Comparing Assimilation of Remotely Sensed Discharge Derived From SWOT and Landsat Into a Global Hydrological Model

Abstract The Surface Water and Ocean Topography (SWOT) satellite has enabled river discharge estimation for global rivers including ungauged regions. This remotely sensed discharge (SwRSQ) generated from SWOT's cloud‐penetrating measurements of river widths, water surface elevation, and slope is expected to have improved accuracy and spatiotemporal coverage over existing methods using Landsat‐derived river widths (LsRSQ). Previous studies demonstrated that the assimilation of LsRSQ into a global hydrological model resulted in improved estimation of river discharge, yet to date SwRSQ assimilation has not been explored. Therefore, this study assimilated SwRSQ into a global hydrological model and evaluated the assimilation performance compared to LsRSQ over the Missouri River basin as a hydrologically complex test case. Results show that the RSQ assimilation reduced skill metrics relative to model‐only and RSQ‐only estimates. The assimilation of SwRSQ showed a significant improvement over the baseline simulation, reducing PBIAS by 34.1% and NRMSE by 0.435. Moreover, assimilating SwRSQ outperformed assimilating LsRSQ at 74% and 76% of gauging stations in terms of NSE and KGE, respectively, based on comparisons between the two data assimilation experiments. Improvements were most pronounced in regions with poor baseline performance, where SWOT's higher observation frequency contributed to such improvements over Landsat. In contrast, in areas well represented by the baseline model, LsRSQ occasionally performed better. Simultaneous assimilation of SwRSQ and LsRSQ showed further improvements at rivers with low‐skill baseline result. These findings highlight the value of SWOT‐derived RSQ and its synergistic potential with other satellite products in advancing global‐scale hydrological modeling and monitoring.