A Framework for Estimating Global-Scale River Discharge by Assimilating Satellite Altimetry

Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography ( SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most SWOT data assimilation studies have been conducted at the local scale. We developed a novel framework for estimating river discharge at the global scale by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multimodel runoff forcing and inaccurate model parameters represented by corrupted Manning's coefficient values. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (> 50 degrees) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was related to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling-Gupta efficiency [KGE] > 0.90) in river reaches with >270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy ( KGE approximate to 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates could be improved using SWOT observations. Further advances are needed for global-scale data assimilation. Plain Language Summary River discharge is an important indicator for managing the world's freshwater resources. Advances in computing technology have facilitated the development of hydrodynamic models, which can be used to predict river water states and compensate for a lack of in situ observation facilities. However, these models have inherent limitations, including simplified physics, forcing errors, and inaccurate parameters. Satellite observations, such as those from the Surface Water and Ocean Topography (SWOT) mission, may be incorporated to improve these models. Because the SWOT satellite is due for launch in 2021, assessing the potential benefits of incorporating SWOT observations into global hydrodynamic models is essential. Therefore, we performed observation assimilation experiments using a technique known as Kalman filtering, which assesses model uncertainty and expected observation errors. Note that SWOT observations are not recorded continuously; therefore, the hydrodynamic model was used to extrapolate water states in time and space. We found that incorporating SWOT observations provided accurate river discharge estimates for continental-scale rivers. Furthermore, correcting model parameters will considerably improve river discharge estimates. This framework may be used to generate accurate global river discharge estimates when SWOT observations become available. Therefore, these methods may be helpful for mitigating conflicts in transboundary river basins (e.g., Mekong basin).

Automated summary

Understanding spatial and temporal variations in terrestrial waters is crucial for assessing the global hydrological cycle. The integration of SWOT observations into hydrodynamic models has shown promise in improving river discharge estimates at both high and low latitudes. Improved hydrodynamic models are essential for maximizing the benefits of SWOT observations, and further research is needed for global-scale data assimilation.