Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems
Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems
复制标题
DOI：
10.1016/j.jhydrol.2008.11.033
复制
发表时间：
2009-02
影响因子：
6.4
通讯作者：
H. Franssen;W. Kinzelbach
中科院分区：
文献类型：
--
作者：
H. Franssen;W. Kinzelbach
蒙特-卡罗（MC）型逆建模技术，如顺序自校准（SSC）方法，可以用作测试其他逆建模程序的基准。在比较研究中，MC型逆建模方法优于其他逆参数估计方法，但所需的大量CPU时间可以成为禁止使用这样的方法。因此，人们有兴趣开发性能几乎一样好并且需要更少CPU时间的替代方法。本文提出了一种适用于多节点瞬变地下水流模型离线标定的Enklman滤波方法。一个增广的状态向量的方法被用来校准参数连同状态的更新。在两个校准实验中比较了EnKF和SSC，对于轻度非均匀情况（σlnT2=1.0）和强非均匀情况（σlnT2=2.7）。对于轻度不均匀的情况下，EnKF给SSC非常相似的结果，也在校准的对数transmittance字段。对于强异质性的情况下，EnKF仍然给出了类似的结果，SSC，虽然logT字段的特性改善较少相比，SSC。另一方面，EnKF在这两种情况下都需要比SSC少80倍的CPU时间。此外，在两个预测实验中，EnKF和SSC的性能进行了比较：（1）在不同的流动情况下（无抽水和不同的时间序列的补给率）的地下水流的预测，（2）溶质运移到抽水井的预测。无论是在轻度和强烈的异质性的情况下，与EnKF的预测质量是一样好的SSC。考虑到EnKF的良好性能，EnKF包括多个不确定性来源（例如，与MC型反演模型相比，EnKF具有与外部强迫有关的优点，并且减少了所需的CPU时间，因此EnKF似乎是大型地下水文模型随机校准的一个有趣的候选者。
Monte-Carlo (MC) type inverse modelling techniques like the sequential self-calibration (SSC) method, can be used as a benchmark to test other inverse modelling procedures. In comparison studies MC type inverse modelling methods outperformed other inverse parameter estimation methods, but the large amount of CPU time needed can become prohibitive to use such methods. Therefore, an interest exists to develop alternative methods that perform nearly as good and need much less CPU time. In this paper Ensemble Kalman Filtering (EnKF) is promoted for the off-line calibration of transient groundwater flow models with many nodes. An augmented state vector approach is used to calibrate parameters together with the updating of the states. EnKF and SSC are compared in two calibration experiments, for a mildly heterogeneous case (σlnT2=1.0) and a strongly heterogeneous case (σlnT2=2.7). For the mildly heterogeneous case, EnKF gives very similar results to SSC, also in terms of calibrated log-transmissivity fields. For the strongly heterogeneous case, EnKF gives still similar results as SSC, although the characterisation of the logT field improves less compared to SSC. On the other hand, EnKF needed in both cases around a factor of 80 less CPU time than SSC. In addition, the performance of EnKF and SSC was compared in two prediction experiments: (1) the prediction of groundwater flow in a different flow situation (without pumping and with a different time series of recharge rate), (2) the prediction of solute transport towards a pumping well. Both in the mildly and strongly heterogeneous cases the quality of the predictions with EnKF was as good as for SSC. Given the good performance of EnKF, the strength of EnKF to include multiple sources of uncertainty (e.g., related to external forcing) and the reduced CPU time needed compared to MC type inverse modelling, EnKF seems to be an interesting candidate for the stochastic calibration of large subsurface hydrological models.