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Identification and characterization of watermasses within the Columbia River plume using both traditional and novel statistical analyses: does functional data analysis improve water mass classification?

Released: 2015
Citation:
Broughton, J, J Felis, J Adams, R Kudela. 2015. Identification and characterization of watermasses within the Columbia River plume using both traditional and novel statistical analyses: does functional data analysis improve water mass classification? Second International Ocean Colour Science Meeting, San Francisco, California, 15-18 June 2015. [presentation]

In the northern California Current System (nCCS), intermittent seasonal upwelling/downwelling and the Columbia River plume (CRP) create oceanographic structure that enhance phytoplankton growth and form fronts that aggregate prey for marine predators. Watermass classification and detection of fronts provide useful information for resource management and marine spatial planning. Traditionally, watermass classification (k-means, fuzzy c-means, or hierarchical clustering) used optically-derived biological and chemical properties from few wavelengths to derive unique types. Previous bio-optical description of the CRP and surrounding waters relied on satellite-derived ocean color at coarse spatial and spectral resolution. Although data from sensors (e.g., MODIS) are useful for evaluating larger to global-scale (100s – 1000s of km) processes, they are insufficient for evaluating finer-scale (1-10 km) ecologically-relevant patterns within watermass dynamics in coastal and estuarine ecosystems. To improve spectral and spatial resolution, we conducted low-altitude aerial surveys of SST and hyperspectral ocean color along east-west transects spanning continental shelf and slope waters off southern Washington and northern Oregon. I use SST and hyperspectral ocean color data to characterize watermasses associated with the nCCS in three steps: 1) use along-transect gradients in SST and ocean color to identify and characterize watermass types associated with, and independent from the CRP using k-means clustering, 2) use functional Principal Components Analysis (fPCA) to evaluate watermasses clustered using spectral shape, , and 3) compare traditional and novel statistical analyses to determine benefits of including spectral shape when classifying watermasses.

 

 

 

 



This product is associated with the following project:
Pacific Continental Shelf Environmental Assessment (PaCSEA)

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