Anticipated improvements to river surface elevation profiles from the surface water and ocean topography mission

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  • Langhorst, T.
    • Affiliation: College of Arts and Sciences, Department of Geological Sciences
  • Pavelsky, T.M.
    • Affiliation: College of Arts and Sciences, Department of Geological Sciences
  • Frasson, R.P.D.M.
    • Other Affiliation: The Ohio State University
  • Wei, R.
    • Other Affiliation: The Ohio State University
  • Domeneghetti, A.
    • Other Affiliation: University of Bologna
  • Altenau, E.H.
    • Affiliation: College of Arts and Sciences, Department of Geological Sciences
  • Durand, M.T.
    • Other Affiliation: The Ohio State University
  • Minear, J.T.
    • Other Affiliation: University of Colorado
  • Wegmann, K.W.
    • Other Affiliation: North Carolina State University
  • Fuller, M.R.
    • Other Affiliation: Duke University
Abstract
  • Existing publicly available digital elevation models (DEMs) provide global-scale data but are often not precise enough for studying processes that depend on small-scale topographic features in rivers. For example, slope breaks and knickpoints in rivers can be important in understanding tectonic processes, and riffle-pool structures are important drivers of riverine ecology. More precise data (e.g., lidar) are available in some areas, but their spatial extent limits large-scale research. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission is planned to launch in 2021 and will provide measurements of elevation and inundation extent of surface waters between 78° north and south latitude on average twice every 21 days. We present a novel noise reduction method for multitemporal river water surface elevation (WSE) profiles from SWOT that combines a truncated singular value decomposition and a slope-constrained least-squares estimator. We use simulated SWOT data of 85–145 km sections of the Po, Sacramento, and Tanana Rivers to show that 3–12 months of simulated SWOT data can produce elevation profiles with mean absolute errors (MAEs) of 5.38–12.55 cm at 100–200 m along-stream resolution. MAEs can be reduced further to 4–11 cm by averaging all observations. The average profiles have errors much lower than existing DEMs, allowing new advances in riverine research globally. We consider two case studies in geomorphology and ecology that highlight the scientific value of the more accurate in-river DEMs expected from SWOT. Simulated SWOT elevation profiles for the Po reveal convexities in the river longitudinal profile that are spatially coincident with the upward projection of blind thrust faults that are buried beneath the Po Plain at the northern termination of the Apennine Mountains. Meanwhile, simulated SWOT data for the Sacramento River reveals locally steep sections of the river profile that represent important habitat for benthic invertebrates at a spatial scale previously unrecognizable in large-scale DEMs presently available for this river.
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  • Article
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  • In Copyright
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  • Attribution 4.0 International
Journal title
  • Frontiers in Earth Science
Journal volume
  • 7
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  • English
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  • 2296-6463
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  • Frontiers Media S.A.

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