Earth observation (EO) has demonstrated to be capable of capturing spatially organized information on key hydro-meteorological variables related to the land surface (e.g. soil moisture, evapotranspiration, river discharge, surface and groundwater storage change), atmosphere (e.g. rainfall) and cryosphere (e.g. snow albedo, snow water equivalent).

The availability of EO data alone is, however, in many cases not sufficient for operational utilization, e.g. for integrated water resources management, because the remote sensors typically do not observe the exact same variable that is of interest and at the spatial resolution suitable to correctly catch the process. Integration of EO data with physically based process models is an important strategy for deriving value added products. In this context, EO data as such is employed for reducing uncertainties in the modelling of hydrological processes. An improvement in the reliability of model simulations can be achieved by updating the simulated state with the observed information via a comprehensive data assimilation scheme. Moreover, dual state-parameter updating by assimilating EO data can further increase the reliability of model simulations also in periods when no EO data are available.

This session solicits for contributions that demonstrate improved simulation of hydro-meteorological processes by using EO data either for (i) state updating or (ii) providing model parameters or (iii) model calibration.