Details
| Original language | English |
|---|---|
| Pages (from-to) | 132-147 |
| Number of pages | 16 |
| Journal | Water resources research |
| Volume | 50 |
| Issue number | 1 |
| Publication status | Published - 27 Nov 2013 |
Abstract
When predicting flow in the unsaturated zone, any method for modeling the flow will have to define how, and to what level, the subsurface structure is resolved. In this paper, we use the Ensemble Kalman Filter to assimilate local soil water content observations from both a synthetic layered lysimeter and a real field experiment in layered soil in an unsaturated water flow model. We investigate the use of colored noise bias corrections to account for unresolved subsurface layering in a homogeneous model and compare this approach with a fully resolved model. In both models, we use a simplified model parameterization in the Ensemble Kalman Filter. The results show that the use of bias corrections can increase the predictive capability of a simplified homogeneous flow model if the bias corrections are applied to the model states. If correct knowledge of the layering structure is available, the fully resolved model performs best. However, if no, or erroneous, layering is used in the model, the use of a homogeneous model with bias corrections can be the better choice for modeling the behavior of the system. Key Points Accounting for unresolved subsurface structures using bias aware EnFK Predicting average system behavior using local observations Using EnKF for data assimilation with a nonlinear unsaturated zone model
Keywords
- bias correction, EnKF, model error, parameter estimation, unresolved structure, unsaturated zone
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
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In: Water resources research, Vol. 50, No. 1, 27.11.2013, p. 132-147.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Using a bias aware EnKF to account for unresolved structure in an unsaturated zone model
AU - Erdal, D.
AU - Neuweiler, I.
AU - Wollschläger, U.
PY - 2013/11/27
Y1 - 2013/11/27
N2 - When predicting flow in the unsaturated zone, any method for modeling the flow will have to define how, and to what level, the subsurface structure is resolved. In this paper, we use the Ensemble Kalman Filter to assimilate local soil water content observations from both a synthetic layered lysimeter and a real field experiment in layered soil in an unsaturated water flow model. We investigate the use of colored noise bias corrections to account for unresolved subsurface layering in a homogeneous model and compare this approach with a fully resolved model. In both models, we use a simplified model parameterization in the Ensemble Kalman Filter. The results show that the use of bias corrections can increase the predictive capability of a simplified homogeneous flow model if the bias corrections are applied to the model states. If correct knowledge of the layering structure is available, the fully resolved model performs best. However, if no, or erroneous, layering is used in the model, the use of a homogeneous model with bias corrections can be the better choice for modeling the behavior of the system. Key Points Accounting for unresolved subsurface structures using bias aware EnFK Predicting average system behavior using local observations Using EnKF for data assimilation with a nonlinear unsaturated zone model
AB - When predicting flow in the unsaturated zone, any method for modeling the flow will have to define how, and to what level, the subsurface structure is resolved. In this paper, we use the Ensemble Kalman Filter to assimilate local soil water content observations from both a synthetic layered lysimeter and a real field experiment in layered soil in an unsaturated water flow model. We investigate the use of colored noise bias corrections to account for unresolved subsurface layering in a homogeneous model and compare this approach with a fully resolved model. In both models, we use a simplified model parameterization in the Ensemble Kalman Filter. The results show that the use of bias corrections can increase the predictive capability of a simplified homogeneous flow model if the bias corrections are applied to the model states. If correct knowledge of the layering structure is available, the fully resolved model performs best. However, if no, or erroneous, layering is used in the model, the use of a homogeneous model with bias corrections can be the better choice for modeling the behavior of the system. Key Points Accounting for unresolved subsurface structures using bias aware EnFK Predicting average system behavior using local observations Using EnKF for data assimilation with a nonlinear unsaturated zone model
KW - bias correction
KW - EnKF
KW - model error
KW - parameter estimation
KW - unresolved structure
KW - unsaturated zone
UR - http://www.scopus.com/inward/record.url?scp=84896710135&partnerID=8YFLogxK
U2 - 10.1002/2012WR013443
DO - 10.1002/2012WR013443
M3 - Article
AN - SCOPUS:84896710135
VL - 50
SP - 132
EP - 147
JO - Water resources research
JF - Water resources research
SN - 0043-1397
IS - 1
ER -