Estimating Nearshore Morphological Change through Ensemble Optimal Interpolation with Altimetric Data

Estimating Nearshore Morphological Change through Ensemble Optimal Interpolation with Altimetric Data

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 oft...

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Bibliographic Details
Published in:Journal of marine science and engineering Vol. 12; no. 7; p. 1168
Main Authors: Geheran, Matthew P., DeVore, Katherine R., Farthing, Matthew W., Bak, A. Spicer, Brodie, Katherine L., Hesser, Tyler J., Dickhudt, Patrick J.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2024
Subjects:
Acoustics
altimetry
Bathymeters
Bathymetry
bathymetry estimation
Boundary conditions
Coastal morphology
Coastal processes
Data assimilation
Estimates
Experiments
Interpolation
Methods
Morphology
Polls & surveys
Sand bars
Surveys
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Summary: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 As a result, nearshore models often rely on outdated bathymetric boundary conditions, which may introduce significant errors.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.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12071168