The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

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Abstract. The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths >900 m (>34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level and applied it to all altimeter crossings of ocean draining rivers with widths > 900 m (> 34 % of global drainage area). We evaluated every VS, either quantitatively for VS where in-situ gages are available, or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1,478 VS. After quality control, the final product contained 810,403 measurements distributed over 932 VS located on 39 rivers. Available in-situ data allowed quantitative evaluation of 389 VS on 12 rivers. Median standard deviation of river elevation error is 0.93 m, Nash-Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at DOI 10.5067/PSGRA-SA2V1 (Durand et al., 2016).