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 Surface Water 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 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 models 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. Overall, this study emphasizes the complementary strengths of Earth Observation data in improving the representation of the flood dynamics in the riverbed and the floodplains.

Remote Sensing Data Assimilation with a Chained Hydrologic-hydraulic Model for Flood Forecasting

A chained hydrologic-hydraulic model is implemented using predicted runoff from a large-scale hydrologic model (namely ISBA-CTRIP) as inputs to local hydrodynamic models (TELEMAC-2D) to issue forecasts of water level and flood extent. The uncertainties in the hydrological forcing and in friction parameters are reduced by an Ensemble Kalman Filter that jointly assimilates in-situ water levels and flood extent maps derived from remote sensing observations. The data assimilation framework is cycled in a real-time forecasting configuration. A cycle consists of a reanalysis and a forecast phase. Over the analysis, observations up to the present are assimilated. An ensemble is then initialized from the last analyzed states and issued forecasts for next 36 hr. Three strategies of forcing data for this forecast are investigated: (i) using CTRIP runoff for reanalysis and forecast, (ii) using observed discharge for analysis, then CTRIP runoff for forecast and (iii) using observed discharge for reanalysis and keep a persistent discharge value for forecast. It was shown that the data assimilation strategy provides a reliable reanalysis in hindcast mode. The combination of observed discharge and CTRIP runoff provides the most accurate results. For all strategies, the quality of the forecast decreases as the lead time increases. When the errors in CTRIP forcing are non-stationary, the forecast capability may be reduced. This work demonstrates that the forcing provided by a hydrologic model, while imperfect, can be efficiently used as input to a hydraulic model to issue reanalysis and forecasts, thanks to the assimilation of in-situ and remote sensing observations.