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A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization
Author(s) -
Sikder Md. Safat,
Bonnema Matthew,
Emery Charlotte M.,
David Cédric H.,
Lin Peirong,
Pan Ming,
Biancamaria Sylvain,
Gierach Michelle M.
Publication year - 2021
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr029035
Subject(s) - satellite , environmental science , sampling (signal processing) , altimeter , data set , meteorology , remote sensing , computer science , geology , geography , engineering , filter (signal processing) , artificial intelligence , computer vision , aerospace engineering
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.