Effect of unrepresented model errors on estimated soil hydraulic material properties
Effect of unrepresented model errors on estimated soil hydraulic material properties
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DOI：
10.5194/hess-21-4301-2017
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发表时间：
2017-09
影响因子：
6.3
通讯作者：
S. Jaumann;K. Roth
中科院分区：
文献类型：
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作者：
S. Jaumann;K. Roth
抽象。未表示的模型误差影响有效土壤水力材料特性的估计。由于测量数据的一致性描述所需的模型复杂性是应用程序相关的和未知的先验，我们实现了一个结构误差分析的基础上越来越复杂的模型的反演。我们表明，该方法可以表示未表示的模型误差，并量化其对所得材料性能的影响。为此，一个复杂的2-D地下结构（ASSESS）被迫与波动的地下水位，而时域反射仪（TDR）和液压电位测量设备监测水力状态。在这项工作中，我们分析了未代表的（一）传感器位置的不确定性，（二）小尺度的异质性，（三）2-D流动现象估计土壤水力材料特性与1-D和2-D的研究的定量效果。这些研究的结果证明了三个要点：（i）每种材料可用的传感器越少，未表示的模型误差对所得材料特性的影响就越大。(ii)1-D的研究产生偏差的参数，由于未代表的横向流。(iii)代表和估计传感器的位置，以及小规模的异质性降低了体积含水量数据的平均绝对误差超过2至0的一个因素。004.
Abstract. Unrepresented model errors influence the estimation of effective soil hydraulic material properties. As the required model complexity for a consistent description of the measurement data is application dependent and unknown a priori, we implemented a structural error analysis based on the inversion of increasingly complex models. We show that the method can indicate unrepresented model errors and quantify their effects on the resulting material properties. To this end, a complicated 2-D subsurface architecture (ASSESS) was forced with a fluctuating groundwater table while time domain reflectometry (TDR) and hydraulic potential measurement devices monitored the hydraulic state. In this work, we analyze the quantitative effect of unrepresented (i) sensor position uncertainty, (ii) small scale-heterogeneity, and (iii) 2-D flow phenomena on estimated soil hydraulic material properties with a 1-D and a 2-D study. The results of these studies demonstrate three main points: (i) the fewer sensors are available per material, the larger is the effect of unrepresented model errors on the resulting material properties. (ii) The 1-D study yields biased parameters due to unrepresented lateral flow. (iii) Representing and estimating sensor positions as well as small-scale heterogeneity decreased the mean absolute error of the volumetric water content data by more than a factor of 2 to 0. 004.