This title appears in the Scientific Report :
2022
Please use the identifier:
http://hdl.handle.net/2128/31640 in citations.
Please use the identifier: http://dx.doi.org/10.1029/2021WR031549 in citations.
Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany
Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany
Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify...
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Personal Name(s): | Hung, Ching Pui (Corresponding author) |
---|---|
Schalge, Bernd / Baroni, Gabriele / Vereecken, Harry / Hendricks Franssen, Harrie-Jan | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | Water resources research, 58 (2022) 6, S. e2021WR031549 |
Imprint: |
[New York]
Wiley
2022
|
DOI: |
10.1029/2021WR031549 |
Document Type: |
Journal Article |
Research Program: |
FOR 2131: Datenassimilation in terrestrischen Systemen Agro-biogeosystems: controls, feedbacks and impact |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1029/2021WR031549 in citations.
Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface-subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state-only/state-hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance. |