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Using Satellite Observations to Assess the Spatial Representativeness of the GLNPO Water Quality Monitoring Program.

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The U.S. EPA's Great Lakes National Program Office (GLNPO) annual water quality survey (WQS) collects data at a relatively small number of stations in each lake. The survey was designed… Click to show full abstract

The U.S. EPA's Great Lakes National Program Office (GLNPO) annual water quality survey (WQS) collects data at a relatively small number of stations in each lake. The survey was designed to measure conditions in the open-water regions of the lakes where an assumption of spatial homogeneity was thought likely to be met and the measured variables could be characterized by simple statistics. Here we use satellite observations to assess how well statistics based on samples collected in the GLNPO sampling network represent the lake-wide values of two variables, surface chlorophyll concentration and Secchi depth. We find strong linear relationships between the mean values calculated from the samples and the corresponding averages based on the subsets of the full satellite images. Although overall the means of the values from the sample locations agree well with means calculated from most of the non-coastal regions of the lakes, in terms of water depth, the GLNPO station averages best represent the regions of Lake Huron deeper than 30 m, of Lakes Michigan and Superior deeper than 90 m, and of Lake Ontario deeper than 60 m. When the lake regions are defined by distance offshore rather than by depth, the GLNPO station chlorophyll means in Lakes Huron, Ontario, and Superior are closest to the means for the area of the lakes > 10 km offshore. In Lake Michigan the closest correspondence is with the > 20 km offshore region. On a whole-lake basis in Lake Erie the GLNPO station chlorophyll averages are closest to the average calculated from the entire lake.

Keywords: satellite observations; water; observations assess; water quality; glnpo; lake

Journal Title: Journal of Great Lakes research
Year Published: 2018

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