This title appears in the Scientific Report :
2012
Please use the identifier:
http://dx.doi.org/10.5194/hess-16-573-2012 in citations.
Please use the identifier: http://hdl.handle.net/2128/7479 in citations.
Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter
Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter
The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scena...
Saved in:
Personal Name(s): | Li, L. |
---|---|
Zhou, H. / Hendricks-Franssen, H.J. / Gomez-Hernandez, J.J. | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | Hydrology and earth system sciences, 16 (2012) S. 573 - 590 |
Imprint: |
Katlenburg-Lindau
EGU
2012
|
Physical Description: |
573 - 590 |
DOI: |
10.5194/hess-16-573-2012 |
Document Type: |
Journal Article |
Research Program: |
Terrestrische Umwelt |
Series Title: |
Hydrology and Earth System Sciences
16 |
Subject (ZB): | |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/7479 in citations.
The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations. |