Improving sediment transport prediction by assimilating satellite images in a tidal bay model of Hong Kong

Peng Zhang, Wing Hong Onyx Wai, Xiaoling Chen, Jianzhong Lu, Liqiao Tian

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Numerical models being one of the major tools for sediment dynamic studies in complex coastal waters are now benefitting from remote sensing images that are easily available for model inputs. The present study explored various methods of integrating remote sensing ocean color data into a numerical model to improve sediment transport prediction in a tide-dominated bay in Hong Kong, Deep Bay. Two sea surface sediment datasets delineated from satellite images from the Moderate Resolution Imaging Spectra-radiometer (MODIS) were assimilated into a coastal ocean model of the bay for one tidal cycle. It was found that remote sensing sediment information enhanced the sediment transport model ability by validating the model results with in situ measurements. Model results showed that root mean square errors of forecast sediment both at the surface layer and the vertical layers from the model with satellite sediment assimilation are reduced by at least 36% over the model without assimilation.
Original languageEnglish
Pages (from-to)642-660
Number of pages19
JournalWater (Switzerland)
Volume6
Issue number3
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Data assimilation
  • Deep bay
  • MODIS
  • Optimal interpolation
  • Satellite image
  • Sediment transport model

ASJC Scopus subject areas

  • Aquatic Science
  • Biochemistry
  • Water Science and Technology
  • Geography, Planning and Development

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