This title appears in the Scientific Report :
2014
Development of a data assimilation system for the integrated land surface - subsurface model ParFlow-CLM
Development of a data assimilation system for the integrated land surface - subsurface model ParFlow-CLM
The coupling of land surface and subsurface models might improve the overall predictive accuracy of hydrological and atmospheric models. An example of such an integrated modeling approach is the recently established modeling platform TerrSysMP which consists of three sub-models; ParFlow for subsurfa...
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Personal Name(s): | Kurtz, Wolfgang (Corresponding Author) |
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He, Guowei / Vereecken, Harry / Hendricks-Franssen, Harrie-Jan | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | 2014 |
Imprint: |
2014
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Conference: | CAHMDA/HEPEX-DAFOH Joint Workshop, Austin (USA), 2014-09-08 - 2014-09-12 |
Document Type: |
Poster |
Research Program: |
Terrestrial Systems: From Observation to Prediction Modelling and Monitoring Terrestrial Systems: Methods and Technologies |
Publikationsportal JuSER |
The coupling of land surface and subsurface models might improve the overall predictive accuracy of hydrological and atmospheric models. An example of such an integrated modeling approach is the recently established modeling platform TerrSysMP which consists of three sub-models; ParFlow for subsurface processes, CLM for land surface processes and COSMO for the atmosphere. These components are coupled via state variables and fluxes by the coupling software OASIS-MCT. In general, predictions with such highly parameterized coupled models are associated with a considerable degree of uncertainty due to uncertain initial conditions and the poorly known subsurface and vegetation properties. Therefore, there is a need to constrain the model predictions with field observations which can be achieved with different data assimilation algorithms. We constructed a data assimilation system for the land surface-subsurface part of TerrSysMP (CLM and ParFlow) by linking the model with the PDAF (Parallel Data Assimilation Framework) software. This approach is highly efficient for high-performance computing systems because it avoids frequent re-initializations of the model and allows for a memory based communication between model and data assimilation routines. The feasibility of this approach is demonstrated with a synthetic model setup where soil moisture data are assimilated into a medium-scale hydrological model of the Rur catchment (Germany) with ensemble based data assimilation algorithms. |