Estimating river discharge is important in understanding spatial and temporal variations in terrestrial waters. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the water surface elevation and slope and can be used to estimate river discharge. Even though methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, application of those methods in global scale is still difficult due the computational capabilities of assimilation schemes. We developed a physically based empirical localization technique to overcome global-scale hydrological data assimilation incapacities. With the help of developed hydrological data assimilation method, a framework for estimating river discharge on a global-scale was derived by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. Several experiments were performed with and without model error assumption using multi-model runoff forcing. The inaccurate model parameters were assumed to be represented in Manning’s coefficient. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (>50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was due to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. More accurate global river discharge estimates were obtained in river reaches with large accumulated SWOT overpasses from upstream reaches per SWOT cycle when no model error was assumed. Even though introducing model errors decreased the assimilation efficacy, the accuracy of estimated river discharge remain high. Therefore, estimating correct hydrodynamic model parameters (i.e. Manning’s’ coefficient) are essential for maximizing SWOT information. We also found that basin-wide assimilation, rather than reach-specific assimilation, was required for estimating discharge in river reaches with a large drainage area. These synthetic experiments showed where discharge estimates can be improved using SWOT observations. Further advances are needed for data assimilation on global-scale.
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