Journal
OCEAN ENGINEERING
Volume 73, Issue -, Pages 126-138Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2013.08.002
Keywords
Data-model assimilation; Depth inversion; Nearshore bathymetry; Remotely sensed observation; Kalman filter
Funding
- project BARBEC [ANR N2010 JCJC 602 01]
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A data-model assimilation method is presented that can combine heterogeneous remotely sensed video observations to inverse wave-dominated beach bathymetry. The ability of our method is assessed using synthetic cases from a laboratory experiment. For relatively flat beach profiles, good bathymetry inversion can be obtained with a small number (O(1)) of observations and a simple Gaussian-type background error matrix. For more complex barred-beach profiles, localisation of the background error matrix is crucial to depth inversion and the required number of observations is increased (O(10)). In the latter situation, both relevant location of observations and the corresponding localisation length scales can be properly defined without the knowledge of the local bathymetry, resulting in accurate bathymetry inversion, which is a major asset for practical applications. Our method also allows shifting between several sources of bathymetry proxy as well as overlapping which provides flexibility in data management. Multi-1D reconstruction can also be performed to estimate complex 3D beach morphologies. Our study therefore suggests that high-performing time-efficient nearshore bathymetry inversion can be achieved using a limited set of heterogeneous video-derived observations. (C) 2013 Elsevier Ltd. All rights reserved.
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