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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
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State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

机译:使用集合卡尔曼滤波器和粒子滤波器的两个陆地表面模型的状态和参数估计

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摘要

Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50?cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50?cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.
机译:陆地表面模型(LSM)使用大型参数和状态变量队列来模拟土壤 - 大气界面处的水和能量平衡。这些模型参数中的许多不能直接在该领域中测量,并且需要校准针对测量的二氧化碳,明智和/或潜热的助熔剂,和/或土壤热和/或水分状态的观察。在这里,我们使用5个月校准(同化)期间,评估四种不同数据同化方法的有用性和适用性的可变渗透能力模型(VIC-3L)和社区土地模型(CLM)的估算( 2012年3月 - 2012年7月)在德国西部的EIFEL山脉的ROLLEBROICH实验测试部门中的5,20和50厘米深度的面积平均水分测量。我们分别使用具有状态增强或双重估计的ENKF,以及具有简单,统计上缺陷的或更复杂的基于MCMC的参数重采样方法的残余重采样PF。使用5个月评估期(2012年8月至12月)使用Spade水含量测量来研究校准的LSM模型的性能。正如预期的那样,所有DA方法都增强了VIC和CLM模型描述了卷轴位点的Vadose区内的水分储存时空模式的能力,特别是如果是砂,粘土和有机物质的最大基础速度(VIC)或分数每个层(CLM)与每个土壤层的模型状态共同估计。 VIC-3L和CLM的土壤水分模拟之间的差异远大于四种数据同化方法的差异。具有状态增强或双重估计的ENKF在校准和评估期间产生了VIC-3L和CLM的最佳性能,但使用MCMC重采样的结果与PF密切一致。总体而言,CLM展示了Rollesbroich网站的最佳表现。在评估期问题的前几个月内通过VIC-3L在50℃深度的大量系统低估,部分地,其固定水位深度在模拟的土壤域底部的有效性。

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