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EnKF with closed-eye period - towards a consistent aggregation of information in soil hydrology

机译:EnKF闭眼时期-致力于土壤水文学中信息的一致汇总

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摘要

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.
机译:土壤水分运动的表示暴露了所有模型组件的不确定性。我们使用TDR(时域反射法)测量的含水量评估了一维土壤剖面的特定水力状况的关键不确定性。解决的不确定性包括初始条件,土壤水力参数,小规模非均质性,上边界条件和Richards方程的局部平衡假设。我们采用具有增强状态的集成卡尔曼滤波器(EnKF)来表示和估计所有关键的不确定性,除了间歇性违反局部均衡假设的情况之外。对于后者,我们引入了闭眼EnKF来弥合差距。由于采用了迭代方法,EnKF能够在一次降雨事件中基于TDR测量来估计土壤参数,Miller比例因子和上限条件。引入的闭眼周期确保了恒定的参数,表明它们类似于被认为是真实的材料特性。在满足局部平衡假设的情况下,此闭眼周期可改善预测,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公开的挑战。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。 Miller缩放的简化表示已经产生了令人满意的描述。

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