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

The availability of EO data alone is, however, in many cases insufficient for operational applications, e.g. integrated water resources management; because the remote sensors typically do not observe the exact same variable of interest at the appropriate spatial resolution needed to adequately capture 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 inherent to the modelling of hydro-meteorological 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. This includes also the representation of human impacts on the hydro-meteorological variables that can be captured by EO but is typically not part of physical models directly. 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.