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flow-monitoring data such that the remainder can be assumed to be the RDII component. The <br />' method is generally sound for event analysis and is a reasonable metric to base a comparison <br />~' of pre- versus post-rehabilitation effectiveness, and meshes well with Eugene's historic data <br />~ - collection and recording methods. <br />Accepted with this analysis are the inherent uncertainties associated with flow-monitoring in <br />general and separating dry-weather base sanitary flows from the flow record to achieve RDII <br />flows. Deficiencies in monitoring data can result from any number of the following: <br />• Hysteresis and flutter of monitoring data <br />• Spatial variation of rainfall <br />• Blockages in and/or spillages from the system <br />• Situations where monitors have been relocated during the monitoring period <br />• Random fluctuations in the dry weather flow profiles that are in no way attributable to <br />dry-weather flow processes. <br />Also accepted with this analysis are the uncertainties associated with drawing conclusions <br />based on drawing best-fit lines through scatter graphs of RDII response versus rainfall. <br />Because RDII is influenced significantly by antecedent moisture conditions in the soil, the <br />flow versus rainfall and the RDII volume versus rainfall graphs can be quite scattered in the <br />R-value method. Therefore, comparing the slopes of the lines before and after rehabilitation <br />may not give a full representation of the RDII reduction actually achieved. An extension of <br />the analysis described in this report would consist of calibrating the City's RDII simulation <br />model to the sub-basin level. This would have the advantage of including the antecedent <br />moisture conditions in the soil and providing simulated long-term periods (in addition to <br />monitoring periods) over which to evaluate RDII reduction effectiveness. <br />12 <br />