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首页> 外文期刊>Vadose zone journal VZJ >Estimation of Unsaturated Soil Hydraulic Parameters Using the Ensemble Kalman Filter
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Estimation of Unsaturated Soil Hydraulic Parameters Using the Ensemble Kalman Filter

机译：使用集合卡尔曼滤波器估算不饱和土的水力参数

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摘要

The parameters of soil hydraulic functions are essential to the accurate simulation of soil moisture based on the Richards equation. Optimal values of these parameters can be calibrated by inverse modeling, in which the incomplete consideration of various errors may influence the parameter estimation results, thus further limiting the accuracy of modeling and forecasting. The ensemble Kalman filter (EnKF) is believed to be a flexible and effective sequential data assimilation method that provides a framework of explicit consideration of the various sources of uncertainty and is suitable for real-time, updated observations. The objective of this study was to extend the use of the EnKF to parameter estimation in vadose zone hydrology to improve the treatment of uncertainty in the calibration process. The parameters of soil hydraulic functions were estimated by assimilating observations of soil water pressure dynamics using EnKF with an augmentation technique. The results of the synthetic experimentson 12 soils with different textures indicated that EnKF estimates can quickly approach stable estimates. In contrast to the batch calibration process that used a simple least squares objective function, the EnKF reduced the risk of obtaining suboptimal estimates. The EnKF also performed well in the multiparameter estimation scenarios with synthetic observations and in its application in a heterogeneous soil profile with in situ field observations from a previous study. We further explored the factors that may influence estimation results, including the initial estimate, the ensemble size, the observation error and the model error, the assimilation interval, the water regime, and the variability of the estimated parameters. The result of this study indicates that the EnKF scheme is an effective method for parameter estimation in vadose zone hydrology.
机译：土壤水力函数的参数对于基于Richards方程的精确模拟土壤水分至关重要。这些参数的最佳值可以通过逆建模进行校准，其中对各种误差的不完全考虑可能会影响参数估计结果，从而进一步限制了建模和预测的准确性。集成卡尔曼滤波器（EnKF）被认为是一种灵活而有效的顺序数据同化方法，它提供了明确考虑各种不确定性来源的框架，并且适用于实时更新的观测。这项研究的目的是将EnKF的使用扩展到渗流带水文参数估计中，以改善对标定过程不确定性的处理。通过使用EnKF和增强技术对土壤水压力动态的观察进行同化，从而估算了土壤水力学参数。在12种质地不同的土壤上进行的综合实验结果表明，EnKF估计可以快速接近稳定的估计。与使用简单最小二乘法目标函数的批量校准过程相比，EnKF降低了获得次优估计的风险。 EnKF在综合观测的多参数估算方案中以及在先前研究的现场实地观测的非均质土壤剖面中的应用中也表现出色。我们进一步探讨了可能影响估计结果的因素，包括初始估计，集合大小，观测误差和模型误差，同化间隔，水情和估计参数的可变性。研究结果表明，EnKF方案是渗流带水文参数估计的有效方法。

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