Covariance Analysis and Sensitivity Studies for GRACE Assimilation into WGHM
Covariance Analysis and Sensitivity Studies for GRACE Assimilation into WGHM
复制标题
GRACE 同化至 WGHM 的协方差分析和敏感性研究
DOI：
10.1007/1345_2015_119
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发表时间：
2015
影响因子：
5.4
通讯作者：
P. Döll
中科院分区：
文献类型：
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作者：
M. Schumacher;A. Eicker;J. Kusche;H. Schmied;P. Döll
提出了一种改进WaterGAP全球水文模式（WGHM）的集合卡尔曼滤波方法，该方法同化重力恢复与气候实验（GRACE）数据，同时对模式参数进行校正。该方法使用模型导出的状态和卫星测量及其误差信息来确定更新的储水状态。然而，由于水文模型不提供任何误差信息，需要计算经验协方差矩阵。因此，在本文中，我们分析了WGHM的组合状态和参数协方差矩阵。我们发现，高达0.75的校准参数和存储隔间之间存在的高相关性，这些允许一个有效的校准。此外，还进行了敏感性分析，以确定水隔室对哪些参数最敏感。由于GRACE无法直接观察模型参数，因此进行的分析很重要。我们发现，这些参数，其中水存储是最敏感的，不仅不同的区域，而且相对于水隔间。并不意外，在我们的模型版本中实现的一些气候输入乘数具有整体强大的影响力。我们还发现，灵敏度随时间变化，例如，在0（夏季）和0.5（冬季）之间的雪存储。
An ensemble Kalman filter approach for improving the WaterGAP Global Hydrology Model (WGHM) has been developed, which assimilates Gravity Recovery And Climate Experiment (GRACE) data and calibrates the model parameters, simultaneously. The method uses the model-derived states and satellite measurements and their error information to determine updated water storage states. However, due to the fact that hydrological models do not provide any error information, an empirical covariance matrix needs to be calculated. In this paper, therefore, we analyse the combined state and parameter covariance matrix of WGHM. We found that high correlations of up to 0.75 exist between calibration parameters and storage compartments, and that these allow for an efficient calibration. In addition, a sensitivity analysis is performed to identify those parameters that the water compartments are most sensitive to. The performed analysis is important, since GRACE cannot observe the model parameters directly. We found that those parameters, which the water storage is most sensitive to, differ not only regionally, but also with respect to the water compartments. Not unexpected, some climate input multipliers implemented in our model version have an overall strong influence. We also found that the degree of sensitivity changes temporally, e.g. between 0 (in summer) and 0.5 (in winter) for the snow storage.