Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning's friction coefficient

Talea Mayo, Troy Butler, Clint N. Dawson, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Coastal ocean models play a major role in forecasting coastal inundation due to extreme events such as hurricanes and tsunamis. Additionally, they are used to model tides and currents under more moderate conditions. The models numerically solve the shallow water equations, which describe conservation of mass and momentum for processes with large horizontal length scales relative to the vertical length scales. The bottom stress terms that arise in the momentum equations can be defined through the Manning's n formulation, utilizing the Manning's n coefficient. The Manning's n coefficient is an empirically derived, spatially varying parameter, and depends on many factors such as the bottom surface roughness. It is critical to the accuracy of coastal ocean models, however, the coefficient is often unknown or highly uncertain. In this work we reformulate a statistical data assimilation method generally used in the estimation of model state variables to estimate this model parameter. We show that low-dimensional representations of Manning's n coefficients can be recovered by assimilating water elevation data. This is a promising approach to parameter estimation in coastal ocean modeling. © 2014 Elsevier Ltd.
Original languageEnglish (US)
Pages (from-to)43-58
Number of pages16
JournalOcean Modelling
Volume76
DOIs
StatePublished - Apr 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology and the Gulf of Mexico Research Initiative Center for Advanced Research on Transport of Hydrocarbons in the Environment. This support is gratefully acknowledged.

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Oceanography
  • Computer Science (miscellaneous)
  • Atmospheric Science

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