The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage‐constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

The publicly available global discharge database is limited in spatial and temporal coverage. Although regional exceptions exist, the population of the database has declined over the past several years. As discharge is one of the most important parameters for modeling hydrological interactions, alternative measuring techniques must be sought. In the recent past, satellite altimetry has been investigated as an alternative for monitoring inland water level. In the present study, altimetry footprints in the vicinity of river gauging stations for the Amazon, Amur, Brahmaputra, Danube, Don, Mekong, Niger, Ob, and Vistula rivers are analyzed for a functional relationship between the water level measurements from altimetry and discharge from the gauging stations. Such a functional relationship is conventionally established via a rating curve computed using simultaneous data. This study proposes a statistical approach based on quantile functions to infer this functional relation without the need for having synchronous data sets. The statistical approach provides the opportunity of extracting discharge values from altimetry data for rivers like the Mekong, Brahmaputra, Don, and Vistula for which the discharge measurements at the selected gauges were made before the age of satellites. The algorithm is then validated over those rivers which do have discharge measurements available within periods of altimetry. Our validation shows that our algorithm is in the same quality range as the conventional approach. We are thus able to salvage presatellite altimetry discharge data and turn them into active use for the satellite altimetry time frame.