Editorial Type:
Article
Article Type:
Research Article

Bottom Topography Mapping via Nonlinear Data Assimilation

Edward D. Zaron Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon

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Marie-Aude Pradal Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Patrick D. Miller Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Alan F. Blumberg Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Nickitas Georgas Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Wei Li Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Julia Muccino Cornuelle Center for Maritime Systems, Stevens Institute of Technology, Hoboken, New Jersey

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/JTECH-D-11-00070.1
Page(s):
1606–1623
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Abstract

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.

Corresponding author address: Edward D. Zaron, P.O. Box 751, Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97207-0751. E-mail: zaron@cecs.pdx.edu

Abstract

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.

Corresponding author address: Edward D. Zaron, P.O. Box 751, Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97207-0751. E-mail: zaron@cecs.pdx.edu
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