Estimating Nearshore Morphological Change through Ensemble Optimal Interpolation with Altimetric Data

Abstract

Nearshore bathymetry changes on scales of hours to months in ways that strongly impact coastal processes. However, even at the best-monitored sites, surveys are typically not conducted with sufficient frequency to capture important changes such as sandbar migration. As a result, nearshore models often rely on outdated bathymetric boundary conditions, which may introduce significant errors. In this study, we investigate ensemble optimal interpolation (EnOI) as a method to update survey-derived bathymetry with altimetric measurements that are spatially sparse but have high temporal availability. We present the results of two synthetic examples and two field data experiments that demonstrate the ability of the method to accurately track morphological change between surveys. The method reduces the RMSE relative to a static bathymetry (corresponding to the day before the first assimilation step) by 23% to 68%. When compared with an estimate linearly interpolated between survey-derived bathymetries, the EnOI analysis reduces the RMSE by 19% to 47% in three out of the four experiments.

Keywords

• data assimilation
• bathymetry estimation
• altimetry