Assessment of SWOT water surface elevations for flood monitoring of a narrow river (< 50 m width)

Abstract

Floods are among the most frequent and damaging natural hazards worldwide, and reliable observations of water surface elevation (WSE) are essential for improving flood modelling and risk management. The Surface Water and Ocean Topography (SWOT) satellite, launched in 2022, offers new opportunities to monitor river hydrodynamics from space, but its performance in relatively narrow rivers (< 50 m width) remains poorly documented. This study evaluates the potential of SWOT WSEs for flood monitoring through a site-specific hydraulic application by comparing them with in situ observations as well as simulations from an existing large-scale hydraulic model (LISFLOOD-FP) on the Du Gouffre River (width ≈ 40 m), located in Quebec, Canada. The L2_HR_RiverSP (RiverSP) SWOT product Version D, derived from a priori database (SWORD-version 17b), was first compared with one-minute WSE measurements from a tidal gauge located downstream the Du Gouffre River in the St. Lawrence River. This comparison, based on in situ reference measurements, confirmed the overall quality of the SWOT data in this area, with a Root Mean Square Error (RMSE) of 0.25 m. Then, a major flood event (with a return period of about 60 years) which occurred on 1 May 2023, during the SWOT's calibration orbit, was used to conduct a daily analysis of the entire flood event. Eleven observation cycles, covering the period from 25 April to 7 May 2023, were analysed. Limited ground-based observations were available along the studied reach during the flood, highlighting the added value of SWOT observations in this data-scarce context. The 1D/2D hydraulic model LISFLOOD-FP was run for the discharges corresponding to eleven SWOT cycles. Comparisons between SWOT-derived WSEs and model-simulated WSEs yielded biases ranging from -0.30 to 0.43 m and RMSE values between 0.22 and 0.54 m, indicating generall consistent behavior between observed and simulated WSEs across the analyzed cycles. During the flood peak on 1 May, larger discrepancies were observed, reflecting uncertainties in upstream discharge estimates under extreme flow conditions. In this context, SWOT-derived WSEs provided complementary information that helped diagnose discharge underestimation during the peak event. These results are not intended as an independent validation of SWOT measurement accuracy but are specific to the river and flood event under study and demonstrate the practical usefulness of SWOT observations for hydraulic studies in narrow rivers. They highlight the potential contribution of SWOT data for flood monitoring and hydraulic modelling in similar data-scarce contexts, particularly under extreme hydrological conditions.