appendix 
Chesapeake Bay Water Clarity 
Assessment Framework 
STEP 1. WATER QUALITY PARAMETER INTERPOLATIONS 
Each water quality parameter in each point dataset involved in the particular region¬ 
ally specific regression model is first interpolated across the segment using the 
Ordinary kriging function in the Geostatistical Analyst included in the ArcMap soft¬ 
ware (Figure E-l). All default settings provided by Geostatistical Analyst are used in 
the interpolations, except for those specified in Table E-l. STAC 2006 (cited in U.S. 
EPA 2007) indicates that of the various types of interpolation algorithms available 
and reviewed, ordinary kriging is best positioned to address this issue, i.e., data 
density from DATAFLOW cruise tracks. 
The results of the interpolations are stored in a grid format, where each cell contains 
a value for the associated water quality parameters. For each segment, all grids used 
in this analysis are set to the exact same extent (rounded to nearest 25 m) and grid 
cell size (25 m x 25 m). This ensures that all segment grids correspond spatially 
when overlayed (Figure E-2). 
STEP 2. USING PARAMETER INTERPOLATIONS TO 
DERIVE Kd SURFACE. 
The next step towards calculating water clarity acres is to use the interpolated grids 
to calculate a K d surface. Turbidity, salinity, and chlorophyll were the three parame¬ 
ters used for determining each of the regionally-specific K d models (see Table IV-2 
in Chapter iv, also Appendix D). For each segment, the interpolated chlorophyll, 
turbidity, and salinity grids are input into the appropriate equation on a cell by cell 
basis. The result of this cell-specific calculation based on the region-specific 
multiple regression K d model is a new grid representing the K d surface. 
appendix e 
Chesapeake Bay Water Clarity Assessment Framework 
