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 -