Data assimilation with TerrSysMP-PDAF

Modeling of terrestrial systems is continuously moving towards more integrated modeling approaches where different terrestrial compartment models are combined in order to realize a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. An example of such an integrated earth system model is the recently established modeling platform TerrSysMP (Shrestha et al., 2014) consisting of individual component models for variably saturated subsurface flow and surface water routing (ParFlow), land surface processes (CLM 3.5) and atmosphere dynamics (COSMO-DE). The component models are dynamically linked by the exchange of state variables and fluxes with the coupling software OASIS-MCT in a modular, scale-consistent manner which provides a fully coupled representation of terrestrial processes.
Despite the sophisticated process description in TerrSysMP, model predictions are still associated with uncertainties, e.g., related to imprecise knowledge on initial conditions or the high spatial and/ or temporal variability of model forcing data (e.g., precipitation) or model parameters (e.g., vegetation parameters or hydraulic subsurface parameters). Ensemble-based data assimilation methods can potentially improve the model predictions and reduce the uncertainty by adjusting model simulations with available observation data which can additionally be used to improve the characterization of the model parameters. Therefore, we developed a data assimilation system in combination with TerrSysMP by coupling TerrSysMP with the PDAF (Parallel Data Assimilation Framework) library (Nerger & Hiller, 2013). PDAF is specifically designed for parallel simulation models and provides a variety of global and local data assimilation algorithms, like the ensemble Kalman filter (EnKF) or the local ensemble transform Kalman filter (LETKF).
The data assimilation framework TerrSysMP-PDAF (Kurtz et al, 2016) allows for the assimilation of a variety of observation data (e.g., soil moisture, groundwater levels, surface water levels) into the different compartment models of TerrSysMP. These observation data can be used to adjust model state variables as well as to calibrate model parameters, like saturated hydraulic conductivity, texture and Mannings roughness coefficients. TerrSysMP-PDAF is modular with respect to the chosen forward model, i.e., different model combinations of ParFlow, CLM and COSMO can be used in the ensemble forward integration. Internally, it uses a memory-based communication between model and data assimilation routines which avoids frequent re-initializations of the model and is thus highly scalable and applicable to large-scale hydrological systems (> 20 Mio. unknowns).

Data assimilation with TerrSysMP-PDAF
Data flow in TerrSysMP-PDAFFigure 1: Data flow in TerrSysMP-PDAF.

Examples of applications of the data assimilation framework TerrSysMP-PDAF are:

Link to projects:

TR32

FOR2131

Further information on PDAF can be found here

Papers involving TerrSysMP-PDAF:

Zhang, H., Kurtz, W., Kollet, S.J., Vereecken, H., Hendricks Franssen, H.-J. (20xx). Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial systems model. under review for Adv. Water Resour.


Baatz, D., Kurtz, W., Hendricks Franssen, H.-J., Vereecken H., Kollet, S.J. (2017). Catchment tomography – An approach for spatial parameter estimation, Adv. Water Resour., 107, 147-159, doi:10.1016/j.advwatres.2017.06.006


Kurtz, W., He, G., Kollet, S.J., Maxwell, R.M., Vereecken, H., Hendricks Franssen, H.-J. (2016). TerrSysMP-PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface-subsurface model, Geosci. Model Dev., 9, 1341-1360, doi:10.5194/gmd-9-1341-2016.

References:

Kurtz, W., He, G., Kollet, S.J., Maxwell, R.M., Vereecken, H., Hendricks Franssen, H.-J. (2016). TerrSysMP-PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface-subsurface model, Geosci. Model Dev., 9, 1341-1360, doi:10.5194/gmd-9-1341-2016.


Nerger, L., Hiller, W. (2013). Software for ensemble-based data assimilation systems - implementation strategies and scalability, Comput. & Geosci., 55: 110-118, doi:10.1016/j.cageo.2012.03.026.


Shrestha, P., Sulis, M., Masbou, M., Kollet, S.J., Simmer, C. (2014). A scale-consistent terrestrial systems modeling platform based on COSMO, CLM, and Parflow, Mon. Wea. Rev., 142: 3466-3483, doi:10.1175/mwr-d-14-00029.1.

Last Modified: 25.05.2022