A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization

Despite being a critical component of Earth's water cycle, much remains unknown about freshwater fluxes in the world's rivers. Discharge can be estimated in situ by monitoring water surface elevation yet the declining worldwide coverage of gauges makes global discharge quantification challenging. Numerous studies have shown that satellite radar altimetry could provide global discharge estimates. In anticipation such groundbreaking datasets, one key question remains unanswered: how accurately could the various orbital configurations of altimetry missions capture global discharge distributions under optimal retrieval conditions? We here generate an idealized synthetic global discharge data set following mission orbits, and present the first evaluation of various spatiotemporal sampling strategies on global discharge distribution estimation. Our data are produced by superimposing six measurement footprints representing nine altimetry missions onto existing global discharge simulations. While this approach assumes accurate simulations and ignores uncertainties in spaceborne discharge estimation, it allows for an upper limit assessment of how satellite missions might capture global characteristics of hydrographs. We show that most orbits used could lead to accurate global mean flow distribution (<7%), which was expected but never demonstrated. We also find that accurate distributions of minimum flow (respectively, maximum flow and peak flow duration) require revisit times more frequent than 10 (respectively, 5 and 5) days, that is, finer than allowed by existing orbital strategies, and that global extreme discharge and peak flow distributions rely on temporal frequency rather than spatial coverage. Our analysis could inform future mission development and our data set be used to support potential global gap-filling experiments.

Automated summary

This study examines how satellite radar altimetry can capture global discharge distributions and generates an idealized synthetic global discharge dataset. It found that most orbits can lead to accurate global mean flow distribution, but accurate distributions of minimum flow and maximum flow require more frequent revisit times.