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"Recommended water column concentrations to achieve the required intergravel dissolved oxygen concentrations shown in parentheses. The figures in parentheses apply to species that have early life stages exposed directly to the water column.

Tncludes all embryonic and larval stages and all juvenile forms to 30 days following hatching. cNA = not applicable.

dAll minima should be considered instantaneous concentrations to be achieved at all times. Further restrictions apply for highly manipulative discharges.

"Recommended water column concentrations to achieve the required intergravel dissolved oxygen concentrations shown in parentheses. The figures in parentheses apply to species that have early life stages exposed directly to the water column.

Tncludes all embryonic and larval stages and all juvenile forms to 30 days following hatching. cNA = not applicable.

dAll minima should be considered instantaneous concentrations to be achieved at all times. Further restrictions apply for highly manipulative discharges.

The challenge in evaluating the effectiveness of point source BOD loading reductions is the need to isolate their impacts on downstream DO from impacts caused by urban stormwater runoff and rural nonpoint sources and the natural seasonal influences of streamflow and water temperature. An innovative approach was developed to reduce these confounding factors and screen for water quality station records that inherently contain a "signal" linking point source discharges with downstream DO. It includes the following steps:

• Developing before- and after-CWA data sets of DO summary statistics derived from monitoring stations that were screened for worst-case conditions (i.e., conditions that inherently contain the sharpest signal).

• Assigning the worst-case DO summary statistic to each station for each before-and after-CWA time period and then aggregating station data at sequentially larger spatial scales.

• Conducting a "paired" analysis of spatial units that have both a before- and an after-CWA worst-case DO summary statistic and then documenting the direction (improvement or degradation) and magnitude of the change.

• Assessing how the point source discharge/downstream worst-case DO signal changes over progressively larger spatial scales.

The hierarchy of spatial scale plays an especially important role in this second leg of the three-legged stool approach for examining water quality conditions before and after the CWA. Three spatial scales are addressed in this portion of the study: reach, catalog unit, and major river basin.

Reaches are segments of streams, rivers, lakes, estuaries, and coastlines identified in USEPA's Reach File Version 1 (RF1) and Reach File Version 3 (RF3). In this system, a reach is defined by the confluence of a tributary upstream and a tributary downstream. Reaches in RF1 average about 10 miles in length and have a mean drainage area of 115 square miles. Created in 1982, RF1 contains information for 64,902

reaches in the 48 contiguous states, covering 632,552 miles of streams. Figure 1-2 is a map of the stream reach network in the Chesapeake Bay drainage area.

An individual reach in the RF1 system is identified by an 11-digit number. This number carries much spatial information. It identifies not only the reach itself, but also the hierarchy of watersheds to which the reach belongs. The first eight digits of the identification number are the Hydrologic Unit Code (HUC). Originally developed by the U.S. Geological Survey (USGS), the HUC number identifies four scales of watershed hierarchy. The highest scale, coded in the first two digits of the identification number, is the hydrologic region (commonly referred to as a major river basin). Hydrologic regions represent the largest river basins in the country (e.g., the Missouri River Basin and the Tennessee River Basin). Subregions are identified by the next

Figure 1-2 Reach File Version 1 stream reach network in the Chesapeake Bay drainage area.

two numbers. These are followed by the accounting unit and the cataloging unit, the smallest scale in the hierarchy. Figures 1-3 and 1-4 display the 18 hydrologic regions and the 2,111 cataloging units in the contiguous 48 states.

Developers of RF1 extended the eight-digit HUC code by three digits for the purpose of identifying the reaches within the cataloging unit. Table 1-2 is an example of the RF1 identification codes for a reach of the Upper Mississippi River near Hastings, Minnesota. This 33.1-mile reach is defined by the confluence of the Minnesota River (upstream) and the St. Croix River (downstream).

Many engineering studies have documented the impact of BOD loading on the DO budget in reaches immediately below municipal outfalls. Consequently, one would expect to find a sharp signal linking point source discharges with worst-case DO in those reaches. The key aspect of this investigation, therefore, was to see how the signal changed (or if it could be detected at all) as one aggregated worst-case DO data at increasingly larger spatial scales and then compared summary statistics associated with time periods before and after the CWA. Detection of a statistically significant signal at the catalog unit and major river basin scales would provide evidence that the CWA mandates to upgrade to secondary treatment and greater levels of wastewater treatment yielded broad, as well as localized, benefits.

Figure 1-5 illustrates signal and noise relationships over the range of spatial scales (reach, catalog unit, and major river basin), using the Upper Mississippi River near Hastings, Minnesota, as an example. The line graphs on the left side of the figure display DO data collected at monitoring stations from 1953 to 1997 aggregated by spatial unit. The bar graphs on the right side of the figure compare worst-case DO (mean tenth percentile) for designated time periods before and after the CWA and are produced as the final step of the comparison analysis process described in Chapter 3. The

Figure 1-4 The 2,111 hydrologic catalog units of the 48 contiguous states.

summary statistics they present are derived from station data that have been selected, aggregated, and spatially assessed so that they might have the best chance of inherently containing a "signal" linking point source discharges with downstream DO.

Examining the line graphs in Figure 1-5, one can see that each broader spatial scale aggregation of station data yields a "noisier" data pattern. The bar chart for the reach scale (the finest scale) displays the greatest improvement in worst-case DO, in-

TABLE 1-2 Station and Reach Identification Codes: Reach File Version 1 (RF1)

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