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<br />:-1 <br />l <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />, <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br /> <br />5.2.2 SnoWl)ack Results <br /> <br />The regression equation developed from the historical base periOd data to predict <br />the April 1 snow water equivalent is: <br /> <br />Yc=.48+. 59 eel <br /> <br />(1) <br /> <br />where (Yc) is the calculated average target snow water content (inches) and eel <br /> <br />is the observed average control snow water content. The correlation coefficient (r) is <br />.922. This means that 85 percent of the variance (r = .850) is accounted for in the <br />regression equation. <br /> <br />The observed April 1, 1995 snow water content (C) was averaged for the six <br /> <br />control stations and the predicted water equivalent ( Y cl for the target area was <br /> <br />calculated using equation (1). These amounts were compared to the observed snow water <br /> <br />content ( Yo) , averaged over the four target stations, to estimate the seeding effect. <br /> <br />This seeding effect (SE) can be expressed as the ratio (R) of the average observed target <br />snow water content to the average calculated target snow water content. Thus, <br /> <br />y <br />SE=R-.!3. <br />Yc <br /> <br />(2) <br /> <br />where (Yo) is the target area average snow water (in inches) and (Yc) is the <br />target area average calculated snow water content. <br /> <br />The seeding effect can also be expressed as a percent excess (or deficit) of the <br />expected snow water; such that, <br /> <br />Yo-Yc(lOO) <br />SE= <br />Yc <br /> <br />(3) <br /> <br />5-7 <br />