Assimilation of SWOT Altimetry Data for Riverine Flood Reanalysis: From Synthetic to Real Data

Floods are one of the most common and devastating natural disasters worldwide. The contribution of remote sensing is important for reducing the impact of flooding both during the event itself and for improving hydrodynamic models by reducing their associated uncertainties. This article presents the innovative capabilities of the SurfaceWater and Ocean Topography (SWOT) mission, especially its river node products, to enhance the accuracy of riverine flood reanalysis, performed on a 50-km stretch of the Garonne River. The challenge addressed here is quantifying how SWOT river observations, alone and in combination with in-situ gauges, can improve hydraulic parameter estimation and river water level prediction in flood reanalysis. The experiments incorporate various data assimilation strategies, based on the ensemble Kalman filter (EnKF), which allows for sequential updates of model parameters based on available observations. The experimental results show that while SWOT data alone offers some improvements, combining it with in-situ water level measurements provides the most accurate representation of flood dynamics, both at gauge stations and along the river. The study also investigates the impact of different SWOT revisit frequencies on the model's performance, revealing that assimilating more frequent SWOT observations leads to more reliable flood reanalyses. In the real event, it was demonstrated that the assimilation of SWOT and in-situ data accurately reproduces the water level dynamics, offering promising prospects for future flood monitoring systems. Results show that in the OSSE framework, assimilation reduced water level errors by an order of magnitude, while in the real 2024 event the errors were reduced to below 17 cm, demonstrating the reliability of the approach. This study underscores the complementary role of Earth Observation data in enhancing flood dynamics representation in the riverbed and the floodplains.