SWOT-QQ: Global River Discharge Estimates at Gauging Stations from SWOT Observations

River discharge is a core element of the global water cycle and an Essential Climate Variable (ECV), yet direct observations remain limited in both space and time. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution (~100 m) measurements of water surface elevation over rivers and lakes worldwide, creating new opportunities to advance the monitoring of surface water dynamics. Here, we present the SWOT-QQ data set, a global SWOT-based river discharge dataset developed by combining SWOT surface water elevation measurements with an extensive collection of historical and contemporary in situ discharge records from over 60,000 gauging stations. Relying on SWOT’s global coverage, SWOT-QQ incorporates substantially more gauges than previous satellite-based discharge products (e.g., SAEM, RSEG), thereby extending both the geographic and hydrological representativeness of the estimates. Discharge time series are derived using the non-parametric quantile mapping (NPQM) approach, enabling the translation of SWOT water surface elevations into discharge across diverse climatic and hydrological regimes. In addition, we develop a near-real-time (NRT) framework in which incoming SWOT observations are converted into discharge using non-parametric rating relationships established during the mission period.

The results show consistent skill across multiple performance metrics in several regions, highlighting the potential of SWOT-QQ to support hydrological studies. We further compare our discharge estimates with outputs from existing SWOT discharge algorithms, including neoBAM, HiVDI, MetroMan, MOMMA, SAD, SIC4DVar, and the consensus product from the latest L4 dataset. Our results show that, after matching gauges to SWOT reaches, SWOT-QQ exhibits a betteragreement with in situ discharge than the reach-based SWOT L4 products. SWOT-QQ is intended as a complementary resource for river discharge algorithm validation, as prior information for inferring flow-law parameters, and as input for hydrological modeling and data assimilation. Through this work, we aim to foster discussion and collaboration within the SWOT community and contribute to improved global river discharge characterization.