Towards Optimizing Experiments for Maximum-confidence Model Selection between Different Soil-plant Models
Towards Optimizing Experiments for Maximum-confidence Model Selection between Different Soil-plant Models
复制标题
不同土壤植物模型之间最大置信度模型选择的优化实验
DOI：
10.1016/j.proenv.2013.06.058
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
H. D. Wizemann
中科院分区：
文献类型：
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作者：
Th. Wöhling;A. Geiges;W. Nowak;S. Gayler;P. Högy;H. D. Wizemann
我们提出了一种实验设计方法，优化数据采集，使土壤-植物模型选择任务具有最大的置信度。该方法考虑了参数、测量和模型结构的不确定性。本文将贝叶斯模型平均（BMA）与数据价值分析相结合，探讨了在不同数据类型选择下模型权重的决定作用。这允许评估不同数据类型、数据密度和数据位置在从一组合理的模型中识别最佳模型结构方面的能力。本研究中考虑的模型是作物模型CERES， SUCROS， GECROS和SPASS，它们与建模框架Expert-N中模拟土壤过程的相同例程相耦合。这四种模型在描述作物生长和根系水分吸收的详细程度上差别很大。考虑到这些模型在土壤水力特性和所选作物模型参数中的不确定性，对每个模型进行了蒙特卡罗模拟。利用Bootstrap滤波对贝叶斯模型进行了更新，并以德国西南部奈林根地区一个冬小麦生长期的土壤湿度、叶面积指数（LAI）和蒸散速率为条件，对模型进行了野外测量。根据我们的方法，我们推导了使用所有数据或其不同子集时的BMA模型权重。我们讨论了后验BMA均值在多大程度上优于先验BMA均值和所有个体后验模型，数据类型对减少实际蒸散发预测不确定性的信息量有多大，以及基于不同数据类型和子集的模型结构识别的程度。
We present a method for experimental design, optimizing data acquisition for maximum confidence in the soil-plant model selection task. The method considers uncertainty in parameters, measurements and model structures. We combine Bayesian Model Averaging (BMA) with worth-of-data analysis and investigate how decisive the model weights are under different selections of data types. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modeling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. With Bayesian model updating by a Bootstrap filter, the models were then conditioned on field measurements of soil moisture, leaf area index (LAI), and evapotranspiration rates during a vegetation period of winter wheat in Nellingen, Southwestern Germany. Following our approach, we derived the BMA model weights when using all data or different subsets thereof. We discuss to which degree the posterior BMA mean outperformed the prior BMA mean and all individual posterior models, how informative the data types were for reducing prediction uncertainty of actual evapotranspiration, and how well the model structure can be identified based on the different data types and subsets.