New Perspectives for Nonlinear Depth-inversion of the Nearshore Using
Boussinesq Theory
Abstract
Accurately mapping the evolving bathymetry under energetic wave breaking
is challenging, yet critical for improving our understanding of sandy
beach morphodynamics. Though remote sensing is one of the most promising
opportunities for reaching this goal, existing depth-inversion
algorithms using linear approaches face major theoretical and/or
technical issues in the surf zone, limiting their accuracy over this
region. Here, we present a new depth-inversion approach relying on
Boussinesq theory for quantifying nonlinear dispersion effects in
nearshore waves. Using high-resolution datasets collected in the
laboratory under diverse wave conditions and beach morphologies, we
demonstrate that this approach results in enhanced levels of accuracy in
the surf zone (errors typically within 10%) and presents a major
improvement over linear methods. The new nonlinear depth-inversion
approach provides significant prospects for future practical
applications in the field using existing remote sensing technologies,
including continuous lidar scanners and stereo imaging systems.