EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology
EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology
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DOI：
10.5194/hess-20-4999-2016
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发表时间：
2016-12
影响因子：
6.3
通讯作者：
H. H. Bauser-H.;S. Jaumann;Daniel Berg;K. Roth
中科院分区：
文献类型：
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作者：
H. H. Bauser-H.;S. Jaumann;Daniel Berg;K. Roth
抽象的。土壤水分运动的表示暴露了所有模型组件中的不确定性。我们使用TDR(时域反射仪)测量的水分来评估一维土壤剖面特定水力状况的关键不确定性。所涉及的不确定性包括初始条件、土壤水力参数、小尺度非均质性、上边界条件和Richards方程的局部平衡假设。我们使用具有增广状态的集合卡尔曼滤波(EnKF)来表示和估计除了间歇性地违反局部平衡假设之外的所有关键不确定性。对于后者，我们引入了闭眼EnKF来弥补这一差距。由于采用迭代方法，EnKF能够基于单次降雨过程中的TDR测量来估计土壤参数、Miller标度因子和上边界条件。引入的闭眼周期确保了参数的恒定，这表明它们与人们相信的真实材料性质相似。这种闭目期改善了满足局部平衡假设的时期的预测，但需要对局部非平衡阶段的动力学进行描述才能预测它们。这样的描述仍然是一个悬而未决的挑战。最后，对于给定的表示，我们的结果表明了包括小规模异质性的必要性。米勒标度的简化表示已经给出了令人满意的描述。
Abstract. The representation of soil water movement exposes uncertainties in all model components. We assess the key uncertainties for the specific hydraulic situation of a 1-D soil profile with TDR (time domain reflectometry)-measured water contents. The uncertainties addressed are initial condition, soil hydraulic parameters, small-scale heterogeneity, upper boundary condition, and the local equilibrium assumption by the Richards equation. We employ an ensemble Kalman filter (EnKF) with an augmented state to represent and estimate all key uncertainties, except for the intermittent violation of the local equilibrium assumption. For the latter, we introduce a closed-eye EnKF to bridge the gap. Due to an iterative approach, the EnKF was capable of estimating soil parameters, Miller scaling factors and upper boundary condition based on TDR measurements during a single rain event. The introduced closed-eye period ensured constant parameters, suggesting that they resemble the believed true material properties. This closed-eye period improves predictions during periods when the local equilibrium assumption is met, but requires a description of the dynamics during local non-equilibrium phases to be able to predict them. Such a description remains an open challenge. Finally, for the given representation our results show the necessity of including small-scale heterogeneity. A simplified representation with Miller scaling already yielded a satisfactory description.