Details
| Original language | English |
|---|---|
| Pages (from-to) | 1301-1323 |
| Number of pages | 23 |
| Journal | Hydrology and Earth System Sciences |
| Volume | 27 |
| Issue number | 6 |
| Publication status | Published - 27 Mar 2023 |
Abstract
Variably saturated subsurface flow models require knowledge of the soil hydraulic parameters. However, the determination of these parameters in heterogeneous soils is not easily feasible and subject to large uncertainties. As the modeled soil moisture is very sensitive to these parameters, especially the saturated hydraulic conductivity, porosity, and the parameters describing the retention and relative permeability functions, it is likewise highly uncertain. Data assimilation can be used to handle and reduce both the state and parameter uncertainty. In this work, we apply the ensemble Kalman filter (EnKF) to a three-dimensional heterogeneous hillslope model and investigate the influence of updating the different soil hydraulic parameters on the accuracy of the estimated soil moisture. We further examine the usage of a simplified layered soil structure instead of the fully resolved heterogeneous soil structure in the ensemble. It is shown that the best estimates are obtained when performing a joint update of porosity and the van Genuchten parameters and (optionally) the saturated hydraulic conductivity. The usage of a simplified soil structure gave decent estimates of spatially averaged soil moisture in combination with parameter updates but led to a failure of the EnKF and very poor soil moisture estimates at non-observed locations.
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: Hydrology and Earth System Sciences, Vol. 27, No. 6, 27.03.2023, p. 1301-1323.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model
AU - Brandhorst, Natascha
AU - Neuweiler, Insa
N1 - Funding Information: This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. NE 824/12-1).
PY - 2023/3/27
Y1 - 2023/3/27
N2 - Variably saturated subsurface flow models require knowledge of the soil hydraulic parameters. However, the determination of these parameters in heterogeneous soils is not easily feasible and subject to large uncertainties. As the modeled soil moisture is very sensitive to these parameters, especially the saturated hydraulic conductivity, porosity, and the parameters describing the retention and relative permeability functions, it is likewise highly uncertain. Data assimilation can be used to handle and reduce both the state and parameter uncertainty. In this work, we apply the ensemble Kalman filter (EnKF) to a three-dimensional heterogeneous hillslope model and investigate the influence of updating the different soil hydraulic parameters on the accuracy of the estimated soil moisture. We further examine the usage of a simplified layered soil structure instead of the fully resolved heterogeneous soil structure in the ensemble. It is shown that the best estimates are obtained when performing a joint update of porosity and the van Genuchten parameters and (optionally) the saturated hydraulic conductivity. The usage of a simplified soil structure gave decent estimates of spatially averaged soil moisture in combination with parameter updates but led to a failure of the EnKF and very poor soil moisture estimates at non-observed locations.
AB - Variably saturated subsurface flow models require knowledge of the soil hydraulic parameters. However, the determination of these parameters in heterogeneous soils is not easily feasible and subject to large uncertainties. As the modeled soil moisture is very sensitive to these parameters, especially the saturated hydraulic conductivity, porosity, and the parameters describing the retention and relative permeability functions, it is likewise highly uncertain. Data assimilation can be used to handle and reduce both the state and parameter uncertainty. In this work, we apply the ensemble Kalman filter (EnKF) to a three-dimensional heterogeneous hillslope model and investigate the influence of updating the different soil hydraulic parameters on the accuracy of the estimated soil moisture. We further examine the usage of a simplified layered soil structure instead of the fully resolved heterogeneous soil structure in the ensemble. It is shown that the best estimates are obtained when performing a joint update of porosity and the van Genuchten parameters and (optionally) the saturated hydraulic conductivity. The usage of a simplified soil structure gave decent estimates of spatially averaged soil moisture in combination with parameter updates but led to a failure of the EnKF and very poor soil moisture estimates at non-observed locations.
UR - http://www.scopus.com/inward/record.url?scp=85151635263&partnerID=8YFLogxK
U2 - 10.5194/hess-27-1301-2023
DO - 10.5194/hess-27-1301-2023
M3 - Article
AN - SCOPUS:85151635263
VL - 27
SP - 1301
EP - 1323
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
SN - 1027-5606
IS - 6
ER -