Abstract. Along rivers, where local insitu gauges are unavailable, estimation of river discharge are undirectly derived from the Manning formula that relate discharge to geomorphological characteristics of the rivers and flow conditions. Most components of the Manning formula can currently be derived from spaceborne products except for two features: the unobserved always-wet bathymetry and the roughness coefficient. Global-scale applications use simplified equivalent riverbed shapes and empirical parameters while local-scale applications rely on finer model dynamics, field survey and expert knowledge. Within the framework of the incoming Surface Water and Ocean Topography (SWOT) mission, scheduled for a launch in 2022, and more particularly, the development of the SWOT-based discharge product, fine-resolution but global discharge estimates should be produced. Currently implemented SWOT-based discharge algorithms require prior information on bathymetry and roughness and their performances highly depend on the quality of such priors. Here we introduce an automatic and spaceborne-data-based-only methodology to derive physically-based roughness coefficients to use in one-dimensional hydrological models. The evaluation of those friction coefficients showed that they allow model performances comparable to calibrated models. Finally, we illutrate two cases of application where our roughness coefficients are used as-is to initiate the experiment: a data assimilation experiment designed to correct the roughness parameters and an application around the HiVDI SWOT-based discharge algorithm. In both cases, the roughness coefficients showed promising perspectives by reproducing, for the data assimilation application, and sometimes improving, in the SWOT discharge algorithm case, the calibrated-parameter-based performances.