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Bottom Topography Mapping via Nonlinear Data Assimilation
Journal of Atmospheric and Oceanic Technology
  • Edward D. Zaron, Portland State University
  • Marie-Aude Pradal, Stevens Institute of Technology
  • Patrick D. Miller, Stevens Institute of Technology
  • Alan F. Blumberg, Stevens Institute of Technology
  • Nickitas Georgas, Stevens Institute of Technology
  • Wei Li, Stevens Institute of Technology
  • Julia Muccino Cornuelle, Stevens Institute of Technology
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Publication Date
  • Hydrology -- Statistical methods,
  • Rivers -- Topography -- Remote sensing,
  • Estuaries -- Topography -- Remote sensing
A variational data assimilation method is described for bottom topography mapping in rivers and estuaries using remotely sensed observations of water surface currents. The velocity field and bottom topography are related by the vertically integrated momentum and continuity equations, leading to a nonlinear inverse problem for bottom topography, which is solved using a Picard iteration strategy combined with a nonlinear line search. An illustration of the method is shown for Haverstraw Bay, in the Hudson River, where the known bottom topography is well reconstructed. Once the topography has been estimated, currents and water levels may be forecast. The method makes feasible 1) the estimation of bottom topography in regions where in situ data collection may be impossible, dangerous, or expensive, and 2) the calibration of barotropic shallow-water models via control of the bottom topography.

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Citation Information
Zaron, E. D., Pradal, M., Miller, P. D., Blumberg, A. F., Georgas, N., Li, W., & Cornuelle, J. (2011). Bottom Topography Mapping via Nonlinear Data Assimilation. Journal of Atmospheric & Oceanic Technology, 28(12), 1606-1623.