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Project Details

Refining Ecosystem Model Inputs for Sea Level Rise Vulnerability in the San Francisco Bay Estuary

Project Status: This project began in January 2015 and is projected to be completed in December 2017

We are enhancing a locally relevant marsh model with new field data on the impacts of sea level rise to allow coastal managers to evaluate the vulnerability and inform restoration of San Francisco Bay tidal marshes.

Why We Care
Tidal marshes support important coastal food webs, improve water quality, and offer a buffer against storm and wave damage. Sea level influences the structure and function of coastal marshes in ways that alter the services provided.

What We Are Doing
We are collecting data to refine existing coastal management tools. Specifically, we are collecting data to meet three information needs of existing models:

  • Improved estuary-wide data on vegetation and marsh elevation, which will come in the form of estuary-wide LiDAR-derived data sets based on remotely sensed and on-the-ground vegetation and RTK GPS data.
  • An understanding of productivity and decomposition responses of tidal marsh plant species under a series of elevation and salinity gradients.
  • Sediment deposition rate based on plant species composition, season, storms, and tidal elevation to better parameterize marsh sea level rise models.

This research will provide managers of the San Francisco Bay Estuary with improved management tools, such as vegetation-corrected, digital elevation models (DEMs). DEMs are high-resolution, digital representations of the topographic landscape that coastal managers can use to better understand marsh vegetation structure and sediment dynamics to inform marsh restoration and augmentation planning. This project is led by Oregon State University and the U.S. Geological Survey, and is funded through the Ecological Effects of Sea Level Rise program.

Region of Study: California

Primary Contact: David Kidwell

Research Theme: Climate Impacts (Impacts of Sea Level Rise)

Related NCCOS Center: CSCOR


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