Siqi Ke (Institute of Geodesy, University of Stuttgart, Germany); Omid Elmi (Institute of Geodesy, University of Stuttgart, Germany); Benjamin M. Kitambo (Institute of Geodesy, University of Stuttgart, Germany); Fabrice Papa (Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France); Mohammad J. Tourian (Institute of Geodesy, University of Stuttgart, Germany)

River discharge is a fundamental component of the global water cycle and an Essential Climate Variable (ECV), yet its observation remains spatially and temporally sparse. The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution (~100 m) observations of water surface elevation over global rivers and lakes, offering a unique opportunity to enhance our understanding of surface water dynamics. Building on these new observations, we present an ongoing effort to estimate river discharge during the SWOT mission period by using an unprecedented available of historical and contemporary in situ discharge measurements (over 52,000 gauges) and SWOT-derived water level observations. SWOT’s broad spatial coverage allows us to use more gauges than previous satellite-based studies (e.g., SAEM, RSEG, etc), enhancing the geographic and hydrological reach of discharge estimation. Using the non-parametric quantile mapping (NPQM) approach introduced by Elmi et al. (2021), we derive discharge time series from SWOT water levels across a wide range of hydrological and climatic regimes. We also develop a near-real-time (NRT) framework, in which SWOT water surface elevation is converted into discharge using the derived non-parametric rating curve.

Preliminary results show encouraging consistency in several regions, suggesting the potential value of these estimates for supporting SWOT-related studies. We compare our estimates with outputs from SWOT discharge algorithms, including neoBAM, HiVDI, MetroMan, MOMMA, SAD, SIC4DVar, and the consensus product. Our discharge estimates can serve as a complementary source for validating discharge algorithms, provide prior information to infer the flow law parameters, and also support hydrological modeling and data assimilation. We aim to stimulate discussion and foster collaboration within the SWOT community to improve global discharge characterization and make the most of SWOT's observational capabilities.


Elmi, O., Tourian, M. J., Bárdossy, A., & Sneeuw, N. (2021). Spaceborne river discharge from a nonparametric stochastic quantile mapping function. Water Resources Research, 57(12), e2021WR030277. https://doi.org/10.1029/2021WR030277