functions, the ROSETTA pedotransfer functions was used. The posterior distribution of the water characteristic parameters was obtained, and modelling performance using the best estimated parameter set and the 95% prediction confidence interval of the model prediction were analyzed. The results show that the Bayes method based on DREAMZS sampling can be used to identify the soil water characteristic parameters and predict the soil water dynamics at the field scale. The parameter identification results show that the saturated conductivity Ks is the least sensitive, and saturated water content θs is the most sensitive and easily identified. The parameter θs estimated from the laboratory experiment can be used for the modeling at the field scale. With the increase of soil depth, the higher the PUCI (Percentage of observations bracketed by the Unit Confidence Interval) value, the higher the performance of the model (reliability and accuracy). The prediction uncertainty is mainly caused by model structural uncertainty, and this implies that the effort to improve model prediction credibility in future should focus on diagnosis of model structure.
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