Bio-optical Algorithm for Lake Michigan

["Shannon Briggs" <briggssl@michigan.gov>]

J. Great Lakes Res. 32(2):258-279.
Internat. Assoc. Great Lakes Res., 2006

web site for press release
http://www.iaglr.org/jglr/release/32/32_2_258-279.php 

Verification and Application of a Bio-optical Algorithm for Lake Michigan Using SeaWiFS: a 7-year Inter-annual Analysis

Robert Shuchman1, Anton Korosov2, Charles Hatt1, Dmitry Pozdnyakov2, Jay Means3 and Guy Meadows4, 1Altarum Institute (formerly ERIM), Ann Arbor, Michigan, 48105-1579; 2Nansen International Environmental and Remote Sensing Centre, 197101, St. Petersburg and Russia; 3Department of Chemistry, Western Michigan University, Kalamazoo, Michigan, 49008-3842; 4Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, Michigan, 48109-2145

http://www.iaglr.org/jglr/db/view_contents.php?pub_id=2422&mode=view&table=yes&topic_id=&mode=toc&volume=32&issue=2

ABSTRACT: In this paper we utilize 7 years of SeaWiFS satellite data to obtain seasonal and interannual time histories of the major water color-producing agents (CPAs), phytoplankton chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals (sm) for Lake Michigan. We first present validation of the Great Lakes specific algorithm followed by correlations of the CPAs with coincident environmental observations. Special attention is paid to the satellite observations of the extensive episodic event of sediment resuspension and calcium carbonate precipitation out of the water. We then compare the obtained time history of the CPA?s spatial and temporal distributions throughout the lake to environmental observations such as air and water temperature, wind speed and direction, significant wave height, atmospheric precipitation, river runoff, and cloud and lake ice cover. Variability of the onset, duration, and spatial extent of both episodic events and seasonal phenomena are documented from the SeaWiFS time series data, and high correlations with relevant environmental driving factors are established. The relationships between the CPAs retrieved from satellite data and environmental observations are then used to speculate on the future of Lake Michigan under a set of climate change scenarios.

 


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