Details
| Original language | English |
|---|---|
| Pages (from-to) | 360-370 |
| Number of pages | 11 |
| Journal | Advances in water resources |
| Volume | 110 |
| Publication status | Published - 16 Oct 2017 |
Abstract
For predicting flow in the unsaturated zone, an adequate choice of the model parameters, especially the soil hydraulic parameters, is essential. It is difficult to determine these parameters, as the parameter estimation problem easily becomes ill-posed, e.g. due to pseudo-correlations among two or more of the unknown parameters. In the field, this problem is strongly related to the available observations which, in monitoring networks, are not optimized to be used for parameter estimation. In this paper, we investigate the potential of data assimilation using the ensemble Kalman filter (EnKF) with unsaturated zone models under conditions where model parameters are highly uncertain and not identifiable. Different ways of dealing with the parameter uncertainty, such as parameter updates and bias correction, are discussed and compared. It is shown that jointly updating all uncertain parameters and states is the best method to account for the error induced by parameter uncertainty.
Keywords
- Data assimilation, EnKF, Parameter uncertainty, Soil moisture prediction, Unsaturated flow
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
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In: Advances in water resources, Vol. 110, 16.10.2017, p. 360-370.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Soil moisture prediction with the ensemble Kalman filter
T2 - Handling uncertainty of soil hydraulic parameters
AU - Brandhorst, N.
AU - Erdal, D.
AU - Neuweiler, I.
N1 - Funding information: This research is funded by the German Science Foundation ( DFG ) in the framework of research unit FOR 2131 under NE 824/12-1 .
PY - 2017/10/16
Y1 - 2017/10/16
N2 - For predicting flow in the unsaturated zone, an adequate choice of the model parameters, especially the soil hydraulic parameters, is essential. It is difficult to determine these parameters, as the parameter estimation problem easily becomes ill-posed, e.g. due to pseudo-correlations among two or more of the unknown parameters. In the field, this problem is strongly related to the available observations which, in monitoring networks, are not optimized to be used for parameter estimation. In this paper, we investigate the potential of data assimilation using the ensemble Kalman filter (EnKF) with unsaturated zone models under conditions where model parameters are highly uncertain and not identifiable. Different ways of dealing with the parameter uncertainty, such as parameter updates and bias correction, are discussed and compared. It is shown that jointly updating all uncertain parameters and states is the best method to account for the error induced by parameter uncertainty.
AB - For predicting flow in the unsaturated zone, an adequate choice of the model parameters, especially the soil hydraulic parameters, is essential. It is difficult to determine these parameters, as the parameter estimation problem easily becomes ill-posed, e.g. due to pseudo-correlations among two or more of the unknown parameters. In the field, this problem is strongly related to the available observations which, in monitoring networks, are not optimized to be used for parameter estimation. In this paper, we investigate the potential of data assimilation using the ensemble Kalman filter (EnKF) with unsaturated zone models under conditions where model parameters are highly uncertain and not identifiable. Different ways of dealing with the parameter uncertainty, such as parameter updates and bias correction, are discussed and compared. It is shown that jointly updating all uncertain parameters and states is the best method to account for the error induced by parameter uncertainty.
KW - Data assimilation
KW - EnKF
KW - Parameter uncertainty
KW - Soil moisture prediction
KW - Unsaturated flow
UR - http://www.scopus.com/inward/record.url?scp=85033499240&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2017.10.022
DO - 10.1016/j.advwatres.2017.10.022
M3 - Article
AN - SCOPUS:85033499240
VL - 110
SP - 360
EP - 370
JO - Advances in water resources
JF - Advances in water resources
SN - 0309-1708
ER -