A Direct Application of Data Assimilation to Multi-Scale Regionalized Parameters

Streamflow observations are integrated signals of a catchment. This data is only weakly correlated to local observations (e.g. soil moisture and groundwater heads) or local parameters (e.g. hydraulic conductivity) of the catchment. On the one hand, this makes it next to impossible to estimate model parameters from streamflow observations alone. On the other hand, local observations only make parameter estimation possible in their immediate proximity. With data scarcity in mind, this multi-variate data assimilation alone has limited potential to solving the problem of estimating model parameters.
Therefore, we propose to not apply data assimilation to the model parameters directly, but to the global parameters of the multi-scale regionalization (MPR, Samaniego et al. 2010) approach. This approach relates a very limited number of global parameters through transfer functions to the model parameters. By doing so, the number of parameters to be estimated can be drastically reduced, saving computing time and with robust transfer functions, the local parameters can be estimated not only in the proximity of observations, but also throughout the catchment.
Using the DA-MPR approach, we investigate different experiment setups for estimating model parameters, e.g. a stationary cosmic ray sensor vs. a mobile one or how many local observations are actually needed in order to uniquely identify the model parameters.

Samaniego L., R. Kumar, S. Attinger (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46